esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 2 State of the Art
The work in [15] presents a covert channel that utilizes the supported rates field in PR frames. The supported rates field contains information about the data rates supported by STA or AP and is typically used to ensure compatibility between both devices. The covert channel encrypts secret data in the most significant bit (MSB) of the supported rates field while transmitting PR frames. Instead of the STA genuinely scanning for available networks, its primary focus is to send covert data. Another approach that modifies the content of the MAC header is presented in [16]. This covert channel technique encodes the secret message within the two-bit protocol version field. It exploits the fact that the 802.11 standards currently allocate only one specific value (binary 00) to this field, while the remaining three combinations (binary 01, 10, 11) are left unused. This allows a covert channel by utilizing unused combinations to transmit hidden information. Analyzing the current state of IEEE 802.11 covert channels, the focus has mainly been establishing the possibility of creating a covert channel rather than its bandwidth. Our contribution introduces the pioneering concept of an IEEE 802.11 covert channel that utilizes a random MAC address as the secret message, providing a significant bandwidth of 48 bits per frame, high throughput, and minimal delay. This novel covert channel can be replicated in simulated environments and real-life Wi-Fi device drivers, marking a significant advance in the field.
## 4 Reed-Solomon Codes Effectiveness against Single-Frequency EMD  
With regard to the implementation, the Python-based RS Codes library provided by [22] is used within this paper. Furthermore, all simulations were carried out based on the parameters specified in Table 2 and the experimental setup that was previously used in [20]. The conceptual overview of this experimental setup is shown in Figure 1.  
In addition, for the sake of clarity, a simulation example is provided in Appendix A. As shown in Table 2, there are two groups of simulation parameters. In 'G1', the length of data words is one symbol, and the generated code words use the maximum Hamming distance as well as the maximum correction capability. On the other hand, the length of data words in 'G2' is three symbols, but the generated code words have smaller Hamming distance and correction capability.  
Using the aforesaid experimental setup, several graphs were generated of which the most important ones are provided here. Furthermore, due to the periodic behavior of the applied single-frequency interferences, only one period is represented in all graphs.
## 4 Experimental Results
### Subjective Evaluation of Defective Area Detection  
In addition to analyzing the classification accuracy, we also provide the subjective evaluation for the four kinds of Autoencoder models. In Figure 27, we compare the detection results of the four models on nanofiber, carpet, and grid datasets. Due to the assistance of the DEC loss function, the IAE + DEC and SAE + DEC models can have good completeness of repairing defect areas. At the same time, by observing the output image, it is found that the IAE + DEC model has the best repair capability, and the detection of defective areas is also the best in the subjective evaluation.  
Figure 26: Analysis of using multi-thresholding to identify the weak defect carpet image. (**a**) Weak difference image. (**b**) Apply Otsu multi-thresholding method to detect the defective area. (**c**) The result of multi-thresholding.  
Figure 27: The defect detection results of the four models on (**a**) nanofiber, (**b**) carpet, and (**c**) grid datasets for the subjective evaluation.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 2 Machine-Learning-Based Path Loss Prediction
### Data Collection and Feature Extraction  
The collected data refer to samples obtained from measurement, and each sample should include the path loss value and the corresponding input features. The input features can be divided into two categories, system-dependent parameters and environment-dependent parameters. System-dependent parameters are those independent of the propagation environment, such as carrier frequency, heights and positions of the transmitter and receiver, and so on. According to the above parameters, more system-dependent features can be acquired, such as the antenna-separation distance and the angle between the line-of-sight path and the horizontal plane.  
Environment-dependent parameters are those determined by the geographic environment and the weather conditions. Parameters related to the geographic environment include the terrain, building conditions, and vegetation conditions. Most of them can be obtained from three-dimensional (3D) digital maps, topographic databases, and land cover databases. The weather parameters include temperature, humidity, precipitation rate, and so on.  
The performance of the path loss model is closely related to the number of training samples. After obtaining enough data, these samples should be divided into the training dataset and the test dataset. The former is used to build the prediction model, whereas the latter is used to verify and further improve the model performance.
## 3 Background on YANG and OPC UA
### Opc Ua  
This section aims to provide the reader with the basic overview of fundamental technical concepts of OPC UA required to comprehend the article's content. OPC UA is an industrial automation standard used to exchange information and configure and control industrial devices and applications from different manufacturers. Figure 3 defines the scope of OPC UA in industrial automation [16].  
OPC UA primarily employs a client/server communication model based on standard TCP/IP, Web Sockets, and HTTP technologies. Figure 4 demonstrates the typical client/server communications in OPC UA [38]. The OPC UA framework provides a fundamental information model framework and a range of services for creating and sharing vendor-specific information in a standardized way. The OPC UA information models encapsulate the structure, behavior, and semantics of the OPC UA server. They consist of a network of OPC UA nodes that represent a variety of information types, including configurations, observability data, procedures/functions, and their interrelations with other OPC UA nodes.  
Figure 4: OPC UA client-server communication.  
Figure 3: OPC UA’s scope.
## 2 The Mathematical Model  
The governing equation of the two-dimensional unsteady heat conduction problem in rectangular coordinates is Equation (1) (See, e.g., II of Chapter VI of Reference [12]):  
\[\left\{\begin{aligned} \rho c\frac{\partial u}{\partial t}& =\mu(\frac{\partial^{2}u}{\partial x^{2}}+\frac{\partial^{2}u}{ \partial y^{2}})+S(x,y,t)\\ u(x,y,0)&=S_{1}(x,y)\\ u_{t}(x,y,0)&=S_{2}(x,y)\end{aligned}\right. \tag{1}\]  
where \((x,y)\in\Omega_{i}(x,y,t)\in\Omega\times[0,T]\), \(\Omega=[0,1]\times[0,1]\subset R^{2}\), \(u(x,y,t)\) represents heat transfer temperature, \(\rho\) is the material density (kg/m\({}^{3}\)), \(\mathbf{c}\) is the specific heat capacity of the material (\(J/(\text{kg}\cdot\text{k})\)), \(\mu\) is the thermal conductivity of the material (\(\text{w}/(\text{m}\cdot k)\)) and \(S(x,y,t)\) is the term function of the known radiation heat source. Both \(S_{1}(x,y)\) and \(S_{2}(x,y)\) are initial conditions, T is the total time period.  
Boundary condition:  
In order to facilitate the programming, the three kinds of boundary conditions are uniformly expressed as the expression of heat flux. (see, e.g., II of Chapter II of reference [24]).  
\[q_{w}=-b_{1}\mu\frac{\partial u}{\partial n}+b_{2}q+b_{3}h_{f}(u_{w}-u_{f}) \tag{2}\]  
where in the first boundary condition \(b_{1}=1\), \(b_{2}=0\), \(b_{3}=0\); in the second boundary condition \(b_{1}=0\), \(b_{2}=1\), \(b_{3}=0\); in the third boundary condition \(b_{1}=0\), \(b_{2}=0\), \(b_{3}=1\).  
In the second boundary condition, \(q\) is the heat flux, and in the third boundary condition, \(u_{f}\) is the fluid temperature. In the analysis of this article, it considers the position of adjacent points around the boundary. It is generally assumed that the line between the boundary points and its adjacent points are perpendicular to each other. Thus, the discrete expression of Equation (2) can be obtained:  
\[q_{w}=-b_{1}\mu\frac{u(A_{0})-u(A_{1})}{\chi}+b_{2}q+b_{3}h_{f}(u(A_{0})-u_{f}) \tag{3}\]  
In Equation (3), \(A_{0}\) is the number of the valid boundary node, the number of its adjacent internal nodes is marked as \(A_{1}\), and \(\chi\) is the effective distance between \(A_{0}\) and \(A_{1}\) nodes.
# Performance Analysis of Quantum Key Distribution Technology for Power Business
## 5 Experimental Results
Note that, for the delay standard requirements, the distribution automation business (control area, one-way) has a latency of less than 500 ms, and the latency of the power information collection service was less than 60 s.  
In summary, based on the core business test results of power QKD technology, the proposed scheme meets various indicators for stable transmission of the power grid. It can effectively ensure the security of energy information interaction and further strengthen the support for intelligence and digitalization of power systems. Figure 12: Schematic diagram of QKD-based dispatching automation test.
# A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances
## 6 Simulation Results and Discussion
### The Performance with Both Uncertain Wind Disturbance and Friction Coefficients  
In this section, both the uncertain wind disturbance and friction coefficients were considered in obstacle avoidance conditions. The effectiveness of the tube MPC system when under both uncertain wind disturbance and friction coefficients is demonstrated. The disturbance range of the cross-wind and friction coefficients were the same as that in Sections 6.2 and 6.3. The results were compared to a traditional MPC without robustness guarantees. A total of 20 trials were run for each controller with random disturbances. Figure 13 shows the trajectory performances of the traditional MPC. In Figures 13 and 14, the blue and red lines represented the trajectories that successfully avoided the obstacle and failed to avoid the obstacle, respectively. The trajectories of the traditional MPC were scattered over a large area, and there was a failure to avoid the obstacle in eight of the trials. The tube MPC system was carried out under the same cross-wind disturbances. The trajectory performances of the tube MPC are shown in Figure 14. Compared with Figures 13 and 14, it can be observed that the range of the trajectories of the tube MPC  
Figure 11: Trajectories of the traditional MPC system when under cross-wind disturbances.  
Figure 10: The state disturbances that resulted from the cross-wind disturbance. (**a**) The lateral position and yaw angle error. (**b**) The error rate of the lateral position and yaw angle.  
system was much tighter and that all of the trajectories successfully avoided the obstacle on the road. The tube MPC system was robust under both uncertain wind disturbance and friction coefficients.
## 2 Mathematical Models
### OMTSP Model for OWFCCLP  
OMTSP can be defined as follows: \(K\) traveling salesmen must visit \(M\) cities from a predetermined departure city, and a suitable travel path should be defined to meet the scheduled goal (such as the shortest path or minimum cost) under certain constraints. The constraints of OMTSP include the following:  
Constraint 1: Each traveling salesman must start from the same departure city before visiting other cities, whereas each one does not have to return to the departure city;  
Constraint 2: Each city (except the departure city) can be visited only once by one traveling salesman;  
Constraint 3: Each traveling salesman should maintain continuity during the visits; that is, the traveling salesman cannot visit two cities at the same time after visiting one city (except the departure city).  
Comparing the definition of OMTSP with OWFCCLP, it can be seen that the departure city, visited cities, and access path of a traveling salesman in the OMTSP can be analogized as the offshore substation (OS), WTs and the cable connection scheme of feeder lines in the OWFCCLP. The OMTSP is completely consistent with the definition of OWFCCLP. However, OWFCCLP only meets constraint 1 and constraint 2 of the OMTSP while constraint 3 is violated. Hence, a further constraint is added into the OWFCCLP to meet constraint 3 of the OMTSP; that is, only two cables are allowed to be connected to one WT (one entering and one leaving cable) in each feeder line. The constraint can be expressed as:  
\[\begin{array}{l}g_{G_{T},k,m,p}+\sum\limits_{\begin{array}{c}m^{\prime}=1 \\ \forall k=1,\;2,\;\cdots,\;K,\forall m=1,\;\cdots,\;M\end{array}}^{\prime}g_{G_ {T},k,m,\;m^{\prime},p}^{\prime}+g_{G_{T},k,m,p}^{\prime\prime},\end{array} \tag{8}\]
## 3 Results and Discussion  
### Original Spectral Analysis  
Figure 3 shows the original spectral curves for all samples. Due to the influence of noise, there are obvious distortions in the front and posterior parts of the original spectral curve, so five wavelengths were truncated in the front and back, respectively, and the spectrum with a wavelength range of 883.6-1685.3 nm was selected. In total, 244 wavelengths were selected for analyses. It can be seen from the figure of original spectral curve that the maximum reflectance is not higher than 0.75 in the range of 883.6-1685.3 nm. The 914.6-nm absorption peak band corresponds to the second overtone stretching of C-H, which is caused by the molecular structure CH\({}_{2}\). The 1064.8-nm absorption peak band corresponds to the second overtone stretching of N-H, which is caused by the molecular structure RNH\({}_{2}\). The 1259.0-nm absorption peak band corresponds to the second overtone stretching of C-H, which is caused by the molecular structure CH\({}_{3}\). Figure 4 shows the average spectral curve over 7 days. The trends of the spectral curves of white shrimps with different refrigeration days are similar, but in the range of 1150-1300 nm. As can be observed, the spectral reflectance of white shrimps differs significantly in different days and as the refrigeration days increase, the reflectance rises gradually. Overall, the difference between the average spectral curves on day 1 and day 7 is the largest one, while the difference between the average spectral curves on days 2, 3, 4, and 5 is tiny.  
Figure 3: Original spectral curve.
## 2 Related Work
### Attention Mechanism  
Attention mechanisms contribute to a fair number of improvements for defect detection, especially on complex scenarios and small object detection, by dynamically estimating the relative importance of features in the input data. The role of the attention mechanism is to grant higher weights with regard to relevant features and allow the model to focus on important information and disregard the less relevant one, thereby enhancing model accuracy and efficiency. Some of them include Squeeze-and-Excitation Networks (SENets) [78], Coordinate Attention (CA) [79], the Normalisation-based Attention Module (NAM) [80], Efficient Channel Attention (ECA) [31], and the Convolutional Block Attention Module (CBAM) [81], which can be used as modules in network architectures to enhance the generalisation capabilities of the CNNs.  
The ECA is an efficient channel attention method, inspired by the blocks of SENet for a good balance between performance and architectural simplicity (Figure 4).  
The SENet attention mechanism uses spatial dimensions to pool and capture dependencies between channels through two fully connected layers. While this method achieved good results, it also increased computational complexity. The ECA module uses fast one-dimensional convolution in place of a fully connected layer, thereby reducing computational overhead and improved channel attention learning by capturing nonlinear information between channels. With this approach, the network has learned better capture of channel attention, reducing the number of parameters in the network and improving its performance [31].  
The ECA module in Figure 4 takes the aggregated features from global average pooling (GAP), applies a fast 1D convolution in \(k\) dimension, and produces channel weights. Here  
Figure 4: ECA module.  
Figure 3: Ghost module, Ghost Bottleneck, and C3 Ghost module structure: (**a**) Ghost module; (**b**) Ghost Bottleneck with stride = 1; (**c**) Ghost Bottleneck with stride = 2; (**d**) C3 Ghost module [77].  
is adaptively calculated according to the channel dimension \(C\). Representing the coverage rate of cross-channel information, \(k\) is the kernel size of the one-dimensional convolution and is proportional to the channel size \(C\). There is mapping between the total channel size \(C\) and \(k\), where the mapping \(\phi\) is a linear function \(\gamma\times k-b\), as in Equation (6):  
\[\phi(C)=\gamma\times k-b \tag{6}\]  
Since the number of channels in convolutional networks is set to a power of 2, and the mapping relation with a linear function is simple, the linear function is obtained with a nonlinear function, as in Equation (7):  
\[C=\phi(k)=2^{(\gamma\times k-b)} \tag{7}\]  
Thus, for a given channel size \(C\), the kernel size \(k\) is obtained through an adaptive function defined in Equation (8):  
\[k=\psi(C)=\left|\frac{c}{\gamma}+\frac{b}{\gamma}\right|_{odd} \tag{8}\]  
where \(k\) is the kernel size, \(c\) is the channel size, and \(\gamma\) and \(b\) are set to 2 and 1, respectively, to make the kernel size close to an odd number.
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 5 Discussion  
### Appropriateness of the Experimental Design and Analysis  
The experiments were conducted in a controlled 220 m\({}^{2}\) laboratory environment, designed to effectively simulate real-world point cloud acquisition conditions. Two scanning start positions were selected based on the locations of the columns; however, the results indicate that the influence of starting position on the measurements was negligible.  
The sensitivity analysis was conducted to evaluate the impact of random downsampling. The results confirmed that the error distribution patterns in the raw point clouds were preserved even after applying random downsampling. This finding supports the  
Figure 14: Overall error of MLS-RANSAC and MLS-RANSAC with correction methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**MLS Type**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Column**} & \multicolumn{2}{c}{**MLS-**} & \multirow{2}{*}{**RANSAC**} & \multirow{2}{*}{**Accuracy**} \\  & & & **RANSAC** & & **with** & **Improve-** \\  & & & **(mm)** & & **Correction** & **(mm)** \\ \hline \multirow{8}{*}{MLS-03} & \multirow{2}{*}{01} & A & 5.4 & 3.2 & 2.2 \\  & & B & 6.3 & 3.7 & 2.6 \\ \cline{2-6}  & \multirow{2}{*}{02} & A & 4.7 & 2.6 & 2.1 \\  & & B & 4.2 & 2.0 & 2.3 \\ \cline{2-6}  & \multirow{2}{*}{03} & A & 2.0 & 2.4 & \(-\)0.4 \\  & & B & 2.6 & 2.0 & 0.6 \\ \cline{2-6}  & \multirow{2}{*}{04} & A & 3.0 & 2.9 & 0.1 \\ \cline{2-6}  & & B & 4.9 & 3.9 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._validity of our experiment in appropriately analyzing the inherent error patterns in the MSL point clouds.  
The proposed method integrates robust algorithms, including random down-sampling, ICP registration, and RANSAC. Apart from the precise fabrication and installation of the mock-ups, the overall process is significantly simpler than conventional manufacturer calibration procedures. While manufacturer calibration remains the most accurate approach for error correction, it is time-consuming and often impractical to perform frequently in real-world settings.  
In contrast, the proposed method enables efficient and repeatable assessment of MLS error distribution patterns with minimal setup, requiring only the installation of basic analysis targets. Therefore, this approach can meaningfully contribute to improving the accuracy of as-built BIM models generated from MLS point clouds, particularly in construction contexts where geometrical precision is critical.
## 3 Proposed Method
### Spatiotemporal Graph Connection Configuration for Skeleton  
The original skeleton data that can be obtained from pretrained video pose estimation algorithms or motion capture devices are provided as a sequence of vectors. We define \(N\) as the number of joints in skeleton and \(T\) as the total number of frames. For each person, a spatiotemporal graph is established as \(G=(V,E)\), where \(V=\{v_{tn}\mid t=1,2,\cdots,T;n=1,2,\cdots,N\}\) is the set of all the joint nodes as the vertices of the graph, and \(E\) represents the set of all the edges describing natural connections in the human body structure and time as the edge of the graph. Further more, \(E\) consists of two subsets \(E_{s}\) and \(E_{t}\), where \(E_{s}=\{(v_{si},v_{sj})\mid s=1,2,\cdots,T;i,j=1,2,\cdots,N\}\) represents the connection of any pair of joints \((i,j)\) in each frame \(t\). \(E_{t}=\{(v_{tn},v_{(t+1)n})\mid t=1,2,\cdots,T;n=1,2,\cdots,N\}\) represents the connection between each frame along the continuous time. Figure 2a shows an example of the constructed spatiotemporal graph, where the joints are represented as vertices and their natural connections in the human body are represented as spatial edges (the blue lines in the Figure 2a) and the corresponding between two adjacent frames are connected as temporal edges (the green lines in the Figure 2a).  
We adopt the spatial configuration partition [1] to divide the neighborhood of a node into three subsets according to graph distance. First, the center of gravity is determined as the average coordinate of all joints of the skeleton in the frame, then the first subset is the root node itself (red node in Figure 2b), the second subset is the neighbor nodes closer to the center of gravity than the root node (green node in Figure 2b), and the third subset is adjacent nodes away from the center of gravity (blue node in Figure 2b).  
Figure 2: (**a**) The description of the spatiotemporal graph follows ST-GCN [1]; (**b**) The configuration of spatial configuration partitioning. The three colors represent three different subsets.