esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Weighted Sum Model (WSM) and Weighted Product Model (WPM)  
Due to the WSM and WPM approach's simple calculation process, which does not require complex calculation skills, it has emerged as a common solution method for decision-making problems. In this approach, the summation of all target values for each alternative is performed, with the alternative yielding the largest sum value being recommended as the top-ranked solution. The operational procedure of the WSM and WPM approach is as follows [27; 36; 37].  
1. Creating the initial decision matrix (\(A_{ij}\)) \[A_{ij}=\begin{bmatrix}a_{11}&a_{12}&\cdots&a_{1n}\\ a_{21}&a_{22}&\cdots&a_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ a_{m1}&a_{m2}&\cdots&a_{mn}\end{bmatrix},\;i=1,2,\ldots,\;m,\;j=1,2,\ldots,n\] (1) where \(i\) is the number of alternatives and \(j\) represents the number of criteria.
2. Calculating the WSM and WPM values of alternative \(i\) \(P_{i}^{1}\) represents the relative importance of the \(i\)th alternative of the WSM approach, and \(P_{i}^{2}\) represents the relative importance of the \(i\)th alternative of the WPM approach. \[P_{i}^{1}=\sum_{j=1}^{n}A_{ij}\cdot w_{j}\] (2)\[P_{i}^{2}=\prod\
olimits_{j=1}^{n}{A_{ij}}^{w_{j}} \tag{3}\]  
where \(w_{j}\) is the weight of the evaluation criterion \(j\).
## 1 Introduction
The innovative architecture incorporates a multi-scale feature extraction network comprising residual blocks and channel attention mechanisms (SE modules) alongside an up-sampling architecture using sub-pixel convolution and global residual connections to strengthen low-frequency information propagation. This approach enables sub-grid-level uncertainty quantification while preserving high reconstruction accuracy [13; 15; 33; 34; 35]. By leveraging deep learning's robust feature extraction capabilities and the rigorous aleatoric (data noise) and epistemic (model ambiguity) uncertainties of the Bayesian framework, this method effectively tackles the complexities of reconstructing intricate seafloor topography. The primary contributions of this research are as follows:  
1. It introduces a hybrid Bayesian deep learning framework that integrates MC-Dropout with adaptive dropout, achieving the concurrent optimization of high-precision reconstruction and uncertainty quantification in seafloor topography reconstruction. 2. It analyzes the comprehensive impact of various regularization and uncertainty quantification modules on seafloor topography reconstruction. Traditional models apply uniform regularization, whereas SE-guided adaptive dropout dynamically adjusts to local seabed complexity, enabling risk-sensitive decision-making in marine operations. 3. It presents the design of a multi-scale feature extraction network that integrates residual blocks with SE modules and employs sub-pixel convolution and global residual connections during up-sampling, effectively enhancing low-frequency information transfer and recovering high-frequency details.
## 3 Pre-Processing
### Median Filter  
The median filter performs the image data smoothing by removing the noise from images. It operates as a sliding window to improve the results in later processing, leaving the image cleaner, which calculates the median value on the neighborhood from the specific pixel considered, removing the impulsive noise and preserving the edge border, which is important for detecting objects in the image. In image processing, the median filter replaces the central pixel with the median of the neighboring area pixels. Thus, it is an efficient method for noise reduction of individual peaks coming from the environment where the noise could lead to high contrasts in the image, which would be propagated to the binarization stage [23].  
Figure 7 illustrates the process of median filter application in the image at the pixel level. First, there is a specific set of pixels with their respective intensity as an input image. The image is then analyzed pixel by pixel, with the mirrored edges along the border of the initial image, to solve the problem of executing a neighborhood operation in a border pixel. Finally, the average value of each pixel is determined, and a new image is generated as an output image, as depicted by the yellow and red marks.  
The neighborhood averaging can suppress isolated out-of-range noise for this method, but the side effect also blurs sudden changes, such as line features, sharp edges, and other image details corresponding to high spatial frequencies.  
Figure 6: Attenuation of noise with image buffering in grayscale.  
Figure 7: The median filter in the intensity image.  
This way, a median filter is an effective method that, to some extent, can distinguish out-of-range isolated noise from legitimate image features, such as edges and lines. Specifically, the median filter replaces a pixel by the median, instead of the average, of all pixels in a neighborhood \(w\). Equation (2) details the application of the median filter, where \(w\) represents a neighborhood defined by the user, centered around image location \([m,n]\)[24].  
\[y[m,n]=median\{x[i,j],(i,j)\in w\}. \tag{2}\]  
The median filter has noise an attenuation efficiency that can generate random points in the binarization step. For example, Figure 8 shows a case study in which noise is identified, such as '1' binary points, and when applying the median filter in the intensity image, 1-bit binarization of that noise is avoided. Therefore, the definition of the filter neighborhood must satisfy the need for attenuation of possible noises in the image.  
Figure 8 shows that the image is already in a specific ROI containing the recognized objects. The image still has some small undesirable noises, but that does not affect the main objects of interest.  
Although we have already treated the frame in several aspects to obtain a clean and good quality image, it can still have some characteristics of blur or noise. Then, the most common technique adopted to remove these undesirable situations is a Gaussian smoothing operator [25; 26], a 2D convolution operator.
## 3 Hydroxyapatite Particles in Scaffold Manufacturing and Cell Signaling
### Porous HA-Based Scaffolds
Bacterial cellulose is a polysaccharide secreted by _Gluconacetobacter_ microorganisms through oxidative fermentation processes and it has a great number of hydroxyl groups [OH\({}^{-}\)] in its structure. This aspect is very important in terms of creating a friendly environment for proteins and cellular attachment and migration. Moreover, bacterial cellulose has a fibrous aspect, a good degradation rate, biocompatibility, and increased surface reactivity. Magnetic nanoparticles have been widely applied in the biomedical field and their inclusion in scaffolds for bone tissue engineering can be associated with their high susceptibility to be guided by magnetic fields directly to the targeted site. Owing to their ability to give an osteoinductive character to the scaffold with or without the influence of an external magnetic field, magnetic nanoparticles are suitable candidates for cellular signaling as well. As an example, magnetic bone scaffolds are taken into account by researchers due to their potential to activate the mechano-transduction process and to signalize the desired differentiation pathway. A study reported that the integration of magnetic nanoparticles in polycarbonate polymeric scaffold promoted the osteogenic differentiation pathway of rat mesenchymal stem cells, and, additionally, in vivo investigations revealed the start of the angiogenesis process. Starting from these concepts, [PERSON] et al. conducted an experiment where they developed composite scaffolds with the aim of creating a fibrous matrix of bacterial cellulose with HA and magnetic nanoparticles dispersed in its structure. The physicochemical analysis concluded that a highly porous scaffold with mechanical properties close to those of the native trabecular bone was obtained. In vitro tests were conducted in contact with an MC3T3-E1 cell line and revealed non-cytotoxicity, high biocompatibility, and great adhesion and proliferation of the osteoblastic cell line [49; 50]. Hence, it can be stated that porosity plays an important role in both mechanical and biological performances of a scaffold. From the biological point of view, it seems that cell anchorage is strongly influenced by pore size and architecture when compared to control samples. Currently, there is no doubt that porosity (nanopores, micropores, or macropores) has a positive impact in terms of promoting bone ingrowth and offering a biomimetic architecture to bone scaffolds. Even though porosity is not enough in terms of creating an ideal scaffold, it is a parameter that must be taken into account when developing bone matrices in order to achieve a native-like performance.
# Effect of CeO2@GNPs on the Corrosion Properties of 2024 Alloy
## 3 Results
### Corrosion Resistance
On the other hand, the poor interfacial bonding properties between the GNPs and the Al matrix resulted in the presence of defects, such as pores and cracks in the composites [33], which disrupted the integrity of the surface oxide film and allowed the NaCl electrolyte to bypass the surface oxide layer and react directly with the \(\alpha\)-Al. The corrosion resistance of the 0.5 wt% CeO\({}_{2}\)@GNPs/2024 Al nanocomposite was improved compared to the previous sample. The CeO\({}_{2}\) modification of the GNPs surface was equivalent to the surface coating of a layer of CeO\({}_{2}\) ceramic phase passivation film, which impeded the galvanic coupling corrosion behavior between the GNPs and \(\alpha\)-Al. Moreover, it was shown that CeO\({}_{2}\) nanoparticles could react weakly with aluminum to form trace amounts of Al\({}_{4}\)Ce intermediate grains [34; 35; 36], which promoted wetting bonding with the matrix. This reduced the microporosity due to the agglomeration of GNPs or the poor binding properties of the matrix, reducing the corrosion area and improving the corrosion resistance of the composites. The existence of CeO\({}_{2}\) refined the grains and improved the densities of the materials, thus improving the corrosion resistance of the nanocomposites.
## 7 Discussion  
For TSP optimization, the search space for the optimal solution becomes complex as the problem's size increases. We used the parallel ant colony system to explore the optimal path. Rather than just relying on brute force parallelization, our implementation periodically exchanges information in terms of pheromone value, so that the common paths taken by different colonies are likely optimal, and the exploration can be focused on paths for which the different ant colonies disagree. This is analogous to driving from one source address to a destination address, with highways most likely covering the shortest distance, and side roads leading to the highways offering alternatives routes. Our parallel ACO with pheromone exchange discovered this, as depicted in Figures 8 and 9. All colonies shown in Figure 8 took the sub-paths indicated by solid lines in Figure 9. Further, as can be seen in Table 1, our approach achieved optimal routes for the TSPLIB benchmarks with sizes up to a few hundred nodes. The value of the optimal tour for some of the TSPLIB benchmarks is publicly available on the TSPLIB website; therefore, we could compare our results to these values. The optimal solution consists in the shortest path from the start node and back to it, visiting each node only once.  
The ability of quantum computing to optimally solve NP-hard problems such as the TSP is great. An issue lies in the size of quantum computers, i.e., the large number of qubits needed. To address this issue, we developed a hybrid algorithm that allowed us to partition a large TSP in sub-graphs, which could then be optimized on a quantum computer. Even though partitioning approaches were attempted previously, the novelty of our approach lies in the assembly of individually optimized sub-graphs using a parallel ACO algorithm with pheromone exchange, so that the negative effect of partitioning on the overall optimization is overcome by the cooperating ant colonies, which allows refining the assembled solution. Furthermore, we performed iterations; we produced the partitions, assembled them, optimize them, then partitioned the system again until convergence. The size of each partition depends upon the available number of qubits.  
In Figure 13, it can be seen that the larger the partition size, the better the overall optimization. Intuitively, the partitioning causes fixed end nodes in individual sub-graph optimization. By using a different partitioning in each iteration, as shown in Figure 14, we were able to overcome the slight loss in optimization quality.  
Figure 13: Effect of the sub-graph partition size on the d2103 benchmark.  
Note that other metaheuristics can be used for the TSP instead of ACO; however, for the TSP, ACO allows an extremely quick execution time as compared to other metaheuristics. Further, it is easily parallelizable, and our parallel ACO uses cooperation between ant colonies to yield better optimization results.
## 2 Methodology for Measuring the Rate of Corrosion Reinforcement in Cores Drilled from The Structure
### Description of the Tested Structure and Its Condition  
Tests for verifying the developed methodology were conducted on ground-embedded reinforced concrete tanks for fresh water. The tanks were made in the 1990s in the west of Poland. This technological facility divided into two parts (marked as T1 and T2) was composed of four isolated rectangular tanks with dimensions of 36 \(\times\) 24 m (Figure 4). Overall dimensions of the structure were 72 \(\times\) 48 m. The tanks were founded on the reinforced concrete slab with a thickness of 120 cm. The thickness of perimeter walls was 45 cm, and of the internal walls was 40 cm. Floor slabs of the tanks were supported by intermediate elements, that is, reinforced concrete frames with beams with cross-section of 90 \(\times\) 45 cm, monolithically connected to columns with cross-section of 45 \(\times\) 45 cm. The reinforced concrete floor slab with a thickness of 25 cm was loaded with a 20-cm layer of sand and a 70-cm layer of topsoil.  
Conditions of the tanks were regularly inspected following the current regulations. After one inspection, the facility administrator obtained worrying information on white discoloration on internal sides of the tank walls, and serious wear of protective coats on concrete (Figure 5). Decision on immediate shut-down of the tanks was considered due to hazard related to corrosion process of reinforced concrete. However, due to technological aspects such actions were not possible for both tanks, and preparation and repair works were expensive and time-consuming. Therefore, the facility administrator made a decision to precede the scheduled repair works by the construction expertise. This expertise was to  
Figure 4: The tested structure of the rectangular reinforced concrete tanks for fresh water; (**a**) cross-section A-A; and (**b**) longitudinal section B-B; 1—perimeter walls, 2—internal walls, 3—columns; symbols T1-1–T1-6 and T2-1–T2-6 show places of drilling cylindrical concrete oores with embedded reinforcement for corrosion tests.  
be the verification developed from specialised tests, the opinion contained in conclusion from the technical inspection, based on the visual inspection of the facility.  
This construction expertise was oriented to the evaluation of corrosion risk to reinforced concrete structural system of the tanks. The corrosion tests were conducted on concrete cores with embedded rebar pieces drilled from concrete structures. Places of core drilling from the wall tanks are shown in Figure 4a.
## 3 Results  
### Chondroprotective Effect of NEM In Vitro  
Figure 1 illustrates the isolation and 24 h incubation of primary chondrocytes from rat knee joints with NEM at concentrations ranging from 0.0001 to 1 mg/mL in distilled water. Across all tested concentrations (0.001, 0.01, 0.1, 1, and 10 mg/mL) of NEM, there was no significant change in the cell viability of NEM-treated primary cultured rat articular chondrocytes compared with the vehicle-treated normal controls (Figure 1A). In the rhIL-1\(\alpha\)-induced cell injury model, cell viability decreased in the vehicle-treated rhIL-1\(\alpha\) model control compared with that in the normal control (Figure 1B). However, pretreatment with NEM at 0.01, 0.1, and 1 mg/mL suppressed the cell viability reduction induced by rhIL-1\(\alpha\) treatment. In addition, the model control group treated with rhIL-1\(\alpha\) showed decreased expression of chondrogenic genes such as Col-2, SOX9, and aggrecan (Figure 1C-E), and increased MMP2 and MMP9 levels compared with the normal control group (Figure 1F). However, we noted significantly decreased levels of MMP-2 and MMP-9 and increased expression levels of the three genes in the 0.01, 0.1, and 1 mg/mL NEM treatment groups compared with in the rhIL-1\(\alpha\)-treated model control group. In the LPS-induced cell inflammation model, 5-LPO activity and PGE\({}_{2}\) content significantly increased after LPS treatment compared with the normal control (Figure 1H,I). Nonetheless, pretreatment with NEM at concentrations of 0.01, 0.1, and 1 mg/mL effectively reduced these increases compared with the LPS model control group.  
Figure 1: Cytoprotective effects in vitro (**A**) Cell viability in primary articular chondrocytes. (**B**) Cell viability in rhIL-1\(\alpha\)-exposed chondrocytes. (**C-E**) Relative expression levels of chondrogenic genes (Col-2, aggrecan, and Sox9) when exposed to rhIL-1\(\alpha\). (**F,G**) MMP-2 and MMP-9 levels upon exposure to rhIL-1\(\alpha\). (**H,I**) 5-lipoxygenase (5-LPO) activity and prostaglandin E\({}_{2}\) (PGE\({}_{2}\)) level in LPS-exposed chondrocytes. Values are expressed as mean \(\pm\) SD in six independent experiments after NEM treatment at the indicated doses. a \(p<0.01\) and b \(p<0.05\) as compared with the control; c \(p<0.01\) and d \(p<0.05\) as compared with rhIL-1\(\alpha\)- or LPS-treated control.
# Improved Retinex-Theory-Based Low-Light Image Enhancement Algorithm
## 4 Experiments
### Experimental Discussion  
From the above comparison of the subjective evaluation and objective metrics, we can see that the method presented in this paper is a significant improvement compared to existing methods. This paper's proposed method could be applied to various practical applications. For example, the method can effectively improve the brightness of medical images (ultrasound images, X-rays, etc.) and provide more accurate information. It can be applied to biological experiments to obtain images with high signal-to-noise ratios from low-expression samples, and it can also be applied to surveys of mines, mining caves, etc.
# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 3 Results
### Spatial Details in Landsat Circumpolar Albedo  
The ability to characterize local changes and determine their influence on the landscape scale has important implications for understanding the role of finer scale changes in the arctic and boreal domains. In Figure 6, for example we highlight a coastal section of the Seward Peninsula in western Alaska in mid-summer and early spring. The MODIS landcovers are pulled from the MCD12Q1 data product [57], and 30 m landcovers are resampled from Sentinel-1 and Sentinel-2 derived landcover maps over selected areas of Alaska, Canada, and Siberia [58]. However, detail captured in the Landsat imagery allows for the differentiation in the albedo values of both small-scale water bodies and vegetation classes not possible at the MODIS scale. As both shrubification and changing surface water are considered major drivers of albedo change across the tundra biomes [9; 17; 59; 60] the better characterization of these albedo signature can help better understand and model the impact of shrub expansion and surface water change in the northern latitudes.  
Figure 5: Comparison of blue-sky albedo derivation using Aerosol Optical Depth (AOD) data from different sources: (**a**) The comparison of blue-sky albedo calculated from MODIS AOD and AERONET tower AOD; (**b**) The comparison of blue-sky albedo calculated from MODIS AOD and using a fixed climatology value of 0.20.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Feature Extraction from Quad-Polarimetric SAR Data
#### 2.2.6 Co-Polarized Phase Difference  
The co-polarized phase difference (CPD) is defined as the phase difference between the _HH_ and _VV_ channels [3]:  
\[\varphi_{c}=\varphi_{HH}-\varphi_{VV} \tag{13}\]  
From multilook SAR data, it can be also derived as:  
\[\varphi_{c}=\arg(\langle S_{HH}S_{VV}^{*}\rangle) \tag{14}\]  
where arg(*) stands for phase calculation.  
The standard deviation of CPD has been proposed as a very efficient parameter indicating sea surface scattering mechanisms [3]. It can be estimated from \(\varphi_{c}\) using a sliding window. On slick-free sea surfaces, the _HH-VV_ correlation is high, and a narrow CPD distribution is expected. This resulting CPD will have a small standard deviation, similarly for weak-damping surfactant-covered areas. In oil slicks where the Bragg scattering is weakened and other scattering mechanisms increase, the _HH-VV_ correlation largely decrease. As a result, the CPD pdf becomes broader, and its standard deviation largely increases.
## 1 Introduction
If the optical lengths are equal for each pair of beams, and the optical length difference between two pairs is longer than the coherent length, the two pairs of beams can illuminate the surface at the same time without cross-interference [17]. However, the temporal phase-shift method is used in this research, making the real-time measurement impossible to conduct. The temporal phase-shift technique solves the phase distribution from multi-interferograms in a time series [1; 18]. Given that the phase may vary dramatically while acquiring multi-interferograms, the temporal phase-shift technique is mainly suitable for displacement measurement with a static or quasi-static loading. By employing the Fourier transformation method, the phase distribution can be calculated from a single interferogram [19; 20]. By using three illuminating sources, three interference fringe patterns can be recorded in a single interferogram, and are spatially separated in the Fourier domain, allowing the simultaneous measurement of two in-plane displacement components from one image [21]. However, in this study, the spatial carrier is not introduced, making the measurement of complex displacement unsuitable. By following particular procedures to generate the spatial-carrier frequency, the phase map can be obtained from a single interferogram on the basis of the Fourier transformation procedure, which is named the spatial carrier phase-shift method [22; 23; 24]. Many researchers have recently proposed digital speckle pattern interferometry, which can measure three-dimensional (3D) displacements by using spatial carrier phase-shift method. Multi-directions of illumination [24; 25] or observation [26; 27] are used to generate three different sensitivity vectors. Then, the three orthogonal displacement components can be evaluated. The two in-plane displacements can be derived from three-dimensional displacement, which is measured in real time simultaneously. The post calculation of three-dimensional displacement enlarges the uncertainty of measurement. The two in-plane displacements are not independently measured, leading to the coupling of measurement errors, yielded from speckle noise and optical misalignment. This paper reports a DSPI for the simultaneous and independent measurement of in-plane displacement in two orthogonal directions. Spatial carrier phase-shift technique is used to perform dynamic measurement: only one camera is used, image registration is not needed, and polarization property of light is used to solve the problem of cross interference. Theory derivation, spectrum analysis, and experiment results are shown in detail.