esa-sceva/satcom-chunk-collection huggingface dataset:
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 3 Types of Physical Covert Channels  
Physical covert channels can be classified into five main categories: electromagnetic, power, acoustic, thermal, and optical, as previously shown in Figure 1. A summary of bandwidth, bit error rates, example works, and detectability of various types of physical covert channels is listed in Table 1, as discussed above.  
Covert channels can be created from optical emissions of light-emitting diodes (LEDs) in many types of devices, such as monitors [45], keyboards [46], hard drives [34], etc. As long as line-of-sight is maintained, optical covert channels could have a very high transmission rate. However, it is very unlikely for a secure computer to be in an environment that would also have a malicious, undetected camera to act as a receiver and for the flashing LEDs to go unnoticed. Similarly, acoustic covert channels can be created from computer  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Bandwidth (bps)** & **BER (\%)** & **Detectability** & **Example Works** \\ \hline Thermal & 0.002–300 & 1–11 & Medium & [33; 37] \\ Power & 3–1000 & 0–5 & Low & [32; 38] \\ Electromagnetic & 480–300,000 & 0.25–10 & Medium & [31; 41; 42] \\ Optical & 15–4000 & 1–8 & High & [34; 43] \\ Acoustic & 0.25–230 & 1–2 & High & [30; 44] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of characteristics of physical covert channels.  
speakers [30; 44], or even the noise from fans [47]. Acoustic channels generally have lower bandwidth and their waves do not travel very far. Additionally, an observant user may be able to notice the presence of audible noise, which can make this type of covert channel detectable. Since optical and acoustic channels are relatively easier to detect, these covert channels are not discussed in this work. Thermal (Section 3.1), power (Section 3.2), and electromagnetic (Section 3.3) covert channels are summarized with specific novel methodologies discussed in detail in the following sections.
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 3 Hierarchical Age Estimation with Semantic Attention  
In this section, we propose a hierarchical age estimation model based on deep neural networks, as shown in Figure 2. The architecture consists of two main networks: a global network for coarse age prediction and a sub-network for local refined age estimation. In the first stage, the input image is processed through the Swin Transformer architecture, which extracts hierarchical features using patch partitioning and multiple transformer blocks. This global network performs an initial age prediction. The prediction errors are then corrected by a local adjustment module in the second stage, which incorporates semantic attention module and fine-grained feature learning to refine the prediction. The total loss function is designed by combining both global prediction and local prediction losses, optimizing the overall model performance for more accurate age estimation.
###### Abstract  
Simultaneous Localization and Mapping (SLAM) is an essential feature in many applications of mobile vehicles. To solve the problem of poor positioning accuracy, single use of mapping scene, and unclear structural characteristics in indoor and outdoor SLAM, a new framework of tight coupling of dual lidar inertial odometry is proposed in this paper. Firstly, through external calibration and an adaptive timestamp synchronization algorithm, the horizontal and vertical lidar data are fused, which compensates for the narrow vertical field of view (FOV) of the lidar and makes the characteristics of vertical direction more complete in the mapping process. Secondly, the dual lidar data is tightly coupled with an Inertial Measurement Unit (IMU) to eliminate the motion distortion of the dual lidar odometry. Then, the value of the lidar odometry after correcting distortion and the pre-integrated value of IMU are used as constraints to establish a non-linear least-squares objective function. Joint optimization is then performed to obtain the best value of the IMU state values, which will be used to predict the state of IMU at the next time step. Finally, experimental results are presented to verify the effectiveness of the proposed method.  
simultaneous localization and mapping; dual lidar inertial odometry; IMU; time synchronization; tight coupling +
Footnote †: journal: Applied  
Article
# Finite Element Analysis of Geogrid-Incorporated Flexible Pavement with Soft Subgrade
## 4 Conclusions  
The primary aim of this investigation was to assess how geogrid reinforcement impacts flexible pavement, employing finite element analysis. A comprehensive analysis of a 3D  
Figure 14: Profiles of vertical displacement obtained at the top of the subgrade.  
Figure 15: Displacement of the full pavement model.  
layered pavement was conducted, incorporating a geogrid layer atop the subgrade layer while varying subgrade strengths and base layer thicknesses. Key findings from this study are:  
1. Geogrid significantly enhances the overall performance of flexible pavement by effectively managing tensile strain under traffic loads. It demonstrates notable tensile strain in proximity to the loading area while exhibiting minimal compressive strain at the central section.
2. Geogrid significantly decreases the vertical strain exerted at the top of the subgrade. By incorporating geogrid, a noticeable decrease of approximately 36-38% in the maximum vertical strain is evident when compared to pavement without reinforcement.
3. As the CBR value rises, vertical strain diminishes, though the zone of influence extends to a depth of 300 mm within the subgrade, beyond which strain variations are insignificant. At the subgrade's surface, vertical strain decreases by approximately 17%, 39%, and 49% as the CBR value escalates from 1% to 3%, 5%, and 8%, respectively.
4. The plastic strain observed in geogrid-incorporated pavement suggests that the elastic strain resulting from stress is comparatively minor. This implies that the presence of the geogrid layer has a more significant impact on the development of plastic strains than on resilient strains.
5. Overall, this study shows the effectiveness of geogrid incorporation in flexible pavement and the role of geogrid in reducing the total stress, strain, and deflection of the pavement. However, further studies need to be carried out with different traffic loads to identify specific layer thicknesses for weak soil subgrade.  
Conceptualization, S.C. and P.D.; methodology, S.C.; software, S.C. and P.D.; validation, P.D.; data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, P.D.; supervision, P.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## NHL and RCGA Based Multi-Relational Fuzzy Cognitive Map Modeling for Complex Systems  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 3**  
* Information Management Department, Beijing Institute of Petrochemical Technology, Beijing 100029, China
* College of Information Engineering, Capital Normal University, Beijing 100048, China; E-Mail: [EMAIL_ADDRESS]
* Computer Department, North China Institute of Science and Technology, East Yanjiao, Beijing 101601, China; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-10-60228013.  
Academic Editor: [PERSON]  
_Received: 7 October 2015 / Accepted: 13 November 2015 / Published: 27 November 2015_  
**Abstract:** In order to model multi-dimensions and multi-granularities oriented complex systems, this paper firstly proposes a kind of multi-relational Fuzzy Cognitive Map (FCM) to simulate the multi-relational system and its auto construct algorithm integrating Nonlinear Hebbian Learning (NHL) and Real Code Genetic Algorithm (RCGA). The multi-relational FCM fits to model the complex system with multi-dimensions and multi-granularities. The auto construct algorithm can learn the multi-relational FCM from multi-relational data resources to eliminate human intervention. The Multi-Relational Data Mining (MRDM) algorithm integrates multi-instance oriented NHL and RCGA of FCM. NHL is extended to mine the causal relationships between coarse-granularity concept and its fined-granularity concepts driven by multi-instances in the multi-relational system. RCGA is used to establish high-quality high-level FCM driven by data. The multi-relational FCM and the integrating algorithm have been applied in complex system of Mutagenesis. The experiment demonstrates not only that they get better classification accuracy, but it also shows the causal relationships among the concepts of the system.  
**Keywords:** fuzzy cognitive map (FCM); multi-relational data mining (MRDM); nonlinear Hebbian learning (NHL); real code genetic algorithm (RCGA); complex system
## 5 Conclusions  
In this study, multi-period high-resolution aerial photogrammetry images and self-photographed UAV photogrammetry data were used to quantify the landslide and channel sediment volumes to a meter-scale accuracy in the Laishe River catchment from 2009 to 2015, which provided reliable data for discussing sediment transport and morphological changes. From 2009 to 2015, a total of 36.16 M m\({}^{3}\) of landslide material were generated. Typhoon Morakot in 2009, caused 31.63 M m\({}^{3}\) of landslide sediment, which is equal to 87% of the six-year sediment production volume. From 2009 to 2015, the total depositional volume in the channel was 4.4 M m\({}^{3}\). Typhoon Morakot caused deposition of 8.2 M m\({}^{3}\), almost double the six-year channel volume input, which means about 3.78 M m\({}^{3}\) of sediment in the channel was removed from the observed area. Considering the spatial distribution of change in channel depth, the average erosion depth of the upstream channel was 8.5 m, the average deposition thickness of the middle channel was 5.5-6.5 m, and the average deposition thickness of the downstream channel was 3-6 m. This study used high-resolution DEMs to quantify landslide volume and channel erosion on long-term and event scales at the catchment scale.  
This study emphasizes the potential of high-resolution topographic surveys. In particular, it demonstrated the feasibility of using UAVs to quantify the migration of landslide material and the changes in the channel area and volume. In addition, after Typhoon Morakot, the main channel of the Laishe River was blocked by a landslide in the upstream area, and a landslide dam was formed. Meanwhile, the UP2 area in this study also identified the possible location of the landslide dam. Regular monitoring of landslide and channel sediment can effectively determine the formation, location, and possible break form of landslide dams. Therefore, the results of this study have significant potential for application in the monitoring of threshold landscapes. As the intensity of social and economic activities gradually increase, the need for threshold landscapes will also increase. The results from this study would help ensure the security of the residents, and would, therefore, reduce the impact of natural disasters.  
Conceptualization, K.-J.C., C.-W.T., C.-M.T., T.-C.L., C.-J.Y.; methodology, K.-J.C., C.-W.T., C.-M.T.; investigation K.-J.C., T.-C.L.; writing--original draft preparation, C.-W.T. C.-J.Y.; writing--review, K.-J.C., C.-W.T., C.-M.T., C.-J.Y.; editing, K.-J.C., C.-W.T., C.-M.T., C.-J.Y.; supervision, K.-J.C. All authors have read and agreed to the published version of the manuscript.  
This project was partially supported from the Ministry of Science and Technology, MOST 107-2625-M-027-004 and MOST 108-2119-M-027-001, and from the Central Geological Survey, MOEA, B10831.  
The authors declare no conflict of interest.
## 5 Evaluation and Results
### Simulation Setup
#### 5.1.3 Data Inputs and Logging  
Input JSON files:  
* generated_users.json
* mining_licenses.json
* access_policies.json  
Outputs were written to simulation_results.csv and included user ID, resource ID, access status, latency, and timestamp.
## 4 Validation  
This section validates the analytical expressions derived in Section 3.1 to calculate the probability \(P_{c}\) that a packet is not correctly received due to packet collisions with other UEs. To this aim, we compare the results achieved with the analytical expressions, with that obtained through simulations.  
We have implemented a system level simulator in Matlab(tm) that accurately models the 5G NR grant-free scheduling process with \(K\)-repetitions and shared resources. The simulator emulates a single cell with \(N\) UEs that generate aperiodic traffic. Each UE models the packet traffic arrival, using a Poisson distribution with exponential inter-arrival time. The average packet inter-arrival time is equal to \(1/\lambda\), where \(\lambda\) is the average number of packets generated per second. The simulator implements the time/frequency resource grid map of 5G NR. The time and frequency duration of RBs is configurable based on the considered 5G NR numerology \(\mu\). It is possible to also configure the number \(U\) of RBs available per time slot. The number \(K\) of replicas can also be configured in the simulation platform.  
We have conducted a large number of simulations to ensure the accuracy of the simulation results, and compare them to those obtained with our analytical expressions and methodology. Simulations are here shown for \(K\) equal to 2, 4 and 8, \(\lambda\) equal to 0.1 packets, \(\mu\) equal to 3, and \(U\) equal to 6 RBs per slot. UEs transmit small packets with a size of 32 bytes [22] that can be transmitted in a single RB. Figure 3 compares the value of \(P_{c}\) achieved analytically and through simulations for a varying number \(N\) of users in the cell. The figure shows that the results achieved analytically precisely match those obtained through the simulations. Similar trends have been observed for other values of the parameters. The results achieved clearly validate the proposed methodology and the analytical expressions presented in Section 3.1.  
Figure 2: Scenarios with possible self-collisions (\(L=1\) ms and \(K=4\), 6 and 8).  
It is important to highlight that this study focuses on URLLC applications that demand very high reliability levels. In simulations, we compute the number of packets for which the \(K\) replicas have collided with those packets transmitted by other UEs, and then compute the achieved reliability (\(P_{rel}=1-P_{c}\)). It is rare that all \(K\) replicas of a packet collide with transmissions from other UEs for low values of \(N\). This is particularly the case when \(K\) increases. In this context, the computational cost of simulations significantly increases if we want to achieve accurate statistical results. This explains why simulation results are not shown for values of \(N\) below 30 when \(K=8\). It also highlights the value of our analytical expressions and methodology to estimate the performance of 5G NR grant-free scheduling for demanding URLLC applications and aperiodic traffic.
## 3 Results
### Preprocessing
#### 3.1.2 Correlation Analysis  
A correlation analysis was performed to analyze the relationship between the response and the continuous explanatory variables. The odor substances having a high correlation with the complex odor were IBA (0.583) and ACA (0.504), and the correlation coefficient of other odor substances was found to be less than 0.5 (Table 5). As shown on the right side of Figure 4, it can be seen that the substances belonging to the same class (sulfur compounds or VOCs) among odor substances have high correlation values. Accordingly, VIF analysis was applied to determine whether an independent relationship between explanatory variables could be assumed.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **Complex** & **Ammonia** & **Hydrogen** & **Methyl1** & **Dimethyl1** & **Dimethyl1** & **Acetic** & **Propionic** \\  & **Odor** & **Sulfide** & **Mercaptan** & **Sulfide** & **Disulfide** & **Acid** & **Acid** \\ \hline Mean & 800 & 2.72 & 208.51 & 6.04 & 5.65 & 0.12 & 240.52 & 184.06 \\ STD & 1467 & 3.99 & 386.96 & 15.53 & 40.51 & 0.44 & 407.90 & 291.47 \\ Min & 3 & 0.00 & 0.04 & 0.06 & 0.00 & 0.02 & 0.15 & 0.13 \\ Median & 300 & 1.06 & 56.25 & 0.07 & 0.08 & 0.05 & 27.81 & 17.70 \\ Max & 10000 & 22.24 & 2484.00 & 120.00 & 462.00 & 4.28 & 2446.00 & 2109.69 \\ \hline  & **Iso-Butyric Acid** & **Butyric Acid** & **Iso-Valeric acid** & **Valeric Acid** & **Phenol** & **p-Cresol** & **Indole** & **Skatole** \\ \hline Mean & 19.28 & 159.94 & 46.071 & 85.78 & 6.78 & 34.27 & 2.18 & 3.15 \\ STD & 37.08 & 262.03 & 83.60 & 192.73 & 12.70 & 60.15 & 7.08 & 9.15 \\ Min & 0.04 & 0.52 & 0.08 & 0.28 & 0.06 & 0.00 & 0.02 & 0.04 \\ Median & 2.01 & 7.93 & 4.42 & 5.52 & 1.85 & 2.89 & 0.86 & 1.52 \\ Max & 380.00 & 1455.52 & 743.69 & 1869.40 & 125.72 & 481.20 & 95.28 & 127.13 \\ \hline \end{tabular}
\end{table}
Table 4: Summary of the data after the imputation method.  
Figure 3: Map of missing data.  
VIF [35] is a criterion for judging whether a multicollinearity problem is present. If it is usually greater than 10, the explanatory variable is judged to have a multicollinearity problem in Equation (2). VIFi is the VIF value of the \(i\)-th variable and \(r_{i}\) is the r-square value excluding the \(i\)-th variable.  
\[\text{VIF}_{i}=\frac{1}{1-r_{i}} \tag{2}\]  
We found that the VIF values of the four variables (PPA, BTA, IBA, and VLA) exceeded 10, indicating multicollinearity. This means that a linear relationship exists between the explanatory variables; thus, a linear model would not be suitable as a predictive model. To use a linear model, techniques, such as variable selection or variable extraction, which remove correlations between explanatory variables, should be used. However, in this case, the effects of all the odor substances examined in this study on the complex odor could not be determined because the shape of the original variable was changed or removed. Therefore, the study was conducted using machine learning, a nonlinear modeling method, rather than a linear model.
# Void Avoiding Opportunistic Routing Protocols for Underwater Wireless Sensor Networks: A Survey
## 2 Routing Protocols
### Void Area Problem in UWSNs
The acoustic channel characteristics: The underwater environment characteristics have an impact on the acoustic communication channel, changing the signal's quality and strength at different water depths due to disturbed pressure, temperature, and salinity at varying water levels.
# Adaptive Learning in AI Agents for the Metaverse:
## 4 ALMA: A Framework for Adaptive AI in the Metaverse
### Implementation Considerations
#### 4.3.2 Challenges and Solutions  
Deploying the Adaptive Learning Model for AI Agents (ALMAA) in the Metaverse presents several challenges that require innovative solutions to ensure effective functionality and sustainability. These challenges are multifaceted and involve technical, ethical, and logistical aspects.  
(1) Technical Complexity  
One of the primary challenges is the technical complexity associated with developing AI agents that can adapt in real-time to the dynamic and diverse Metaverse environment. These agents must process vast amounts of data, make decisions, and learn from interactions quickly. Solution: Implement state-of-the-art computational frameworks that utilize efficient algorithms optimized for speed and accuracy. Leveraging cloud computing and edge processing can also distribute the workload, enhancing responsiveness and scalability.  
(2) Data Privacy and Security  
As AI agents operate within environments that collect and process personal user data, ensuring privacy and security is paramount. Solution: Integrate advanced encryption methods and secure data protocols to protect user data. Employ anonymization techniques where possible and ensure compliance with global data protection regulations like the General Data Protection Regulation (GDPR). Develop a transparent mechanism that allows users to control what data are collected and how it is used.  
(3) User Acceptance and Trust  
Gaining user trust in AI agents, particularly in contexts where they make autonomous decisions, is a significant challenge. Solution: Build transparency into the decision-making processes of AI agents. Provide users with clear, accessible explanations of how their data are used and how decisions are made. This transparency will help build trust and acceptance among users.
# Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating
###### Abstract  
At present, aircraft radome coating cleaning mainly relies on manual and chemical methods. In view of this situation, this study presents a trajectory planning method based on a three-dimensional (3D) surface point cloud for a laser-enabled coating cleaning robot. An automated trajectory planning scheme is proposed to utilize 3D laser scanning to acquire point cloud data and avoid the dependence on traditional teaching-playback paradigms. A principal component analysis (PCA) algorithm incorporating additional principal direction determination for point cloud alignment is introduced to facilitate subsequent point cloud segmentation. The algorithm can adjust the coordinate system and align with the desired point cloud segmentation direction efficiently and conveniently. After preprocessing and coordinate system adjustment of the point cloud, a projection-based point cloud segmentation technique is proposed, enabling the slicing division of the point cloud model and extraction of cleaning target positions from each slice. Subsequently, the normal vectors of the cleaning positions are estimated, and trajectory points are biased along these vectors to determine the end effector's orientation. Finally, B-spline curve fitting and layered smooth connection methods are employed to generate the cleaning path. Experimental results demonstrate that the proposed method offers efficient and precise trajectory planning for the aircraft radar radome coating laser cleaning and avoids the need for a prior teaching process so it could enhance the automation level in coating cleaning tasks.  
coating cleaning; point cloud alignment algorithm; B-spline curve; robot path planning +
Footnote †: journal: Applied sciences