esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research on BPL Decoding Algorithm for Polar Codes Based on Information Geometry
### List Decoding Algorithm for Polar Codes from the Perspective of Information Geometry
#### 3.1.1 Distance Analysis of Log-Likelihood Ratios (\(Llr\))  
The soft information used in Belief Propagation (BP) decoding of polar codes is represented by the Log-Likelihood Ratio (\(Llr\)):  
\[LLR=\ln\frac{p(x=1)}{p(x=0)}. \tag{5}\]  
Inthis context, the sign of the \(LLR\) indicates the decision result: if the \(LLR\) is positive, the decision result is 1; if the \(LLR\) is negative, the decision result is 0. The magnitude of the absolute value of the \(LLR\) represents the reliability of the decision: the larger the absolute value of the \(LLR\), the higher the reliability of the decision result.  
Consider the following four \(LLR\) values: \(LLR_{A}=-0.5,LLR_{B}=0.5,LLR_{C}=10\), \(LLR_{D}=11\) at this point, we have \(|LLR_{A}-LLR_{B}|=|LLR_{C}-LLR_{D}|\), which means, from the perspective of Euclidean geometry, the distance between A and B is the same as the distance between C and D.  
However, in terms of decision outcomes, the difference between C and D is not significant, both indicating a high confidence in deciding as 1, whereas the difference between A and B is substantial, as they lead to completely opposite decision results.  
In other words, the space in which \(LLR\)s reside is not a flat space. Next, we will analyze this space using the concept of information geometry.
## 2 SE-TransUNet Water Seepage Detection Model  
This section details the tunnel lining leakage identification methodology. The SE-Block serves as the integrated attention mechanism module, performing channel-wise weighting on the global semantic features output by the Vision Transformer (ViT). These weighted features are subsequently incorporated during skip connections between the encoder and decoder. Consequently, the network architecture comprises three core components: (1) a TransUNet-based encoder for leakage identification, (2) a TransUNet-based decoder for leakage identification, and (3) skip connections enhanced with the SE-Block module.
## 3 Experiment Setup and Results
### 160 Gb/s Wavelength Division Multiplexing Operation
The power penalty of 1 to 1.5 dB for _ch-1_ to _ch-4_ is attributed to the different optical gain setting in the two EDFA's for the back-to-back case. Figure 14: 40 Gb/s electrical eye diagrams obtained for the four channels at the output of the demultiplexer. Figure 15: Measured BER as a function of the average optical power at the input of the photodetector for the back-to-back and four output channels of the demultiplexer.
## 2 Results
### Analytical Application  
The analytical performances of the proposed method were settled under the optimized conditions, using differential pulse voltammetry (DPV) as a detection technique. The calibration curve was plotted within a concentration range of 5-100 mg mL\({}^{-1}\) (Figure 5). The precision was investigated by the analysis of 10 series of 10 replicates of 25 ng mL\({}^{-1}\) of Rhodamine B standard solution; the relative standard solution calculated was 6.59%. The limit of detection (LOD) and limit of quantification (LQ) were 1.44 and 4.81 ng mL\({}^{-1}\), respectively. The figure of merit of the present method is summarized in Table 3. As can be seen in this table, the detection limit of the MIP/SPCE-MSPE method was found to be 1.44 ug L\({}^{-1}\), which is better than the other methods for detection of Rhodamine B. Thus, it can be said that the MIP/SPCE-MSPE method is very useful for investigation of the electrochemical behavior of Rhodamine B and its determination; SPCE possesses important advantages such as high electrochemical reactivity, commercial availability, good mechanical rigidity, low cost, low technology, ease of modification, and renewable surface. The developed study provides good sensitivity in comparison with methods previously reported for the determination of Rhodamine B (Table 4). The applicability of the described method was investigated by the analysis of Rhodamine B in two types of samples--chili powder and tomato sauce extracts--which were found to contain no Rhodamine B. Thus, these food samples were spiked with distinct concentrations of Rhodamine B that were calculated using the calibration equation. The obtained results are presented in Table 5.
## 2 Related Work
### Explanation Types and Evaluation
Point fidelity measures agreement between the explainer and predictor for individual instances:  
\[\text{PointFidelity}(x_{i})=\mathbb{1}\left(\hat{y}_{f,i}=\hat{y}_{time,i}\right) \tag{2}\]  
The average point fidelity across all instances is calculated as follows:  
\[\text{AvgPointFidelity}=\frac{1}{N}\sum_{i=1}^{N}\text{PointFidelity}(x_{i}) \tag{3}\]  
Neighborhood fidelity extends this concept to consider agreement within local neighborhoods around each instance, providing a more robust measure of explanation quality:  
\[\text{NeighborhoodFidelity}(x_{i})=\frac{1}{k}\sum_{x_{j}\in\mathcal{N}_{k} (x_{i})}\mathbb{1}\left(\hat{y}_{f,j}=\hat{y}_{time,j}\right) \tag{4}\]  
where \(\mathcal{N}_{k}(x_{i})\) denotes the set of \(k\)-nearest neighbors of instance \(x_{i}\) in the feature space. This metric assesses how well explanations generalize to similar instances, capturing the local coherence of explanations. The stability metric quantifies the variability in fidelity scores using total variation:  
\[\text{TV}(\mathcal{F})=\frac{1}{N-1}\sum_{i=1}^{N-1}|f_{i+1}-f_{i}| \tag{5}\]where \(\mathcal{F}=\{f_{1},f_{2},\ldots,f_{N}\}\) represents a set of fidelity scores. Lower total variation indicates higher stability, suggesting that explanations remain consistent across different instances without sudden fluctuations. Stability is crucial for building trust in explanations, as users expect similar explanations for similar instances. These evaluation metrics provide complementary perspectives on explanation quality. Point fidelity focuses on individual accuracy, neighborhood fidelity considers local coherence, and stability measures consistency across instances. A desirable explainable model would achieve high average fidelity scores and low total variation, indicating explanations that are both accurate and stable across different instances. Together, these metrics offer a comprehensive framework for assessing explanation quality that aligns with the multifaceted nature of interpretability [4; 5].
## 5 Conclusions
Specifically, the proposed method achieved an average precision of 93.91%, a recall rate of 95.97%, and an F1-score of 94.91%, with measurement errors for defect length, width, and depth of 3.43%, 4.50%, and 5.12%, respectively, meeting the stringent demands of the magnet tile production industry. Despite its effectiveness, the method has some limitations. The computational cost associated with processing high-density point clouds remains a challenge, and further optimization is needed to improve real-time performance. Furthermore, while this study focused on surface defects, future research will explore the integration of photoacoustic multimodal techniques to simultaneously detect both surface and subsurface defects, as well as assess their applicability to other materials and industrial components. Integrating deep learning-based point cloud processing techniques and enhancing defect classification capabilities are also promising directions for further research. By addressing these challenges, the proposed framework can contribute to more efficient and precise defect detection in industrial settings, paving the way for improved quality control and predictive maintenance strategies. Methodology, J.H. and Q.H. ; software, J.H., W.J. and F.S. ; validation, J.H., Q.H., W.J. and F.S. ; formal analysis, J.H. and Q.H. ; investigation, J.H., W.J. and F.S. ; data curation, Q.H., W.J. and F.S. ; writing--original draft preparation, J.H. ; writing--review and editing, J.H., Q.H., W.J. and F.S. ; visualization, J.H., W.J. and F.S. ; supervision, Q.H. ; project administration, Q.H. ; funding acquisition, Q.H. All authors have read and agreed to the published version of the manuscript. This work was supported by the Sichuan Science and Technology Program (Grant No. 2024 NSFSC0137), the Talent Introduction Project of Sichuan University of Science and Engineering (Grant No. 2021 RC30), and the Graduate Innovation Fund of Sichuan University of Science and Engineering (Grant No. Y2023316).
# ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles
## 3 System Functionalities
### Mission Planning  
A mission can be planned by an RMCC operator executing high-level software that can formally instruct the vehicles [48]. The operator marks one or more zones of interest on an interactive map and generates a pattern controlling both USV and AUV navigation, including specification on how to reach the survey areas and how to perform the mission. During execution, telemetry is provided as it becomes available. The AUV can navigate in gliding mode between different survey areas and then move via thrusters inside the area for acquiring the data. The Figure 4 shows an example of complex seabed survey activity where (a) the hybrid AUV navigates in gliding mode (dashed lines) to reach the survey area (b), then it switches to thruster mode navigation for acquiring data through horizontal transects (continuous lines). After the area (b) is surveyed, the hybrid AUV moves (c) in gliding mode to the survey area d) then switches in gliding mode (e) for reaching (f). Finally, the AUV switches again in gliding mode for autonomous homing (g).  
The mission planner also manages sensors, their (de)activation position and time, execution of algorithms for data analysis on board of both AUV and USV, as well as the USV-RMCC and the USV-AUV communication. After AUV-USV data transfer, a preliminary compressed map (low-resolution or classified areas) is sent to the RMCC. The user can request a higher detail image of a subsection to be transmitted by the USV. This on-demand level of detail conserves bandwidth, while still providing baseline information at all times. Using a progressive image transfer, the user does not need to wait for the transmission to be completed to get an impression of the mission environment. Based on new information, the operator can subsequently refine the mission for the AUV to perform a closer rescan of the area. The complete uncompressed map becomes available after AUV and/or USV recovery.
# Space Weather Effects on Satellites
## 5 Discussion and Conclusions
Spacecraft operators and owners could consider the following mitigation strategies, particularly in view of the fact that 43% of GEO and 6% of LEO satellites are insured (data from 2018, [[https://payloadspace.com/the-space-insurance-landscape/](https://payloadspace.com/the-space-insurance-landscape/)]([https://payloadspace.com/the-space-insurance-landscape/](https://payloadspace.com/the-space-insurance-landscape/)), accessed on 17 August 2023): a delay in the launch in order to avoid ongoing GS or multiple GS-arrival; development of physics-based forecasting schemes tailored to the specific operational needs; consideration of a higher staging orbit (currently being implemented); and investment in the development of improved propulsion systems to counteract the atmospheric drag. Such strategies aim at utilizing in full the nominal lifetime of the spacecraft in order to ensure reliable services but also to regulate space debris pollution. Conceptualization, R.M. ; methodology, R.M., S.W.S. and S.T. ; software, R.M. ; validation, R.M. and S.W.S. ; formal analysis, R.M. and S.W.S. ; writing--original draft preparation, R.M. ; writing--review and editing, R.M., S.W.S. and S.T. ; visualization, R.M. ; project administration, R.M. and S.W.S. ; funding acquisition, R.M. and S.W.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the Bulgarian-Egyptian inter-academy project by the Bulgarian Academy of Sciences IC-EG/08/2022-2024 and Egyptian Academy of Scientific Research and Technology (ASRT)/NRIAG (ASRT/BAS/2022-2023/10116). The authors thank [PERSON] for the valuable discussions. Data Availability Statement: Data and results from open access databases have been used in this work and the links are provided in the text. The authors declare no conflict of interest. The views expressed in this paper are the sole responsibility of the authors and do not necessarily reflect the views of the European Innovation Council and SME Executive Agency. The Authors are not liable for any consequence stemming from the reuse of this publication.
## 2 Related Works and Contribution
### Data Prediction  
Prediction-based approach enables selective transmission of the data collected by sensor nodes [28; 29; 30]. According to this approach, only a subset of the collected data is delivered to the sink node. Each sensor node has to decide whether to send its current data readings to the sink. When data are not transmitted, the sink has to reconstruct them. To this end, the same prediction algorithm is used at the sensor node and at the sink for estimation of the current data readings. If the prediction error is above a predetermined threshold then the sensor node sends its data readings to the sink. In opposite situation, the data are not transmitted and the sink uses the prediction as an accurate estimate of the sensed data.  
A linear prediction algorithm was used in [31] to predict sensor values and transmit the sensor data if the difference between the predicted value and the real sensor reading exceeds a predetermined limit. Simulation experiments have revealed that such approach reduces the number of transmission effectively for signals representing typical control system responses, i.e., step response of a first-order system and step responses of second-order systems.  
The prediction-based method of data transmission reduction was exploited by several authors to address the problem of energy conservation in body area sensor networks. In [32] a generic prediction algorithm was proposed for predicting the data sampled by wireless body sensors on the basis of the proportional-integral-derivative (PID) control technique. That algorithm was designed for monitoring of hemodynamic and electrocardiographic signals. A similar method with application of artificial neural network was presented in [33]. The artificial neural network was used for prediction of physiological data (blood pressure and electrocardiogram) collected by wireless body sensors.  
Recently, the prediction-based data transmission concept was adapted for air quality sensors [34]. To this end, a prediction algorithm was proposed, which uses a wavelet transform and Gaussian processes model. That algorithm transforms the sensor readings to the wavelet domain and then the Gaussian processes prediction is made for each subband of the wavelet transform. It was shown that the multi-resolution analysis can improve the prediction results for complex series of sensor readings.
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
## 4 Comprehensive Comparison
### Summary  
Based on the analysis above, the characteristics of the three methods can be summarized as the following Table 1.  
Method 1 has high accuracy, and it holds a medium CN0 margin against the GNSS signal based on the \(CNSR\) chosen in this paper.  
Considering that positioning based on Doppler can only be used for static targets and the low pseudorange precision, method 2 is not a good choice for navigation.  
Method 3 is of medium accuracy and high CN0 margin. The communication loss is also acceptable. This would be great advantage in the environment when the GNSS signal is deteriorated severely. The accuracy can be improved further with the improvement of receiving algorithm and the usage of carrier phase.  
Considering that the great weakness of GNSS signal is low CN0, method 3 would be a good choice of LEO for GNSS augmentation.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Method 1** & **Method 2** & **Method 3** \\ \hline Pseudorange precision & High & Low & Medium \\ \hline Doppler precision & High & Medium & High \\ \hline CN0 margin & Medium & High & High \\ \hline Positioning method & \
\begin{tabular}{c} Pseudorange/Doppler/ \\ Carrier phase \\ \end{tabular} & Pseudorange/Doppler &
\begin{tabular}{c} Pseudorange/Doppler/ \\ Carrier phase \\ \end{tabular} \\ \hline Communication loss & Low & Low & Medium \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the characteristics of three methods.  
Figure 8: (**a**) CN0 margin versus \(CN0_{\textit{total}}\) for methods 1 and 3. (**b**) The accuracy of pseudorange versus \(CN0_{\textit{total}}\).
## 3 Proposed Hybrid Model  
Hybrid models integrate the different abilities of various models into a combined system, and superior results are achieved [32; 33]. This study used a new generation of neural networks, EANN, and SVR to construct a hybrid SVR-EANN model for BFP prediction. The proposed model was implemented using the special MATLAB 2019a codes. The initial stage of the proposed system was employing the left-out sensitivity analysis using the SVR model with the RBF kernel. We tuned the SVR hyper-parameters using grid search [34] during the training with 4-fold cross-validation. Grid search trains the models using all the possible combinations of hyper-parameters and finds the superior values that produce the highest results. In this study, the final parameters of SVR were set as 5, 0.0313, and 0.25 for C, \(\gamma\), and \(\varepsilon\), respectively, using the grid search of the hyper-parameters in 4-fold cross-validation.  
Machine learning models are capable of learning different numbers of data; however, effective data selection improves the ability of the models to achieve superior results. One of the data selection techniques is the left-out approach which is also performed by the machine learning models and based on artificial intelligence [34; 35]. The left-out approach trains a model with the other variables, while one of the variables is left out in each training phase. At this point, the left-out input is later re-applied to all inputs. The method allows researchers to observe which attributes have the most significant influence on the models' convergence. Therefore, the more efficient the variable, the greater the reduction in model accuracy. When the significant input is retrieved and then switched for a less critical variable, the model performance dramatically decreases (here, left-out variable). Figure 4 presents the algorithm for SVR left-out sensitivity analysis in detail.  
After determining the most significant factors affecting BFP using the SVR model, the EANN model was trained to predict the BFP using the selected variables in the second stage. The training was employed using the backpropagation algorithm. The structure of the EANN model was determined after several experiments, and finally, it was considered with 20 hidden and five hormone neurons. Superior results were obtained using the TanSig activation function, and the EANN was trained for 50 epochs. Figure 5 presents the general block diagram of the proposed hybrid model.  
Figure 4: Algorithm for SVR left-out sensitivity analysis.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 2 Related Works
### 3 DMOP Watermarking Algorithm Based on 3D Mesh  
The 3 DMOP watermark algorithm based on a grid embeds the watermarks into the grid features. [PERSON] et al. (2007) proposed a robust digital watermarking algorithm for 3D mesh models based on wavelet transform [17], and [PERSON] et al. (2009) proposed a 3D model digital watermarking algorithm based on DWT (discrete wavelet transform) and SVD [18]. The above two types of watermarking algorithms are robust against cropping and noise attacks but cannot resist translation and rotation attacks common in the application of 3 DMOP. [PERSON] et al. (2015) used genetic algorithms to embed watermarks into selected points after K-means clustering, and the algorithm has strong robustness, but it is a non-blind watermarking algorithm and has poor practicality [19]. [PERSON] et al. (2014) proposed a robust watermarking algorithm for 3D mesh models based on vertex curvature, which uses the root mean square curvature of vertices for watermark embedding [20]. [PERSON] et al. (2014) extracted significant regions in 3D models, converted them to spherical coordinates, and performed wavelet transform to embed watermarks into low-frequency and high-frequency regions [21]. Algorithms in [20; 21] have good robustness against translation, rotation, noise attacks, etc. [PERSON] et al. (2014) proposed a digital watermarking algorithm for 3D mesh models based on roughness [22], which selects candidate vertices through the normal vector and the angle between the weighted normal vector of the local geometric space closure-ring neighborhood. It can effectively resist simplification attacks, but the above three algorithms are not robust against cropping attacks.
## 2 Materials and Methods  
### Optimization Procedure  
The basic idea of OSP in SHM is to identify a layout for a certain number of sensors that can be used to obtain as much information as possible regarding a dynamic system. To realize this objective, the first step is to express a full-scale analytical model in finite-element software, such as Abaqus. Through modal analysis, modal properties such as mode shapes and natural frequencies can be calculated. Then, the input parameters for an OSP process are computed and the objective functions are defined. Next, the optimization algorithm is executed, and it returns a Pareto set. Finally, a multiobjective decision-making (MODM) strategy is applied to determine the most feasible solution as the final output OSP. Figure 1 illustrates the process adopted in this study.  
When we set the objective function, we considered key factors such as the spatial intersection angle of the modal vector, the redundancy of the collected data, and the intensity of the collected signal. According to Equation (1), the examined MOP has three objective functions, which must be minimized in the exploration space \(\Omega\). The goal of optimization is to calculate the feasible solution sets for the objective functions and to return the optimal sensor layout solution in the context of the decision strategy.  
\[\text{Minimize }F(x)=\{f_{1}(x),f_{2}(x),f_{3}(x)\},\text{ for }x\in\Omega \tag{1}\]  
Figure 1: Flowchart of proposed method.
# A Path Recommendation Method Considering Individual Driving Preferences
## 5 Case Studies
### Results and Analysis of Mid-Trip Secondary Recommendations
Using a secondary path recommendation threshold of 10%, secondary path recommendations were provided for all 33 drivers who traveled through Path 1 during the aforementioned time period. Among them, 12 drivers experienced a satisfaction increase of more than 10%, indicating that 36.36% of drivers would benefit from a secondary recommendation, demonstrating room for further improvement in individual satisfaction. Prior to the secondary recommendation, the average path satisfaction for all 33 drivers was 45.37. After the recommendation, the average satisfaction increased to 51.16, representing a 12.76% increase. Notably, this exceeded the set threshold for individual perception. Additionally, among the twelve drivers who received the secondary recommendation, five of them (41.67%) were guided from denser main roads to less dense secondary roads, which contributed to traffic flow distribution and helped to alleviate the pressure on main roads.