esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Ground Subsidence Vulnerability in Urban Areas Using Spatial Regression Analysis
## 4 OLS Analysis
The z-score is a criterion to determine the statistical significance of Moran's I. If the z-score is statistically significant (\(p\)-value \(<0.05\)), it confirms the existence of spatial autocorrelation in the model. Furthermore, both the Jarque-Bera and Breusch-Pagan statistics are statistically significant, indicating that there is non-normality and heteroscedasticity in the error term. Thus, since the OLS analysis model exhibits non-normality and heteroscedasticity in the error term, and the spatial autocorrelation of the residuals is confirmed, it is appropriate to conduct the analysis with a spatial regression model using a spatially weighted matrix. \begin{table}
\begin{tabular}{c c} \hline \hline
**Underground Facility Information** & **Linear Regression Model** \\  & **(OLS)** \\ \hline Constant & \(-0.0314207\)*** \\ \hline Water pipeline diameter (cm) & \(-0.0314207\)*** \\ Water pipeline average depth (m) & \(0.000063\)*** \\ Water pipeline age (years) & \(-0.010811\)*** \\ Sewer pipeline diameter (cm) & \(0.001171\)*** \\ Sewer pipeline average depth (m) & \(-0.000011\)** \\ Sewer pipeline age (years) & \(-0.0143429\)*** \\ Power cable diameter (cm) & \(0.000140\)** \\ Power cable average depth (m) & \(0.000807\)*** \\ Power cable age (years) & \(-0.033201\)*** \\ Underground facility & Communication pipe diameter (cm) & \(-0.000404\) \\ Information & Communication pipe average depth (m) & \(0.000415\)*** \\ Communication pipe age (years) & \(-0.000119\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: OLS analysis results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Underground Facility Information** & **VIF** \\ \hline Water pipeline diameter (cm) & \(1.555709\) \\ Water pipeline average depth (m) & \(2.338912\) \\ Water pipeline age (years) & \(2.712778\) \\ Sewer pipeline diameter (cm) & \(1.541930\) \\ Sewer pipeline average depth (m) & \(1.469795\) \\ Sewer pipeline age (years) & \(1.518273\) \\ Power cable diameter (cm) & \(5.369876\) \\ Power cable average depth (m) & \(4.826353\) \\ Power cable age (years) & \(1.356797\) \\ Communication pipe diameter (cm) & \(4.055690\) \\ Communication pipe average depth (m) & \(1.107161\) \\ Communication pipe age (years) & \(3.990815\) \\ Heating pipeline diameter (cm) & \(2.802712\) \\ Heating pipeline average depth (m) & \(3.911007\) \\ Heating pipeline age (years) & \(2.918603\) \\ Gas pipe diameter (cm) & \(2.085979\) \\ Gas pipe average depth (m) & \(1.150920\) \\ Gas pipe age (years) & \(2.768914\) \\ Density of all six types & \(1.758216\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: VIF of the dependent variables.
# Dual Threshold Self-Corrected Minimum Sum Algorithm for 5G LDPC Decoders
## 2 Basic Decoding Algorithm of LDPC Codes
### MS Algorithm  
The MS algorithm is an approximation of the LLR BP algorithm. In the update of the check node, the minimum and second minimum values are used instead of the confidence information to be transmitted. In the entire implementation process, there are only \"sum\" and \"comparison\" operations.  
The CNP of the MS algorithm is described as follows.  
For each check node \(j\) and its adjacent check nodes \(i\in R(j)\), at the \(l\)-th iteration the check node message is calculated as [22]:  
\[L\big{(}r_{ji}\big{)}=\prod_{i^{\prime}\in R_{j}\backslash i}\text{sgn}\big{(} L\big{(}q_{i^{\prime}j}\big{)}\big{)}\cdot\min_{i^{\prime}\in R_{j} \backslash i}\Big{(}\big{|}L\big{(}q_{i^{\prime}j}\big{)}\big{|}\Big{)} \tag{5}\]  
The MS algorithm greatly reduces the hardware complexity, but the approximate substitution sacrifices a part of the performance. To make up for the performance loss, the NMS algorithm and OMS algorithm are the typical improved algorithms.  
NMS algorithm: reduce the magnitude of message confidence by dividing by a value, the check message is expressed as [22]:  
\[L\big{(}r_{ji}\big{)}=\prod_{i^{\prime}\in R_{j}\backslash i}\text{sgn}\big{(} L\big{(}q_{i^{\prime}j}\big{)}\big{)}\cdot\min_{i^{\prime}\in R_{j} \backslash i}\Big{(}\big{|}L\big{(}q_{i^{\prime}j}\big{)}\big{|}\Big{)}\cdot \frac{1}{\alpha} \tag{6}\]  
where \(\alpha\) is called the correction factor.  
OMS algorithm: reduce the magnitude of message confidence by subtracting a value, the check message is expressed as [22]:  
\[L\big{(}r_{ji}\big{)}=\prod_{i^{\prime}\in R_{j}\backslash i}\text{sgn}\big{(} L\big{(}q_{i^{\prime}j}\big{)}\big{)}\cdot\max\bigg{(}\min_{i^{\prime}\in R_{j} \backslash i}\Big{(}\big{|}L\big{(}q_{i^{\prime}j}\big{)}\big{|}\Big{)}-\beta,0\bigg{)} \tag{7}\]  
where \(\beta\) is called the offset factor.
# Adaptive Prosthetic Trajectory Estimation Based on Key Points Constraints
## 3 Adaptive Kinematic Estimation Method
### Key Point Extraction Based on D-P Iteration  
Key points can summarize the basic shape of the kinematic curve and affect the trend of the curve. Although the positions of the key points may vary with factors such as individuals' height and weight, the curves follow a general pattern, with the key points changing within a certain range. Initially, the key point positions of the average gait model are determined, and personalized methods can be subsequently employed for further optimization [27]. The core idea of extracting feature points is to reduce the number of nodes with the premise of maintaining the curve shape characteristics as much as possible. At present, the methods for extracting feature points include vertical distance, slope, angle, etc. The Douglas-Peucker (D-P) extraction algorithm is a commonly used method based on the vertical distance method. The idea is to set the threshold in advance, and then compare the vertical distance of the line from the point on the curve to the first and end points of the curve with the size of the given threshold.  
Obviously, the accuracy of the D-P algorithm is related to the chosen threshold. The larger the threshold, the greater the degree of simplification and the fewer key points. On the contrary, the lower the degree of simplification, the more key points, and a curve that is closer to the full shape. The algorithm is prone to the loss of some important feature points when the curvature is large, and to the retention of non-feature points when the bending horizontal distance is small. Therefore, an improved iterative D-P key point extraction method is proposed in this section, which can adaptively extract the global key points of the curve.
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 3 Data and Methods
### Surface Water Mapping
#### 3.2.2 SAR Data Processing  
We used the Level-1 Interferometric Wide Swath (IW) and Ground Range Detected (GRD) Sentinel-1 data. Within GEE, the data was accessed and processed to generate a calibrated, ortho-corrected product free of geometric terrain distortion and projected onto the same 10 m spatial grid as the optical data [69]. As the GEE Sentinel-1 dataset does not apply terrain flattening, we used a DEM to mask out areas affected by radar shadow and layover effects. Rather than using spatial filtering, we computed the monthly mean composites of the VV and VH polarizations to reduce some of the inherent speckle effects. Only the polarization with the highest training accuracy was used in the model prediction. With SAR data, it can be difficult to differentiate water from other surfaces with low backscatter, for example sand and airports. To minimize potential commission errors (cf. non-water areas with permanent low backscatter), we used an exclusion layer to remove SAR water predictions where the optical water predictions cannot confirm water presence within the preceding three-month period.  
Figure 1: Schematic illustration of the classification workflow.
## 4 LiDAR Photon Imaging
### Single-Photon LiDAR (SPL)  
Single-photon avalanche detectors or single-photon avalanche diodes (SPADs) are a variation of GMAPD. SPL is executed by multiplying a single laser line into grid 10 \(\times\) 10 sub-beams (beamlets) by a diffractive optical element [36]. The footprints of the 100 beamlets do not overlap on the ground. The backscattered signal is received by an individual detector which is aligned to the beamlet laser direction. The detectors consist of a matrix ar ray of single-photon sensitive cells operating in Geiger mode. The possible implementations include microchannel plate photomultiplier tubes (MCP-PMT) or silicon photomultipliers (SiPM) [22,37]. SPAD utilizes a very short laser pulse (duration is 400 ps). The very low jitter of the detector element is specified at 50-100 ps, which has a positive impact on the ranging accuracy. In addition, it has a low recovery time, such as 1.6 ns [31].  
The beamlets do not provide more subsampling as different from GMLiDAR, but rather provide a cumulative signal at the detector's output. Some cells of the array respond to closer targets, while others trigger later at more distant targets. This gives the possibility of multiple targets such as vegetation and ground. Each beamlet detector acts as an avalanche photo diode (APD) operated in linear mode. In contrast to GMLiDAR, the cumulative signal intensity can be measured but its radiometric resolution is less compared to multi-photon LMAPDs [36,38]. Thanks to single-photon sensitivity, in aerial measurement, SPL enables higher altitude than GMLiDAR and consequently larger area measurement compared to conventional LiDAR. SPL-APD enables higher frame rates than LMAPD.
# A Channel Compensation Technique Based on Frequency-Hopping Binary Offset Carrier Modulated Signal
## 2 Overview of the Designed System  
In this section, the construction of the designed signal is presented, followed by general descriptions and structures of the transmitter and receiver.
## 1 Introduction
We can determine canopy photosynthesis from vegetation indices (VIs) derived from more than one band of reflectance, and since they have a possible relationship with the substrate supply to R\({}_{\rm s}\) we can gain more insight into the R\({}_{\rm s}\) of the area [14]. Examples of indices include the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) [15]. For this study, we used light detection and ranging (LiDAR) data, which use a laser to measure variable distance to the earth's surface [16]. In addition to vegetation indices, remote sensing can also provide information on the canopy height, density, and change over time through the use of LiDAR or other related technologies. In this study, we use LiDAR data to calculate canopy height and hyperspectral data to calculate vegetation indices (VIs), which were compared to ground data. We calculated canopy height from LiDAR. Hyperspectral data were used to calculate VIs to compare to our ground data. We examined, using regression analysis, whether the amount of variability in the R\({}_{\rm s}\) data set gathered from the ground was explained by the remotely sensed data collected over our study area. The main objectives for this study were to characterize how fine-scale differences in dominant understory or overstory plant communities affect total R\({}_{\rm s}\) and the relationship of R\({}_{\rm s}\) to soil temperature and moisture. Also, we wanted to determine whether remotely sensed data can predict R\({}_{\rm s}\) directly or improve on models using fine-scale land-based measures of R\({}_{\rm s}\).
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Built-Up RS-Based Proxies
#### 4.1.1 Buildings Category
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Proxy** & **Essence** & **Used in Disaster Phase** & **Used RS Data to Extract** & **Mono** & **Multi** & **Key References** \\ \hline
**Blow-out debris** & To detect blocked roads (During a disaster blow-out debris) & Damage & UAV data; HR and VIR & **X** & **P**(2,4,72,73) \\  & block-partition/complexity of the roads & Damage & satellite images & ✓ & ✓ & [2,74,74,75, 76, 77, 78] \\ \hline
**Accessibility analysis/load** & Accessibility analysis shows the transportation capacity of & Damage, Recovery & VIR satellite images & ✓ & ✓ & [2,3,44,69,79,73] \\  & (network validation and & Budgets and transfer-together facilities are essential parts of & Damage, Recovery & VIR satellite images & ✓ & ✓ & [2,3,44,75, 76, 77, 78] \\  & public transport facilities & providing accessibility in a transport network & Damage, Recovery & VIR satellite images & ✓ & ✓ & [2,3,44,75, 76, 77, 79, 78] \\  & Presence of vehicles & Presence of vehicles on the roads provide information & Recovery & VIR satellite images & ✓ & & [2,3,44,75, 76, 77, 78] \\  & about transportation condition and functioning of roads & Energy drift towards those capacity of the & Recovery, Vulnerability & VIR satellite images & ✓ & [2,3,44,75, 76, 77, 79, 78] \\ \hline
**Length of roads** & transport-network & Recovery, Vulnerability & VIR satellite images & ✓ & [2,3] \\  & whole roads are less vulnerable to be completely blocked & Vulnerability & VIR satellite images & ✓ & [2,4] \\  & during a disaster & Vulnerability & VIR satellite images & ✓ & [2,4] \\ \hline
**Read network density** & Higher road road road network density shows vulnerable & Vulnerability & Heart satellite images, ✓ & ✓ & [2,3] \\  & & mismergent network & Radar data & ✓ & [2,3] \\ \hline
**The proportion of** & low-grade highways (e.g., county road) are & Vulnerability & HR satellite images & ✓ & [2,3] \\  & two-grade highways & move vulnerable & Vulnerability & HR satellite images & ✓ & [2] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Remote sensing-based built-up proxies for urban DRM, Transport category. Mono and Multi refer to mono-temporal and multi-temporal RS data, respectively, used for the extraction, respectively.
## 3 Results and Discussion
### Results
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Sectors**} & \multicolumn{2}{c}{**City Gas Supply**} & \multicolumn{2}{c}{**Steam and Hot Water Supply**} \\  & **Values** & **Ranks** & **Values** & **Ranks** \\ \hline
1. & Agricultural, forest, and fishery goods & 0.00025 & 22 & 0.00119 & 20 \\
2. & Mined and quarried goods & 0.00296 & 3 & 0.00011 & 31 \\
3. & Food, beverages and tobacco products & 0.00026 & 21 & 0.00122 & 19 \\
4. & Textile and leather products & 0.00009 & 30 & 0.00036 & 28 \\
5. & Wood and paper products, printing and & 0.00021 & 23 & 0.00083 & 24 \\
6. & Petroleum and coal products & 0.00054 & 16 & 0.01224 & 2 \\
7. & Chemical products & 0.00063 & 15 & 0.00419 & 8 \\
8. & Non-metallic mineral products & 0.00012 & 28 & 0.00065 & 33 \\
9. & Basic metal products & 0.00034 & 18 & 0.00095 & 23 \\
10. & Fabricated metal products, except machinery & 0.00080 & 12 & 0.00216 & 15 \\
11. & Computing machinery, electronic equipment & 0.00068 & 14 & 0.00337 & 11 \\
12. & Electrical equipment & 0.00027 & 20 & 0.00160 & 17 \\
13. & Machinery and equipment & 0.00075 & 13 & 0.00263 & 14 \\
14. & Transport equipment & 0.00011 & 29 & 0.00032 & 29 \\
15. & Other manufactured products & 0.00006 & 31 & 0.00019 & 30 \\
16. & Manufacturing services and repair services of & 0.00136 & 6 & 0.00309 & 12 \\
17. & Electricity and city gas supply & - & - & 0.05662 & 1 \\
17. & Electricity, and steam and hot water supply & 0.00088 & 10 & - & - \\
18. & Water supply, sewage and waste treatment & 0.00015 & 25 & 0.00208 & 16 \\  & and disposal services & 0.00013 & 27 & 0.00127 & 18 \\
19. & Construction & 0.00013 & 7 & 0.00604 & 6 \\
20. & Wholesale and retail trade and commodity & 0.00101 & 9 & 0.00278 & 13 \\
21. & Food services and accommodation & 0.00085 & 11 & 0.00434 & 7 \\
23. & Communications and broadcasting & 0.00117 & 8 & 0.00356 & 10 \\
24. & Finance and insurance & 0.01085 & 1 & 0.01042 & 4 \\
25. & Real estate services & 0.00238 & 4 & 0.00395 & 9 \\
26. & Professional, scientific, and technical services & 0.00198 & 5 & 0.01182 & 3 \\
27. & Business support services & 0.00475 & 2 & 0.00680 & 5 \\
28. & Public administration, defense, and social & 0.00037 & 17 & 0.00107 & 21 \\
29. & Education services & 0.00004 & 32 & 0.00040 & 27 \\
30. & Health and social care services & 0.00017 & 24 & 0.00101 & 22 \\
31. & Art, sports, and leisure services & 0.00013 & 26 & 0.00066 & 26 \\
32. & Other services & 0.00034 & 19 & 0.00076 & 25 \\
33. & Others & 0.00000 & 33 & 0.00000 & 32 \\ Sum (A) & & 0.03597 & & 0.14736 & \\ Self-induced effect (B) & & 0.19168 & & 0.51060 & \\ Total (A+B) & & 0.22764 & & 0.65796 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Value-added creation effects of steam and hot water supply and city gas supply sectors.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments
### Experiment Settings
#### 5.1.4 Evaluation Metrics  
To evaluate the prediction performance of the proposed model, we used four evaluation metrics, including the root mean square error (RMSE), the mean absolute error (MAE), the average displacement error (ADE), and the final displacement error (FDE). The RMSE focuses on measuring the stability of the result, while the MAE evaluates the prediction ability of a model. The ADE stands for the average Euclidean distance error between the predicted position and the actual position. Additionally, the FDE focuses on the final accuracy of the predictions.  
\[RMSE =\sqrt{\frac{1}{n}\sum_{k=1}^{n}(y_{k}-\widehat{y}_{k})^{2}} \tag{13}\] \[MAE =\frac{1}{n}\sum_{k=1}^{n}\|y_{k}-\widehat{y}_{k}\|_{1}\] \[ADE =\frac{1}{n}\sum_{k=1}^{n}\|y_{k}-\widehat{y}_{k}\|_{2}\] \[FDE =\|y_{final}-\widehat{y_{final}}\|_{2}\]  
where \(y_{k}\) and \(\widehat{y}_{k}\) represent the true position and the predicted position, \(n\) is the total number of predicted track points, and \(\|\cdot\|_{1}\) and \(\|\cdot\|_{2}\) denote one norm and the Euclidean distance, respectively. \(y_{final}\) and \(\widehat{y_{final}}\) represent the final position of the actual trajectory and the predicted trajectory, respectively. It is worth noting that the lower the evaluation index, the better the generalization ability of the model.
## 1 Introduction
As a result, many IDS models treat anomaly detection as a static binary or multiclass classification task, which significantly limits their ability to adapt to new or evolving cyber threats. This shortcoming underscores the urgent need for a dynamic, data-driven solution that supports continuous learning and real-time threat identification. In this context, our research introduces a hybrid ensemble framework that integrates an Artificial Neural Network (ANN) and Support Vector Machine (SVM) as base classifiers, with a Random Forest (RF) meta-classifier to intelligently combine their predictions for improved detection accuracy. Furthermore, the framework is deployed in a real cloud-based environment that utilizes real-time packet capture tools to stream live network traffic. This integration enables immediate anomaly detection, allowing the system to dynamically analyze incoming data and respond to threats in real time, thereby enhancing the adaptability and operational relevance of modern IDS deployments. Thus, in this regard, the key contributions of this study include the following:  
* Proposing a novel ensemble learning framework (ANN + SVM with RF meta-classifier) that achieves competitive performance compared to state-of-the-art intrusion detection models. * Employing Recursive Feature Elimination (RFE) to dynamically select the most relevant features and reduce model complexity. * Incorporating explainable artificial intelligence (XAI) to interpret individual detection decisions and improve transparency. Figure 3: Commonly seen network-based cyberattacks. * Developing a real-time deployment pipeline using Flask and live packet capture for immediate anomaly detection and dashboard reporting, in the real cloud environment. On the other hand, a review of the recent literature indicates that most ML-based IDSs operate offline and lack real-time adaptability or interpretability [5; 6; 9; 10; 11; 12]. Thus, this study addresses these limitations by combining real-time inference, feature selection, and explainability in a unified, scalable IDS framework. The study is organized in the following manner. Following the introduction, the next section provides the background and related work relevant to the research. The methodology is detailed in Section 3, while Section 4 presents the evaluation results and analysis. Finally, the paper concludes with a summary of findings and future research directions in the Section 5.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 5 Discussion
### Considering Different Parameters
The generally lower R-values for large earthquakes indicate higher uncertainty and complexity in predictions, possibly due to the multiple factors influencing large earthquakes and their intricate interactions. (5) The results for Regions A and B are not identical, reflecting the regional characteristics of seismicity, which implies that specific seismicity features need to be considered in applying the model to different regions. Overall, by removing parameters one by one, we have successfully identified parameters that significantly impact the prediction performance in different regions and magnitude ranges. These findings help optimize model inputs and improve prediction accuracy.
## 2 System Model  
In this section, an in-depth exploration of the system model that forms the basis of the paper is presented. Firstly, the fundamentals of polarization-amplitude-phase modulation and demodulation are introduced. Secondly, this is followed by an explanation of the operational mechanisms of the MP-WFRFT and DM algorithms.
# Virtual View Synthesis Based on Asymmetric Bidirectional DIBR for 3D Video and Free Viewpoint Video
## 3 Experimental Results and Discussion
### Evaluation of Asymmetric Bidirectional Rendering
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Hole Size**} & \multicolumn{3}{c}{**Hole Reduction**} \\ \cline{2-6}  & **SVR** & **BR** & **ABR** & **BR** & **ABR** \\ \hline BA3 & 99,128 & 3479 & 5311 & 96.49\% & 94.64\% \\ BA5 & 108,549 & 6255 & 6772 & 94.24\% & 93.76\% \\ BR3 & 67,917 & 6557 & 11,049 & 90.35\% & 83.73\% \\ BR4 & 33,997 & 5165 & 8193 & 84.81\% & 75.90\% \\ DA5 & 50,385 & 8843 & 10,520 & 82.45\% & 79.12\% \\ SH5 & 99,857 & 9746 & 11,633 & 90.24\% & 88.35\% \\ KE3 & 31,723 & 2338 & 2928 & 92.63\% & 90.77\% \\ Baby1 & 101,613 & 6124 & 6252 & 93.97\% & 93.85\% \\ Books & 148,127 & 8298 & 9116 & 94.40\% & 93.84\% \\ Art & 269,033 & 18,942 & 22,096 & 92.96\% & 91.79\% \\ Aloe & 181,236 & 37,695 & 44,868 & 79.20\% & 75.24\% \\ Reindeer & 225,956 & 12,302 & 13,439 & 94.56\% & 94.05\% \\ Lampshade1 & 174,334 & 34,085 & 45,799 & 80.45\% & 73.73\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hole-size comparison of different methods. Figure 9: Comparison result of different rendering methods. Images from left to right denote the synthesized images by SVR, BR and ABR: (**a**) Ballet (**b**) Breakdancers; (**c**) Baby1; and (**d**) Aloe.