esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### WGAN-GP Model Development
#### 3.3.1 Algorithm: WGAN-GP with Advanced Architecture  
This section explains the architecture of the WGAN-GP model. Algorithm 1 outlines the steps for model definition, data preparation, gradient penalty implementation, and training with \(\omega_{0}\) as the initial discriminator and \(\theta_{0}\) as the initial generator parameter.  
Input: \(\lambda\) = 10 (gradient penalty coefficient), \(train\_data\) (training dataset), \(num\_epochs\) = 100 (number of epochs), \(batch\_size\) = 100 (size of each training batch), and \(lr\) = 0.001 (learning rate). Figure 2 represents the WGAN-GP architecture. The output of the WGAN-GP model consists of generated synthetic coefficients for \(cA,cD5,cD4,cD3\), \(cD2\), and \(cD1\) for each of the 4 channels.  
**Algorithm 1** WGAN-GP training  
```
1:Define generator model: \(\mathbf{G}_{\theta}(z)=\text{ReLU}(\mathbf{W}_{4}\cdot\text{ReLU}(\mathbf{W}_{3} \cdot\text{ReLU}(\mathbf{W}_{2}\cdot\text{ReLU}(\mathbf{W}_{1}\cdot z))))\)
2:Define discriminator model: \(\mathbf{D}_{\omega}(x)=\text{LeakyReLU}(\mathbf{W}_{4}\cdot\text{Dropout}( \text{LeakyReLU}(\mathbf{W}_{3}\cdot\text{Dropout}(\text{LeakyReLU}(\mathbf{W}_{2} \cdot\text{Dropout}(\text{LeakyReLU}(\mathbf{W}_{1}\cdot x))))))\)
4:Initialize generator and discriminator models: \(G_{\theta}\) and \(D_{\omega}\)
5:Initialize optimizers: Adam optimizers for both models with learning rate \(lr\) and \(\beta\)
6:for epoch = 1,..., num_epochsdo
7: Randomly permute training data indices
8:for\(i=1,\dots,m\)do
9: Sample a batch of \(m\) real data \(x(i)\sim P_{data}\), and \(m\) noisy data \(z(i)\sim P_{z}\)
10: Generate fake data: \(\tilde{x}\gets G_{\theta}(z)\)
11: Interpolate data: \(\tilde{x}\gets ex+(1-\epsilon)\tilde{x}\)
12: Compute gradient penalty: \(\
abla_{\tilde{x}}D_{\omega}(\tilde{x})\), \(L_{GP}=\lambda\big{(}\|\
abla_{\tilde{x}}D_{\omega}(\tilde{x})\|_{2}-1\big{)} ^{2}\)
13: Compute discriminator loss: \(L_{D}=D_{\omega}(\tilde{x})-D_{\omega}(x)+L_{GP}\)
14: Update discriminator: \(\omega\gets [PERSON](\
abla_{\omega}L_{D})\)
15:endfor
16: Every \(k\) iterations, update the generator:
17:if\(i\mod(5\times m)=0\)then
18: Generate fake data: \(\tilde{x}\gets G_{\theta}(z)\)
19: Compute generator loss: \(L_{G}=-D_{\omega}(\tilde{x})\)
20: Update generator: \(\theta\gets [PERSON](\
abla_{\theta}L_{G})\)
21:endif
22:endfor
23:return: trained \(G_{\theta}\)
```  
**Algorithm 1** WGAN-GP training
## 4 Control Effect of Ground Improvement on Influenced Zone of Deep Excavation  
### Effect of Ground Improvement on Tunnel Displacement Distribution Behaviors  
It is known from the above analysis that the ground improvement outside the pit has a positive effect on the tunnel displacement induced by deep excavation. Considering the control effect and engineering economy, it is suggested that the ground improvement strength should be kept within 1.5-2 MPa, the ground improvement depth should be 2\(H_{\text{e}}\), and the ground improvement width should be increased as much as possible if the site condition allows. In this study, the ground improvement strength of 2 MPa, the ground improvement depth of 2\(H_{\text{e}}\), and the ground improvement width extending to 1 m to the edge of the tunnel were adopted to analyze the effect of ground improvement on the displacement distribution characteristics of the tunnel and the scope of the influenced zone.  
Figure 14 shows the horizontal and vertical displacement curves of tunnels at different positions outside the pit after ground improvement. To facilitate comparison with the results under the conditions without ground improvement, the coordinates are the same as those in Figure 2. It is observed that, after ground improvement, the displacement has an obvious decrease. Before ground improvement, the maximum horizontal displacement and settlement of the tunnel are 28 mm and 20 mm. After ground improvement, the maximum horizontal displacement and settlement of the tunnel are 14 mm and 6.5 mm, which is reduced by 50% and 67.5%, respectively. It means that ground improvement outside the pit can effectively control both the horizontal and vertical displacements of the tunnel.
###### Abstract
There's a possibility that the resources of the smart city controller could be depleted due to the constant processing of genuine and DDoS-spoofed packets. If the controller becomes incapable of receiving new legitimate packets, the SDN architecture might falter, rendering the controller unreachable. The use of SDN for network assessment in smart cities has been explored in various studies. Detecting DDoS attacks with speed and precision remains highly challenging. Some DDoS attackers employ packets that mimic normal traffic to disrupt the functioning of smart city systems. During a DDoS attack, the normal network conceals the traffic, leading traditional packet-based intrusion detection systems (IDS) to fail to identify it. Through the application of advanced machine learning algorithms and optimization approaches, a novel countermeasure will be devised through the monitoring and prompt identification of instantaneous network changes. Cyber-attacks on smart cities are escalating, posing a threat to the advancements achieved in technological innovation. Smart cities are more susceptible to DDoS attacks, regardless of the nature of the targeted traffic. Smart cities that rely heavily on software-driven, complex digital networks to operate numerous city systems and services are vulnerable to cyber-attacks. When a DDoS attack is launched against a target in an SDN network, the controller triggers multiple flow entries due to the large volume of spoofed source addresses. This study addresses the following questions:  
1. What is the best SDN framework and testbed for smart cities that can be used for evaluation? 2. How is the effectiveness of the anomaly mitigation schemes determined? 3. How is the best multiclass classification determined? The failure of the controller can lead to the breakdown of entire or partial smart city systems. As a result, this study proposes a secure and intelligent framework based on an SDN scheme to protect smart cities from DDoS attacks. The main goals of this work are to introduce an intelligent learning-based DDoS detection framework that mitigates the DDoS attacks on smart cities. The detailed objectives of this work can be summarized as follows:  
* To propose an intelligent learning-based DDoS framework for eliminating DDoS attacks on smart cities. * To improve the efficiency of the smart cities' SDN-enabled centralized network by further enhancing the model. To simulate the proposed framework in smart city systems for evaluation and benchmarking. The remainder of this paper is organized as follows. Section 2 summarizes and discusses related work on DDoS attacks in smart cities. Section 3 describes the framework, algorithm, and features of the proposed work. Section 4 describes the evaluation and performance metrics of the proposed framework. In Section 5, we make final remarks and directions for future work.
## 5 Attenuation Method Experiment
### Phase III: Building the Final Route and Tuning
Reducing the Route Length  
Although the sensitivity of the used SFP detectors is approximately \(-24\) dBm, the transmitted signal is so damaged by the strong attenuation and subsequent amplification that the system needs to deliver at least \(-21\) dBm for proper operation. Based on the above and Figure 8, it appears that this is coincidentally the same power obtained with the minimum attenuation needed for the quantum channel to operate. This issue is explained in Figure 8.  
However, it is necessary to add that this is a limit state in which keys are indeed transferred but only at a very low rate and for a limited time. The system is unstable, and after a certain period of time, the whole key distribution process restarts. However, as the distance decreases (attenuation along the path is compensated), the quantum channel is less disturbed by Raman noise and is thus able to operate even at higher service channel optical power. Moreover, if the power of the service channels is preserved, this leads to higher system stability and consequently higher transmission rates. The route was therefore shortened four times with a step of 5 km until both DWDM filters were connected directly. The path attenuation was always adjusted by bending to the original value (i.e., of approximately 6 dB). The attenuation of the service channels as well as their final performance was thus maintained. The differences in key rate, error rate and visibility can thus only be attributed to Raman noise. Calculation of its optical power was performed in the same way as in the Section 5.2. Figure 8: The graphic describes the service channel optical power problem. The higher its power, the worse the effect it has on the quantum channel. In contrast, the more the service channel is attenuated, the lower the probability that it works properly. The “benevolence” in terms of power increases as the shared fiber shortens.
# A Class of Perfectly Secret Autonomous Low-Bit-Rate Voice Communication Systems
## 7 Experimental Evaluation
: _Since SKD over the source \(CR\big{(}\hat{S}_{IA},\hat{S}_{IB}\big{)}\) is performed independently of the system operation on the main channel, its parameters, such as sampling rate and resolution of synthesized signals, can be almost arbitrarily different, allowing for secret key generation rates in a much wider range of values. The only limiting factor of the secret key distillation rate is the communication capacity of the public channel. Therefore, the proposed APS-VCS system can operate reliably at other standard vocoder rates (2.4 kb/s, 4.8 kb/s) with the appropriate public channel bandwidth._  
The price paid for the perfect secrecy of the APS-VCS system is the establishment and maintenance of a public channel during secure conversation. However, the main and public channels operate in an asynchronous mode, which significantly simplifies practical \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**LSP-Based Artificial Synthesizer**} \\ \hline SNR [dB] & 50 & 39.90 & 29.80 & 19.70 & 10 \\ KR [\%/s] & 9.96 \(\pm\) 1.54 & 6.44 \(\pm\) L35 & 3.04 \(\pm\) 1.06 & 0.53 \(\pm\) 0.47 & 0.08 \(\pm\) 0.08 \\ KR [\%] & 7.78 & 5.03 & 2.38 & 0.42 & 0.05 \\ KAR [\%] & 100 & 100 & 100 & 75 & 7 \\ LR [b/b] & 0.0011 & 0.0015 & 0.0042 & 0.0399 & 0.0191 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of SKD protocol performed over the 1000 blocks in the secure stage of communication, measured on vPCP-V system. implementation. The only condition that must be met is to maintain the constant FIFO memory read rate of 1.2 kb/s for the Vernam cipher. ```
Input: Binary sequence Output: LSP parameters
1: Read random sequence \(E_{k}\) from FIFO, \(\left|E_{k}\right|=L\)
2: Divide \(E_{k}\) in \(p\) subsequences, i.e., \(E_{k}=\left[E_{k1},E_{k2},...E_{kp}\right]\), \(\left|E_{ki}\right|=\left\lfloor\frac{L}{p}\right\rfloor\), \(i=1,...p\)
3: Transform each \(E_{ki}\) to \(LSP_{i}\) by rescaling \(E_{ki}\) decimal value with factor \(\frac{\pi}{2^{\left\lfloor\frac{1}{p}\right\rfloor}-1}\)
4: Sort obtained \(LSP\) parameters according to (10)
```  
**Algorithm 1** Binary sequence (FIFO\({}_{out\_2}\)) to LSP parameters transformation  
Table 4 shows the bit-error rate results for four typical communication channels, with and without the use of error-correcting code (ECC). For the ECC, Golay(12,24) is used, which is specifically designed to protect the 15% most sensitive bits of the binary representation of LSP parameters that are subject to encryption. It is important to note that the first two types of GSM channels produce exceptionally good results, considering the impact of the input compression block in GSM devices. These experimental results confirm the essential functionality that a highly secure voice protection system must meet [21]. The results demonstrate that the BER shown in Table 4 is independent of the SKD system, provided that the synthesizer parameters are selected to guarantee a key generation rate greater than 2.4 kb/s.
## 2 Geopolymer Mortar (GPM)
### Geopolymer Mortar Mechanical Properties
#### 2.2.2. Geopolymer Mortar Flexural Strength
[45], flexural strength comparison between GPM and PC mortar (Na\({}_{2}\)CO\({}_{3}\) concentration was about 10% by weight of powder mixes (zeolite and slag), and 100% Fine Aggregate). GPM's flexural strength was tested using two distinct mix designs that used slag and zeolite as base ingredients. As shown in Figure 12, the geopolymer and the PC mortars had nearly comparable flexural strengths. [PERSON] et al. [55] examined the properties of GPM comprising natural fibres and high levels of calcium fly ash. The primary materials in this investigation included coir or coconut fibre (CF), glass fibre (GF), and sisal fibre (SF). SF and CF were acquired from a plant farm in the Thai provinces of Prachuap Khiri Khan and Chon Buri, respectively. According to the results, utilising fibres enhanced GPM's flexural strength. In addition, the flexural strength of GPM tended to increase as the fibre content increased. Even though flexural strengths increased with fibre content, the mixtures with more than 1% volume fraction had poor workability and were challenging to compact and cast. The flexural strength of GPM reinforced with natural fibre (CF and SF) varied from 5.3 to 6.6 MPa compared to CGM (3.2 MPa) and GPM reinforced with synthetic fibre (GF) (3.1-3.7 MPa), as shown in Figure 13.  
Figure 11: Influence of GGBFS volume on the ultimate flexural strength of GPM [54]. Figure 12: Flexural strength comparison between GPM and PCM.
## 3 Results
### Noisy Basis Data  
In the subsequent analysis, we delved into a comprehensive comparison between SVM and RF under the influence of noisy training data spanning the spectrum from 7 mm to 12 mm. Throughout this investigation, the noise levels were carefully manipulated, varying between an SNR of 0 and 70, while maintaining consistent test conditions for each corresponding noise level and tumor radius. Figure 5 vividly illustrates the SVMmodel's performance nuances amidst fluctuations in noise and tumor radius, while Figure 6 similarly shows the subtle response of the RF model under the same varying conditions. Notably, both figures exhibit a strikingly similar pattern across all curves. A notable trend in the results is the inverse relationship between F1 score and SNR. This trend is further reflected in relation to the tumor radius, showing that as the SNR decreases, the F1-Score decreases proportionally. For example, for a tumor radius of 7 mm and 8 mm, the F1-Score for the RF model escalates from 0.48 to 0.56 and 0.51 to 0.62, respectively, with increasing SNR. In contrast, the F1-Score for the SVM experiences a larger increase, rising from 0.48 to 0.7 and 0.52 to 0.78, respectively, for the corresponding radii. The peak performance for both the SVM and RF models was achieved at the highest levels of SNR and tumor radius, culminating in an F1 score of 0.94 for both models.
# A Dual-Band Modified Franklin mm-Wave Antenna for 5G Wireless Applications
## 3 Parametric Analysis
### Effect of Incremental Length R  
The effect of the incremental length of the patch elements of the first row on the antenna is analyzed. The length is varied to analyze its effect. The variation in the length does not cause a significant variation in the resonant frequency, but a very significant variation in the antenna's return loss is seen. The variation of the incremental length R is demonstrated in Figure 8.
## 1 Introduction
The current research presents a human-centered driven multi-agent solution that (1) produces eco-friendly multimodal route recommendations, including public transport-reliant routes and fastest routes, and (2) empowers urban traffic management authorities to visualize and monitor the traffic conditions of their city. In essence, our intelligent transport solution models seven transport layers of a given city, including road segments, public transport journeys (e.g., buses, trans), bike paths, and pedestrian paths, and calculates the best combination of these modes while considering commuters preferences. The eco-friendly multimodal route guidance system (aka EMRG system) forms a detailed understanding of the typography of the transport network (i.e., physical infrastructure) and the live traffic data representing traffic stream properties (e.g., speed, travel time, and estimated CO2 emissions). In the validation section, we demonstrate the closeness of our simulations to real-world scenarios by applying routing algorithms to compute journey plans that not only fulfill commuter preferences (i.e., multi-modality and departure time), but also reduce traffic congestions and the resulting CO2 emissions. Although past research claims that the provision of public transport information does change travel decisions [15; 16], it remains unclear to what extent mobile applications change travel behavior, and further research is advocated [17]. For instance, a recent study has shown that personalized accessibility information may inflict more walking and less driving time; however, the self-reported travel behavior change was less than 10% [18]. Therefore, another motive for conducting this research is to explore the prospect of inducing sustainable travel behavior using efficient route recommendations. The remainder of the paper comprises eight sections. Section 2 reports on the recent works in the field of multimodal transport management, with an emphasis on their strengths and weaknesses. Section 3 details the inner workings of our eco-friendly route guidance traffic management system. Sections 4 and 5 describe the transport layers and traffic properties modelled by the multi-agent architecture. Section 6 presents a technical validation, along with the types of journey recommendations offered by our multimodal route guidance system. Section 7 shows the user validation results of the system in two European cities. Section 8 discusses the implications and limitation of the eco-friendly traffic management system. Finally, Section 9 concludes the paper with future research prospects.
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 3 Results and Discussion
### Regeneration Studies  
Table 7 shows the regeneration numbers on the removal effect of methylene blue. It can be seen that the secondary removal effect was 90 \(\pm\) 2%. After three regenerations, the removal effects of AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) dropped to 79.50%, 66.60%, and 66.85%, respectively. This may be due to the active sites on the surface being slowly occupied with the repeated use of the composite materials, resulting in the gradual blockage of the pore size, resulting in a continuous decrease in the removal performance.  
Table 8 shows the regeneration loss rates for the removal of methylene blue. It can be seen that the regeneration loss rates of AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) increased from 4.24, 6.12, 4.83 to 25.33, 30.17, and 34.24 in four cycles.  
Combining the removal effect of methylene blue and the regeneration loss rate of AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\), which indicates that AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) had relative stability for methylene blue removal.
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 2 Methodology
### Deep Learning-Based Surrogate Models
#### 2.2.3 Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)  
The LSTM network shows its ability to capture both long- and short-term dependencies [25]. In contrast to traditional RNNs, LSTM networks have extra cell states that serve as memory units for storing information. The fundamental structure of an LSTM cell, as illustrated in Figure 8a, includes \(x_{t}\) as the input vector, \(h_{t}(h_{t-1})\) representing the hidden state of the LSTM cell at time step \(t(t-1)\), and \(c_{t}(c_{t-1})\) indicating the cell state of the LSTM cell at time step \(t(t-1)\). The architecture of the LSTM cell ensures that both its cell state (\(c_{t}\)) and hidden state (\(h_{t}\)) are propagated to the next time step.  
The LSTM unit comprises three gates: the forget gate (\(t_{t}\)), input gate (\(i_{t}\)), and output gate (\(o_{t}\)), which regulate and update the cell state (\(c_{t}\)) and hidden state (\(h_{t}\)). These gates act as filters, each performing a distinct function. The forget gate (\(f_{t}\)) determines which information to discard from the cell state (\(c_{t}\)). The input gate (\(i_{t}\)) decides which new  
Figure 8: (**a**) The structure of a LSTM cell. (**b**) The structure of a GRU cell.  
Figure 7: (**1**) Depthwise separable convolution neural network (DSCNN) (**2**) Detailed internal operation flow of a DS-CNN. (**a**) Depthwise convolution (D-Conv2D). (**b**) Pointwise convolution (P-Conv2D).  
information should be incorporated into the cell state (c\({}_{t}\)). The output gate (o\({}_{t}\)) specifies the information derived from the cell state will be utilized as output (o\({}_{t}\)). Through the coordinated actions of these three gates, the LSTM cell can effectively capture complex dependencies in both short- and long-term time series, making a significant improvement over traditional RNNs.  
The complex structure of LSTM networks requires significant time for training. To expedite the training process, the GRU network was introduced as a modification of the LSTM network, featuring a more straightforward architecture. The specific structure of the GRU unit is illustrated in Figure 8b, where the hidden state (h\({}_{t}\)) and cell state (c\({}_{t}\)) are consolidated into a single component. The GRU contains two control gates: the update gate (z\({}_{t}\)) and the reset gate (r\({}_{t}\)). The update gate (z\({}_{t}\)) regulates the extent to which state information from the previous time step (h\({}_{t-1}\) or c\({}_{t-1}\)) is carried over to the current time step t. A higher update gate value indicates more information from the previous time step is retained. Conversely, the reset gate (r\({}_{t}\)) controls how much state information from the previous time step is passed to the current time step. A lower reset gate value implies that less information is retained.