esa-sceva/satcom-chunk-collection huggingface dataset:
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion

Table 4: _Cont._There are several similarities between various feature extraction techniques adopted by various researchers. One prominent similarity is the use of pre-trained CNNs, such as ResNeXt, InceptionV3, and Xception, to extract spatial features from individual frames. Many studies, including those by [PERSON] and [PERSON] [32], [PERSON] et al. [43], and [PERSON] [69], utilise ResNeXt-50 due to its modular architecture, which makes it adaptable to various tasks and effective in high-level spatial feature extraction. Similarly, the Xception CNN is frequently used by [PERSON], [PERSON], and [PERSON] [36] and [PERSON] and [PERSON] [65] for spatial feature extraction, particularly because its depthwise separable convolutions improve computational efficiency without sacrificing accuracy. Another shared approach across these studies is the integration of LSTM or other RNNs for temporal feature extraction. LSTMs are particularly adept at capturing long-range dependencies between frames, as utilised by [PERSON] and [PERSON] [40], [PERSON] et al. [38], and others. This combination of CNN for spatial extraction and LSTM for temporal analysis has become a standard practice in the field, given its effectiveness in processing sequential data like videos. Several studies also implement additional layers to enhance feature extraction. For instance, [PERSON] et al. [61] incorporate optical flow analysis into a CNN-LSTM model to capture motion between frames, adding depth to the temporal analysis. [PERSON] et al. [38] take this further by integrating a multi-head attention layer, allowing the model to focus on the most relevant temporal features. This highlights the growing trend of using attention mechanisms to improve the performance of hybrid CNN-LSTM models, especially for complex sequential tasks. While many studies share architectural similarities, there are key differences in the specific models and techniques employed. One notable difference lies in the choice of the CNN backbone. While ResNeXt and Xception are popular, some works like [PERSON] et al. [39] opt for InceptionV3, focusing on textural feature extraction, which highlights a different aspect of video frames. Additionally, [PERSON] et al. [60] introduced a multi-stream network with EfficientNet-B3, showing that different CNN architectures bring varying degrees of efficiency and accuracy depending on the task requirements. There is also considerable variation in how the models process the frames. Some approaches, like [PERSON] et al. [31], apply hybrid CNN-LSTM models with PSO for fine-tuning, adding an optimisation layer that can enhance the accuracy of the predictions. This contrasts with models that rely on more straightforward CNN-LSTM architectures without such optimisation techniques, as seen in studies like [PERSON], [PERSON], and [PERSON] [57]. Furthermore,
## 4 Artificial Neural Network Classifiers Used to Select EA Signals
### Wavelet Neural Network  
A wavelet neural network (WNN) structure consists of an input, output, and hidden layer of wavelet neurons (Figure 7).  
The function of wavelet neurons' activation \(\varphi\) is described by the relationship [41]:  
\[\varphi(\mathbf{x})=\prod_{i}^{m}\psi\bigg{(}\frac{x_{i}-\zeta_{i}}{\varepsilon_{i} }\bigg{)}. \tag{10}\]  
where \(\mathbf{x}\) is the input signal vector, \(\psi\) the wavelet function, \(\zeta\) the translation parameter, and \(\varepsilon\) the scale parameter.  
The process of such network teaching involves the selection of the scale parameters and shifting each wavelet \(\psi\) used inside a wavelet neuron nucleus. For the study, the authors used Mexican hat wavelet function.  
Contrary to other network types, drawing of the initial values of the wavelet parameters \(\zeta\) and \(\xi\) may result in the teaching algorithm being stuck in the local minimum. That is why network teaching is preceded by the initialization of initial parameters. A feature selection method developed by [PERSON] and [PERSON] was used for the study [42]. It consists of the following steps:  
* Step 1: creating a library of wavelets with different parameters.
* Step 2: removing those wavelets not falling within the variability range of each input parameter.
* Step 3: developing a ranking of wavelets and iterative selection of the best wavelets using the Gram-Schmidt ortho-normalization algorithms.  
Figure 6: RBF network diagram.  
Figure 7: Wavelet neural network structure.  
Subsequently, fully-initialized WNN was learned with the backpropagation algorithm. A detailed description of the WNN's structure and learning algorithm used for the study can be found in [41].
## 5 Experimental Results
### Time Efficiency Analysis of Dynamic Key Replacement  
Given the computational limitations of microcontrollers in IoT devices, evaluating the time efficiency of the proposed key replacement mechanism is essential to ensure it operates effectively in resource-constrained environments. To this end, experiments were conducted on an Arduino UNO development board to assess the performance of the key replacement mechanism at various frequencies. During the tests, plaintext messages were encrypted using AES-128, with the encryption key replaced at different intervals. For each scenario, a total of 3000 encrypted packets were transmitted, and the time required for each transmission was recorded to measure the time overhead associated with different key replacement frequencies.  
Table 5 presents a detailed breakdown of the average transmission time per packet when the key was replaced after every 1, 10, 30, and 50 packets. The results indicate that replacing the key for every transmission increases the average time per packet from 12 ms (with infrequent replacements) to 50 ms. Although this represents a measurable increase, it remains well within acceptable limits for real-world applications. To provide context, we compared these results to typical access control systems, where the average time for a card swipe operation ranges between 0.5 and 1 s. Even with the most frequent key replacement scenario, the computational overhead remains negligible compared to the user interaction time in such systems.  
The experimental results confirm that the proposed key replacement mechanism effectively enhances security by preventing key reuse while maintaining computational feasibility. The mechanism's average transmission time across all tested frequencies demonstrates its adaptability to resource-constrained environments. Additionally, the low transmission times validate that frequent key replacements do not degrade IoT device performance, making this mechanism highly suitable for practical applications in smart access control systems and other low-latency IoT environments.
# Underwater Biomimetic Covert Acoustic Communications Mimicking Multiple Dolphin Whistles
## 1 Introduction
The MOS test confirmed the high similarity of the proposed technique to actual dolphin whistle sounds. The contributions of the proposed methods are as follows:  
1. This paper proposes an underwater biomimetic covert communication method that mimics multiple whistles produced by dolphin groups, which offers higher covertness and data rates compared to conventional underwater biomimetic communication methods. 2. The proposed approach combines both TFSK and CV-CFM to generate multiple whistles and develops a mitigation method for interference when multiple whistles are overlapped. 3. To achieve high transmission rates, a sequential decoding method is proposed to demodulate the multiple complex whistles resulting from the combination of the two modulation schemes. 4. To evaluate the communication performance and degree of mimicking, this paper conducts computer simulations, ocean experiments, and MOS tests, and the superiority of the proposed method is proven.
# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 3 Methodology  
We propose an innovative cross-modal attention mechanism that not only integrates geometric, shape, and semantic features but also significantly enhances feature representation through a hybrid architecture combining cross-attention and self-attention mechanisms. Unlike frameworks such as SNI-SLAM, which predominantly rely on single-type feature fusion, our approach incorporates multi-scale feature extraction and full-attention mechanisms, enabling more comprehensive capture of hierarchical environmental information. Specifically, the cross-attention mechanism facilitates deep interaction between heterogeneous feature modalities, while the self-attention mechanism emphasizes critical elements within each feature type, endowing the system with superior adaptability and robustness in complex scenarios.  
As Figure 1 describes, our approach conducts dense semantic modeling of scenes using consecutive RGBD frames. This section primarily encompasses three main components: forward feature fusion, inverse semantic mapping, and loss functions. In the feature fusion component, we elaborate on how to fuse appearance, semantic, and shape features through cross-attention and self-attention mechanisms. Cross-attention enables the exchange of information across different feature modalities, while self-attention helps in highlighting the significant elements within each feature type. By combining these two attention mechanisms, we can effectively integrate diverse features, enhancing the representational power of the data. The inverse semantic mapping component details how to represent multi-semantic features through a hierarchical approach. This hierarchical structure allows for a more organized and comprehensive handling of semantic information. Specifically, it facilitates semantic modeling, where the system can identify and classify different objects and their relationships in the scene. Additionally, it is utilized for color prediction, enabling the system to estimate the color of objects based on the learned semantic and geometric information. Moreover, depth estimation is also achieved through this hierarchical semantic representation, as the system can infer the distance of objects from the camera, contributing to a more complete 3D understanding of the scene.  
Attention mechanisms are powerful tools in machine learning, assigning weights to input elements to prioritize critical features and improve performance. In SLAM, the complex environment with varying lighting and geometric features benefits from attention mechanisms, which can capture these details effectively.  
The cross-attention mechanism enables deep interaction between heterogeneous feature modalities, integrating complementary information. The self-attention mechanism highlights key elements within each feature type through adaptive weights. Empirical studies, like in automated fault diagnosis of air-handling units, prove the effectiveness of attention-based approaches [21]. We apply attention mechanisms to SLAM to enhance environmental perception and scene reconstruction.
## 4 Results and Discussion
### Influence of Emulsifier Diffusion and Association on Emulsion Stabilization  
The stabilization of oil droplets within HIPEs depends critically on the rapid interfacial migration and association behavior of emulsifier molecules. In the absence of sufficient interfacial coverage, oil droplets tend to coalesce in order to minimize surface energy. This phenomenon necessitates the dynamic diffusion of emulsifier molecules toward the interface to arrest droplet deformation and maintain emulsion morphology. As demonstrated in Figure 6(a1-e1), when adequate quantities of emulsifier reached the droplet interface in time, the structural integrity of the dispersed oil phase was preserved, resulting in successful W/O emulsion formation. Conversely, insufficient emulsifier presence led to unstable interfacial structures and eventual phase transition, as shown in Figure 6(a2-e2). This transition was particularly apparent in emulsifier-oil combinations lacking strong interfacial interactions.  
Moreover, the role of \(\pi\)-\(\pi\) stacking among emulsifier molecules emerged as a key factor in providing additional mechanical reinforcement to the interfacial film. Emulsifier AS2, in particular, demonstrated the ability to form dimeric and trimeric stacking configurations that effectively bridged adjacent droplets, thereby withstanding the destabilizing forces within the emulsion system. This was especially critical when stabilizing aromatic oils that inherently possess high interfacial tension and strong coalescence tendencies. As shown in Figure 7a,b, AS2 maintained its integrity through stacking bridges even under such challenging conditions, while AS3 failed to form such networks. The stabilizing mechanisms observed across the simulations could be categorized into four distinct modes: (1) nanomicelle-based steric hindrance and bridging by stacked emulsifier aggregates (e.g., AS1), (2) interfacial bridging with non-polar aliphatic oils (e.g., AS2/AS3 with alkanes), (3) polar functional group interactions with moderately active oils (e.g., AS2/AS3 with amides or ethers), and (4) full interface coverage and bridging by the emulsifier itself, particularly for challenging systems like aromatics (e.g., AS2 with aromatic oils). These mechanisms collectively emphasize the critical roles of van der Waals interactions and \(\pi\)-\(\pi\) stacking in sustaining HIPE morphology.  
Figure 6: Time evolution of W/O emulsions and phase transitions for different emulsifier–oil systems. (**a1–e1**) represent successful stabilization via interfacial emulsifier coverage; (**a2–e2**) depict emulsions undergoing phase transition due to insufficient interfacial diffusion. Time increment is 2 \(\times\) 10\({}^{5}\) steps.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 3 Cooperative Data Fusion in JSC Network for Vehicular Scenarios
### Modeling Complex Targets: The Vehicle  
The objects of interest for sensing, such as automobiles, bicycles, pedestrians, and drones, occupy a physical space in the real world. Whether an object is observed as a mere point or exhibits an extended spatial presence primarily depends on the sensor's characteristics, particularly its resolution. When a sensor offers high resolution relative to the object's size, it can yield multiple detections of the same target during each scanning cycle. These detections appear as spatially distributed measurements surrounding the object, whereas a lower-resolution sensor would lead to perceiving the object as a single scattering point.
# An Investigation of CTP Use for Wireless Structural Health Monitoring of Infrastructure
## 2 Attempting to Make CTP Cooperative for the Infrastructure Shm
### Ctp  
In this section, a brief description of the CTP is given. As it can be seen in [49], CTP uses a routing gradient that is based on ETX. ETX is an additive metric that is calculated per node as the ETX of its parent plus the ETX of its link to its parent. Packet retransmissions are included in the calculation as well. For the forwarding of the packets, CTP creates a hierarchical tree-based network, where each node selects its forwarding parent based on the minimum ETX value of a parent node, as we can see in Figure 2. When it comes to the infrastructure monitoring application, this tree can be useful for the transmission of the data to the basestation, which may reside in one end of the infrastructure.  
A problem that routing protocols experience is the routing loops. This occurs when a new route that has a much higher ETX than its old one, often in response to losing connectivity with a potential parent. As such a route may include a node that is essentially a descendant and a routing loop takes place. CTP has two mechanisms for handling routing loops. Initially, when CTP receives a data packet with a gradient value lower than its own, it remarks the routing procedure as problematic, in terms of the tree infrastructure. Note that every packet includes the routing gradient as a field. CTP attempts to solve the problem by sending a beacon with the purpose that the node responsible for the loop adjusts its route. The second mechanism of CTP is not to consider routes with an ETX higher than a reasonable constant, which is dependent on the implementation.  
The duplication of packets is another problem that CTP is facing. For example, when the data packet is received but the acknowledgment is not. This creates a significant problem as duplication is exponential. Duplicate suppression occurs when packets come from the same origin and has the same sequence number. Additionally, a frame that declares the time that a packet has lived, which becomes incremented in every hop, can result in duplicate messages being dropped.
## 6 Future Work and Conclusion
### Conclusion  
In this paper, we discussed different approaches of combining MANETs and P2P systems into MP2P systems. We analyzed major challenges in securing MP2P networks by analyzing attack vectors of MANETs and P2P systems. We considered attacks such as Sybil or routing attacks that affect both MANETs and P2P systems and, thus, will also affect MP2P networks. We discussed the efficiency of security mechanisms that were proposed for MANETs and P2P systems independently in the context of MP2P systems. Most P2P networks and most security measures for P2P systems were developed under the assumption of a large amount of available resources. Due to this, limitations introduced by the MANET underlay affect the P2P overlay and the security measures for P2P systems strongly. On the other hand, security mechanisms developed for MANETs are still applicable in MP2P systems. Yet, the security measures can be improved when the security mechanisms designed for MANETs and P2P systems are combined in a cross-layered approach. We introduced an adapted security mechanism for MP2P networks that harnesses a cross-layer information exchange. This security mechanism provides robustness against multiple routing attacks while keeping the generated overhead on a reasonable level, thus considering the resource constraints of MP2P systems.
# Erosion Resistant Hydrophobic Coatings for Passive Ice Protection of Aircraft
## 2 Materials and Methods  
### Materials  
Desmodur(r) Z4470 BA (Z4470, NCO% = 11.9%, functionality \(\sim\)3.5, Covestro, Leverkusen, Germany), anhydrous N,N-dimethylformamide (DMF, Sigma-Aldrich, St. Louis, MO, USA), 1H,1H,2H,2H-perfluoro-1-octanol (PFOA, Career Henan Chemicals, Zhengzhou, China), Terathane(r) PTMEG-650 (average Mw of 650 g/mol, Invista, Wichita, KS, USA), MCR-C18 (monocarbonol terminated polydimethylsiloxane, average Mw of 5000 g/mol, Gelsen Inc., Bucks County, PA, USA) and dibutyltin dilaurate (DBTDL, Sigma-Aldrich) were used in the as-received conditions. Fiberglass plates (FR-4, 2 \(\times\) 2 \(\times\) 1/4 in) were purchased from Curbell plastics. Aeordur HS 2118 CF primer (Akzo Nobel, Amsterdam, The Netherland) was purchased from Hypercoat-Downing Ltd., Mississaug, ON, Canada, X-9032/G401 Nix Stix(r) release agent was acquired from Stoner Molding Solutions. The matrix coating, namely PU-R, is a 2-part solvent-borne polyurethane elastomer coating developed at the National Research Council Canada for erosion protection of helicopter rotor blades against high-speed solid particles and rain droplets. Its formulation will be published elsewhere.
## 5 Discussion
### Assessing the Approaches Used in the Remote Sensing of Pasture Monitoring
Remote sensing products must be appropriately quantified regarding what they represent on the ground. Adaptive pasture management integrates anthropogenic, environmental, and climate variables and remote sensing to provide insight into intensively grazed pasture dynamics, thus making pastures and land management sustainable. This review shows that a combination of different remote sensing strategies, e.g., aircraft imagery and Landsat [121], Phenocam camera [80], and UAS and satellite [114], can be used to understand the temporal and spatial variability of pastures to seasonal and climate change in establishing a framework for adaptive management. For example, a very high temporal time-lapse camera has been used to study the phenology of pasture species at the paddock scale compared with that from the landscape scale using MODIS and Landsat instruments [80]. Consequently, remote sensing products have been used with anthropogenic variables (i.e., stocking rate, grazing survey data, fire, GPS (livestock distribution)) and environmental/climate data (rainfall, temperature, soil) as predictors to improve model accuracy significantly [3; 176; 212]. However, only 18 studies considered the adaptive management of pastures with remote sensing in this review (Figure 4b). Therefore, more research is encouraged to demonstrate how adaptive management principles with remote sensing tools can support the sustainability of pasture management.
## 3 Results and Discussion
### 28 Days Compressive Strength  
Figure 10 shows the compressive strength of mortar using converter slag at 28 days of age. The compressive strength of the mortar using as received converter slag was 53.12 MPa, and the compressive strength of the mortar using reacted converter slag was measured to be 50.16 MPa. A slight decrease in compressive strength was observed, and it is primarily associated with the increase in absorption capacity associated with generation of cracks from the converter slag after reaction with SCD.  
Considering that the compressive strength of the mortar using standard sand (plain) at 28 days of age was measured to be 37.99 MPa, it was found that the converter slag after reaction with SCD can be a good source of fine aggregate because the strength was still more than 32% higher than mortar made of standard sand. Although a slight decrease in compressive strength is expected on mortar using converter slag after reaction with SCD, completely carbonated converter slag will be away from the worries that may produce potential expansion caused by lime and periclase, and therefore worth more for its recycling with added value of mineralization of CO2 to contribute global warming issue.  
Figure 9: Destruction process during expansion test of mortar bar using converter slag as fine aggregate.
## Acknowledgments  
The authors thank [PERSON] and [PERSON] for their assistance in preparing the Queso Fresco.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**60 °C hold for 10 min**} & \multicolumn{2}{c}{**40 °C hold for 25 min**} \\ \cline{2-5}
**Compound** & \begin{tabular}{c} **Retention** \\ **time** \\ **(min)** \\ \end{tabular} & \begin{tabular}{c} **Relative** \\ **abundance** \\ **(thousands)** \\ \end{tabular} & \
\begin{tabular}{c} **Retention** \\ **time** \\ **(min)** \\ \end{tabular} &
\begin{tabular}{c} **Relative** \\ **abundance** \\ **(thousands)** \\ \end{tabular} \\ \hline
3-Methylbutanal & 2.7 & 2600 & 2.7 & 69,800 \\
2-Methylbutanal & 3.3 & 11,300 & 2.8 & 80,100 \\
3,3-Dimethyl-2-butanone & ND & & 3.5 & 49,100 \\ Dimethylsulfide & ND & & 4.1 & 20,700 \\
1-Pentanol & 5.1 & 26,000 & ND & \\
3-Hexanone & 5.6 & 57,900 & 4.9 & 36,200 \\
2-Hexanone & ND & & 5.0 & 18,100 \\ Hexanal & 6.3 & 422,300 & 5.3 & 1,654,100 \\
3-Ethyl-2-pentanone & ND & & 4.3 & 28,400 \\
3-Heptanone & ND & & 7.0 & 20,700 \\
2-Heptanone & ND & & 7.1 & 43,900 \\
1-Heptanal & 12.9 & 59,300 & 7.4 & 124,100 \\
2-Heptanal & ND & & 8.5 & 12,900 \\ Heptanol & 16.6 & 7200 & 8.7 & 15,500 \\
2-Pentylfuran & ND & & 9.1 & 49,100 \\
1-Octen-3-ol & 17.0 & 28,900 & ND & \\
2-Ethylhexanol & 19.1 & 59,300 & 9.8 & 124,100 \\ Nonanol & 20.8 & 23,100 & ND & \\
2-Nonen-1-ol & 21.3 & 5800 & 9.9 & 23,300 \\ Nonanal & 22.0 & 458,500 & 11.1 & 121,500 \\ Octanoic acid & 24.2 & 65,100 & ND & \\ Decanal & 25.3 & 18,800 & ND & \\ Nonanoic acid & 27.1 & 36,200 & ND & \\ Decanoic acid & 29.8 & 89,700 & ND & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Relative abundance of volatile compounds in whey protein concentrate, and variations with temperature conditions. ND = not detected.