esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization Scheme
### The First-Level Approximation Problems by BMA
\begin{array}{ll}\text{Find}&\mathbf{X}_{p}=\left\{x_{1(p)},x_{2(p)},\cdots, x_{n(p)}\right\}^{T}\\ &\alpha=\left\{\mathbf{\alpha}_{1(p)},\alpha_{2(p)},\cdots,\alpha_{n(p)}\right\}^{T }\\ Min&W^{(p)}(\mathbf{X}_{p},\mathbf{\alpha}_{p})=\sum_{i=1}^{n}\!\alpha_{i(p)}W_{i} \Big{(}x_{i(p)}\Big{)}\\ \\ S.t.&g_{j}^{(p)}(\mathbf{X}_{p},\mathbf{\alpha}_{p})\leq 0\\ &\alpha_{i(p)}x_{i(p)}^{L}+\Big{(}1-\alpha_{i(p)}\Big{)}x_{i}^{b}\leq x_{i(p)} \\ &x_{i(p)}\leq\alpha_{i(p)}x_{i(p)}^{UI}+\Big{(}1-\alpha_{i(p)}\Big{)}x_{i}^{b} \\ &\alpha_{i(p)}=0\quad\text{ or }\quad\alpha_{i(p)}=1\quad\quad\quad\quad \quad i=1\cdots n\\ \\ x_{i(p)}^{UI}=\min\Bigl{\{}x_{i}^{UI},\tilde{x}_{i(p)}^{UI}\Bigr{\}}\\ x_{i(p)}^{L}=\max\Bigl{\{}x_{i}^{L},\tilde{x}_{i(p)}^{L}\Bigr{\}}\\ \end{array} \right. \tag{7}\]  
where \(g_{j}^{(p)}(\mathbf{X},\mathbf{\alpha})\) is the approximate constraint function and \(W^{(p)}(\mathbf{X},\mathbf{\alpha})\) is the objective function at the \(p\)-th stage. \(J_{1}\) is the number of active constraints related to the primal problem in Equation (1). \(x_{i(p)}^{UI}\) and \(x_{i(p)}^{L}\) are the upper and lower bounds of \(x_{i}\) at the \(p\)-th stage; \(\tilde{x}_{i(m)}^{UI}\tilde{x}_{i(p)}^{UI}\) and \(\tilde{x}_{i(p)}^{L}\) are the moving limits of \(x_{i}\) at the \(p\)-th stage. In the \(p\)-th stage of optimization, the optimal result, as a new point, in the \(p\)-\(1\)-th stage is added to update the approximate functions. The data of the initial first point is deleted; thus, the number of counted points in the approximate function remains constant in the iteration of the first-level approximation problem. Since the first-level approximation problem involves both continuous and discrete variables, it can never be solved directly by using general mathematical programming methods. If GA is directly used and the continuous and discrete variables are encoded simultaneously, the scale of the design variables and the computational costs will become tremendous. Thus, a layered optimization strategy is introduced: discrete variables are optimized by GA in the external layer, and continuous variables are optimized in the internal layer by using the dual method, which can significantly reduce the gene code length in GA while improving the optimization efficiency and accuracy.
# A Robust Fixed-Time Sliding Mode Control for Quadrotor UAV
## 3 Robust Stabilization  
The altitude \(z\) can be stabilized by using the following thrust control input.  
\[u=\frac{m(g+r)}{\cos\theta\cos\phi} \tag{2}\]  
where \(r\) is defined as:  
\[r=-k_{21}\dot{z}-k_{22}(z-z_{d}) \tag{3}\]  
Figure 1: UAV Configuration.  
where \(k_{z}\) is a positive constant and \(z_{d}\) is the desired altitude; notice from Equation (2) and the above that if \(|\theta|<\frac{\pi}{2}\), then \(u>0\).  
The yaw angular position can be controlled by applying a sliding mode approximation,  
\[\tau_{\psi}=\dot{\psi}-k_{\psi}\text{sgn}(\dot{\psi}+\beta\psi) \tag{4}\]  
where \(k_{\psi}\geq|\Gamma_{\psi}|\), \(\beta\) and \(k_{\psi}\)>0; thus, Equation (1) can be rewritten as:  
\[\ddot{x}=\frac{\tan\theta(g+r)}{\cos\phi}+\Gamma_{x}\] (5a) For the Y-axis, it is: \[\dot{y}=\tan\phi(g+r)+\Gamma_{y}\] (5b) Now for the Z-axis, we obtain: \[\ddot{z}=r+\Gamma_{z}\] (5c) Subsequently, for the rotation angles we have: \[\ddot{\theta}=\tau_{\theta}+\Gamma_{\theta} \tag{6a}\] \[\ddot{\phi}=\tau_{\phi}+\Gamma_{\phi}\] (6b) \[\ddot{\psi}=\tau_{\psi}+\Gamma_{\psi} \tag{6c}\]
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## Discussion
### Four Approaches to the Correction of Vehicle Trajectory Data  
Four approaches to the correction of vehicle trajectory data are presented in the literature. As mentioned above, because the historical GPS trajectory must be rectified before training the DQN model, this study adopted the approach presented in [28], whereby the roads on the map are matched by calibrating known GPS trajectory information with accurate map data. The second approach, the Differential Global Positioning System (DGPS), is an improved GPS method that references ground stations as well as GPS satellites [29]. The DGPS can be used to correct trajectory data by comparing the positions of reference stations and signal sources to correct the difference error in the trajectory data and, therefore, obtain more accurate positional information. The third approach is to smooth GPS trajectory data, which can be another feasible method to obtain more precise data [30]. The errors in the trajectory can be corrected based on historical data and real-time information from other sensors, making the trajectory closer to the actual path. Finally, gradient descent minimizes GPS errors by adjusting parameters to measure the difference between the corrected trajectory and actual geographic positions [31]. Through gradient descent, this loss function is minimized by iteratively adjusting the positions of trajectory points, gradually reducing errors. However, the latter three approaches require relevant real-time information from other reference stations or sensors to correct GPS locations, and cannot be applied to the offline phase of the proposed IDRF.
# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 1 Introduction
** We integrated the rendering of images from new perspectives, utilizing a neural radiance field implicit model, into the construction of traditional co-visibility clusters. This method addresses the challenge of viewpoint occlusion and low overlap resulting from flight path and environmental factors. In contrast to traditional co-visibility clusters, implicitly synthesized co-visibility clusters leverage the capabilities of the NeRF model to implicitly render new perspective images, compensating for viewpoint gaps induced by flight trajectories and environmental conditions. The NeRF model learns the depth and illumination information of a 3D scene from images, providing implicit synthesis co-visibility clusters with robustness against variations in lighting, occlusions, and low-texture regions. Consequently, this robustness facilitates guiding the dense point cloud reconstruction module to achieve more robust reconstructions. **(3) An implicit texture image-pose-recovery-based high-accuracy mesh modeling is proposed. ** This method innovatively restores image poses that were not considered in the reconstruction process by reversing the neural radiance field. This decouples the mapping of image textures from the constraints of the 3D scene reconstruction. Through the implicit model, we accurately recover image poses that were excluded during the reconstruction phase, thereby incorporating more texture information into the mesh model. Figure 1: Comparison with the SOTA open-source reconstruction method COLMAP (a general-purpose structure-from-motion and multi-view stereo pipeline with a graphical and command-line interface), shows that our method outperforms in handling UAV images with low overlap, achieving the best performance. Specifically, the mesh model takes dense point clouds as input and undergoes surface mesh computation through Poisson reconstruction. Additionally, local optimization of the mesh is performed by integrating illumination models, achieving a more realistic and finely detailed 3D mesh modeling of large-scale UAV scenes.
## 5 Experimental Results and Discussion
### Experiments on Microblog Network
Personalized push can be realized. And collaborative filtering can be implemented to recommend the following accounts for grouped users. The user clustering of the SSC algorithm and K-means clustering algorithm were compared on the Microblog network data set. The clustering result was evaluated by the modularity index. The modularity index is used to evaluate the results of unlabeled network partitioning [22], which is defined as:  
\[\text{Q}=\frac{1}{2m}\sum\
olimits_{i,j}\left(A_{ij}-\frac{k_{i}k_{j}}{2m} \right)\left(C_{i},C_{j}\right). \tag{22}\]  
In Equation (22), \(A_{i,j}\) is an element of the network adjacency matrix which is the edge weights of nodes \(i\) and \(j\); \(k_{i}=\sum\limits_{j}A_{i,j}\) represent the degree of node \(i\); \(C_{i}\) represents the community to which node \(i\) belongs; \(m\) represents the total number of sides of the community network. When \(i\) and \(j\) are in a community, \(C_{i}=C_{j}\), \(\sigma\left(C_{i},C_{j}\right)=1\), otherwise \(\sigma\left(C_{i},C_{j}\right)=0\). The range of modularity Q is in [\(-0.5\), \(1\)]. With a larger value, the better the effect of community division is. Table 4 is the comparing result  
Figure 4: Clustering results of the Microblog network SSC. Figure 5: Visualization results of three groups of user comment cloud graphs. From Table 5, it can be seen that the SSC algorithm results are more closely connected within the user group in the case of different cluster numbers compared with the K-means algorithm. Experimental results showed that SSC can effectively cluster short texts. Similar to traditional clustering methods, the reduction of feature dimension will result in the loss of data information and affect the effect of the SSC algorithm. However, as the dimension increases, the introduced noise and abnormal points will increase, and the accuracy of clustering will decrease. We took the post of user product feedback issued by Xiaomi Company as an example. The followers effectively evaluated the use of the Xiaomi mobile phone. This work constructed a value model of Microblog users from three dimensions: attribute characteristics, behavior characteristics, and text features. The user grouping of the SSC algorithm was then implemented. And the word cloud tool was used to extract the keywords from the grouped user comments to obtain the interesting points and concerns of different groups.
## 3 Simulating HMAC Pavement Deformation in Curved Ramp
### Rutting Prediction Model for HMAC Pavement in Curved and Sloped Road  
Since there is no rutting predicting equation applicable to HMAC pavement in curved and sloped road in current literature, this research conducted multivariate regression on the simulating data and presented the following model. The correlation coefficient shows this model fits well with the simulating results.  
\[\begin{split} RD&=0.0435P^{1.021}N^{0.831}-3.089H_{u} ^{0.5}-7.650\ln H_{m}+1.685\ln^{2}H_{b}+\frac{0.133}{H_{ba}}+13.677\\ (R^{2}&=0.959)\end{split} \tag{4}\]  
where, \(RD\) is the rutting depth (mm),  
\(P\) is the tire pressure (MPa),  
\(N\) is the ESAL number (\(10^{4}\) times),  
\(H_{u}\) is the thickness of top asphaltic course (cm),  
\(H_{m}\) is the thickness of middle HMAC course (cm),  
\(H_{b}\) is the thickness of binder asphaltic course (cm),  
\(H_{ba}\) is the thickness of semi rigid base (cm).
## 3 Materials and Methods
### Encoding and H.264/AVC Syntax-Based Selective Encryption  
Due to the huge size of videos, they must be compressed to reduce their size to accommodate bandwidth limitations. Video is a series of frames that rapidly change over a given time to illustrate moving objects. Video codecs arrange these video frames in groups, known as a group of pictures (GOP), which are compressed and encoded as independent sets of video frames. Each frame is composed of a set of slices, which are composed of macroblocks. These macroblocks are further divided into a number of blocks. Each macroblock is a 16 \(\times\) 16 array of pixels. A block is represented by a matrix of 4 \(\times\) 4 pixels, which is the smallest unit of compression system. The H.264 video codec has two forms of entropy coders, both of which perform lossless compression: context-adaptive binary arithmetic coding (CABAC) and context-adaptive variable-length coding (CAVLC). Both of these forms are context adaptive, which means that compression is dependent on the patterns of the coefficients that are extracted from the coefficient matrix formed after transform coding and quantization. As both of these entropy coders perform lossless coding, the resultant bitstream is very close to the original input stream. The purpose of this study was error recovery to improve the visual quality of the received videos that has been compromised during transmission. CAVLC is more sensitive to errors, whereas CABAC is not much affected by channel errors, which makes it a better choice for implementing the proposed FEC algorithm [26].  
There are multiple residual parameters of the H.264/AVC CABAC entropy coder, including TCs, MVDs, delta QPs, and the arithmetic signs of TCs and MVDs. We have used the sign bits of TCs and MVDs for XOR-based selective encryption at the final step of the encoding process in our suggested framework to preserve format compliance and security during transmission. The video frame is divided into 4 \(\times\) 4 blocks. The bits within a 4 \(\times\) 4 block are first shuffled and the TCs and MVDs are selected for applying XOR encryption on the encoded bitstream of the shuffled blocks using a 128-bit encryption key. Let \(C_{MVD}\), \(C_{TC}\), and \(k\) represent the ciphered MVDs, ciphered TCs, and the key, respectively, then the ciphertext is generated through the bitwise XOR encryption process \(E\), as follows:  
\[C_{MVD}:=E(k,\;MVD)=k\oplus MVD \tag{1}\]  
\[C_{TC}:=E(k,\;TC)=k\oplus TC \tag{2}\]  
The process is reversed on the receiving end by the decryption algorithm \(D\), where the symmetric key is XORed with the ciphered MVDs and TCs of each block after extraction of the encrypted MVDs and TCs to reconstruct the original bitstream by combining the decrypted blocks, as follows:  
\[M_{MVD}:=D(k,\;C_{MVD})=k\oplus C_{MVD} \tag{3}\]  
\[M_{TC}:=D(k,\;C_{TC})=k\oplus C_{TC} \tag{4}\]  
where \(M_{MVD}\) and \(M_{TC}\) are the original MVDs and TCs after decryption used to reconstruct the original block.
## 2 Experiments
### Ship-to-Ship Wireless Channel Measurement  
The S2S wireless channel measurement was conducted using the same channel sounder and boats in the S2I measurement. Both the Tx and Rx antennas in the S2S measurement were omni-directional antennas with antenna gains of 2 dBi and 10 dBi, respectively. The installation height of the Tx antenna was 3 m, while it was 3.12 m for the Rx antenna.  
In the S2S measurement, the Tx and Rx boats sailed in the same direction. The sailing speed of the Tx was between 0.06 knots (0.108 km/h) and 13.2 knots (24.44 km/h), and the speed of the Rx was between 0.66 knots (1.224 km/h) and 11.47 knots (21.24 km/h). The maximum distance between Tx and Rx boats was 0.09 nautical mile (about 168.1 m).  
In order to give a more intuitive description, we list the system and measurement parameters in Table reftab:para of the S2I and S2S measurement campaigns.  
In addition, the measurements are recorded by video as well. Our channel measurements in this paper serve the data collection of a broadband wireless communication channel over the offshore water regions, based on which the typical channel characteristics extraction and analysis of marine wireless channels will be carried out.
# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 3 Methodology
### Multilayer Perceptron  
The Multi-Layer Perceptron (MLP) is a type of artificial neural network (ANN) and widely used in artificial intelligence (AI) applications [37]. It is particularly effective for binary classification tasks involving nonlinear data [38]. The MLP model consists of three layers: an input layer, a hidden layer, and an output layer [39]. The layers are interconnected through weighted connections, which are adjusted during the training process to enhance the model's stability and performance [40]. Research has also demonstrated the significant impact of weight optimization on MLP outcomes in various applications [41]. Compared with traditional classification methods, MLP offers superior predictive capability due to its ability to model complex, nonlinear relationships [38]. However, it also has certain limitations, such as a tendency to become trapped in local minima and slow learning speeds. Given that a landslide occurrence is a highly nonlinear process, MLP's nonlinear mapping capabilities make it a suitable choice for landslide susceptibility prediction (LSP).  
In the present study, the MLP model was applied for landslide susceptibility mapping, with the model architecture consisting of three layers: an input layer with 16 landslide conditioning factors, a hidden layer that served as the classification tool, and an output layer, which assigned a binary classification (1 for landslide, 0 for non-landslide) to each data point. The MLP neural network, in general, follows a unidirectional signal flow, where data are processed sequentially from the input layer to the output layer. Within this three-layer structure, weighted connections link the input layer to the hidden layer and the hidden layer to the output layer. These weights are iteratively adjusted during the training phase to refine the model's predictive accuracy. The MLP model was developed using Weka software v3.8.6, where its parameters were fine-tuned to optimize its performance. Using the training dataset, the model was iteratively trained and validated to identify the optimal parameter configuration, ensuring stable and reliable susceptibility predictions.
## 4 Discussion  
### Sensitivity Analysis  
The experimental outcomes indicate that the method put forward is capable of attaining superior performance in various datasets through the integration of feature extraction and position attention modules. The put-forward method's effectiveness predominantly stems from feature selection in both the spatial and channel dimensions, as well as label refinement by learning higher-order structural features. When dealing with building edge details, FEPA-Net can capture multi-scale features from local to global scales, facilitating the more effective capture of multi-scale spatial correlations among pixels. In particular, the spatial and channel attention mechanisms enhance useful features; they enable the model to segment buildings with high precision across various terrains and effectively filter out background information. Moreover, the designed model demonstrates outstanding scalability, and it is possible to further optimize this scalability by strengthening the encoder structure.  
Although the ablation experiments described in this paper are mainly used to assess the influence of each module on the performance of the model, they also provide a certain basis for analyzing the influence of the above-mentioned features. Through the comparison of the findings from the experiment between U-Net and the U-Net+ Feature Extraction Module, it is evident that upon the insertion of the feature extraction module, the recall rate of the model rises when handling complex features. This observation suggests that the feature extraction module can identify the multi-scale features of buildings more effectively. Moreover, it mitigates, to some degree, the interference brought about by elements like lighting, color, image resolution, and texture features.
## 3 Results
The ML-generated to surgeon-corrected error variation was non-inferior compared to the inter-surgeon variation for all metrics and surgeons. For surgeons A and B, the error between the ML model and surgeon correction was smaller than the inter-surgeon variations. For surgeon C, the ML model to surgeon-corrected error was higher, but was still found to be non-inferior to the inter-surgeon error. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Total** & \begin{tabular}{c} **Dice** \\ **Coefficient** \\ \end{tabular} & \begin{tabular}{c} **Distance** \\ **Map Error (\%)** \\ \end{tabular} & \begin{tabular}{c} **Correction** \\ **Ratio (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Volume Diff** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **FI Diff (\%)** \\ \end{tabular} \\ \hline \hline All Patients & 31 (100\%) & 1.0 [0.97, 1.0] & 1.58 [0.0, 8.97] & 0.55 [0.0, 5.49] & 0.28 [0.0, 8.97] & 0.06 [0.0, 2.19] \\ \hline Accepted & 30 (97\%) & 1.0 [0.98, 1.0] & 1.54 [0.0, 7.24] & 0.5 [0.0, 3.66] & 0.22 [0.0, 3.32] & 0.06 [0.0, 1.42] \\ \hline Rejected & 1 (3\%) & 0.74 & 48.11 & 67.2 & 58.66 & 15.93 \\ \hline Minor & 25 (81\%) & 1.0 [0.98, 1.0] & 2.38 [0.0, 6.38] & 0.88 [0.0, 3.55] & 0.49 [0.0, 2.64] & 0.08 [0.0, 1.38] \\ \hline Major & 2 (6\%) & 0.86 [0.73, 0.98] & 26.92 [6.56, 49.93] & 32.74 [3.4, 71.17] & 27.34 [3.4, 64.2] & 8.23 [0.55, 17.09] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the quantitative validation. The ML to surgeon variations for all cases, as well as for cohorts that were accepted or rejected, or needed corrections, are shown. Note that all presented values represent the median followed by the 5 th and 95 th percentiles. Figure 3: Comparing deltoid masks generated by ML and surgeons. The ML generated deltoid mask is shown in green, the deltoid mask corrected by surgeon A is shown in yellow, and the deltoid masks corrected by surgeon B is shown in red. Samples 1 and 2 demonstrate deltoid masks that were clinically acceptable and needed only minor corrections. Sample 3 is a deltoid mask that was clinically rejected and required a major correction. In Sample 3, the posterior deltoid exhibited a significant level of fatty infiltration relative to that exhibited by the anterior and middle deltoid.
# Real-Time Simulation of Wave Phenomena in Lung Ultrasound Imaging
## 2 Simulation Method
### Simulation Model  
There are typically two main models used in ultrasound simulations: vector-based and volumetric. The vector-based model is created by 3D graphic artists and often relies on CT data as a foundation. This model involves the generation of geometric shapes to simulate ultrasound wave propagation.  
On the other hand, the volumetric model is derived directly from CT data, allowing for a more accurate representation of the scanned anatomy. By utilizing the actual CT data, the volumetric model offers a higher level of realism, anatomical details and precision in simulating ultrasound images [26; 27; 28; 29; 30; 31; 32; 33; 34].  
Both approaches have their strengths and are commonly employed in ultrasound simulation technology. Vector-based (or triangle-based) models are easier to edit, animate and apply interactions to. The Ultrasound simulation can then be performed using a cross-section of that 3D model or using a raytracing technique [35], where rays are sent from the transducer position and travel through the model, simulating a wavefront propagation.  
On the other hand, the volumetric model may have lower temporal resolution, may be corrupted by different artifacts, is more challenging to process, and occupies more memory space. A volume model can be sampled using a ray-marching technique, which can implement most of the required phenomena to produce a realistic final ultrasound image.  
Figure 1: Schematic representation of the acquisition landmarks on chest anatomic lines. (**a**) From guidelines [22]; (**b**) represented on the virtual patient in the simulator.