esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Correlation between Land Use and Land Cover and Water Quality
With the increase in water covering the soil, there is a reduction in the quantity of other land use classes [37]. When analyzing the correlations between the land use \"Wetland Areas\" and water quality parameters in IA17, low correlations were observed, lower than corr = \(\pm\)0.5, except for the parameter \"Total Nitrogen,\" which showed a high coefficient (corr = \(-0.82\)). [PERSON] et al. [38] evaluated 40 small wetland areas in the Blue Ridge ecoregion, South Carolina, United States, where they found that total dissolved nitrogen depends on both the hydrological connection between wetland areas and the surrounding land use (Figure 8). Figure 6: Correlogram of land use and land cover with water quality in the Miranda River watershed. Note: Class: Agri = Agriculture, Water = Water Body, Not_Veg = Non-Vegetated, Past = Pasture, Wet = Wetland Areas, Urb = Urban Area, Veg_Nat = Native Vegetation. Water quality parameters: DO = dissolved oxygen, BOD = biochemical oxygen demand, Therm. Coli. = thermotolerant coliforms, TN = total nitrogen, TP = total phosphorus, TS = total solids, Tu = turbidity, WQI = water quality index. Figure 8: Correlogram of wetland areas with water quality by influence area in the Miranda River watershed. Note: Class: Agri = Agriculture, Water = Water Body, Not_Veg = Non-Vegetated, Past = Pasture, Umide = Wetland Areas, Urb = Urban Area, Veg_Nat = Native Vegetation. Water quality parameters: DO = dissolved oxygen, BOD = biochemical oxygen demand, Therm. Coli. = thermotolerant coliforms, TN = total nitrogen, TP = total phosphorus, TS = total solids, Tu = turbidity, WQI = water quality index. Figure 7: Correlogram of land with water quality by influence area in the Miranda River watershed. Note: IA: Influence Areas. Water quality parameters: DO = dissolved oxygen, BOD = biochemical oxygen demand, Therm. Coli. = thermotolerant coliforms, TN = total nitrogen, TP =total phosphorus, TS = total solids, Tu = turbidity, WQI = water quality index. These low correlations can be explained by the non-linear variation in the quantity of Wetland Areas over the study period, resulting from the seasonality of interannual periods of drought and flood in the Pantanal. The dynamics of land use and land cover in this region do not behave linearly over the study period, showing higher variance compared to other Influence Areas in the MRW. Because of this, the relationship between land use and land cover and water quality may not be well explained by linear correlations, suggesting that non-linear correlations should be investigated in future studies.
## 1 Introduction
Finally, we compared the results of the DELM, DBN, and SVM. We concluded that when the DBN was attached to the alpha-beta filter, and the performance was very high compared to the other algorithms. Figure 1 shows the conceptual model of the alpha-beta filter model. Each algorithm recorded different best-case accuracy results; for the DBN, we achieved 3.60 and 2.61; for the DELM, we obtained best-case results of 3.90 and 2.81; and finally, for the SVM, 4.0 and 3.21 were attained in terms of the RMSE and MAE, respectively, as compared to 5.21 and 3.95. In summary, the main contributions of our paper are as follows:  
1. The development of a deep-belief-network-based alpha-beta filter;
2. The development of an SVM-based alpha-beta filter;
3. The development of a DELM-based alpha-beta filter;
4. The performance evaluation of both conventional algorithms and the proposed algorithm;
5. A shift from the static approach to a dynamic approach. Figure 1: The conceptual model of the proposed learning algorithm for the alpha-beta filter model. The paper is organized as follows: In Section 2, related work regarding similar filters is discussed in detail. Section 3 sheds light on the proposed methodology of the different learning methods for the alpha-beta filter, including the DELM, DBN, and SVM. In Sections 4 and 5, the implementation and results are discussed briefly. Finally, Section 6 concludes the paper. The abbreviations used in this paper are defined in Table 1.
# Blur Kernel Estimation by Structure Sparse Prior
## 2 Materials  
As a long standing computer vision problem, blind image deblurring aims to recover a sharp image from a blurry image when the blur kernel is unknown. Blind image deblurring is severely ill-posed since both the original image \(x\) and the blur kernel \(k\) are unknown [24]. Mathematically, a blurry image \(y\) can be modeled as:  
\[y=k*x+\mathbf{N}, \tag{1}\]  
where \(y\) and \(x\) represent the input blurred image and the latent sharp image, respectively; \(k\) denotes the blur kernel; and \(\mathbf{N}\) is the additive noise, which is usually assumed as Gaussian distribution. In the formulation, * is the convolution operator and thus the image deblurring problem is converted to a deconvolution problem.  
For comprehensive evaluation, a real-world benchmark dataset is publicly available ([[http://webday.is.mpg.de/pixel/benchmark4](http://webday.is.mpg.de/pixel/benchmark4) camerashake/]([http://webday.is.mpg.de/pixel/benchmark4](http://webday.is.mpg.de/pixel/benchmark4) camerashake/)). As shown in Figure 2a-d, four true sharp images were recorded by a camera (Canon Eos 5D Mark II). The camera was set to ISO 100, aperture f/9.0, exposure time 1 s. The corresponding blurred images are generated by Equation (1), as shown in Figure 2e-h. The blur kernel is shown in Figure 3. This dataset contains all information to evaluate the image deblurring methods.  
Figure 2: A real-world benchmark dataset: (**a–d**) the four original images; and (**e–h**) the blurry images.
## 4 Results and Discussion
### Experimental Results of the Individual Components
#### 4.1.2 Experimental Results of Repair Mortar  
To investigate the tensile strength of repair mortar, the results of the tensile strength tests on the briquet specimens at increasing temperatures (presented as mean value with standard deviation) are summarized in Figure 10. The tensile strength of the investigated mortar decreased by 15% with increasing temperature levels compared to 20 \({}^{\circ}\)C. The results  
Figure 8: Stationary fiber strand tensile strength test: (**a**) mean values of the tensile strength adapted from [10]; (**b**) reduction factor K\({}_{\text{T}}\) shown as mean values of tensile strength with standard deviation [1].  
Figure 9: Mean values of ultimate strain of CTR with standard deviation at increasing testing temperatures.  
at 40 \({}^{\circ}\)C exhibited a significant reduction; however, this can be attributed to test outliers, considering a variation coefficient of 13%. The authors assume that the decrease in tensile strength of mortar can be attributed to mass loss due to evaporation. However, these results can be consistently aligned with the literature findings. According to the results in [37; 38], higher strength reduction is known to occur in the tensile zone compared to the compressive zone under elevated temperature conditions. Furthermore, our experimental test results are consistent with the reduction factors identified by [39]. In order to consider creep phenomena, transient tests with constant load levels and increasing heating rates until failure should be investigated.
## 2 Materials and Methods
### Dynamic Selection Adaptive Genetic Algorithm (DSAGA)  
The scheduling challenge in solid-wood-panel processing is identified as the flexible job-shop scheduling problem (FJSP) [25], wherein operations are not limited to preassigned processing equipment but can select from various available options, introducing a degree of flexibility in machining processes.  
This study introduces the dynamic selection adaptive genetic algorithm (DSAGA) as an optimization solution to address FJSP. The algorithm, drawing from natural selection and genetics principles, iteratively evolves to seek an optimal scheduling solution. DSAGA is adept at addressing complex optimization tasks, such as FJSP and the traveling salesman problem, which are categorized as NP-hard due to their intricate combinatorial properties. These tasks entail organizing manufacturing processes, allocating specific workpieces to respective operations, and selecting suitable machines for each machining task. Figure 13 presents the flow of the DSAGA in a detailed flowchart.  
Figure 12: Technology architecture.  
Three enhancements contribute to DSAGA. First, integrating a dynamic selection strategy improves the algorithm's adaptability by adjusting selection intensity based on the problem's specific needs and the current population's fitness level. Second, implementing the partially mapped crossover (PMX) technique during the crossover phase facilitates a more effective recombination of genetic traits [26]. Third, introducing an innovative mutation operation aids in expanding the pool of potential solutions, thus reducing the risk of premature convergence [27]. Together, these strategic improvements boost the algorithm's searching efficiency and enhance the quality of generated solutions.  
The subsequent analysis will detail DSAGA through its algorithmic flowchart, methodically examining the five components: coding strategy and initialization of the population, objective function, adaptive selection operation, crossover operation, and mutation operation.
## 4 Implementation Procedure
### Validation
#### 4.3.3 Successful Data Fragmentation  
The RFO-SVM classifies the data into fragments based on the optimized features and trained model to ensure that the data are divided into subsets that capture different classes or patterns present in the dataset. The performance of the RFO-SVM model, data fragmentation, and allocation process is evaluated using various metrics. The precision, recall, and F1 score assess the model's performance for individual classes.
## 5 Conclusions  
In summary, we propose an adaptive beam divergence angle control method to mitigate the effect of scintillation caused by pointing errors. We verified the performance of the proposed technique using FSO channel modeling. In addition, we analyzed the relationship between pointing error and scintillation, which is necessary for determining the optical beam divergence angle from the aspect of beam divergence angle. In addition, theoretical evaluations proved that the suggested method mitigates scintillation when a pointing error exists. Adaptive beams caused an improvement in SNR compared to fixed beams, and it was shown that applying the secured power could lead to more progress. Furthermore, the data rate could be enhanced by concurrently using adaptive beam divergence control and modulation. The proposed technique would help minimize the scintillation effect in vertical FSO links, which incur considerable losses owing to random fluctuations in the refractive index of the atmosphere. Furthermore, the proposed technique can be used to configure a system in an environment with frequent occurrence of pointing errors by moving along a specific trajectory because the UAV cannot hover. We believe the proposed technique would help next-generation industry backhaul networks considering a combination of pointing error and scintillation.  
Conceptualization, H.-M.P.; methodology, H.-M.P. and Y.-J.H.; software, H.-M.P.; validation, H.-M.P. and S.-K.H.; formal analysis, H.-M.P. and Y.-J.H.; investigation, H.-M.P. and Y.-J.H.; resources, H.-M.P.; data curation, H.-M.P.; writing--original draft preparation, H.-M.P.; writing--review and editing, H.-M.P., [PERSON]-[PERSON] and S.-K.H.; visualization, H.-M.P.; supervision, S.-K.H.; project administration, S.-K.H.; funding acquisition, S.-K.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by an Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean Government (MSIT) (No. 2019-0-00685, Free space optical communication based vertical mobile network).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Adsorption Tests  
The potential application of the activated carbons as adsorbents in aqueous phase was evaluated using sulfamethoxazole (SMX) as model impurity, which has a solubility in water of 610 mg\(\cdot\)L\({}^{-1}\) at 298 K. Samples of AC (12.5 mg, \(\approx\)100 \(\upmu\)m) were contacted in stoppered glass bottles with 50 mL of SMX aqueous solutions (25 to 150 mg\(\cdot\)L\({}^{-1}\)). A commercial activated carbon (C: 89.5 wt.%, ABET: 800 m\({}^{2}\cdot\)g\({}^{-1}\); Vmicro: 0.67 cm\({}^{3}\cdot\)g\({}^{-1}\); Vmeso: 0.53 cm\({}^{3}\cdot\)g\({}^{-1}\)), supplied by Merck, was also used as reference for comparison purposes. Experiments were carried out at 20 \({}^{\circ}\)C and the natural pH of the SMX solution (4.6) in a thermostatized shaker (Optic Ivymen System, Biotech, Madrid, Spain) at equivalent 200 rpm. A kinetic experiment showed that 5 days was time enough to reach the adsorption equilibrium in the operating conditions. SMX concentration was determined by UV-vis spectrophotometry (Cary 60 UV-Vis, Agilent Technologies, Santa Clara, CA, USA) at 265 nm wavelength. The Langmuir equation was used to fit the equilibrium data. The reported results are the average values from triplicate runs, being the standard errors always below 5%.
## 2 The NGLog-X Family  
Let \(r(t)\) be the PDF of a RV (random variable), say \(T\), where \(T\in[\pi_{1},\pi_{2}]\) for \(-\infty\leq\pi_{1}<\pi_{2}<\infty\), and let suppose that \(K[F(x;\Theta)]\) be a function of \(F(x;\Theta)\) CDF of a RV, say \(X\), satisfying the conditions given bellow:  
1. \(K[F(x;\Theta)]\in(\pi_{1},\pi_{2})\);
2. \(K[F(x;\Theta)]\) is monotonically increasing and differentiable; and
3. \(K[F(x;\Theta)]\to\pi_{1}\) as \(x\to-\infty\) and \(K[F(x;\Theta)]\to\pi_{2}\) as \(x\to\infty\). According to ([PERSON] et al. [23]), the CDF \(G(x)\) of the T-X family is defined by  
\[G(x)=\int_{\pi_{1}}^{K[F(x\Theta)]}r(t)dt,\,x\in\mathbb{R}. \tag{4}\]  
The \(g(x)\) PDF corresponding to (4) is given by  
\[g(x)=\bigg{\{}\frac{d}{dx}K[F(x;\Theta)]\bigg{\}}r\{K[F(x;\Theta)]\},\,x\in \mathbb{R}.\]  
Using the T-X idea, several novel classes of distributions have been proposed in the literature. Table 1 lists different \(K[F(x;\Theta)]\) functions for special members of the T-X family. Now, taking inspiration from Equation (4), we propose the novel-introduced family of distributions. Let \(T\sim\exp(1)\), and then its CDF is given by  
\[R(t)=1-e^{-t},\,\,\,t\geq 0 \tag{5}\] \begin{table}
\begin{tabular}{c c c} \hline \hline \(K[F(x;\Theta)]\) & **Support of X** & **Member of T-X Family** \\ \hline \(F(x;\Theta)\) & \([0,1]\) & Beta–G ([PERSON] et al. [26]) \\ \hline \(-\log[F(x;\Theta)]\) & \((0,\infty)\) & Gamma–G type 1 ([PERSON] and [PERSON] [27]) \\ \hline \(-\log[1-F(x;\Theta)]\) & \((0,\infty)\) & Gamma–G type 2 ([PERSON] and [PERSON] [28]) \\ \hline \(\frac{F(x\Theta)}{1-F(x;\Theta)}\) & \((0,\infty)\) & Gamma–G type 3 ([PERSON] and [PERSON] [29]) \\ \hline \(\log[-\log(1-F(x;\Theta))]\) & \((-\infty,\infty)\) & Logistic–X family ([PERSON] et al. [30]) \\ \hline \(-\log\Big{(}\frac{1-F(x;\Theta)}{e^{-t(x;\Theta)}}\Big{)}\) & \((0,\infty)\) & Weighted T–X family ([PERSON] et al. [31]) \\ \hline \(-\log\Big{[}\frac{e^{(1-K(x;\Theta))}-1}{e^{-(1-K(x;\Theta))}}\Big{]}\) & \((0,\infty)\) & New Expo–X family ([PERSON] et al. [32]) \\ \hline \(-\log\Big{(}\frac{e^{\prime}\,F(x\Theta)}{(e-\log F(x;\Theta))^{2}}\Big{)}\) & \((0,\infty)\) & NGLog–X family (proposed) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different \(K[F(x;\Theta)]\) functions for some members of the T–X family. The PDF corresponding to Equation (5) is  
\[r(t)=e^{-t},\;\;t>0 \tag{6}\]  
Setting \(K[F(x;\Theta)]=-\log\left(\frac{e^{\delta}\;F(x;\Theta)}{\left(e-\log F(x; \Theta)\right)^{\delta}}\right)\) and \(r(t)\) as Equation (6) in Equation (4), we obtain the \(G(x;\delta,\Theta)\) CDF of the New Generalized Logarithmic-X (NGLog-X) family of lifetime distributions, which is given by  
\[G(x;\delta,\Theta)=\frac{e^{\delta}\;F(x;\Theta)}{\left(e-\log F(x;\Theta) \right)^{\delta}},\;\;\delta\in\mathbb{R}^{+},x\in\mathbb{R} \tag{7}\]  
where \(\delta\in\mathbb{R}^{+}\) is an extra shape parameter, \(x\in\mathbb{R}\) and \(F(x;\Theta)\) is the CDF of any baseline (or existing) probability distribution which may depend on a parameter vector \(\Theta\in\mathbb{R}\).
## 5 Implementation, Evaluation Details
### Metrics for Retinal Vessel Segmentation
#### 5.1.3 Precision  
Precision measures the accuracy of the positive predictions made by the segmentation algorithm. It is calculated as the ratio of true positive predictions to the total number of positive predictions (both true positives and false positives). Precision is given by:  
\[\text{Precision}=\frac{TP}{TP+FP}\]  
where:  
* \(FP\) is the number of false positive predictions.
# The EGNOS Augmentation in Maritime Navigation +
## 4 Results and Discussion  
After a preliminary analysis of the operational scenarios, raw data collected in static and kinematic modes were processed using the different setups described in Section 3 such as GPS SPP, GPS+SBAS SPP and DGPS configurations. The aim was to analyze the performance of EGNOS augmentation in terms of solution accuracy, availability and continuity.  
For the performance analysis in terms of accuracy, positioning solutions obtained using the three configurations have been compared to reference ones computing vertical and horizontal positioning errors. The figures of merit adopted for this analysis are Root Mean Square (RMSE), mean and maximum errors for both horizontal (H), vertical (Up) and tridimensional (3D) components of position. Furthermore, the capability to meet accuracy requirements as defined by IMO about horizontal accuracy of 10 m was also checked.  
In addition, the performance of the different configurations was also verified in terms of solution availability (computed as the time percentage when the estimated solution is simply available, i.e., the number of available satellites is sufficient for the positioning) and continuity (considered as the probability that the positioning solution is available over a temporal period of 15 min).
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 3 Main Idea of 3 DMOP Security Protection  
A mesh model is a representation of a 3D model, which usually contains vertex data, texture coordinate data, normal data, face data etc. Together they constitute the basic geometry and texture of a mesh model.  
Vertex coordinates are the coordinates of each vertex in a 3D space; texture coordinate data is usually used to determine how the texture fits on the surface of the model; normal vectors are used to describe the orientation of each vertex or face, affecting the lighting and rendering of the model; face data defines the polygon faces of the model, usually consisting of vertex indices and texture coordinate indices. When correctly constructing the mapping relationship between face data and texture data, texture data is a two-dimensional image that contains information such as the color and texture of the model surface.  
As shown in Figure 1, this article encrypts the vertex coordinate data, texture coordinate data, and texture image data in the mesh model separately (i.e., use the vertex coordinate watermark algorithm for vertex coordinate data, and use the texture coordinate watermark algorithm for texture coordinate data). The next section further introduces the principles and implementation details of related encryption algorithms.  
Figure 1: Main idea of the 3 DMOP security protection.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 3 System Model
### Network Architecture  
In this paper, as shown in Figure 1, the network consists of two types of nodes: a sensor node and a sink node. A sensor node is an isomorphic node equipped with a half-duplex omnidirectional acoustic modem and has the capability of collecting, sending, and forwarding information. A sink node is integrated with an acoustic modem receiving information from the sensor nodes and a wireless modem sending information toward a satellite or onshore station. In the networks, each of the nodes are uniformly anchored in different depths of the specific region of the sea. Once sensing information, the source nodes transmit directly or through multi-hop to the sink, which forms many-to-one convergent traffic. Then, the sink will forward the data to the onshore station via the satellite in space [5; 30; 31].
## 4 Discussion
### Chl Map
In addition, in order to convert SPAD readings into actual chlorophyll concentration calibration equations have to be applied; however, due to logistical and equipment constrains in this study it was not possible to derive calibration equations from the SPAD readings. Therefore, a published equation based on the dominant species of mangrove was used. To partially overcome some of these issues, it is recommended to sample a larger number of ESUs including field measurements of LAI at different seasons and to develop Chl meter and CC calibration equations. Finally, as this is a pioneer study the authors acknowledge that the limited number of ESUsmay lead to optimistic results. For the reasons explained above, in this paper the focus is on the seasonal variation of CC rather than in the absolute values of CC. The results of this research have implications for the use of a new generation of satellites that include a spectral band in the red edge position such as the Sentinel 2 from the European Space Agency. At a leaf level, the hyperspectral indices tested in this study that had a red edge band in their formulation achieved a larger correlation with leaf CC. At ESU level, the Landsat 8 broad band index NDVI green achieved the largest correlation with Chl measured on the ground. Further, Sentinel 2 will enable computation of commonly used broad band indices such as the NDVI green plus the highly correlated VI's using the red edge bands. Sentinel 2 transcends the capabilities of the Landsat mission in terms of swath width, spatial resolution, revisit time, and number of spectral bands [92]. Information in the red edge combined with the frequent revisit time of Sentinel 2 (5 days) is expected to increase the accuracy of leaf CC estimation. To date, algorithms to estimate CC based on Sentinel 2 simulated spectral bands are being revised, created, and validated in crops across Europe, showing promising results [93, 94, 95]. Therefore, there is much scope for the application of these algorithms to estimate CC in mangrove forests once Sentinel 2 is operational.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 3 Proposed Statistical Dimensioning Model
### Expressions of \(m_{N}\) and \(v_{N}\)  
In this subsection, we derive the expressions of \(m_{N}\) and \(v_{N}\) in (16) and (17) considering the single-user and multiuser cases. We recall that all sensor devices are equipped with a single antenna and the number of antennas at the collector is \(n_{r}\). The distribution of the fading coefficient in (4) will be specified in the next section depending on the antenna configuration and transmission mode.