esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Mini Case Study: Stabilisation Under Incomplete Observations
This indicates that, when no information is missing, the algorithm successfully identifies and retains high-quality solutions, leading to a steadily improving best individual over generations. * P(loss) = 5% (orange line) The optimisation process is noticeably slower and less dynamic compared to the 0% dropout scenario. The function value stabilises at around 200, with fewer and smaller performance jumps observed throughout the generations. This suggests that missing sensor data limit the algorithm's ability to accurately evaluate individuals, thereby reducing the efficiency of selection and recombination. * P(loss) = 20% (green line) The GA struggles to improve performance under severe information loss. The function value remains low, with only minimal improvements observed over generations. The lack of reliable state information significantly impacts the evaluation of candidate solutions, leading to a stagnation of the evolutionary process. These results highlight the detrimental effect of missing observations on evolutionary optimisation. The primary limitations caused by sensor dropout include the following:  
* Increased uncertainty in fitness assessments leads to less effective selection. * The GA is less capable of discovering high-quality solutions due to incomplete state information. * Instead of making stepwise improvements, the algorithm struggles to identify better solutions, particularly in high-dropout scenarios. The findings indicate that even a relatively low probability of sensor dropout (5%) significantly hinders the optimisation process, while a higher dropout rate (20%) renders the GA nearly ineffective in improving agent performance. This suggests that in real-world applications, where sensor failures are common, additional mechanisms are required to mitigate information loss. Possible solutions include the following:  
* Data imputation techniques to estimate missing values and maintain more reliable fitness evaluations. * Adaptive selection strategies that account for uncertainty in the objective function. * Hybrid evolutionary approaches that balance exploration and exploitation to compensate for information  
The most pronounced impact is observed for the learning rate and replay buffer size, highlighting the need for more advanced optimisation techniques to enhance robustness in environments with partial observability.
## 3 Strain Transfer Efficiency of the Surface-Bonded FBG Sensor  
### Strain Analysis Model of the FBG Sensor  
The schematic diagram of the FBG sensor structure is shown in Figure 6, where D, H, and h represent the width of the adhesive layer, the thickness of the adhesive layer, and the thickness of the adhesive interlayer, respectively. \(r_{\mathrm{c}}\) and \(r_{\mathrm{p}}\) are the radii of the bare fiber and the cladding layer.  
After a mechanical analysis and formula derivation, the strain transfer efficiency (\(\eta\)) of the FBG strain transfer and error correction factor (\(k\)) are defined as  
\[\eta=\frac{\overline{\varepsilon_{\mathrm{c}}(x,t)}}{\varepsilon_{0}}=L^{-1}[ \frac{1}{s}(1-\frac{2\mathrm{tanh}(\overline{\xi}L/2)}{\overline{\xi}L}], \tag{6}\]  
\[k=\frac{1}{1-\eta}, \tag{7}\]  
\[\mathrm{where}\frac{1}{\overline{\xi}}=s\cdot\overline{f}_{\mathrm{a}}(s) \cdot\frac{(2\pi r_{\mathrm{p}}+D)r_{\mathrm{p}}E_{\mathrm{c}}h}{4D}+\frac{(r _{\mathrm{p}}+r_{\mathrm{c}})r_{\mathrm{p}}E_{\mathrm{c}}}{4G_{\mathrm{p}}}, \tag{8}\]
# Numerical Simulation on Dynamic Behavior of Slab-Column Connections Subjected to Blast Loads
###### Abstract  
Although many studies on the blast-resistant performance of structures have focused mainly on single members such as beams and columns, there is little research on the behavior of joints that are subjected to blast loads. In this study, the structural behavior of a slab-column connection subjected to blast load was investigated using a numerical analysis method. LS-DYNA was used as a finite element analysis program, and in order to improve the accuracy of numerical analysis, mesh size, material model, and simulation method of blast load were determined through preliminary analysis. The effect of different restraints of the joints, depending on the position of the columns in the slab, on the blast resistance performance was investigated. As a result, the highly confined slab-interior column connection showed better behavior than other edge and corner columns. The drop panel installed between the lower column and the slab was effective in improving the blast-resistance performance of the slab-column connection. For a more accurate evaluation of blast resistance performance, it was suggested that various evaluation factors such as ductility ratio, reinforcing stress, and concrete fracture area can be considered along with the support rotation, which is an important evaluation factor suggested by many standards.  
blasti loads; slab; column; connections; numerical analysis +
Footnote †: journal: Applied Sciences
###### Abstract  
In arid regions, climatic fluctuations significantly affect vegetation structure and function. Sun-induced chlorophyll fluorescence (SIF) can quantify certain physiological parameters of vegetation but has limitations in characterizing responses to climate change. This study analyzed the spatiotemporal differences in response to climate change across various ecological regions and vegetation types from 2000 to 2020 in Xinjiang. According to China's ecological zoning, R1 (Altai Mountains-Western Junggar Mountains forest-steppe) and R5 (Pamir-Kunlun Mountains-Altyn Tagh high-altitude desert grasslands) represent two ecological extremes, while R2-R4 span desert and forest-steppe ecosystems. We employed the standardized precipitation evapotranspiration index (SPEI) at different timescales to represent drought intensity and frequency in conjunction with global OCO-2 SIF products (GOSIF) and the normalized difference vegetation index (NDVI) to assess vegetation growth conditions. The results show that (1) between 2000 and 2020, the overall drought severity in Xinjiang exhibited a slight deterioration, particularly in northern regions (R1 and R2), with a gradual transition from short-term to long-term drought conditions. The R4 and R5 ecological regions in southern Xinjiang also displayed a slight deterioration trend; however, R5 remained relatively stable on the SPEI24 timescale. (2) The NDVI and SIF values across Xinjiang exhibited an upward trend. However, in densely vegetated areas (R1-R3), both NDVI and SIF declined, with a more pronounced decrease in SIF observed in natural forests. (3) Vegetation in northern Xinjiang showed a significantly stronger response to climate change than that in southern Xinjiang, with physiological parameters (SIF) being more sensitive than structural parameters (NDVI). The R1, R2, and R3 ecological regions were primarily influenced by long-term climate change, whereas the R4 and R5 regions were more affected by short-term climate change. Natural grasslands showed a significantly stronger response than forests, particularly in areas with lower vegetation cover that are more structurally impacted. This study provides an important scientific basis for ecological management and climate adaptation in Xinjiang, emphasizing the need for differentiated strategies across ecological regions to support sustainable development.  
sun-induced chlorophyll fluorescence (SIF); standardized precipitation evapotranspiration index (SPEI); natural forests and grasslands; climate change; spatiotemporal variation characteristics ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Dilated Filters for Edge-Detection Algorithms
## 3 Methodology
### Shen-Castan Algorithm  
The unique feature of the [PERSON] algorithm is that it uses the infinite symmetric exponential filter (ISEF) [18], which offers better noise reduction. The detection steps used by [PERSON] and Canny are similar, but the Shen-Castan ISEF filter provides better signal-to-noise ratios and better localization. The implementation of [PERSON]'s algorithm approximates his optimal filter by the derivative of a Gaussian, whereas Shen-Castan uses the optimal filter directly [51].  
ISEF is described as a real continuous function for one dimension by Equation (12) and for two dimensions by Equation (13), where \(p\) is the thinning factor and \(a\) is the normalization coefficient; more details can be seen in [52]. To speed up the convolution, we used the discrete version of ISEF, the recursive filter, Equation (14), where \(b\) is the thinning factor and its values lie in between 0 and 1.  
\[f(x)=\frac{p}{2}e^{-p|x|} \tag{12}\]  
\[f(x,y)=a\cdot e^{-p(|x|+|y|)} \tag{13}\]  
\[f(x,y)=\frac{(1-b)\cdot b^{(|x|+|y|)}}{1+b} \tag{14}\]Shen-Castan edge detector has a unique step that overcomes noise in the input image by using the ISEF, see [53]. The algorithm has the following steps: (1) smooth the image with the ISEF filter, (2) convolve the smoothed image with a Laplace binary operator, (3) threshold the edge strong edge pixels using zero crossing, (4) link the edge points by hysteresis and (5) thin the results at the end. Details regarding the steps we use for obtaining the edge map are presented in Algorithm 6.  
The approximation of the Laplacian is computed by subtracting the original image from the smoothed one. The result is a band-limited Laplacian image. Next, a binary Laplacian image is generated by setting all the positive valued pixels to 1 and all others to 0, as in [53].  
For our analysis we do not use the Laplace binary operator, which is considered to be optimal as run time for this algorithm but instead use the versions of Laplace isotropic kernels presented in Section 3.5.
```
0: RGB image
0: Binary edge map
0: Parameters: Smoothing Factor of ISEF (\(SF\)), Threshold of Laplace (\(TG\)), Zero crossing window (\(W\)), Threshold for zero crossing (\(R\)), Thinning Factor (\(TN\))
1:for edge operator do
2: Convert image to gray-scale image
3: Apply recursive ISEF filter smoothing
4: Apply Laplace Operator
5: Apply Zero Crossing replacing the pixel values by 1 for positive and 0 for negative
6: Apply non-maximum suppression using adaptive gradient method with fixed width W
7: Apply edge tracking by hysteresis using double threshold
8:endfor
```  
**Algorithm 6** Shen-Castan Operator steps.
# AHC: Advanced Network-Hiding Access Control Framework
## 1 Introduction
By enhancing the network's ability to hide services, AHAC improves overall system security, reduces computational costs, and significantly enhances key verification efficiency. AHAC represents a substantial refinement of SPA's security architecture and encryption protocols, enhancing security, performance, stability, and extensibility. This framework employs a distributed and scalable architecture that seamlessly integrates with existing zero-trust frameworks such as SDP and MSG, providing robust protection for both north-south and east-west network traffic. The main contributions of the paper are summarized as follows:  
1. In the AHAC framework, the separation of the Port Knocking module from the access control module, combined with mutual authentication, allows users to access resources without prior knowledge of their IP addresses. This approach enables users to customize mapping relationships, thereby ensuring that actual IP addresses remain undisclosed. 2. To cope with complex scenarios and multi-level security requirements, we have implemented an extensible two-way communication mechanism, which not only provides richer expansion capabilities for data communication connections but also transmits the contents of the resource requestor's door-knocking request packet to the authorized service provider for advanced authentication and privilege qualification, thus providing stricter and more granular identity authentication and privilege control. 3. We separate the Port Knocking module from the access control module; thus, the two modules can be deployed to different network entities. By doing this, we not only avoid knocking verification occupying the computing and bandwidth resources of the host where the protected resources are located but also allow for on-demand elastic expansion of the knocking module, and access control module, achieving cluster and load balancing to meet the response requirement of services under high concurrency and high load conditions. 4. We replace the RSA signature in the SPA protocol by introducing Noise Protocol into encrypted access and combining random Elliptic Curve Cryptography (ECC) key pair and Elliptic Curve Difference Hellman Key Exchange (ECDH) algorithms, which not only enhances security but also reduces calculation cost and improves verification efficiency. The remainder of this paper is structured as follows. Section 2 explores the evolution of network-hiding technology and provides background on related research. Section 3 defines key terms central to the AHAC framework, establishing a theoretical foundation for subsequent discussions. Section 4 elaborates on the overall architecture and processing flow of the AHAC framework, and introduces the Noise Protocol within this framework. In Section 5, we validate the feasibility and effectiveness of the AHAC framework through experiments. Finally, Section 6 summarizes the entire paper and discusses future research directions.
# Fast 3D Semantic Mapping in Road Scenes +
## 6 Conclusions  
We have presented a fast monocular 3D semantic mapping system which runs on a CPU coupled with a GPU. An incremental fusion method is introduced to combine 2D semantic segmentation and 3D reconstruction online. We exploit a state-of-the-art deep CNN to accomplish scene parsing in the road context. Direct monocular visual SLAM provides a quick 3D mapping based on selected keyframes and corresponding depth estimation. Since the semantic segmentation only runs and propagates on the keyframes, this reduces the computational cost and improves the accuracy of semantic mapping. The offline regularization with a CRF model can enhance the mapping further.  
Since the original LSD-SLAM is hard to handle in the case of sharp turns which are frequent in ordinal driving, our system is not stable in such conditions. In addition, semi-dense 3D reconstruction should be replaced by a dense model. In future work, we plan to introduce several state-of-the-art SLAM methods to improve the initialization and resistance to serious movements. Research on how labeling boosts 3D reconstruction of SLAM would be an interesting direction. The optimization of the regularization module would be another effective effort on the wide-range mapping.  
Conceptualization, X.L.; methodology, X.L.; software, X.L. and H.A.; validation, X.L., D.W. and H.A.; formal analysis, X.L.; investigation, X.L. and H.A.; resources, X.L.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, R.B. and D.G.; visualization, X.L.; supervision, X.L.; project administration, X.L. and D.W.; funding acquisition, X.L. and D.W. This research was funded by the Natural Science Foundation of Jiangsu Province grant number No. BK20160700 and No. BK20170681. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 10 Numerical Simulations and Results
### Comparison of IMM-CKF MoP with Varying Measurement Time Interval  
Figures 13 and 14 present the RMS position and velocity errors in the 4\(g\) case with the measurement time intervals of 0.5 s and 1 s using the IMM-CKF. We observe that as the measurement time interval increases, the RMS position and velocity errors also increase, as expected. This can also be observed from Table 8, which shows the RTAMS errors for both position and velocity.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Azimuth SD** & **Segment** & **RTAMS Error** & **IMM-KKF** & **IMM-UKF** & **IMM-CKF** & **PCRLB** \\ \hline \multirow{4}{*}{\(1\) mrad} & \multirow{2}{*}{NCT-} & Position (m) & 6.809 & 6.694 & 6.694 & 5.225 \\ \cline{3-6}  & & Velocity (m/s) & 0.925 & 0.859 & 0.859 & 0.341 \\ \cline{2-6}  & \multirow{2}{*}{Last NCV} & Position (m) & 9.076 & 8.986 & 8.986 & 8.712 \\ \cline{3-6}  & & Velocity (m/s) & 1.123 & 1.120 & 1.120 & 1.106 \\ \hline \multirow{4}{*}{\(2\) mrad} & \multirow{2}{*}{NCT-} & Position (m) & 4.689 & 4.470 & 4.470 & 3.330 \\ \cline{3-6}  & & Velocity (m/s) & 0.740 & 0.701 & 0.701 & 0.298 \\ \cline{2-6}  & \multirow{2}{*}{Last NCV} & Position (m) & 5.985 & 5.845 & 5.845 & 5.655 \\ \cline{3-6}  & & Velocity (m/s) & 0.995 & 0.990 & 0.990 & 0.978 \\ \hline \multirow{4}{*}{\(4\) mrad} & \multirow{2}{*}{NCT-} & Position (m) & 16.265 & 16.118 & 16.119 & 11.676 \\ \cline{3-6}  & & Velocity (m/s) & 1.464 & 1.184 & 1.184 & 0.401 \\ \cline{2-6}  & \multirow{2}{*}{Last NCV} & Position (m) & 21.531 & 21.234 & 21.236 & 19.219 \\ \cline{3-6}  & & Velocity (m/s) & 1.436 & 1.430 & 1.423 & 1.374 \\ \hline \hline \end{tabular}
\end{table}
Table 7: RTAMS errors for 4\(g\) using IMM-CKF and PCRLB for \(T\) = 0.5 s.  
Figure 12: RMS velocity errors for (**a**) 1 mrad, (**b**) 2 mrad, and (**c**) 4 mrad azimuth error SD in the 4\(g\) case.
## 4 Conclusions  
The stabilization of expansive soil is investigated using two combined additives, namely, calcium chloride and cement. Calcium chloride and cement as single chemical additives are known to be successful but each has its limitations. Excessive calcium chloride can reduce swelling, but it causes the strength to decrease sharply due to the soluble nature of the salt. Cement is an excellent binder, but it causes the material to be rigid and stiff and not reusable if added in high concentrations. This study encourages the use of 4-8% of calcium chloride with the addition of 2% cement. The results presented herein indicated a significant improvement in the swell potential, swell pressure, and compressibility. A  
Figure 14: The XRD profile for Al-Qatif natural clay compared with the addition of 2% cement and 8% CaCl\({}_{2}\).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Item** & **Unit** & **Unit Price for Cement** & **Unit Price for Calcium** & **Estimated Cost** \\  & **per Ton in USD** & **Chloride per Ton in USD** & **of the Stabilizer in USD** \\ \hline Quantity of road volume to be stabilized & 1000 m\({}^{3}\) & & - \\ \hline Weight of cement required for 2\% stabilizer & 36,698 kg & & 5504.7 \\ \hline Weight of cement required for 4\% dose & 73,396 kg & 150 & 110 & 11,009.4 \\ \hline Weight of cement required for 6\% dose & 110,094 kg & & 16,514.1 \\ \hline Weight of calcium chloride for 4\% dose & 73,396 kg & & 8073.56 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The estimated cost of cement and calcium chlorides stabilizers for a 1000 m\({}^{3}\) road section.  
reduction in the swell potential by 8% to 25% and a reduction in the swelling pressure by 28% to 37.4% were observed for 4% to 8% calcium chloride with a cement addition of 2%.  
Cement was found to add sufficient bonding to clay particles and maintain an intact structure, and the calcium chloride will be less affected by leaching. The use of calcium chloride with cement can make crinkled clay particles flatter, and the pore intensity can decrease. This study presents a new choice for an expansive soil treatment that is likely cheaper and more effective compared to single additive approaches.  
A.A. Supervision, funding acquisition, review, and editing--writing original draft and final version; M.D.: conceptualization, review, and editing--writing original draft and final version; A.A.S.: methodology, formal analysis, investigation, data curation, review, and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by King Saud University through the Researchers Supporting Project, grant number [RSP 2023/279] and The APC was funded by the same project.  
All data related to this manuscript are available upon request.  
The authors gratefully acknowledge the Researchers Supporting Project number RSP-2023/279, King Saud University, Riyadh, Saudi Arabia, for their financial support for the research work reported in this article.  
The authors declare no conflict of interest.
# OGSM: A Parallel Implicit Assembly Algorithm and Library for Overlapping Grids
## 3 Parallel Implicit Assembly Method
### Automatic Determination of Overlapping Relation between Component Grids
#### 3.2.2 Intersection Relation Judgement between Two Grids  
As shown in Figure 5, the minimal external boxes of the component grids are all simple convex polyhedrons. Consequently, we take advantage of the spatial plane-separation algorithm [25] to judge the intersection relation between two grids.  
The normalized eigenvectors of \(G^{i}\) and \(G^{j}\) are denoted by \(n_{s}\)\((s=0,1,\cdots,5)\). If there exists a spatial plane \(SP\) with certain \(n_{s}\) as its normal vector, and meanwhile \(SP\) makes the two minimal external boxes \(B^{i}_{X}\) and \(B^{j}_{X}\) located separately at its two sides, then grids \(G^{i}\) and \(G^{j}\) do not intersect; otherwise, they intersect with each other.