esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise
### The Improved NLM Denoising Algorithm
#### 2.3.2 The Improved Distance Weight Function  
When the similarity of two pixels, i.e., the Euclidean weighted distance of two neighboring blocks is calculated, the NLM algorithm needs to assign a corresponding weight value to the distance. The smaller the distance, the greater the similarity between the two neighboring blocks, and the greater the weight value assigned. The weight value and the distance are similar to the inverse relationship. At the same time, the weight function also requires that the weight value assigned can drop rapidly when the distance increases, and its purpose is to ignore the effect of neighboring pixels that are not similar to the current pixel structure as much as possible.  
The weight function in the traditional NLM algorithm is the Gaussian function, and the weight value follows Gaussian distribution. Based on the Gaussian function, the weight function of the proposed algorithm is set as the power function of the Gaussian function (power value \(n\geq 2\)). The value of power can be flexibly set according to the noise variance of the noised image and the smoothing coefficient \(h\). In this way, the weight function is increased from one parameter (smoothing coefficient \(h\)) to two parameters (smoothing coefficient \(h\) and power value \(n\)). Although the increase of parameters increases the complexity of the algorithm to a certain extent, it also makes the distribution of weight values more flexible and accurate. Moreover, the improved weight function drops faster than the original weight function, which is more in line with the definition standard of the weight function.  
The improved weight function is defined as follows:  
\[w(i,j)=\frac{1}{Z(i)}exp\left(-\frac{d(i,j)}{h^{2}}\right)^{n},\ (n\geq 2) \tag{21}\]  
where \(Z(i)\) is a normalized factor, which is the sum of all weights, that is, the sum is 1 after each weight and is divided by the factor. The power function of the Gaussian function corresponding to formula (21) is \(y=exp\left(-\frac{x^{2}}{h^{2}}\right)^{h}\). Figure 4 shows the values of the weight function when \(h=1.2\) and \(n=1\), 2, 3, and 4 in order to clearly reflect the comparison of function values when n takes different values.  
It can be seen from Figure 4 that the curve corresponding to \(n=1\) is the Gaussian function used by the original weight function, and the corresponding weight value decreases gradually with the increase of Euclidean distance. However, its weight decreases more slowly compared with the weight function curve when \(n=2\), \(3\), and \(4\). The larger the \(n\) value is, the faster its weight decreases. However, experiments have proved that a larger value of \(n\) is not necessarily better; it should be considered comprehensively according to the noise level of the image, the variance \(\sigma\) of the Gaussian kernel function, and the smoothing coefficient \(h\). At the same time, the running time of the algorithm should also be considered.
## 3 Experiment
### Sensitivity Analysis with Respect to Dimensionality of Random Subspace  
The dimensionality of random subspace is an influential parameter for the random subspace method [2; 23]. To study the sensitivity of mixture graph with respect to the dimensionality of random subspace, we conduct two experiments on ORL and Pose27 with \(d\) varying from 10 to 100. The corresponding experimental results are reported in Figure 3. For reference, we also include the results of GFHF, LGFHF, LNP, RMGT, LGC and CGSSL in Figure 3.  
From Figure 3, we can observe that the accuracy of SSCMG increases slightly as \(d\) rising at first and then turns to downgrade, especially on Pose27. The reason is twofold: (i) when \(d\) is very small (_i.e._, \(d=10\)), there are too few features to construct a graph that describes the distribution of samples projected in that subspace; (ii) as \(d\) is close to 100, more and more noisy features are included into the subspace and hence the graph is more and more distorted by noisy features. When \(d=100\) all the features are included, these \(T\) graphs are the same, and thus the performance of SSCMG is similar to that of GFHF. Overall, irrespective of the setting of \(d\), the performance of SSCMG is always no worse than that of GFHF. However, how to choose an effective value of \(d\) (or design a graph less suffered from \(d\)) is still a future pursue.  
Figure 3: Sensitivity under different values of \(d\). (**a**) Accuracy _vs._\(d\) (ORL); (**b**) Accuracy _vs._\(d\) (Pose27).
## Appendix C  
Algorithm A1 presents the steps for the simulated annealing method to solve volume minimization defined in Equation (2).  
```
1. Input:
2. \(\mathrm{P}\in\mathbb{C}\) (point cloud), \(\mathrm{T}\in(\mathbb{P},\ \mathbb{R},\mathbb{R}^{n})\to\mathbb{P}\) (motion model), \(\mathbf{r}_{0}\in\mathbb{R}^{n}\) (initial search boundaries)
3. \(t_{init}\in\mathbb{R}\) (initial temperature), \(\mathbf{r}_{min}\in\mathbb{R}\) (minimum temperature), \(t_{max}\in\mathbb{Z}_{+}\) (maximum iteration number),
4. \(c\in[01]\) (cooling coefficient), \(n_{n}\in\mathbb{Z}_{+}\) (number of neighborhoods)
5. Initialization:
6. \(i\gets 0;t\gets t_{init}\)
7. \(\mathbf{x}\), \(\mathbf{x}_{best}\leftarrow\frac{\mathbf{x}+\mathbf{x}_{k}}{2}\)
8. \(v\gets Vol(\mathrm{P})\)
9. Iterations:
10. while\(\mathbf{r}_{i}>\mathbf{r}_{min}\)
11. \(t\gets c^{i}\)// Decreasing the temperature
12. \(\mathbf{r}_{i}=\mathbf{r}_{0}\ ^{*}t\)// Searching radius for inner iteration
13. \(k\gets 0\)
14. while\(k<n_{n}\)
15. \(\mathbf{x}^{\prime}\leftarrow\mathbf{x}_{best}+\mathbf{r}_{i}*rad()\)// Pick a neighbor randomly
16. \(\mathrm{P}\prime\leftarrow\mathrm{U}_{\mathbf{p}_{i}\in\mathrm{P}}(\mathbf{p}_ {i},t,\mathbf{x})\)// Calculate the cloud based on the motion model
17. \(v\gets Vol(\mathrm{P}\prime)\)// Calculate volume
18. if\(v^{\prime}<v\) or \(\exp(-|e^{\prime}-e|))>\(rnd()\)// Update local best
19. \(\mathbf{x}\leftarrow\mathbf{x}\prime\)
20. \(v\gets v^{\prime}\)
21. end if
22. \(k\gets k+1\)
23. end while
24. \(\mathbf{x}_{best}\leftarrow\mathbf{x}\)// Update best
25. \(i\gets i+1\)
26. end while
27. Output:\(\mathbf{x}_{best}\)
```  
**Algorithm 1** Simulated annealing algorithm for volume minimization
## 2 Results
### Cell Viability  
The metabolic activity of hPDL-MSCs grown on different materials and measured using the CCK-8 and resazurin assays are presented in Figures 3 and 4. Over the whole observation period, the metabolic activity of hPDL-MSCs grown in different conditions gradually increased, suggesting continuous cell proliferation and cells on TCP exhibited a significantly higher metabolic activity than those grown on two other substrates (\(p<0.05\)). Additionally, the metabolic activity of hPDL-MSCs grown on GPP on cell proliferation was significantly higher than VSCM at all time points except CCK-8 assay after 14 days (\(p<0.05\)). After 14 days of cell seeding, although the detection level of CCK-8 from nanofiber and VSCM no longer showed significant differences, cells grown on the former demonstrated a faster proliferation trend (\(p=0.056\)).  
Figure 2: The morphological characteristics of hPDL-MSCs observed on different surface types. hPDL-MSCs were grown on GPP (**a**,**d**,**g**), VSCM (**b**,**e**,**h**) or TCP (**c**,**f**,**i**) for 3, 7, or 14 days and stained with a focal adhesion staining kit. F-actin was stained with TRITC-conjugated phalloidin (red) and the nucleus with DAPI (blue).The images were captured at 100-fold magnification; scale bars correspond to 50 \(\upmu\)m. The cells exhibited different morphologies and distributions on the three different materials.
# Overview of the Research Status of Intelligent Water Conservancy Technology System
## 5 Challenges and Countermeasures
### Standardization and Normalization  
The smart water conservancy platform lacks standard references such as an information infrastructure, data base, model platform, knowledge platform and business application, resulting in difficulties in the integration of the water conservancy model and data and knowledge, and weak system compatibility and interoperability, resulting in the phenomenon of data, knowledge and model island and system chimney [97]. The water conservancy data base also lacks the guidance of data classification and representation, data organization, data fusion, data service and other standards. The digital twin water conservancy model platform is the core engine of intelligent water conservancy. Currently, there is a lack of standards for model classification and representation, model encapsulation, model storage, model assembly, etc., resulting in the situation of useless models, although there are many models. Due to the lack of guidance on the classification and representation of water conservancy knowledge, storage of water conservancy knowledge, integration of water conservancy knowledge and other standards, the digital twin water conservancy knowledge platform still lacks an available business knowledge base in the water conservancy field [29].  
At present, the construction of smart water conservancy standards is still in its initial stage, lacking perfect organization, research and application. When examining the digital twin intelligent water conservancy framework in this paper, it can be seen that the current progress of standardization is still lagging behind. Although the framework envisions a layered structure that includes a perception layer, a network layer, a platform layer and an application layer, the effectiveness of this layered structure is hampered by the lack of standardized processes and organizational structures. In order to realize the sustainable development of smart water conservancy, the standardization process should be promoted [29]. It is necessary to make full use of the existing advantages of basic research, promote interdisciplinary development, and continuously improve the standard system of the water conservancy industry [98], so that it can take a leading position in the world in multiple fields of water conservancy science and technology. Other key measures include: government support [99], top-level design and prioritization of key standards [100], selection of standards advocacy and implementation strategies, and efficiency orientation.
# Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks
###### Abstract  
Contemporary deep learning approaches for post-earthquake damage assessments based on 2D convolutional neural networks (CNNs) require encoding of ground motion records to transform their inherent 1D time series to 2D images, thus requiring high computing time and resources. This study develops a 1D CNN model to avoid the costly 2D image encoding. The 1D CNN model is compared with a 2D CNN model with wavelet transform encoding and a feedforward neural network (FNN) model to evaluate prediction performance and computational efficiency. A case study of a benchmark reinforced concrete (r/c) building indicated that the 1D CNN model achieved a prediction accuracy of 81.0%, which was very close to the 81.6% prediction accuracy of the 2D CNN model and much higher than the 70.8% prediction accuracy of the FNN model. At the same time, the 1D CNN model reduced computing time by more than 90% and reduced resources used by more than 69%, as compared to the 2D CNN model. Therefore, the developed 1D CNN model is recommended for rapid and accurate resultant damage assessment after earthquakes.  
seismic damage assessment; convolutional neural networks; feedforward neural networks; ground motion records; wavelet transform Article  
Faster Post-Earthquake Damage Assessment Based on 1D  
11
## 4 Performance Evaluation and Validation
### Damage Detection and Classification Results  
Table 7 presents the prediction results obtained under various environmental conditions, including surface contamination and natural elements that may be misidentified as damage. These results were generated to evaluate the applicability of the proposed model in real-world railway bridge inspection scenarios. Based on a comparative evaluation of different loss functions within the proposed U-Net framework, the IoU Loss function was selected as the most appropriate optimization method. The model was trained and analyzed using this approach, which led to the results shown in Table 7.  
The results demonstrate that the model applying IoU Loss consistently outperformed those using other loss functions in terms of detection reliability and classification robustness. In particular, it showed superior capability in distinguishing true damage from environmental noise and background elements, an essential feature for practical deployment in field conditions. This suggests that the proposed deep learning model, when optimized with IoU Loss, is highly effective for multi-class damage detection in complex real-world settings. \begin{table}
\begin{tabular}{c c c c} \hline
**Distinction** & **Original** & **Actual** & **Predicted** & **Overlap Image** \\ \hline \hline Crack & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the damage detection results. Figure 6: Localized damage detection using \(10\times 8\) grid-based evaluation method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distinction** & **Original** & **Actual** & **Predicted** & **Overlap Image** \\ \hline
**Concrete Scaling/Spalling** & **Actual** & **Predicted** & **Overlap Image** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._In image datasets where background regions significantly outnumber damaged areas, evaluation metrics such as Accuracy and Precision tend to be overestimated. This was confirmed to be inadequate for assessing the performance of models intended for structural damage inspection. Specifically, these metrics may appear high even when actual damage is not detected, as the model correctly classifies most of the background as non-damage. While this indicates good background discrimination, it does not align with the primary objective of infrastructure maintenance, which is to accurately detect and assess damage. Therefore, Recall and the F1-Score are more appropriate metrics for performance comparison in this context. As shown in Table 8, for damage types with relatively large amounts of training data, such as cracks, the differences across loss functions were relatively small (Recall ranging from 0.831 to 0.866, F1-Score ranging from 0.776 to 0.869). However, for more complex and underrepresented classes, particularly concrete scaling/spalling and leakage, the proposed IoU Loss model demonstrated substantial improvements; for concrete spalling, the Recall increased from 0.355 (Cross-Entropy) to 0.950 (IoU Loss), representing a 167.6% relative
# Integrated Double-Sided Random Microlens Array Used for Laser Beam Homogenization
###### Abstract  
Double microlens arrays (MLAs) in series can be used to divide and superpose laser beam so as to achieve a homogenized spot. However, for laser beam homogenization with high coherence, the periodic lattice distribution in the homogenized spot will be generated due to the periodicity of the traditional MLA, which greatly reduces the uniformity of the homogenized spot. To solve this problem, a monolithic and highly integrated double-sided random microlens array (D-rMLA) is proposed for the purpose of achieving laser beam homogenization. The periodicity of the MLA is disturbed by the closely arranged microlens structures with random apertures. And the random speckle field is achieved to improve the uniformity of the homogenized spot by the superposition of the divided sub-beams. In addition, the double-sided exposure technique is proposed to prepare the rMLA on both sides of the same substrate with high precision alignment to form an integrated D-rMLA structure, which avoids the strict alignment problem in the installation process of traditional discrete MLAs. Then the laser beam homogenization experiments have been carried out by using the prepared D-rMLA structure. The laser beam homogenized spots of different wavelengths have been tested, including the wavelengths of 650 nm (R), 532 nm (G), and 405 nm (B). The experimental results show that the uniformity of the RGB homogenized spots is about 91%, 89%, and 90%. And the energy utilization rate is about 89%, 87%, 86%, respectively. Hence, the prepared structure has high laser beam homogenization ability and energy utilization rate, which is suitable for wide wavelength regime.  
laser beam homogenization; microlens array; random; double exposure; integration Article
## 3 Results and Discussion
### In Vitro Cell Viability  
In Figure 5, LENM-1, LENM-2, and LENM-3 are immersed in MEM for 48 h in order to obtain LENM extracts. The extracts and cells were culture and examined using a 24-h incubation short-term assay evaluating the influence of the maximum release amount on the cell viability of fibroblasts (L929). Figure 5 shows that the viability of fibroblasts increased by 157.5% for LENM-1, 130.7% for LENM-2, and 120.2% for LENM-3 when \(p<0.05\). The test results show that the drug concentration that all three groups released and accumulated is suitable for cell growth. The difference in cell viability among LENM-1, -2, and -3 is ascribed to their different EEs, DLCs, and drug release curves. An appropriate drug concentration to a certain level is helpful with cell viability, but excessive concentrations would suppress cell growth, as seen in Figure 1.
## 4 Simulation and Result Analysis
### Fishhook Test
The comparison of Figure 8c,d shows that when the left side needs a very large damping force, the right side of the vehicle with CDC-ODDF also increases correspondingly. The critical state of rollover from the 1 st second to the 3.5 th second is taken for analysis, and the corresponding local enlarged contrast view is shown in Figure 8. In the interval of the 1.4 th second to the 1.6 th second, the maximum forward steering wheel condition is reached. The roll angle rate fluctuates at approximately the 1.4 th second. To ensure the stable driving of the vehicle, the damping forces on the right side of the vehicle with CDC-ODDF achieve the first peak to compensate for the small damping force on the left side. At the same time, the damping forces on the right side with CDC-LTR are only approximately half of the forces with CDC-ODDF because LTR is very small, and CDC-LTR control is in the off state. When the steering system starts to turn right at approximately the 1.57 th second, the damping forces with CDC-ODDF react immediately, while the CDC-LTR cannot respond in time and the output of damping force is still very small. At the peak value of LTR at approximately the 2.3 th second, the compensation effect with CDC-ODDF is more evident. During this period, the damping forces with CDC-ODDF on the right side of the front axle compensate for the left side once at approximately the 2 nd second, while the left side has several large compensations for the right side from the 2.4 th second to the 2.6 th second. The compensation of the left side of the rear axle and the right side of the front axle is the most evident from the data of the view with CDC-ODDF. Because the damping forces of CDC are controlled by electrical signal, the sudden changes of damping forces often lead to the sudden change of control current. The damping force current signal can be collected and identified by appropriate methods, and the driver can be given a corresponding real-time rollover warning, which improves the rollover prediction effect. The main comparison results of CDC-LTR and CDC-ODDF are shown in Table 4.
# Image Analysis and Processing for Generating Camouflages from Digital Earth Photographs
## 4 Selected Technical Aspects of the Camouflage Generator Approach
### Logic Layer  
The logic layer has two components: _ColorExtractor_, which provides an interface for extracting colours from an image, and _CammoGenerator_, which provides an interface for generating and editing camouflage, and gives the presentation layer access to the data layer. Such a division of logic allows for the separation of functions into independent components, which can be changed and expanded in the future, depending on the needs.
## 3 Presentation of the Proposed Model
### Sub-Modules of the Presented Model
#### 3.3.6 Product Classification and Allocation Module
Every link should be contained in the matrix of relationships to take into account the relationships between products. The main problem is the arrangement of products, such that the products that more frequently occurred together on the lists were allocated close to each other. To solve this, a model has been developed to organize the products according to the previously mentioned matrix of relationships. The methodology for sorting products on the picking lists according to the links between them can be presented in the following steps:  
**Step 1--**Creating the matrix A, the matrix of frequencies occurring on the picking lists (11)  
\[A=\left[ \begin{array}{cccc}w_{11}&w_{12}&\cdots&w_{x1}\\ w_{21}&w_{22}&\cdots&w_{x2}\\ \vdots&\vdots&\ddots&\vdots\\ w_{1y}&w_{2y}&\cdots&w_{xy}\end{array} \right] \tag{11}\]  
**Step 2--**Counting the sum \(s_{u}\) (12) of rows of the matrix A  
\[s_{u}\ =\ \sum_{idx=1}^{i}w(idx,idy) \tag{12}\]  
**Step 3--**Entering the sum \(s_{u}\) to matrix S (13)  
\[S\ =\ \left[ \begin{array}{cccc}id_{prod1}&id_{prod2}&\cdots&id_{prody}\\ s_{u1}&s_{u2}&\cdots&s_{uy}\end{array} \right] \tag{13}\]  
**Step 4--**Determination of the identifier (_ident_) for the element of the highest value in matrix S. If several elements have the same maximum value, then all identifiers must be read;  
**Step 5--**Rewriting element values from matrix A to the new matrix B for predetermined identifiers (_ident_);  
**Step 6--**Assigning the identifier to vector W that stores IDs for products arranged by several links in decreasing order;  
**Step 7--**Reading product IDs for identifiers _idx_ and _idy_ of the element of the highest value in matrix B;  
**Step 8--**If several elements have the same maximum value, then IDs should be read for the value which corresponds to the element of the highest value in matrix S;  
**Step 9--**Verification of whether _idx_ or _idy_ are in vector W;  
If they are not, both values _idx_ and _idy_ must be added to the vector W before the next step. It they are, proceed to the next step;  
**Step 10--**Replacing the current value in matrix B with a value 0 for the maximum selected in step 7;  
**Step 11--**Adding values from matrix A to the B element of the identifier (_ident_) that was the last one added. Checking the condition (14),  
\[\max(B)\ =\ 0 \tag{14}\]If it is fulfilled, then the algorithm can be terminated. If it is not fulfilled, then proceed to the next step;  
**Step 12**--Verification of whether the number of values in vector W is equal to the number of analyzed products. If it is, then the algorithm can be terminated. If it is not, go back to step 7. The product allocation in the warehouse was based on the designation of the location characterized by the shortest access time from the packaging zone described by the formula (15). \[POp\sum_{x=1}^{xu}\sum_{y=1}^{ua}\sum_{p=1}^{pu}(t_{d}(x,y)+t(p)) \tag{15}\]