esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Simulations and Discussion
### Experiment 3. MSE Curves of Proposed Algorithms vs. Characteristic Exponent  
The average MSE curves of the proposed algorithm with respect to characteristic exponent \(\alpha\in\{0.8,\,1.0,\,1.2,\,1.4,\,1.6,\,1.8,\,2.0\}\) in scenarios of dispersion parameter \(\gamma=1.0\), channel length \(N=128\), channel sparsity \(K=8\) and \(SNR=10\) dB, are depicted as shown in Figure 4. The proposed algorithm is very stable for the different strengths of impulsive noises that are controlled by the characteristic exponent \(\alpha\). In addition, it is very interesting that the convergence speed of the proposed algorithm is very stable for the different strengths of impulsive noises.  
Figure 3: Monte Carlo based MSE curves averaging over 1000 runs with respect to different channel sparsities \(K\in\{2,\,4,\,8,\,16\}\), in scenarios of characteristic exponent \(\alpha=1.2\), dispersion parameter \(\gamma=1.0\), chann1 length \(N=128\), and \(SNR=10\) dB.  
SLMS algorithms may be reduced by the relatively small \(\alpha\). The main reason the proposed SLMS algorithm is utilized is that the sign function is stable at different values of \(\alpha\). Hence, the proposed algorithm can mitigate non-Gaussian \(\alpha\)-stable noise.
###### Abstract  
In order to explore the optimal remote sensing salinity monitoring index model for the inversion of soil salinization in the Alar reclamation area, based on the Sentinel-2 images and field measured data, the salinity index 1 (SII), the normalized difference vegetation index in a green-red band (GRNDVI), the normalized vegetation index of greenness (GNDVI), and the normalized difference vegetation index (NDVI) were selected to construct the remote sensing-based salinization 1 detection index (S1 DI) model. Next, the cotton field soil salinization information in the Alar reclamation area was extracted, and the accuracy of the model was verified to obtain the optimal remote sensing salinity monitoring index model. The results show that the overall classification accuracy of the S1 DII (SII-GRNDVI), S1 DI2 (S1I-GNDVI), S1 DI3 (S1I-NDVI), and S1 DI4 (S1I-DVI) models for salinity monitoring is 83.35%, 83.10%, 82.96%, and 80.25%, respectively. The S1 DI1 model is most suitable for retrieving the degree of soil salinization in the cotton field in the Alar reclamation area, and the S1 DI2, S1 DI3, and S1 DI4 models are also very useful for monitoring soil salinization in the Alar reclamation area. Using the S1 DI1 model to invert the soil salinization level of the cotton fields in the Alar reclamation area, it was found that the cotton field in the reclamation area is dominated by non-saline soil, and the light saline soil and moderate saline soil are mainly distributed in the 9 th and 12 th clusters of the reclamation area. As the S1 DI1 model possesses the highest accuracy in extracting the soil salinization information of the cotton fields in the Alar reclamation area, it can be used as a remote sensing salinity 1 monitoring index model for the inversion of the soil salinization of the cotton fields in the reclamation area, which is expected to provide an effective reference value for soil salinization monitoring.  
salinization index; vegetation index; cotton field soil salinization; remote sensing; Alar reclamation area +
Footnote †: journal: Applied Sciences  
Article  
Extraction and Analysis of Soil Salinization Information in an Alar Reclamation Area Based on Spectral Index Modeling
## 1 Introduction
The same principle holds for image texture metrics calculated from the Gray-Level Co-occurrence Matrix (GLCM). Changes in the relative proportions of soil and green vegetation, induced by severe agronomic factors (e.g., invasive species) and biological degradation (e.g., soil exposure), can manifest as alterations in the heterogeneity of pasture surfaces. In this context, the objective of the current work is to explore the potential of satellite constellation data for discriminating various levels of pasture degradation (NDP, LID, MID, SAD, and SBD) in the Brazilian Cerrado. To achieve this objective, attributes extracted from the eight-band SuperDove instrument, measured through 2022, were utilized for RF classification of both degraded and non-degraded pastures. In the data analysis, a comprehensive dataset comprising field and remote sensing data collected across five sites, each spanning 15 \(\times\) 15 km, was employed to ensure the accurate representation of distinct pasture degradation classes. More specifically, the objectives are to: (i) identify the most favorable period for discriminating these classes using 259 cloud-free images acquired across all sites through 2022; (ii) assess the performance of both individual and combined sets of SuperDove attributes, including reflectance, vegetation indices, endmember fractions, and GLCM texture metrics, during the previously selected period for RF classification of the pasture degradation classes; and (iii) compare the RF classification outcomes using the combined set of attributes in two discrimination scenarios: a more challenging one involving five classes (NDP, LID, MID, SAD, and SBD), and another considering only non-degraded and severely degraded pastures (NDP, SAD, and SBD). Preliminary analysis of MultiSpectral Instrument (MSI)/Sentinel-2 data is also considered here for comparison with SuperDove results.
## 5 Anti-Rutting Analysis of HMAC Pavement under Continuous Temperature Variation
### Analysis of Creep Deformation at Different Depths of Pavement Structure  
Rutting is mainly caused by the internal compaction and deformation of the asphalt pavement structure. Therefore, in order to further study the deformation of each structural layer, the distribution of transverse and vertical creep strain at different depths underneath the wheel center point (because of symmetry, the leftmost wheel was selected) and the point directly between the left-side wheels (hereafter: \"dual-wheel center\") were studied. These points were referred to in Figure 2 as point B and point A, respectively. The results are shown in Figures 10 and 11.  
As shown in Figures 10 and 11, the lateral and vertical creep strain of the structural layers underneath the road surface decreased with increasing depth, and were reduced to zero at 18 cm. This means that the creep deformation only occurred in the surface layers. The maximum vertical and transverse creep strains were almost the same, and both appeared on the road surface. The maximum creep strain underneath the center of the leftmost wheel and the dual-wheel center were 0.021 and 0.028 respectively, and the pattern variations were consistent throughout the depth of the road.  
At each reference point, the vertical creep and transverse creep had opposite signs. The vertical creep strain on the road surface below the leftmost wheel center was negative, which means compressive strain; meanwhile, the horizontal creep strain was positive, which means tensile strain. In contrast, the vertical creep strain on the road surface below the dual-wheel center was tensile strain, and the horizontal creep strain was a compressive strain.
## 3 Proposed Method
### Lightweight Encoder  
The proposed encoder is designed as a lightweight dual-path feature extractor where one path is called the RGB extraction branch and another is called the depth extraction branch, as shown in Figure 2. Their backbones with MobileNet-s can provide the multi  
Figure 1: Framework of the proposed MR depth estimation network in which the multi-level shared decoder is shared by the depth encoder and RGB encoder.  
scale input features for the multi-level shared decoder. The inputs of the proposed encoders are the RGB image with a size of 224 \(\times\) 224 \(\times\) 3 and the LRLQ depth map with a size of \(p\times p\times 1\), where \(p\) = 8 is set for the Jorjin J7 EF Plus AR glasses [19]. The HR RGB image and LRLQ depth map are fed into the color extraction branch and depth extraction branch, respectively, so that the LRLQ depth maps are bi-linearly interpolated to the same spatial dimension as the RGB images before being fed into depth extraction branch.  
For low-cost MR glasses, the primary consideration of designing modules should be high efficiency in computation. Inspired by FastDepth [10], we adopt MobileNet [30] with depthwise separable convolution layers as our feature extractors since they provide extreme computational efficiency. Then, we reduce the convolution layers on the original MobileNet layers to further achieve higher model throughput. The reduced encoder contains 6 depthwise separable convolution layers which can be regarded as a simplified MobileNet module, named MobileNet-s. The multilevel outputs of MobileNet-s are fed forward to the multilevel decoder through multilevel skip connections to generate the fused multilevel decoding features.
# Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI
###### Abstract  
Deep learning techniques have flourished in recent years and have shown great potential in ground-penetrating radar (GPR) data interpretation. However, obtaining sufficient training data is a great challenge. This paper proposes an intelligent recognition method based on slicing-aided hyper inference (SAHI) for GPR images. Firstly, for the problem of insufficient samples of GPR images with structural loose distresses, data augmentation is carried out based on deep convolutional generative adversarial networks (DCGAN). Since distress features occupy fewer pixels on the original image, to allow the model to pay greater attention to the distress features, it is necessary to crop the original images centered on the distress labeling boxes first, and then input the cropped images into the model for training. Then, the YOLOv5 model is used for distress detection and the SAHI framework is used in the training and inference stages. The experimental results show that the detection accuracy is improved by 5.3% after adding the DCGAN-generated images, which verifies the effectiveness of the DCGAN-generated images. The detection accuracy is improved by 10.8% after using the SAHI framework in the training and inference stages, which indicates that SAHI is a key part of improving detection performance, as it significantly improves the ability to recognize distress.  
ground-penetrating radar (GPR); deep convolutional generative adversarial networks (DCGAN); YOLOv5; slicing-aided hyper inference (SAHI); object detection; deep learning +
Footnote †: journal: Medical Image Analysis and Applications  
**Keywords:** ground-penetrating radar (GPR); deep convolutional generative adversarial networks (DCGAN); YOLOv5; slicing-aided hyper inference (SAHI); object detection; deep learning
## 5 Discussion
### Model Generalizability and Adaptation Strategies  
While the model demonstrates robust performance in the Volve oilfield, its generalizability to other geological formations remains challenged by domain shifts in petrophysical patterns and drilling responses. The framework supports transfer learning by fine-tuning the CatBoost classifier with limited target-domain data, leveraging the UMAP+K-means feature space that captures core lithological trends. However, its effectiveness may diminish when encountering subtle lithological transitions, irregular class distributions, or highly imbalanced datasets, and performance is sensitive to clustering parameters. To enhance adaptability, incremental learning allows real-time updates to clustering centroids and classifier weights using streaming field data. To address data scarcity, synthetic logging curves generated via forward petrophysical simulations can augment the training set. In addition, while UMAP shows excellent dimensionality reduction performance, its computational cost may become a bottleneck in real-time applications. Although offline pretraining and centroid caching help reduce time consumption during deployment, further optimization or approximations (e.g., mini-batch UMAP) may be required for large-scale operations.  
Future research will explore semi-supervised learning for improved label efficiency, alternative clustering techniques such as DBSCAN to better delineate complex boundaries, and adversarial domain adaptation to align source and target feature distributions. Preliminary validation in analogous North Sea reservoirs shows that the framework maintains >85% accuracy when calibrated with only 15% of local data, indicating promising transferability under limited-data conditions.
## 3 Numerical Simulations  
Three-dimensional simulations are employed to illustrate the performances of the proposed technique. Typical parameters of a soft X-ray FEL facility are chosen to perform these simulations, as shown in Table 1. The processes of energy modulation in the wiggler and the transmission processes in the chicane are simulated with FALCON [40] and ELEGANT [41]. The processes of FEL lasing are simulated with GENESIS [42].  
Figure 1: Schematic layout of the proposed technique. After passing through the conventional ESASE section, the well bunched electron beam is sent into downstream short afterburners.  
The initial electron beam is assumed to have an energy of 2.5 GeV, an energy spread \(\Delta\sigma\) of 0.25 MeV, an average beam radius of 30 \(\upmu\)m (rms), an emittance of 0.4 \(\upmu\)m (rms), and a peak current of 800 A. The few-cycle modulation laser pulse employed in the wiggler is about 8 fs (FWHM) with a central wavelength of \(\lambda_{0}=2400\) nm, a power of 40 GW, and a radius of 0.4 mm. The periods for both the radiator and the afterburners are chosen to be the same value of \(\lambda_{1}=3\) cm. The central wavelength of the FEL is 3 nm.
# Reliability and Validity of the Portable KForce Plates for Measuring Countermovement Jump (CMJ)
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics were used, with means and standard deviations. A Kolmogorov-Smirnov test was used in order to check the normality of the data. A paired \(t\)-test was used for the evaluation of systematic bias between the 2 sessions (1 st and 7 th days). The intraclass correlation coefficient (ICC) was performed in order to assess the test-retest reliability of the KForce Plates system. ICC values indicate the level of reliability as follows: less than 0.5 = poor, between 0.5 and 0.75 = moderate, between 0.75 and 0.9 = good, and greater than 0.90 = excellent ICC [28]. The ICC between the two methods (the portable KForce Plates and the My Jump 2 applications) for evaluating the CMJ was calculated in both measurements (1 st and 7 th days) [28]. In addition, the Bland-Altman plot evaluated the limits of agreement for jump height and jump time for the two methods of measurement. Moreover, the lower and upper limits of the 95% confidence interval for the Bland-Altman plot were also computed [29]. To assess the concurrent validity, the Pearson correlation coefficients (r) were calculated between the two methods (the portable KForce Plates and the My Jump 2 application) of measurement. Pearson correlation coefficients (r) range in magnitude from \(-\)1.00 to 1.00 [28]. The statistical significance was set at the \(p\leq 0.05\) level. All analysis was performed using the SPSS v. 26 statistical package (Statistical Package for the social sciences, SPSS Inc., Chicago, IL, USA).
## 3 Results
### Process Scope: Partial vs. Complete  
A critical dimension in evaluating the effectiveness of a fault diagnosis framework is the extent of diagnostic pipeline--namely, whether the system addresses only detection or also includes subsequent stages such as localization and resolution. In this study, each  
Figure 4: Articles that proposed solutions.  
article was assessed to determine whether it proposed a complete diagnostic pipeline or was limited to partial coverage.  
The analysis revealed that only 56 out of 185 articles (30.3%) explicitly covered all three stages: fault detection, fault localization, and corrective or adaptive response. The remaining 69.7% of the reviewed works focused solely on detection (often through signal monitoring or anomaly classification) or included partial localization without a formal intervention mechanism.  
The term \"diagnostic pipeline\" in this study refers to the sequential stages of fault detection, fault localization, and resolution or decision support.  
This limited process scope may be attributed to several factors:  
* Data Availability and System Access: Researchers often lack access to detailed process data or physical systems where controlled fault injection and response validation can be performed;
* Complexity of Localization and Control Design: While detection can be achieved with relatively general-purpose classifiers or statistical models, localization requires domain-specific models and often a deep understanding of plant topology and interdependencies;
* Disciplinary Separation: In many cases, the development of detection tools and control logic is split between different research communities (e.g., data science vs. process control), which can lead to fragmented solutions.  
Interestingly, when broken down by source, Elsevier showed the highest proportion of full-process articles, followed by Google Scholar and then MDPI. This may reflect both editorial preferences and target audience differences among these platforms.  
The gap between detection-only frameworks and those capable of full-process integration highlights a critical opportunity for future research. As power plants become more complex and interconnected, the ability to trace a fault from signal deviation to actionable correction becomes not just desirable, but essential. Research efforts should increasingly strive for holistic, interoperable, and validated systems that span the entire diagnostic spectrum.
## 3 Settings, Data, and Method
### Methods  
#### 3.3.1 Kernel Density  
Kernel density analysis is a spatial analysis method based on the distribution characteristics of the research object. The sample points in the research area are given different weights through the kernel density function, showing a smooth density map to reveal the regional density attribute. It has obvious advantages in the quantitative analysis of the spatial agglomeration characteristics of points in the whole region. Therefore, kernel density analysis was used to calculate the density of spatial point and line elements within the neighborhood around the station to simulate the continuity of the industry density distribution. The kernel function can be expressed as:  
\[f(x,y)=\frac{1}{n\hbar^{2}}\sum_{i=1}^{n}k\bigg{(}\frac{d_{i}}{h}\bigg{)} \tag{1}\]  
where \(f(x,y)\) is the location density estimation, \(h\) represents the bandwidth, \(d_{i}\) represents the distance between position \((x,y)\) and observation point \(i\), and \(k\) represents the kernel function.
## 4 System Model
### Technical Layer  
The technical layer comprises different types of smart devices, servers, and applications [53] (as shown in Figure 2). Technically, this layer is further divided into IoT, fog, and cloud layers. The IoT layer has smart devices, which read and send the required data to the local fog servers [54]. The local server analyses the data and sends or receives data from the cloud servers according to the need.
## 3 Structural Design and Optimization
### BUSP Design  
In continuation of the improved structural configuration, this study introduces further optimization by proposing a novel bi-directional U-ribbed stiffening plate (BUSP) for the top of the crossbeam system. The BUSP is designed to enhance the structural performance of the bridge deck. The modeling is done in the same way as other steel components, such as steel beams. To complement the implementation of the BUSP, adjustments are made to the PBL shear connectors. These modifications are aimed at ensuring proper integration and compatibility between the BUSP and the PBL shear connectors.  
Figure 7 provides an illustration of the proposed BUSP, and Figure 8 provides the adjusted PBL shear connectors, showcasing their design and arrangement. Table 3 presents a comparison between the computational results of the BUSP scheme and the original scheme. This comparison aims to evaluate the performance and effectiveness of the proposed BUSP configuration in contrast to the original configuration.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{2}{c}{**Stress (MPa)**} & \multicolumn{2}{c}{**Displacement (mm)**} & \multicolumn{2}{c}{**Material**} \\  & & & & & **Consumption** \\ \cline{2-7}  & **Concrete** & **Steel** & **Concrete** & **Steel** & **Concrete** & **(m\({}^{3}\))** & **Steel (t)** \\ \hline Original Scheme & 1.28 & 63.81 & 4.95 & 7.01 & 92.96 & 161.50 \\ Optimization & 0.91 & 56.77 & 4.15 & 6.45 & 74.37 & 162.81 \\ Scheme & \multirow{2}{*}{\(-\)28.91} & \multirow{2}{*}{\(-\)11.03} & \multirow{2}{*}{\(-\)16.16} & \multirow{2}{*}{\(-\)7.99} & \multirow{2}{*}{\(-\)20.00} & \multirow{2}{*}{0.81} \\ Amplitude of & & & & & & \\ Variation (\%) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between the original scheme and the improved scheme.  
Figure 7: Schematic illustration of BUSP.  
Based on the information provided in Table 3, it is evident that the adoption of the BUSP solution yields significant improvements compared to the original scheme. The displacement of the concrete overlays is reduced by 10.27% when using the BUSP configuration. Additionally, there is a notable decrease of 29.69% in the maximum tensile stress experienced by the structure.  
These findings demonstrate that the implementation of the BUSP effectively enhances the stiffness and performance of the concrete structure within the bridge deck system. The BUSP solution proves to be successful in mitigating displacement and reducing tensile stress, indicating its potential as an effective design modification for improved structural performance.
# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
###### Abstract  
This study mainly addresses the issues of an excessive model parameter count and computational complexity in Detection Transformer (DETR) for remote sensing object detection and similar neural networks. We propose an innovative neural network pruning method called \"ant colony evolutionary pruning (ACEP)\" which reduces the number of parameters in the neural network to improve the performance and efficiency of DETR-based neural networks in the remote sensing field. To retain the original network's performance as much as possible, we combine population evolution and ant colony algorithms for dynamic search processes to automatically find efficient sparse sub-networks. Additionally, we design three different sparse operators based on the structural characteristics of DETR-like neural networks. Furthermore, considering the characteristics of remote sensing objects, we introduce sparsity constraints to each network layer to achieve efficient network pruning. The experimental results demonstrate that ACEP is effective on various DETR-like models. After removing a significant number of redundant parameters, it greatly improves the inference speed of these networks when performing remote sensing object detection tasks.  
s +
Footnote †: journal: Applied sciences  
article