esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Machine Learning and 5G Security
### Security Protocol Optimization with Machine Learning
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Machine Learning** & **Applications** & **Key Techniques** & **Advantages** & **Challenges** \\ \hline \multirow{5}{*}{\begin{tabular}{c} Potential of ML in \\ security \\ \end{tabular} } & - & Intrusion detection & - & Supervised learning (e.g., \begin{tabular}{c} - \\ SVM, decision trees) \\ \end{tabular} & - & \begin{tabular}{c} Enhanced network \\ security \\ - \\ \end{tabular} & - & Data quality and \\  & - & Predictive security & - & Unsupervised learning & detection & labeling issues \\  & - & Adaptive security & - & Adaptive security & - & Resource-intensive \\  & mechanisms & - & Reinforcement learning & mechanisms & & \\ \hline \multirow{5}{*}{\begin{tabular}{c} Pertinent ML \\ techniques in 5G \\ security \\ \end{tabular} } & - & Intrusion detection & - & Supervised learning (e.g., \begin{tabular}{c} - \\ SVM, decision trees) \\ \end{tabular} & - & \begin{tabular}{c} Effective intrusion \\ detection \\ - \\ \end{tabular} & - & \begin{tabular}{c} Training data \\ availability \\ - \\ \end{tabular} \\  & - & Network intrusion & - & Unsupervised learning & - & Anomaly detection & - & Scalability for large \\  & response & - & Reinforcement learning & - & Adaptive response & networks \\ \hline \multirow{5}{*}{\begin{tabular}{c} Applications of ML \\ in 5G security \\ \end{tabular} } & - & Intrusion detection & - & Classification & - & Early threat detection & - & Model overfitting \\  & - & Prodictive security & - & Time-series forecasting & - & Improved network & - & False \\  & - & Anomaly detection & - & Deep learning (e.g., \begin{tabular}{c} LSTM) \\ \end{tabular} & - & resilience & & positives/negatives \\  & - & Predicting DDoS attacks & - & \begin{tabular}{c} Clustering (e.g., \\ k-means) \\ \end{tabular} & - & \begin{tabular}{c} - \\ Classification (e.g., SVM, \\ decision trees) \\ \end{tabular} & - & Precise anomaly & - & Complex model tuning \\  & - & \begin{tabular}{c} Scalable for \\ high-speed networks \\ \end{tabular} \\  & - & Reduced detection time & - & Deep learning (e.g., \begin{tabular}{c} autoencoders) \\ \end{tabular} & - & \
\begin{tabular}{c} Early threat \\ anticipation \\ - \\ \end{tabular} & - & Model drift over time \\  & - &
\begin{tabular}{c} Data labeling \\ challenges \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 12: _Cont._while [PERSON] et al. [101] have worked on the intricacies of deep neural networks within a 5G context. The unsupervised learning section of the table is similarly exhaustive, covering methodologies ranging from k-means clustering to Affinity Propagation Clustering.
## 4 Performance Analysis
### Effective Capacity
#### 4.4.1 Single-Link System  
By substituting (7) into (51), the integral term \(I_{5}\) in (51) can be yielded as  
\[I_{5}=\frac{\alpha}{2\Gamma(\mu)\Gamma(n)}\int_{0}^{\infty}\gamma^{-1}(1+\gamma) ^{-A}H_{1,1}^{1,1}[\lambda\gamma^{0.5\alpha}|_{(\mu,1)}^{(1-n,0.5\alpha)}]d\gamma. \tag{52}\]  
To solve the integral in \(I_{5}\), we first represent \((1+\gamma)^{-A}\) as \(H_{1,1}^{1,1}\left[\gamma|_{(0,1)}^{(1-\Lambda,1)}\right]/\Gamma(\Lambda)\) in terms of [PERSON]'s H-function by using the identities in [45] (Equations (8.4.2.5) and (8.3.2.21)). Then with the aid of [46] (Equation (2.8.4)), after some mathematical manipulations, \(I_{5}\) can be calculated as  
\[I_{5}=\frac{\alpha}{2\Gamma(\mu)\Gamma(n)\Gamma(A)}H_{2,2}^{2,2}[\lambda|_{( \mu,1),(A,0.5\alpha)}^{(1-n,0.5\alpha)}]. \tag{53}\]  
Then, plugging (53) into (51), the exact expression of the effective capacity for the single-link system over \(\alpha\)-\(\mu\)/IGA fading channels can be obtained. When we substitute (10) into (51), the approximated expression of the effective capacity for the single-link system over the \(\alpha\)-\(\mu\)/IGA fading channels can be deduced as  
\[\Re=-\frac{1}{A}\log_{2}\left(\sum_{i=1}^{N}\frac{0.5\alpha\xi_{i}}{\Gamma(\mu )\Gamma(A)}H_{1,2}^{2,1}[\psi_{i}{}^{-0.5\alpha}|_{(\mu,1),(A,0.5\alpha)}^{(1,0.5\alpha)}]\right). \tag{54}\]
# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 2 Bearing Fault Diagnosis Method Based on Knowledge Reasoning
### Decision Conflict Resolution Method Based on Sensitive Differences of Multi-Model Fault
#### 3.2.1 Sensitivity Analysis of Bearing Faults in Different Environments  
During long-term research and practice, scholars in related fields have found that under different environments or certain operating conditions, the fault data collected by different types of sensors at the same time as industrial bearings show obvious differences in sensitivity. For example, motor bearings can be classified into two types according to the installation location, namely, bearings installed inside the motor (inner bearings) and bearings installed outside the motor (external bearings). When the external bearing of the motor fails, the current signal will have a large attenuation in the process of transmission along the transmission system. In addition, it is easy to be overlapped by the interference generated during the operation. Hence, the effect of fault diagnosis based on the vibration sensor signal at this time should be better than that based on the current sensor signal. Due to the different physical properties of the vibration signal and the current signal itself, it is more effective to use the current signal to detect bearing faults for faults with low characteristic frequencies. Bearing fault diagnosis is more sensitive for the acoustic sensor signal than the vibration sensor signal because the vibration signal generated at low speed is covered by strong background noise, making the components of the vibration signal too complex. In addition, through the research on the position of the measuring point of the industrial motor bearing sensor network, it can be known that when the bearing fails, the sensor signals at different positions will change, but the degree of change is different. Data collected by sensors at some locations may vary significantly, while data collected by sensors at other locations may vary slightly. Based on the above analysis, when a high degree of evidence conflict or contradiction occurs in the industrial motor bearing fault diagnosis process, it is unreasonable to directly adopt the fusion rule of \"same default information source sensitivity\" in the classic D-S evidence theory. Because of the above problems, we combine the idea of evidence trust degree distribution with the degree of sensitivity difference between evidence and improve the classic D-S evidence theory so that the basic probability assignment of highly conflicting evidence is more in line with the actual situation. In the following, the degree of sensitivity difference between conflicting evidence is subdivided and discussed into two categories: balanced sensitivity of faults and unbalanced sensitivity of faults.
## 3 Results and Discussion
### Result of LC-MS Analysis  
_Protein Identification_  
The SDS-PAGE analysis of CPL-Rx revealed the presence of a clear protein band (Figure 2). In order to identify its content, the band was subjected to MS/MS analysis. The obtained results showed the evident presence of one polypeptide, namely \"Protein Plastid Transcriptionally Active 14-Like\" (Figure S1 in the Supplementary material). The identified protein appears as \"predicted\" in the NCBI database (LOCUS XP_024155957). Therefore, the sequence found was aligned to an analogue from _Arabidopsis thaliana_ identified at the protein level (UniProtKB-Q84 JF5) in order to confirm the similarity using the SIM alignment tool from www.expasy.org, accessed on 30 Augusts 2022. The identified sequence showed 70.68% identity when compared to Protein Plastid Transcriptionally Active 14 from _A. thaliana_ (Supplementary material; Figure S2). The protein is a methyltransferase engageed in regulation of the transcription of chloroplast development, especially for thylakoid formation. This component of transcriptionally active chromosome complexes is mostly expressed in leaves, flowers and seedlings and, to a lesser extent, in stems and roots. It was found that the protein interacts with PTAC12 and PTAC7, i.e., expression factors that are essential for the initiation of photomorphogenesis mediated by phytochromes (PHYs), by mediating PHY localisation to photobodies and thylakoid formation, respectively [32]. It can be concluded that the detected protein is probably an element of the transcription machinery regulating the expression of proteins responsible for the development of chloroplasts by modulating the activity of RNA polymerase.  
Figure 2: SDS-PAGE analysis of rugosa rose root crude polysaccharide (CPL-Rx). Lanes: M—standard marker; Rx—crude root polysaccharide.
## 3 Results
### Articulated Object Model  
The average parameter estimation time for a single articulated object in 3D space is shown in Table 7. Most of the time is spent on the estimation of the axis of rotation of a given object in 3D space. It takes 3.11% less time to estimate the parameters of the front. The estimation of the translation axis takes a shorter time, by 8.30%, than the estimation time of the rotation axis. This time was calculated from the moment of determining the plane of the translation front in 3D space to finding the normal to this plane, which is the translation axis of the object.  
Table 8 shows the results of the detection of objects and their parameters on sequences from the test set. The system succeeded in estimating the parameters of 96.88% of the fronts and 96.88% of the axis parameters of the translational objects, as well as 97.50% of the handles of the translational objects. The total number of handles of translational objects is greater than the total number of fronts and axes of these objects, as some objects have more than one handle. In the case of rotating objects, the system was able to estimate 94.34% of  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Correct Detections of** & **False Positive Detections of** \\  & **Rotational Axis** & **Rotational Axis** \\ \hline before postprocessing & **249** & 136 \\ after postprocessing & 191 & **7** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detection of the rotational axis before and after postprocessing the results from the CNN. Bolded are best results.  
Figure 8: Example joint detections obtained from the rotational axis estimation module.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Front** & **Handle** & **Rotation Axis** & **Translation** \\  & **Parameters** & **Parameters** & **Estimation** & **Axis Estimation** \\ \hline Avg. processing & & & & \\ time [s] & 0.0374 & 0.0167 & 0.0386 & 0.0354 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average parameter estimation time of articulated objects.  
the parameters of the fronts and 88.10% of the parameters of the axes of these objects, and 94.34% of the handles.  
Figure 9 shows the results of the system when the camera did not change its position between object states. In Figure 10, we show the results of the system when the camera changed position with respect to the observed objects. The planes of fronts and handles are represented as a point cloud, the colors of which depend on the object class. Rotation fronts take on shades of blue and purple, translation fronts take on shades of green, and handles are red. The translation and rotation axes in the environment model are visualized as straight lines, colored green and light blue, respectively.
###### Abstract  
Runway icing presents a significant challenge to aviation safety, especially in saline environments, where comprehending the icing mechanisms and predicting the icing onset are crucial for efficient airport operations. This study developed a specialized experimental apparatus to examine the mechanisms of airport pavement icing under controlled conditions. The apparatus, comprising an environmental chamber, a data acquisition system, and a scaled pavement structure, allowed for detailed simulations of various environmental factors. The experiments specifically examined the effects of the air temperature (\(-3\)\({}^{\circ}\)C to \(-20\)\({}^{\circ}\)C), wind speed (2 m/s to 6 m/s), and deleting salt concentration (0% to 80%) on the icing process. The results demonstrated that higher wind speeds and lower temperatures significantly accelerated the pavement surface cooling, leading to earlier icing onset. Under the most extreme conditions, the pavement reached critical icing temperatures within 15 min. In contrast, higher deleting salt concentrations delayed the icing onset by up to 67 min and 33 s at an 80% concentration, effectively lowering the pavement surface temperature. A comparison of the experimental data with the theoretical predictions showed initial consistency, although the discrepancies increased over time. This study culminated in the development of a simplified prediction model, which was validated against the experimental results, offering a practical tool for airport operators to manage runway safety during winter conditions.  
runway icing; saline environment; icing mechanisms; prediction model +
Footnote †: journal: Applied Sciences  
Article An Experimental Validation-Based Study of Airport Pavement Icing Mechanisms in Saline Environments and the Development of a Simplified Prediction Model  
[PERSON], [PERSON], [PERSON], [PERSON], S. An Experimental Validation-Based Study of Airport Pavement Icing Mechanisms in Saline Environments and the Development of a Simplified Prediction Model
# Damping Rates of Anti-Vibration Gloves Made of Different Materials
## 3 Results and Discussion
### Results of Laboratory Measurements  
Hand-arm vibration exposure was measured 3 times, with and without gloves, using a fixed vibrating sieve set in the Mining Engineering Mineral Processing Laboratory. The average values of the measurement results were considered for comparison. The hand-arm vibration exposure value measured without gloves was determined to be 0.74 m/s\({}^{2}\), and the values measured while wearing 12 different anti-vibration gloves are shown in Table 4, along with their vibration damping rates (%).  
To visualize the resulted listed in Table 4, Figure 7 shows a bar graph of the vibration damping rates (%) for the 12 gloves. Accordingly, the highest vibration damping rate is observed for glove No. 1 (44.59%), and the lowest vibration damping rate (%) is observed for glove No. 12 (22.97%).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Glove No.**} & \multicolumn{2}{c}{**Average Values of the Vibration Exposure**} & \multirow{2}{*}{**Vibration Damping**} \\  & & **A(8)\({}_{\text{avg}}\), (m/s\({}^{2}\))** & **Rate** \\ \cline{2-3}  & **Ungloved** & **Glyed** & **(\%)** \\ \hline
1 & & 0.41 & 44.59 \\ \hline
2 & & 0.44 & 40.54 \\ \hline
3 & & 0.45 & 39.19 \\ \hline
4 & & 0.46 & 37.84 \\ \hline
5 & & 0.47 & 36.49 \\ \hline
6 & & 0.48 & 35.14 \\ \hline
7 & & 0.49 & 33.78 \\ \hline
8 & & 0.50 & 32.43 \\ \hline
9 & & 0.52 & 29.73 \\ \hline
10 & & 0.53 & 28.38 \\ \hline
11 & & 0.54 & 27.03 \\ \hline
12 & & 0.57 & 22.97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results obtained in a laboratory environment.  
Figure 6: Hand–arm vibration exposure values measured in the field using 12 different gloves.  
The vibration damping rates of the gloves observed in the laboratory environment were higher than those observed in the field environment. This difference is thought to result from different vibration spectra, standardized contact conditions in the laboratory, and varying grip patterns in the field.  
The properties of the material used in a glove affect the transmissibility of vibrations through the material to the palm of the hand [21,22].  
Chloroprene (Neoprene) is a type of synthetic rubber known for its chemical resistance, flexibility, and thermal stability. Chloroprene rubber is commonly used in anti-vibration gloves, typically as an inserted padding or surface coating. Its texture is generally firmer and more durable, and it may be slightly thicker. Latex Foam, a foam material derived from natural rubber obtained from rubber trees, is characterized by its softness, high elasticity, and flexibility, making it suitable for comfort-focused applications. It is especially used as a palm coating in gloves because it is both flexible and has vibration damping properties. Its texture is softer, spongy, and flexible. However, its chemical resistance is lower compared to chloroprene. This situation may help explain the difference in vibration damping rates between glove No. 1 and glove No. 12.
## 5 BN-MAC Protocol Design
### Inter-Synchronized Transmission Schedule
As the number of transmitting nodes increases, the energy spent for each node increases in competing hybrid MAC protocols compared with BN-MAC. Other protocols consume 18%-45% more energy than BN-MAC during heavy traffic, mainly because these protocols use many continuous preamble messages, whereas BN-MAC uses a short preamble to guarantee the efficient delivery of data. Another reason for BN-MAC's superior throughput performance is the use of BNs, which have automatic buffering capacity to store packets instead of discarding them.
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
###### Abstract  
The rapid development of diffusion models in image generation and processing has led to significant security concerns. Diffusion models are capable of producing highly realistic images that are indistinguishable from real ones. Although deploying a watermarking system can be a countermeasure to verify the ownership or the origin of images, the regeneration attacks arising from diffusion models can easily remove the embedded watermark from the images, without compromising their perceptual quality. Previous watermarking methods that hide watermark information in the carrier image are vulnerable to these newly emergent attacks. To address these challenges, we propose a robust and traceable watermark framework based on the latent diffusion model, where the spread-spectrum watermark is coupled with the diffusion noise to ensure its security and imperceptibility. Since the diffusion model is trained to reduce information entropy from disordered data to restore its true distribution, the transparency of the hidden watermark is guaranteed. Benefiting from the spread spectrum strategy, the decoder structure is no longer needed for watermark extraction, greatly alleviating the training overhead. Additionally, the robustness and transparency are easily controlled by a strength factor, whose operating range is studied in this work. Experimental results demonstrate that our method performs not only against common attacks, but also against regeneration attacks and semantic-based image editing.  
Keywords:image watermarking; spread spectrum; latent diffusion model; information hiding
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 3 Techniques and Methods
### Trust Based Secure and QoS Routing Scheme
#### 3.3.2 Trust Update  
Our trust-based model takes the mobility of the network into account. Without further updates or continuous interactions between nodes, trust values would decay over time. Node mobility may also cause continuous interactions between a node and other group members, lowering the chance of nodes' evaluating each other in the same group. This includes cases such as breakage of links to a node, causing disconnection from the current group, voluntary disconnection (for saving power) or involuntary disconnection (due to physical terrain or low energy). We also assume the malicious behavior of individual nodes in resource-constrained MANETs. In a routing process, neighbor node's trust is evaluated by the sender by observing activities carried out by that neighbor and forwarding behaviors of neighbors. To be specific, a source node will observe the trust of its neighbor node based on its data forwarding behaviors and QoS parameters [5]. In our proposed trust-based model, we calculate historical trust consistently after a particular time interim called trust update so that we can identify all the nodes that behave maliciously and then update secure routes towards destinations by  
Figure 2: Trust recommendation using HELLO message exchange.  
updating information in the routing table. The overall neighbor trust value is derived based on the following equation:  
\[\begin{array}{ll}Neighbor_{\text{Trust}}=&w1\times CFR\ +\ w2\times DFR\ +\ w3\times Intimacy_{\text{level}}\\ &+\ w4\times Residual_{\text{Energy}}+\ w5\times Link_{\text{Quality}}\ +\ w6\times Channel_{\text{Quality}}\end{array} \tag{5}\]  
In Equation (5), \(CFR\) is the ratio of number of control packets forwarded correctly by a node against total number of control packets supposed to be forwarded, and DFR is the ratio of total number of data packets forwarded correctly by a node against total number of data packets supposed to be forwarded [23]. \(w1\), \(w2\), \(w3\), \(w4\), \(w5\), \(w6\) are the weights where \(0\leq w1\), \(w2\), \(w3\), \(w4\), \(w5\), \(w6\leq 1\) and \(w1+w2+w3+w4+w5+w6=1\). The values for the weights are purely decided by the empirical way. At the same time, they are decided by MANET application and QoS parameters that a user would give higher priority [24].  
Meanwhile, according to the behavior of neighbor nodes, trust value varies over the time. We use trust threshold \(\eta\) for the nodes to differentiate malicious nodes from benign ones. During the whole trust update process shown in Figure 3, nodes having poor quality and false behavior are marked as malicious and the routing table is updated with the most recent routing information continuously in order to build optimal and secure paths.  
Figure 3: Trust update.
## 2 Materials and Methods
### UAV Data Collection and Processing  
The fieldwork was conducted at the end of the dry season (October-November) in 2019, focusing on two delta lobes (northern and southern deltas) with a total area of approximately 0.3 km\({}^{2}\). This timeframe was expected to have a higher probability of clear satellite images with limited cloud cover, and more sunlight as well as less shadows to reach an optimum condition for unmanned aerial vehicle (UAV) image acquisition. UAV/ drones are widely accepted as standard survey tools in many environment settings [38], generating high-resolution data in a safe, straightforward, and cost-effective way [39]. Coastal environments are challenging for the UAV-based surveying method because of the low texture and contrast of the bed surface [39]. Careful planning of the GCPs placement and flight path can lead to reach centimetres of vertical accuracy [15; 39].