esa-sceva/satcom-chunk-collection huggingface dataset:
# Inland Water Level Monitoring from Satellite Observations:
## 3 Review Results
### Satellite Missions for Inland Water Level Monitoring  
During the last three decades, spaceborne missions have been used as a key tool for the monitoring of inland water surfaces, as well as the hydrological cycle. Especially satellite altimetry has been widely used to monitor water levels since GEOS-3 and Seasat missions are considered the first altimetry satellites that provided exploitable data for the ocean. However, the trend of a particular research field, such as inland water level monitoring, may be reflected in the trend of a specific satellite mission or in a category of satellites, such as radar or laser altimetry missions, since each mission has its own unique characteristics. In the last fifteen years, the use of satellite altimetry has been facilitated by the advent of two different developments: Open-Loop Tracking Command (OLTC), and operation in Synthetic Aperture Radar (SAR) mode [53]. These developments have established altimetry as the prevailing spaceborne technique for inland water level monitoring. Thus, an analysis was conducted on our 48 included publications in order to demonstrate the distribution of literature based on satellite missions. Figure 7 displays the annual number of publications for altimetry missions used for the inland water level monitoring, while in the following subsection different types of satellite missions used for water level monitoring are depicted, such as gravimetry and GNSS-R missions.  
Figure 5: Number of published articles per year.  
Figure 6: Percentage of published articles that present or not novel or improved retrackers.
## 4 Catalyst Ink: Effect of Its Formulation on Its Microstructure and Macroscopic Properties
### Solvent  
To optimize the microstructure and macroscopic properties of the catalyst ink and hence the multiscale structure of the CLs made of the ink, there are two common pathways, i.e., the chemical route and physical route. The former is dependent on the chemical methods to change or modify the physicochemical properties of catalyst or ionomer, e.g., the distribution of Pt on carbon, functional groups on the carbon surface, or the side chain of ionomer, as described in Sections 4.1 and 4.2. The latter is based on the physical methods to change the dispersion characteristics of catalyst and ionomer or their interface through varying the dispersion medium [22; 125], the order of ingredients mixing, or dispersion process (the latter two are discussed in Section 5). Although solvent evaporates completely and is not present in the CLs, as the dispersion medium, its physical properties exert a significant impact on the microstructure and macroscopic properties of the ink and hence the CL structure. This mainly occurs through its influence on the interactions between ionomer and catalyst, the dispersion of ionomer and catalyst, the interaction between the ink film and substrate, coating process, or drying process [23; 26; 33; 35; 27; 126; 127; 128]. The physical properties of a solvent include dielectric constant (\(\epsilon\)), solubility parameter (\(\delta\)), viscosity, boiling point, vapor pressure, and surface tension (Figure 8). The solvents commonly used to prepare the catalyst ink and the solvents that have been used to prepare the catalyst ink in the literature are summarized in Table 2 with their physical properties. The dielectric constant of a solvent is related to its polarity and reflects the ability to separate opposite charges. For example, water is a polar solvent and has a high dielectric constant value (80), in which NaCl can be dissociated to Na\({}^{+}\) and Cl\({}^{-}\); hexane is a non-polar solvent and has a small value (1.9). The solubility parameters provide a numerical estimate of the interaction degree between materials and can be used to indicate the solubility of a polymer in a solvent. Two materials with similar solubility parameters are miscible, following the 'like dissolves like' law. For the time being, there are two kinds of solubility parameters usually used, i.e., [PERSON] solubility parameter and [PERSON] solubility parameter. [PERSON] solubility parameter (\(\delta\)) was proposed by [PERSON] in 1936 [129], and defined as the square root of the cohesive energy density:  
\[\delta=\sqrt{\frac{E_{coh}}{V_{m}}}=\sqrt{\frac{\Delta H_{v}-RT}{V_{m}}} \tag{2}\]  
where \(E_{coh}\) is cohesive energy, which can be derived from vaporization heat \(\Delta H_{v}\), \(V_{m}\) is molar volume of a solvent, \(R\) is gas constant, and \(T\) is temperature. The units of solubility
###### Abstract  
Tight sandstone reservoirs, as typical unconventional reservoirs, have attracted more and more attention worldwide. Compared to conventional reservoirs, the complex microscopic pore structures and micro-fractures of the tight reservoirs are developed, and fluid flow in the tight sandstone reservoir is very complicated. Therefore, studying the influence of fractures on the rock topological structure and the flow characteristics between micro-fractures and the matrix is critical to the development of tight sandstone reservoirs. In light of the intricate nature of micro-fractures, digital rock analysis technology is utilized to address this issue instead of traditional experimental methods. Firstly, the selected tight rock is scanned by X-ray computed tomography (XCT), and then the natural tight digital rock is reconstructed by filtering and binary segmentation. Secondly, micro-fracture with different shapes is added to the digital rock. Then, a pore network model extracted by the maximum ball method is adopted to conduct the flow simulation. The study finds that the distribution of pore throat radius changes with increasing fracture aperture and length and that the permeability and capillary pressure curves are influenced by fracture aperture and length in the direction of connectivity. The impact of micro-fracture dip angle on pore throat structure is significant with changes in permeability and fluid flow in different directions. Overall, the study emphasizes the importance of fracture aperture and length in determining the properties and behavior of tight sandstone reservoirs and highlights the usefulness of digital rock analysis technology in providing insights into complex microstructures.  
tight sandstones reservoirs; micro-fracture; pore scale modeling; digital rock; roughness +
Footnote †: journal: Applied Sciences
## 5 Experimental Validation
### Optimizer Selection Experiment
#### 5.1.2 Step Type Data Generation  
As shown in Figure 5, except for the step jump in the value range, the overall fluctuation trend of the step type data in the original data is very smooth. Only the data generated by Adam optimization method have the most similar fluctuation trend, and the results of other optimization methods are far from it. Therefore, aiming at the change of step characteristics, this paper improves the Adam optimization method and applies it to the generative countermeasure network.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 3 Engineering Background and Database Construction
### Data Processing and Dataset Construction
#### 3.3.2 Data Standardization  
All numerical features (e.g., distance from river, slope angle, rainfall amount) were transformed using Z-score standardization. This technique rescales each feature to have a mean (\(\upmu\)) of 0 and a standard deviation (\(\sigma\)) of 1.  
Features naturally exhibit different units and scales (e.g., meters for distance, degrees for slope, mm for rainfall). Standardization eliminates these magnitude disparities. This prevents features with larger native ranges from dominating the model's learning process solely due to their scale, leading to more stable and efficient training, and ensuring all features contribute proportionally to the model's objective function.
# Post-Filtering of Noisy Images Compressed by HEIF
## 3 Distortion Statistical Analysis
The obtained data are presented in Table 2. In addition to the aforementioned statistical parameters, we present the CR values useful for further analysis. The conclusions that can be drawn are as follows:  
1. There were cases when \(\Delta_{ij}\), \(i=1,..,I_{Im}\), \(j=1,..,J_{Im}\) had a Gaussian distribution; this happened for the complex structure image Diego when the QF was large enough and the CR was quite small, and also took place for the test image Fricso when the CR was quite small;
2. On the contrary, \(\Delta_{ij}\), \(i=1,..,I_{Im}\), \(j=1,..,I_{Im}\) became non-Gaussian when the QF decreased and, respectively, the CR increased; the conclusions in items 1 and 2 were confirmed by both the analysis of kurtosis and [PERSON] K-squared test;
3. Mean and skewness of \(\Delta_{ij}\), \(i=1,..,\ I_{Im}\), \(j=1,..,J_{Im}\) were both close to zero;
4. For the same \(\sigma^{2}\), the variance of \(\Delta_{ij}\), \(i=1,..,I_{Im}\), \(j=1,..,J_{Im}\) was within wide limits; this ranged from 11 to 34 for \(\sigma^{2}\) = 25 and from 24 to 108 for \(\sigma^{2}\) = 100, where the minimal values corresponded to the OOP observed for the test image Fricso, and the maximal values related to the small QF for the test image Diego, for which the OOP was absent. This means that the variance can be either slightly larger than \(\sigma^{2}\) or several times smaller than \(\sigma^{2}\), which could be problematic in terms of setting the filter parameters correctly. Let us also visualize \(\Delta_{ij}\), \(i=1,..,I_{Im}\), \(j=1,..,J_{Im}\) using the preliminary transformation \(\delta_{ij}=5\Delta_{ij}+128\). Our goal here is to check whether we have something like white noise or the structure of \(\Delta_{ij}\), \(i=1,..,I_{Im}\), \(j=1,..,J_{Im}\), which is more complex. Figure 3 shows \(\delta_{ij}\), \(i=1,..,I_{Im}\), \(j=1,..,I_{Im}\) for QF = 50, \(\sigma^{2}\) = 100, for two test images. It was interesting to see that fluctuations in homogeneous regions of the test image Fricso were considerably less intensive compared with the fluctuations in locally active areas of this image as well as for practically the entire image of Diego. This was due to the noise-filtering effect, which appeared more in homogeneous image regions. Meanwhile, it is also possible to state that some \"structures\" were present in \(\Delta_{ij}\), \(i=1,..,I_{Im}\), \(j=1,..,I_{Im}\) due to the presence of some structures (objects, textures, edges) in the information component of the images subject to lossy compression. As a result, we cannot expect that positive features of known denoising techniques can be realized for the considered application to the full extent since they are designed to suppress \(\Delta_{ij}\), \(i=1,..,I_{lm}\), \(j=1,..,I_{lm}\), which is obviously not exactly additive white Gaussian noise. However, it is possible to hope that some positive effects due to post-filtering can be achieved.
## 6 Results and Discussions
### Study System
#### 6.2.3 Analysis of Results Under Grid Price and Optimized Price
The penalty cost of the DNO was greatly reduced, as shown in Figure 13c, as the maximum powers of all braches under the grid price greatly exceeded the power limit (see Figure 11). The operating cost of each unit under the grid price and optimized price is shown in Table 2. It can be seen that the operating costs of the MGOs and the DNO under the grid electricity price were much higher than those under the optimized price. Thus, the optimization method based on the dynamic game strategy significantly reduced the operation costs of the system, which verifies the effectiveness of the proposed strategy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **MGO1** & **MGO2** & **MGO3** & **DNO** \\ \hline
**Cost under grid** & & & & \\
**electricity price/CNY** & 522.4030 & 978.7477 & 1145.5 & 11,140 \\ \hline
**Costunder optimized** & & & & \\
**electricity price/CNY** & 348.5904 & 689.1699 & 827.2671 & 4417 \\ \hline
**Reduction rate** & 33.27\% & 29.59\% & 27.78\% & 60.35\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between operating costs under grid price and those under optimized price. Figure 13: **(a)** Operating cost between DNO with DN, (**b**) Operating cost between DNO with MGO and (**c**) penalty before and after optimization.
## 6 Discussion
### Limitations of This Study  
The study has several limitations. The study was conducted in a single location which limits its findings to a localized level rather than generalization to a national level. However, the rural populations in Bangladesh are similar in terms of socio-demographic and education levels [72], and, hence, we believe that our findings provide important insights that can be used to understand other rural Banglades communities. Blood pressure was measured with an automated device, which might yield slightly varied results between participants. However, we checked the accuracy of the readings using a standard analog device from a sub-sample and found the accuracy level was high. Although we studied salt and fruit consumption, we neither studied vegetable consumption nor provided an objective measure of food frequency data, where people usually follow specific patterns of diet rather than an individual diet.
## 2 Applications of Nanotechnology in the Petroleum Industry
### Production and Stimulation  
#### 2.3.1 Production Problem Solution  
Various problems that occur during the production process can reduce the production efficiency. [PERSON] et al. [68] proposed nanotechnology as an alternative solution to the liquid loading problem in deep gas wells during the production process. They proved that engineered silica NPs with a temperature resistance up to 150 \({}^{\circ}\)C, salinity resistance of about 250 g/L, and H\({}_{2}\)S resistance up to 0.04% could solve the liquid loading problem by creating stable gas-liquid foams. Silica NPs are also beneficial for preventing the development of wax in the pipe during oil and gas production. Recently, [PERSON] et al. [69] discovered that modified nano-silica/EVA (ethylene vinyl acetate) could prevent the development of wax crystals and disperse them by heterogeneous nucleation. Other production problems such as hydrate formation could be solved by injecting self-heating Ni-Fe NPs into the hydrate area. Ni-Fe NPs are preferred because they have a high nucleation latency period, fast heat transfer property, and coofriendly characteristics [35].  
Nanoparticles are also suggested for improving water treatment, as inhibitors for asphaltene, and for improving the separation of the water-oil emulsion during hydrocarbon treatment. [PERSON] et al. [41] studied the effect of NP aggregation on polyether-sulfone (PES)-TiO\({}_{2}\) that can potentially improve the treatment by reducing the fouling effect. [PERSON] et al. [42] reported that TiO\({}_{2}\), ZrO\({}_{2}\), and SiO\({}_{2}\) NPs have potential uses for stabilizing asphaltene particles via hydrogen bonding between NPs and asphaltene in acidic conditions. Similarly, [PERSON] et al. [70] applied the hyperthermia concept of magnetic induction heating to prevent wax deposition during production and refining. In addition, NPs can also be utilized for separating oil-water emulsion. Studies proved that magnetic NPs [43] and silica NPs [71] can remove oil droplets from the emulsion significantly faster than conventional demulsifiers.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 5 Modification Processes of Kaolin: Enhancing Industrial Applicability
### Acid-Base Chemical Treatment  
Chemical activation is a widely utilized method for modifying kaolin clay, with numerous studies documenting the improvement of surface properties through this procedure. The chemically modified kaolin acquires improved physical and chemical properties, making it suitable for more specialized applications, particularly in catalysis, to improve the activity and product selectivity of chemical reactions of interest [16]. This technique has been extensively researched not only for kaolin but also for enhancing the surface and catalytic properties of fibrous clays, such as sepiolite and palygorskite, and smectites clays, including saponite and montmorillonite [37].
## 2 Theoretical Background and Proposed Method
### Procedures of the Proposed Method  
Drawing on adaptive threshold segmentation algorithm and morphological reconstruction operation, the proposed hub defect segmentation solution consists of the following steps:  
1. Choose a smoothing operator of a suitable size to smooth the wheel X-ray image to obtain a smoothed image.
2. The smoothed image is subtracted from the original image to obtain a difference image.
3. Choose a smaller threshold value for the difference image to perform binarization to obtain the first-time segmentation result, and the result is used as a mask image for morphological reconstruction.
4. Choose a larger threshold value for the difference image to perform binarization to obtain the segmentation result, and the result is used as the marker image for morphological reconstruction.
5. Perform morphological reconstruction using the marker image and the mask image to obtain the preliminary defect segmentation result.
6. Perform preliminary analysis of the defect segmentation result having regard to the physical facts of the wheel defect, and this produces the final defect segmentation result.  
Throughout the segmentation process, the parameters involved include the size of the smoothing operator and the threshold value for binarization, the determination of which has been discussed in Section 2.1. In step 3, the small threshold binarization produces whole defect regions, but pseudo-defects such as noise and structural interference are inevitable. In step 4, the large threshold binarization only produces partial region of defects, but most pseudo-defects are removed. The morphological reconstruction operation in step 5 restores complete defect regions obtained in step 3 but removes the pseudo-defects not tagged in step 4, to produce the preliminary segmentation result. For many wheel images, the geometry of the hub itself creates sharp edges in the X-ray image, and such edges cannot be removed by steps 3, 4, and 5, but their geometric features, such as area and diameter, are significantly larger than common defects. Step 6 includes further analysis of the preliminary result obtained in step 5 with a view to eliminating such pseudo-defects by studying the geometric features of the defect regions, and the product of this step is the final segmentation result.
# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 1 Introduction
To date, the problem of simulating hurricane storm surges has been solved through the usage of computer models capable of capturing these interactions and producing fairly accurate storm surge estimates. Some of the computer models that are available and are currently being used to predict storm surges by agencies such as the Federal Emergency Management Agency (FEMA) or the US Army Corps of Engineers (USACE) are Advanced CIRCulation (ADCIRC), or the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model [7; 8; 9]. There exist other numerical models for different purposes, such as TxBLEND, developed by the Texas Water Development Board (TWDB) used to estimate salinity conditions for Texas estuaries [10]. The estimation of storm surges is not only a matter of accuracy; it is also a problem of time. Emergency preparations are time-sensitive; numerical-based storm surge models such as ADCIRC or SLOSH require a lot of time to be executed, especially if there are not many resources available. Currently, high-performance computer clusters (HPC) are employed to run such models on a large scale and provide enough resources for their computations to be timely. It is important to mention that such models are often coupled with wave models, which add another level of complexity, raising the resource requirements of the models. Some examples of the wave models used in conjunction are the Wave prediction Model (WAM), the Steady-State Spectral Wave Model (STWAVE), and Simulating WAves Nearshore (SWAN) [7; 11]. Models such as ADCIRC run their computations based on an unstructured mesh containing bathymetry information of the area to be simulated; this mesh is a discretization of the area that needs sufficient detail near points of interest to better capture the physics involved. It is because of this that there is always a tradeoff between mesh resolution and the time required to complete computations. The ADCIRC code is optimized to scale and parallelize very efficiently, but if, as mentioned, there is a coupled model meant to simulate waves, then the complexity of the model scales vastly, which can hog the computational resources available. It is easy to see how high-fidelity models such as ADCIRC are out of the reach of endeavors without substantial funding, and the long runtime and high costs represent a limiting factor for timely emergency notifications if resources are lacking. The prohibitive costs of a numerical model and the demand for timely storm surge emergency notificationspushed the search for a way to develop an Artificial Intelligence (AI) model for accurately predicting storm surges without the need for large amounts of computational resources. In this study, a machine-learning-based storm surge forecasting model is proposed and created for predicting storm surges at discrete points along the Laguna Madre in Texas. The goal of the study is to create a machine learning model capable of predicting storm surges by using only a fraction of the computational resources that numerical models use.
## 2 Channel Model
### The Channel Gain of LoS Links  
The Lambertian model can usually characterize the LoS link in VLC [23], and consequently, the \(n\)-th LoS channel gain from the \(n\)-th LED to the \(n\)-th PD is given by  
\[h_{n}^{\text{LoS}}=\frac{A_{PD}(m+1)}{2\pi\Big{(}d_{S_{n}}^{D_{n}}\Big{)}^{2} }\cos^{m}(\Theta_{S_{n}}^{D_{n}})\cos(\Phi_{\text{D}_{n}}^{S_{n}})T_{of}G( \Phi_{\text{D}_{n}}^{S_{n}}), \tag{4}\]  
where \(A_{PD}\) denotes the physical area of the PD, \(m\) is the Lambertian index determined by the LED half-power semiangle \(\Psi_{1/2}\) and calculated by \(m=-1/\log_{2}(\cos(\Psi_{1/2})\). Moreover, \(T_{of}\) represents the gain of the optical filter, and \(G(\Phi_{\text{D}_{n}}^{S_{n}})\) denotes the gain of the optical  
Figure 1: A typical RIS-aided parallel MIMO VLC system.  
concentrator associated with the field-of-view (FoV) \(\psi_{\rm FoV}\). The relationship between the optical concentrator gain and the FoV is formulated as [23]  
\[G(\Phi_{D_{n}}^{S_{n}})=\left\{\begin{array}{ll}a^{2}/\sin^{2}\psi_{\rm FoV},&0 \leq\Phi_{D_{n}}^{S_{n}}\leq\psi_{\rm FoV},\\ 0,&\text{otherwise},\end{array}\right. \tag{5}\]  
where \(a\) denotes the refractive index.
## 3 Materials and Methods
### Defects Detection  
For the defect detection model, we experimented with three state-of-the-art object detection models, namely YOLOv5 and YOLOv8 from the YOLO (You Only Look Once) framework, and RetinaNet [19] with a ResNet50 [20] backbone from the TensorFlow Object Detection API.  
YOLO is a real-time object detection model that offers high accuracy and efficiency by directly predicting bounding boxes and class probabilities from a single neural network pass. YOLO divides the input image into a grid and assigns each grid cell the responsibility of detecting objects present within its boundaries. Each cell predicts bounding boxes and corresponding class probabilities based on predefined anchor boxes.  
RetinaNet is an object detection framework that operates as a one-stage detector and tackles the challenge of class imbalance during training by employing a focal loss function. The ResNet50 backbone serves as the feature extractor in RetinaNet. It is a deep convolutional neural network architecture that comprises multiple residual blocks. These residual blocks enable the model to effectively capture and represent rich, hierarchical features from input images.