esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Analysis
### Network Architecture  
_How can the choice of network architecture for the SRL encoders affect results?_  
In this section, we utilized three different autoencoder methods to compare the influence of encoding network architecture, including original autoencoder, variational autoencoder (VAE) [45] and denoising autoencoder (DAE) [46]. Traditional autoencoder accepts and compresses input, then recreates the original input. This is an unsupervised technique because all you need is the original data, without any labels of known, correct results; A variational autoencoder assumes that the source data has some underlying probability distribution (such as Gaussian) and then attempts to find the parameters of the distribution; Denoising autoencoders are an example of deep architectures that are designed to recover noisy data, trying to achieve a good representation by changing the reconstruction criterion.  
**Results:** Figure 6 shows how significantly learning performance can be affected by simple changes to the embedding network architecture. The autoencoder has mixed results: it solves all environments, yet it sometimes under-performs navigation tasks. When we explored the latent space using the S-RL Toolbox visualization tools [52], we noticed that one state space dimension can act on both robot and target positions in the reconstructed image. In addition, this approach does not use additional information that the environment provides, such as actions and rewards, leading to a latent space that may lack informative structure.
## 4 Security Threat Landscape in 5G Networks
### Fifth-Generation Network Potential Vulnerabilities
Table 5 provides a summary of the key security threats in 5G networks, their potential impacts, and specific attack methods and techniques [85; 86]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Security Threat** & **Description** & **Potential Impact** & **Attack Methods and Techniques** \\ \hline Cyberattacks [83; 87] & Threats aimed at disrupting network operations or gaining unauthorized access to sensitive data, including DDoS attacks and APTs. & Network disruption, data breaches, service degradation. & DDoS attacks leveraging higher bandwidth in 5G. APTs using techniques like social engineering, zero-day exploits, and rootkits. \\ \hline User privacy [64; 4; 64] & Risk of unauthorized data access or leakage due to increased data transfer and processing on 5G networks. & Data breaches, privacy violations, identity theft. & Unauthorized data access or eavesdropping. Data leakage through insecure channels. \\ \hline Security vulnerabilities [56; 57; 58; 59; 74; 86; 59; 75; 84; 85; 86] & 5G’s capabilities like network slicing and edge computing introduce new vulnerabilities. Virtualized network functions and data distribution can be exploited. & Network compromise, data breaches, system instability. & Exploiting virtualized network function vulnerabilities. \\ \hline IoT devices [68; 71; 72; 73; 74; 88] & Proliferation of IoT devices with weak security can be targeted to compromise the network or for unauthorized data access. & Botnet creation, network compromise, data theft. & Creating botnets through vulnerable IoT devices. Exploiting weak IoT security. Unauthorized access via compromised IoT devices. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the key security threats in 5G networks, their potential impacts, and attack methods and techniques. Table 5 delineates the key security threats in 5G networks, offering a comprehensive description of each threat, its potential impact, and prevalent attack methods and techniques. The threats range from cyberattacks aimed at disrupting network operations to potential vulnerabilities inherent in 5G's capabilities like network slicing and edge computing. Other notable concerns include the proliferation of the IoT devices with weak security and the enhanced attack surface resulting from the adoption of cloud services [94]. Decentralization and virtualization technologies like SDN and NFV further amplify the risks. Moreover, the diversity of potential attackers--from cybercriminals to state-sponsored entities--adds another layer of complexity.
## 3 Results
### Change Detection Analysis
#### 3.5.1 Spatio-Temporal Mangrove Distribution in Chwaka Bay
the yellow color indicates the area of the mangrove forest lost to the other LCLU classes, i.e., water, bare land, agriculture, and other forests. Overall, mangroves lost, c., 2797 ha to water, bare land, agriculture, and other forests and gained, c., 515 ha. The overall destruction of the mangroves was, c., 2281 ha. Change in the area covered by mangrove forests was lowest between 1995 and 2000 and highest between 2009 and 2020. Therefore, the general trend shows that the area of mangrove forests lost to the other LULC classes increased with time. Figure 8 shows the spatiotemporal mangrove distribution in Chwaka Bay, Unguja, Zanzibar, Tanzania from 1973 to 2020.
## 6 Simulations Results and Discussions
### Network Average Energy Reduction over the Rounds  
Figure 12 depicts how the network average energy decreases over the rounds. The figure clearly illustrates that for the proposed EEUCH, the average network energy decreases almost linearly with the rounds spent. This is because of the fact that each SN sends data packets to the UAV, which is relatively closer than the BS, and hence the transmission energy is reduced. In the case of benchmark protocols, the CH is responsible for sending data packets collected from SNs to the BS. Initially, all SNs have enough energy, but as rounds are spent, the energy of the SNs decreases, and a stage is reached when the elected CH from SNs has very little energy, preventing it from transmitting data packets to the BS; thus, the CH dies during the round, resulting in nonlinearity in the graphs.  
The amount of total network average energy reduction when first SN dies, ten, fifty, and one hundred percent of SNs die is shown in Figure 13. The amount of average network energy reduced from the total initial network energy is represented by the number mentioned above a bar. The linear consumption of network energy for the EEUCH can also be observed in this figure. Furthermore, when the first SN dies, the energy of the EEUCH is much lower, indicating lower performance of the proposed EEUCH, but when half of the SNs die, the EEUCH shows the best performance among the benchmark protocols. This is because CHs died prematurely in benchmark protocols.  
Figure 11: Network energy consumption versus percentage of node deaths.  
Figure 12: Reduction in the average energy of the network over the number of rounds.
## 3 Results
### Comparison and Discussion  
Accurate prediction of scour depth plays an important role in the optimal design of hydraulic structures. So, the present study employs the application of three metaheuristic algorithms (i.e., HHO, PSO, GA) integrated with ANN and traditional ANN for SD prediction around the experimental setup of the ski-jump spillway. Their accuracy was evaluated through performance metrics and  
Figure 7: Taylor diagrams of ANN-HHO, ANN-PSO, ANN-GA, ANN models during (**a**) training, and (**b**) testing periods.  
graphical interpretation. A comparison of results reveals better feasibility, suitability, and predictability of the ANN-HHO model, closely tracked by the ANN-PSO and ANN-GA models. Additionally, the outcomes of the best ANN-HHO model were compared with the existing Wu model (Equation (2)) as shown in Table 5. It can be seen from Table 5 that the ANN-HHO model had a better prediction than the WM model for SD in terms of performance metrics. Furthermore, to validate the reliability of models, the percentage of prediction accuracy is computed using MAE/RMSE values, which reveals reductions of 5.1%/19.6%/32.6%, and 7.7%/19.6%/25.8% during training and 16.0%/19.2%/29.4%, and 12.2%/14.9%/19.5% during testing with ANN-HHO model over ANN-PSO, ANN-GA, and ANN models, respectively. This analysis also confirms the viability of the Harris Hawks Optimization (HHO) algorithm embedded with ANN over the Particle Swarm Optimization (PSO) and Genetic algorithm (GA) in scour depth prediction.
# Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning
## 3 CNN and LSTM for Sequence Recognition
### Implemented Architecture  
In this study, to perform handwriting recognition using accelerometer data, three different architectures were implemented and evaluated: a CNN, an LSTM, and an hybrid CNN-LSTM.  
The CNN architecture (Figure 2) basically consists of three convolutional layers followed by _ReLU_ activation functions. Indeed, in the first and second layers, 16 convolutional filters are used to extract the same number of features from data, where the size of the filters is \(1\times 3\), and the stride is set to one. After the first two convolutional layers, max-pooling is applied to reduce the dimensionality by half. Additionally, a third convolutional layer was implemented using 32 convolutional filters with the same size and stride as in previous layers, applying the ReLU activation function. A dropout technique with a dropout rate of 0.1 was used in each convolutional layer to prevent overfitting. Finally, a fully connected (Dense) layer is used with a Soft-Max activation function to compute the probability distribution over 36 classes. The proposed LSTM architecture employs 250 memory blocks (Figure 1). This is followed by a fully connected layer with a Soft-Max activation function to compute the probability distribution over all classes. In the hybrid CNN-LSTM model (Figure 3), the output of the last convolutional layer of the CNN is connected to the LSTM for recognition and classification.  
Figure 2: A CNN architecture with three convolutional layers, max pooling, and a fully connected layer. Each convolutional layer and dense layer is followed by an activation function.  
The input data for the networks consist of the 3D accelerometer data represented in (\(x\), \(y\), \(z\)) Cartesian coordinates.  
The evaluated architectures were trained using the Adam optimization algorithm to find the minimum of the proposed stochastic objective function, and the network parameters were updated using the backpropagation algorithm.  
Tables 2-4 show the parameters of the three architectures.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Parameters** \\ \hline Input & (None, 116, 3) & 0 \\ Conv1D & (None, 114, 16) & 160 \\ Dropout & (None, 114, 16) & 0 \\ Conv1D & (None, 112, 16) & 784 \\ Dropout & (None, 112, 16) & 0 \\ MaxPooling & (None, 56, 16) & 0 \\ Conv1D & (None, 54, 32) & 1568 \\ Dropout & (None, 54, 32) & 0 \\ Flatten & (None, 1728) & 0 \\ Dense & (None, 36) & 62,244 \\ \hline Total & & 64,756 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of parameters of the CNN architecture.  
Figure 3: CNN-LSTM architecture.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Parameters** \\ \hline Input & (None, 116, 3) & 0 \\ LSTM & (None, 116, 250) & 254,000 \\ Flatten & (None, 1728) & 0 \\ Dense & (None, 36) & 1,044,036 \\ \hline Total & & 1,298,036 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of parameters of the CNN-LSTM architecture.
## 4 Proposed Method
### DNC Method
#### 4.2.4 Time Complexity  
When utilizing the DNC to compute the score of nodes in a network, it is necessary to compute the degree of nodes as well as the sum of their first-order neighbors' local clustering coefficients. First, calculate the local clustering coefficient for all nodes in the network. The computation of local clustering coefficients involves traversing the neighbors of nodes and calculating their connections, making the time complexity for calculating the local clustering coefficients for all nodes in a network \(O(|E|)\). To calculate the importance score for each node in the network, as it involves traversing the first-order neighbors of nodes, the time complexity for this part is \(O(\bar{k})\), where \(\bar{k}\) is the average degree of nodes. In a network, \(|E|\) is significantly larger than \(\bar{k}\), which results in the overall time complexity of DNC being \(O(|E|)\).
# How Environmental Factors Affect Forest Fire Occurrence in Yunnan Forest Region
## 3 Results
### Spatial Heterogeneity of Environmental Factors  
This study establishes 94 county-level LR models; 23 other counties have too few fire points to be statistically significant. Thus, the spatial heterogeneity of the environmental factors was analyzed by using the 94 abovementioned county-level LR models. The AUCs of all these county-level LR models exceed 90%, and the top three environmental factors for the county-level models are RH\({}_{\text{min}}\), T\({}_{\text{max}}\), and altitude (Figure 6a). RH\({}_{\text{min}}\) plays a dominant role in most county-level models, with an overall bias toward Northwest Yunnan, Southwest Yunnan, and Southeast Yunnan. T\({}_{\text{max}}\) is the dominant factor in Central Yunnan and in a few regions in Northwest Yunnan and Northeast Yunnan. Altitude is the dominant factor in Central Yunnan and in a few counties in Northeast and Southeast Yunnan. A correlation analysis of 63 county-level models reveals a significant negative correlation between RH\({}_{\text{min}}\) and the relative importance coefficient of T\({}_{\text{max}}\) (\(r=-0.61\), \(p=0.003\)), whereas no significant relationship appears between the other two pairs of correlation factors. The correlation coefficients of RH\({}_{\text{min}}\) and altitude are not significant in 54 counties (\(p=0.372\)) and T\({}_{\text{max}}\) and altitude are not significantly correlated in 40 counties (\(p=0.235\); Figure 6b-d).  
Figure 6: (**a**) Most important environmental variables in different counties. (**b**) Ranking of relative importance of RH\({}_{\text{min}}\) in each county. (**c**) Ranking of relative importance of T\({}_{\text{max}}\) in each county. (**d**) Ranking of relative importance of altitude in each county.  
At 50% forest fire probability, the threshold of RH\({}_{\min}\) trends downward from Northwest Yunnan, through Southwest Yunnan and Southeast Yunnan, and finally to Central Yunnan (Figure 7a), where the partial derivative RH\({}_{\min}\) < \(-\)8 mainly occurs in Northwest Yunnan, Southeast Yunnan, Southwest Yunnan, and in the areas around the Lancang River and the Nujiang River, which indicates that for every 1% decrease in RH\({}_{\min}\), the marginal forest fire probability of 50% in this region increases by at least 8% (Figure 7b). The high T\({}_{\max}\) threshold mainly occurs in Central Yunnan, Northwest Yunnan, and Southwest Yunnan, decreasing in these three regions upon approaching the surrounding area. The highly correlated partial derivative is concentrated in Central Yunnan and Southwest Yunnan, and its marginal variation is much less than that of RH\({}_{\min}\) (Figure 7c,d). Compared with RH\({}_{\min}\) and T\({}_{\max}\), the altitude threshold for 50% forest fire probability is relatively large, and the degree of variation of the threshold and the related partial derivative reveals no obvious regularity. Overall, the variation of threshold and the related partial derivatives differ between counties (Figure 7e,f).
## 5 Security Analysis
### Queries  
During the process, robust verification is performed at each communication step to ensure mutual authentication, integrity, and freshness using the inj-event function. If freshness is not maintained, replay attacks may occur. Integrity verification queries for messages are modeled as Q1, Q2, and Q3, with freshness verification conducted to counteract replay attacks. Furthermore, both Q2 and Q3 are employed to verify that mutual authentication and secure key exchange are achieved while also ensuring the freshness of the ServerHello and ClientFinish messages. For further validation of the secure key exchange, the queries Q4 through Q6 are applied to evaluate the negotiated session keys for potential leakage. Finally, the confidentiality of the application data transmitted over public channels is verified based on the queries Q4 through Q6. On the other hand, perfect forward secrecy is validated in cases where the long-term key is compromised, as outlined in Algorithm 6 using the 'phase' function. In the process macro section, all event functions used for verification are emphasized, as shown in Table 2.
# Heat Transfer in Thermoelectric Generators for Waste Energy Recovery in Piston Engines
## 1 Introduction
[17] used dimples in the heat exchanger of the vehicle TEG. Results showed that the heat transfer was enhanced and the backpressure was quite low. [PERSON] et al. [18] researched the flow resistance and heat transfer performances in square channels with different cylindrical-shaped channels, and it was concluded that a real improvement in heat transfer operation and a pressure loss decrease can be reached by changing the cylinder channel shape. It was concluded that this kind of channel could make the boundary layer around the edges thinner, enhancing the amount of flow in the turbulent regime present in the fluid. [PERSON] et al. [19] assessed the optimization and evaluation of a temperature-controlled thermoelectric generator to be used in a commercial 16 L heavy-duty vehicle. The system consisted of a heat exchanger with wavy fins embedded in an aluminum matrix along with heat pipes machined directly into the matrix that granted thermal control based on dissipating the local excess heat by phase change. A maximum average electrical production of 2.4 kW was predicted, which resulted in fuel savings of around 2% and CO\({}_{2}\) emissions reductions of around 37 g/km. In addition, work focused on heat sinks has been carried out. [PERSON] et al. [20] studied the cooling performance of a heat sink with flat fins on a TEG. The same authors also investigated whether a fin heat sink was used to obtain higher efficiency by cooling the TE more effectively [21]. [PERSON] et al. [22] proposed a method to obtain the optimum plate-fin heat sink with forced convection. Most previous works dealt with a flat plate heat exchanger with more or less complex internal geometries. In this work, a new approach to TEG has been followed: instead of a typical flat-shaped TEG, a compact squared design with low pressure drop is proposed. This new concept could be particularly useful for aircraft piston engines since it saves weight and volume. In addition, this type of TEG has a more even temperature distribution, helping to reduce the electrical mismatch effects [23]. Usually, TEGs require a cross-sectional area expansion in order to have enough surface to place the thermoelectric modules. As a novelty, the effects on wall heat transfer of the recirculation regions created by this characteristic expansion have been studied in this paper. In addition, the effect of including internal fins is also quantified.
# Quasi-Yagi Slotted Array Antenna with Fan-Beam Characteristics for 28 GHz 5G Mobile Terminals
## 2 Antenna Design and Performance
### Effect of an RF Connector and a Feeding Network  
To investigate the effects of an RF connector and a feeding network on the antenna performance in actual antenna fabrication and measurement, simulations using the connector model and the feeding network should be carried out. Since the connector is an electrically large structure at 28 GHz, the full ground plane with the dimensions of \(150\times 70\) mm with the feeding network and the connector is very time-consuming to analyze using the simulation software. Therefore, as shown in Figure 16a,b, a modified ground plane with the dimensions of \(31\times 70\) mm was considered. The connector model was based on the datasheet for a 2.92 mm end-launch connector [26]. The only difference between Figure 16a,b is an excitation type. As shown in Figure 16a, the proposed array antenna was excited by the feeding network and a lumped port is located at the end of the feeding network. However, as shown in Figure 16b, the lumped port at the end of the feeding network was substituted by the connector. Figure 16c,d shows comparisons of simulated S\({}_{11}\) values and radiation patterns for two different excitation types. The simulated \(-10\) dB S\({}_{11}\) bandwidth of the proposed array antenna with the feeding network and the lumped port excitation is 1.58 GHz (27.06-28.64 GHz). The simulated \(-10\) dB S\({}_{11}\) bandwidth of the proposed array antenna with the feeding network and the connector excitation is 1.59 GHz (26.79-28.38 GHz). Therefore, the proposed antenna covers the target band regardless of the excitation type. The simulated radiation patterns according to the excitation types are similar to each other, as shown in Figure 16d. In addition, the modified ground plane size has little effect on the antenna radiation performance.  
Figure 15: The \(1\times 8\) array antenna with a simplified skin phantom: (**a**) simulation setup, (**b**) simulated radiation patterns in the \(xz\) plane for various \(g\) values at 28 GHz, (**c**) simulated S-parameters when \(g\) = 5 mm, (**d**) simulated S-parameters when \(g\) = 10 mm, and (**e**) simulated S-parameters when \(g\) = 20 mm.
## 4 Proposed Scheme
### Phase 3: Verifying and Aggregation  
After receiving reports from the sensing nodes in cluster \(j\), \(CH_{j}\) starts checking and verifying the integrity of each received report \(R_{i}\). It needs to check the embedded watermark and tries to regenerate it by using the shared secret key \(K_{cj}\). Therefore, the verification phase relies on three conditions:  
1. The freshness of the timestamps \(Time\) attached to each report.
2. Verifying the watermark embedded within the measurement.
3. Having \(T\) or more reports where \(T\) is the number of sensor nodes belonging to cluster \(j\).  
Cluster heads can know whether or not the report is the newest measurement based on the field \(Time\). This helps in detecting replayed false reports. For condition 2 and 3, the cluster head first extracts the watermark by generating the same number of random numbers to know exactly where the watermark was embedded. Next, it extracts \(W_{i}\) from the received \(R_{i}\) to obtain \(E_{i}\). A new watermark \(W^{\prime}\) is generated from the value of \(E_{i}\) received. If \(W_{i}\) and \(W^{\prime}\) are the same, the report is accepted. After that, \(CH_{j}\) will have to wait for \(T\) reports. After receiving \(T\) reports, \(CH_{j}\) aggregates the measurements received to obtain \(agg_{j}\), generates \(W_{j}\), embeds it, and then sends it to the AP as \(R_{j}=\left(agg_{j}\left|\ W_{j}\right.\right|\left.Time\right).\) Algorithm 2 details the steps of Phase 3.  
```
Input: reports \(\mathrm{R}_{i}\) from sensor nodes in the cluster Output: \(\mathrm{R}_{i}\) = aggW\({}_{i}\left|\ \right.\) | Timestamp
1 ifTimestamp is not freshthen Reject packet;
2 for count=1 to numberOfPositionsdo
3\(X_{n+1}\) = (\({}_{i}\)\(X_{n}+c_{j}\)) mod \(m_{i}\)
4\(pos[count]\) = \(X_{n+1}\)
5 end if
6E\({}_{i}\), \(W_{i}\) = Extract(EW\({}_{i}\), pos[]) \(\triangleright\) Extract watermark W\({}_{i}\) and E\({}_{i}\) from E\({}_{i}\)W\({}^{\prime}\) = HMAC(E\({}_{i}\) | \({}_{i}\) | \(1\)Timestamp)
7 if\(W^{\prime}\) = Witthen
8 Accept packet
9 agg\({}_{i}\) = aggregate(E)
10 else
11 Reject packet
12
13 end if
14\(W_{j}\) = HMAC(agg\({}_{j}\) | \(CH\)JD | | Timestamp)
15\(agg\)W\({}_{j}\) = Embed (\(W_{j}\), agg\({}_{i}\), pos[])
16\(R_{i}\) = agg\({}_{j}\)W\({}_{j}\) | \(1\)Timestamp
17\(Send(R_{i}\), AP)
```  
**Algorithm 2** Verify and aggregate algorithm (\(\mathrm{R}_{i}\))