esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## Appendix A
### Meeting Minutes' Sharing  
In the meeting minutes' data query model, in order to improve the efficiency of multiple queries by users, the blockchain transaction information is cached in the B+ tree. If the ciphertext information of the meeting minutes is indexed by the same keyword, increase the memory space of the leaf node according to the ciphertext length and save it to the same leaf node. The blockchain data are updated after each meeting, so the user needs to perform a synchronization operation to insert the newly generated transaction information of the blockchain into the B+ tree. The user searches the B+ tree of the buffer area according to the keyword index. According to the search principle of B+ tree, the leaf node associated with the keyword index is retrieved, and the ciphertext information of the meeting minutes is taken out. The information includes the quantum key ID and the ciphertext of the meeting minutes. Using the quantum key ID as the query condition in the database, then the decryption operation is performed with the meeting ciphertext to obtain the plaintext of the meeting, which realizes the sharing of meeting minutes. See Algorithm A2 for the specific process.
# LAMDA Controller Applied to the Trajectory Tracking of an Aerial Manipulator
## 4 Experimental Results
### Experiment 1: Kinematic Controller  
In this experiment, the kinematic controller is implemented within the aerial manipulator robot to track the lemniscate trajectory. Figure 8 depicts the described trajectory, observing that there is an expected error in the tracking, due to the fact that the dynamics of the aerial manipulator robot had not been considered.  
Figure 6: Aerial manipulator robot used in the in experimentation.  
Figure 7: Wind velocity during experimentation.  
Figure 9 depicts the position errors obtained for the Kinematic controller, showing errors of up to 1 m of amplitude. These errors are because the dynamics of the aerial manipulator robot is not considered. In addition, the presence of wind influences the performance of the controller since it is considered as an external disturbance.
## 3 Results and Discussion
### Evaluation of EF Performance for Micro-Nano Structured Mesh Electrode  
(7)  
where is the viscosity constant of the liquid.  
(8)  
where is the Boltzmann constant, is the absolute temperature, and is the mass of the particles.  
As the bubble size approaches or less, the flotation velocity and Brownian motion speed become comparable, causing the bubbles to ascend more slowly. For nanobubbles with a diameter below 1, Brownian motion becomes dominant, resulting in extremely slow ascent. Moreover, these nanobubbles are stabilized by a layer of charged ions surrounding their surface, preventing rapid dissolution and allowing them to remain in the solution for extended periods [58; 59].  
This stability and prolonged presence of fine bubbles in the liquid offer distinct advantages for EF. In addition to the traditional collision-based agglomeration, fine bubbles benefit from Brownian motion-induced collisions and electrostatic interactions between the charged bubble surfaces and charged foreign particles. These effects significantly enhance the overall EF performance (Figure 7).  
Figure 7: Schematic of EF process (a) without or (b) with micro-nano strucutured surface.  
To verify this effect, EF tests were conducted using both the MP and MMN electrodes with a uniform oil-in-water mixture. The separation times were compared, and it was observed that the MP electrode required over 5 min to achieve phase separation, while the MMN electrode achieved the same result in approximately 30 s (Figure 8).  
To further assess the purification efficiency of the MMN electrode in EF, additional experiments were performed with various oil types, including n-dodecane, octane, petroleum ether, n-hexane, and toluene. The EF performance of the MMN electrode was compared to that of spontaneous flotation. As shown in Figure 2, after the flotation process, the residual oil concentrations in the solution were measured using Total Organic Carbon (TOC) analysis. The initial concentration of oil in the emulsion was approximately 80,000 mg/L. Figure 9 demonstrates that the EF performance using the micro-nano structured mesh electrode showed improvements ranging from a minimum of 52.5% for diesel to a maximum of 92.5% for hexane, compared to spontaneous flotation.
# Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols
## 4 Performance Analysis
### Simulation Conditions
#### 4.1.1 NAV Time  
Figure 8 shows the total NAV, which is the sum of the NAVs of individual nodes with varying traffic loads. The user traffic is assumed to be generated by the Poisson arrival process at a rate between 0.04 and 0.14 packets per second. As shown in the figure, the proposed scheme and that of [25] have much smaller NAVs than the scheme in [24], as in [24], the NAV is extended for every packet overheard. In contrast, the proposed scheme and that of [25] selectively extend the NAV under specific conditions. However, the proposed scheme achieves better reduction of the NAV than in [25] by considering the internodal propagation delay. Not only does the proposed scheme achieve the lowest NAV times, it is also highly time efficient because unnecessary wait times are eliminated.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Network dimensions & 3 km \(\times\) 3 km \\ Propagation speed & 1500 m/s \\ Node transmission range & 500 m \\ Data rate & 1 kbps \\ Contention window size & 15–1023 (binary exponential back-off) \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters.  
Figure 8: Total NAV time.
## 5 UAV-Assisted IoV and Blockchain Solutions for Smart Cities
### Decentralized Storage  
Besides increasing network coverage, UAVs can execute data and computational offloading tasks. Due to the latter, they are considered vital technology of future IoT services provisioning, being used as dynamic platforms of storage, computation, and sensing in the air. Also, IoT advances in many industries, that is, agriculture, environmental monitoring, surveillance, logistics, health care, disaster monitoring, production process, are just examples of how wide it could be [83].  
Vulnerabilities and more prominent exposure to security attacks result from such growth without proper security research to following along. Blockchain can then be used to secure distributed storage mechanisms because data needs to be assured to be legit, auditable, and safeguarded through proper cryptographic techniques.  
Since offloading can be computational or of data, the use of decentralized UAV storage can ease the data carried by UAVs and end-users, therefore not overwhelming any of them, most of the time.  
The crucial part of Blockchain use will be to secure data storage and forwarding from UAV nodes. Because this type of service is based on the opportunistic use of devices, Blockchain could implement a distributive reward network (such as cryptocurrencies). These rewards could be modeled as a mutual benefit earning, in which nodes will rationally tend to their maximum gains.  
One example of published work on this topic is [PERSON] et al. [84] work, where a resource trading system was implemented to help distribute computing resources concerning necessity in a smart city, assisted by IoV technology and Blockchain.
# Bearing Fault Diagnosis Based on Small Sample Learning of Maml-Triplet
## 4 Experimental Process and Results
### Experimental Process and Results of the Maml-Triplet Learning Method  
The triplet neural network structure is constructed using the methods mentioned above, the triplet dataset is constructed using Group B data in each iteration, and the neural network weights of the completed three-way five-shot and three-way ten-shot Maml models are extracted to construct the Maml-Triplet learning models of Maml5-Triplet and Maml10-Triplet. Next, the triplet neural network part is trained. During the training process, the Adam gradient descent method is used, the learning rate \(\beta\) is set to 0.001, and the maximum interval \(\gamma\) of the triplet loss function is set to 0.5. In the experiment, the Maml-Triplet learning method is compared with Triplet, CNN, and Maml-CNN learning methods. The effects of the number of fault types and the number of signals in a single fault type on each learning method are studied respectively.
The accuracy of the Maml-Triplet model and the triplet model fluctuates when the number of samples is small, i.e., five signal data for each fault type. When each fault type contains 10 signal data and 15 signal data, the accuracy of Maml-Triplet model is about 95% and 98%, respectively, which is about 2% higher than that of the triplet model. However, with the increase in fault types, the accuracy of the Siamese, Maml-CNN, and CNN models decreases gradually, especially the CNN model. However, the number of fault types is between three and nine, and the increase in fault types has little effect on the Maml-Triplet model.  
3.2 Research on the Influence of the Number of Signals in a Single Fault on Different Learning Methods  
When studying the effect of signal quantity in a single fault type on different models, under the same fault type quantity condition, the average accuracy on the test set after five training sessions of different models is designed under six fault classification tasks of 5,  
Figure 11: \(n\)-way 15-shot classification result.  
Figure 10: \(n\)-way 10-shot classification result.  
10, 15, and 25 signals in a single fault type. As shown in Figures 12 and 13, there are two groups of experiments, that is, the number of fault types is 4 and 10.  
The experimental results show that the accuracy of the Maml-Triplet model increases with the number of fault signals. When the number of fault signals reaches 20, the accuracy of the Maml-Triplet model reaches the maximum of about 99% and tends to be stable. The accuracy of the Maml-Triplet model is significantly higher than that of the other three models when the number of fault signals is at least five. Compared with the Siamese, Maml-CNN, and CNN model, the Maml-Triplet model can better identify faults with less data.
# Manipulator Smooth Control Method Based on LSTM-XGboost and Its Optimization Model Construction
## 3 Inverse Kinematics Problem Solving Method of Serial Manipulator
### Convex Optimization Method  
The method discussed herein continues to follow the optimal control theory. It determines the maximum acceleration and deceleration on the path based on convex optimization and then integrates these factors to calculate the speed curve. With the flexibility to set optimization objectives, this method can address multi-objective optimal trajectories--such as time, energy, stability--and offers a calculation speed faster than other methods.  
One such solution is a time-optimal trajectory planning method based on convex optimization. This technique can find the transition point faster and easier than other methods such as TOTG and TOPP. However, it does not consider acceleration constraints, resulting in sudden acceleration changes in the trajectory. These changes can negatively affect the stability of the trajectory execution.  
Figure 1: Numerical integration. The maximum jerk represents the maximum rate of change of acceleration, and the minimum jerk denotes the minimum rate of change of acceleration, which is indicated by the blue dots. Velocity denotes the rate of the change in position with respect to time, position indicates the displacement from the initial position, and acceleration represents the rate of change of velocity with respect to time.  
To address this, the jerk constraint is approximated as a linear function and is introduced into the convex optimization problem. This adjustment aims to achieve a smooth time-optimal trajectory. Even though this method preserves the convexity of the optimization problem, it adds to the number of optimization variables, leading to a significant increase in computing time.  
In seeking to optimize both time and acceleration, another method employed is a non-dominated sorting genetic algorithm. This algorithm allows for a smooth time-optimal trajectory. However, it does not consider the torque constraint.  
In conclusion, directly introducing the acceleration constraint into the convex optimization problem destroys the convexity of the problem, increasing both the number of optimization variables and calculation time. Nonetheless, compared to dynamic programming and numerical integration methods, the convex optimization method is simpler and more stable. It delivers a faster calculation speed when the optimization variables are fewer. For the same time-optimal trajectory planning problem, the calculation time of the dynamic programming method is 8.5 times that of the convex optimization method, and the numerical integration method is 15.3 times that of the convex optimization method.  
Despite these benefits, it is important to note that convex optimization methods have not yet been widely accepted for handling nonlinear acceleration constraints. This remains a challenge to be addressed in further research and development in the field.
## 4 Numerical Results  
In this section, we give numerical results to manifest that the capture effect affects the channel utilization by MATLAB (version 8.3, The MathWorks, Natick, MA, USA). In the experiments, we consider a scenario that there are \(N\) active users transmitting packets to a base station and each transmits once in the frame length \(L\). For example, Figure 1 gives the case of \(N=8\), where each user randomly chooses a slot in the frame. Slot 1, Slot 3, Slot 6 and Slot \(L\) are all successful slots. Slot 4 and Slot \(L-1\) are collided slots, and maybe they can turn into successful slots when the capture effect occurs. The others are all idle slots, which are not chosen by any users.
## 3 Results and Discussion
### Validation Experiment  
From the response surface analysis, it can be seen that there was an efficient combination of operating parameters for the complex interaction among the three influencing factors. Through the optimization function of the Design-Expert software, the optimal parameters of the reaction system were predicted as follows: pH = 7.20, the dosage of manganese sand was 3.54 g/L, and the initial concentration ratio of iron and manganese was 3.80. Under this optimal condition, the predicted iron ion removal rate was 83.62%, and the manganese ion removal rate was 76.10%. In order to verify the prediction results, three groups of parallel experiments were carried out in the same reaction system. The average removal rate of iron ions was 82.78%, and the average removal rate of manganese ions was 75.89%, both of which were close to the prediction values, and the relative deviation was less than 1%. These results show that the model can truly reflect the influence of the various analyzed factors on iron and manganese ion removal rate.
# A Polarization-Based Method for Maritime Image Dehazing
## 3 Experiments and Proposed Method
### Separate Seawater Area
As depicted in Figure 6b, there is a significant disparity in polarization levels between the sea surface and the non-sea region in the foggy sea scene. Consequently, the decision was made to utilize the Otsu algorithm on the degree of polarization image to segment the seawater area. Subsequent to this, morphological processing techniques were employed on the segmented image. Here, we utilize the morphological closure operation to reduce gaps and refine the boundaries of the initial segmented binary image [29]. Through such processing, the seawater area will be identified more accurately. The results of the processing are presented in Figure 6, with Figure 6c showing the segmented binary image, where the sea areas are highlighted in white. Due to the overestimation of air light in the sea surface region, after segmentation, we adjust the \(A_{\text{sea}}\) to an appropriate value using a bias coefficient \(\varepsilon_{2}(\varepsilon_{2}>1)\). According to the citation provided in reference [19], the entropy function has the capability to autonomously ascertain the most suitable bias coefficient \(\varepsilon_{2}\). The entropy function is defined in Equation (18), with \(p_{k}\) denoting the relative frequency of gray level \(k\). Through the manipulation of \(\varepsilon_{2}\) values and the subsequent creation of a series of dehazed images, the  
Figure 6: The process of seawater region segmentation. (**a**) \(S_{0}\) image; (**b**) the degree of polarization image; (**c**) the segmentation binary image. entropy function is computed for each image. The dehazed image exhibiting the maximum entropy value is ultimately selected as the optimal dehazed image, as follows:  
\[p_{A^{\prime}}=p_{A}\cdot\epsilon_{2} \tag{16}\]  
\[A_{sea}=\frac{A_{p}}{p_{A^{\prime}}}=\frac{I(0)-S_{0}(1-p)/2}{p_{A^{\prime}}\cdot \cos^{2}\theta_{A}}=\frac{I(90)-S_{0}(1-p)/2}{p_{A^{\prime}}\cdot\sin^{2} \theta_{A}} \tag{17}\]  
\[E=-\sum_{k}p_{k}\log_{2}p_{k} \tag{18}\]  
By replacing \(p_{A}\) with \(p_{A^{\prime}}\) in Equation (6), the value of \(A_{\text{sea}}\) is effectively reduced. Subsequently, a Gaussian low-pass filter is applied to \(A_{\text{sea}}\). Finally, based on Equation (19), we obtain the haze-free sea surface region, denoted as \(L_{\text{sea}}\). The whole workflow is shown in Figure 7.  
\[L_{sea}=\frac{I-A_{sea}}{1-A_{sea}/A_{\infty}} \tag{19}\]
# A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
## 3 Roadside and Cloud Architecture
### Roadside Architecture
#### 3.1.3 Vulnerable Road User (VRU)  
Road users who do not typically use motorized vehicles, such as pedestrians and cyclists are classified as VRUs. Their safety can be enhanced through the use of position sharing devices or HMIs.  
The developed application described above can also function in VRU mode, where the smartphone is connected to the cloud MQTT broker through a cellular connection. The user's position is continuously published to the broker and all nearby subscribed data (CAMs, DENMs, CPMs, etc.) are viewable in the application. In OBU mode, the user can also disseminate events published in the cloud broker. The road manager can then act on this information and transmit it to RSUs near the event to be broadcast by ITS-G5 to connected vehicles. Apart from the user's position, no identifiable information is shared.  
Snapshots of the application can be seen in Figure 4. A view of an RSU disseminating CPMs with perceived vehicles (in blue) and a list of the possible warnings that can be generated by the smartphone user are shown. The Android application can be downloaded via Google's Play Store (CCAM App. [[https://play.google.com/store/apps/details?id=com.it2s.it2](https://play.google.com/store/apps/details?id=com.it2s.it2) smobileapp]([https://play.google.com/store/apps/details?id=com.it2s.it2](https://play.google.com/store/apps/details?id=com.it2s.it2) smobileapp), accessed on 25 February 2023).
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 3 System Model and Problem Formulation
### Network and Graph Model  
Our goal is to enable the WSN to utilize the RPL mechanism for routing while meeting multiple QoS constraints. The objective is to identify a set of paths from sources to destinations within a multi-objective and multi-constraint framework, specifically using aggregated additive metrics. We require an exact model based on evaluated graphs to tackle the associated challenges and develop effective propositions. This section outlines a graph model representing the network topology, which is essential for formulating the fundamental routing decision problem. From this model, we will discuss the key issues and assumptions that must be addressed to achieve QoS routing for WSNs. We assume _wireless communication_, where the graph nodes correspond to sensors, relays, actuators, sinks, and more. For simplification, we consider sensors as the sources, which can also function as relays, while the sinks are defined as gateways or BRs. In large networks, sending messages directly to sinks is often impossible, necessitating multi-hop communication.  
We assume _symmetric communication_ links. Under this assumption, the topology is represented by an undirected graph \(G=(V,E)\) (or a directed graph in the case of asymmetric links). Positive values for QoS-related metrics are assigned to the edges in \(E\). It is important to note that critical QoS-related metrics and constraints, such as the remaining energy or state of the routers, can also be considered for routing. However, this study focuses solely on calculating QoS paths based on link values. The most commonly used metrics and their types are mentioned in RFC6551 [49]. Metrics can be categorized as either aggregated or recorded.  
In this discussion, we will assume _aggregated metrics_. The values that characterize a path can be derived or cumulative values from the link values. In summary, metrics are classified into three categories: _additive metrics_: the value associated with the path is the sum of the link values (e.g., delay); _multiplicative metrics_: the path value is the product of the link values (e.g., the probability of lossless transmission); and _bottleneck-type metrics_: the most critical link value characterizes the path (e.g., bandwidth).