esa-sceva/satcom-chunk-collection huggingface dataset:
# Genetic Programming to Remove Impulse Noise in Color Images
## 5 Experimental Results
### Performance of the Proposed Filter  
Tables 4 and 5 show the average results of the quality metrics obtained after filtering the test images to remove each of the impulse noise models: salt and pepper, uniform, and correlated. Table 4 presents the average values of the MAE and PSNR, whereas Table 5 shows those corresponding to the SSIM and FSIM. As observed in Tables 4 and 5, in general, the proposed GP filter ranked second in average values across all quality metrics (MAE, PSNR, SSIM, and FSIM) for the uniform and correlated noise models, closely following the IDCNN filter. This suggests that the GP filter performed well overall compared to the other filters, particularly in conjunction with these noise models. Regarding salt-and-pepper noise, the DBAIN filter excelled across all quality metrics, closely paralleled by the NAFSM and IDCNN filters. This behavior was due to their specialization in detecting and removing this noise model. However, the competitiveness of the NAFSM and DBAIN filters declined for the uniform and correlated noise. Furthermore, the difference in magnitude of their average values for these noise models was significant, becoming more pronounced as the noise became more prevalent, showcasing a specialization that might not generalize well to other noise types. The IDCNN filter, based on a deep learning model, exhibited robustness to variations in the scale, orientation, and lighting conditions of color images, demonstrating its adaptability to different image textures and noise models. VMF, on the other hand, notably produced images with lower-quality values. This is because it did not differentiate between pixels based on their noise levels or the type of noise they exhibited, resulting in excessive blurring in images  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{\(\mathbf{p=0.05}\)} & \(\mathbf{p=0.10}\) & \(\mathbf{p=0.15}\) & \(\mathbf{p=0.20}\) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} \multirow{4}{*}{
\begin{tabular}{c} \multirow{4}  
with large contrasting regions. Finally, the proposed GP filter generally outperformed the GSAPA and FMPGF filters across all noise models, demonstrating competitive performance across different noise models and ranking consistently well compared to other filters.
## Results  
### Interannual Variations in ET, WUE and Climate  
Over the 11-year period, ET and WUE exhibited significant interannual variability (\(p<0.001\)) (Figure 2). The amplitude of the interannual rate of change was 10.4% and 15.8% for ET and WUE, respectively. There was a significant coupling of ET and GPP (data from [25]) (\(R^{2}=0.35\); \(p<0.001\)). Propagating GPP and ET variability into WUE, we found that 39.6% of the variance for WUE came from GPP and 60.4% from ET.  
Inter-annually, radiation, temperature, wind speed and air humidity varied little at the study site (2 to 12% variations between the minimum and maximum values) (Figure 3). Interannual variations in REW and rainfall were stronger (19% and 16%, respectively). Years with highly negative total water stress indicator values (ie. severe water stress and long droughts) include 2005 (SWSIa = \(-\)5.06), 2008 (SWSIa = \(-\)2.11), 2010 (SWSIa = \(-\)5.64) and 2012 (SWSIa = \(-\)17.37) (Table 1 and Figure 11).  
Figure 2: Interannual variations in ecosystem evapotranspiration (ET) (**a**) and (**c**) and water use efficiency (WUE) (**b**) and (**d**) at the whole year scale (left) and during water depletion (WD) periods (right). Daily data from 2004 to 2014 were used. In the boxplots, the thick line shows the median; the box extends to the upper and lower quartiles; and dashed lines indicate the nominal range. For a given graph, different letters denote significant differences among years (\(p\) < 0.05).  
Figure 3: Interannual and seasonal variations for climate variables. (**a**) Global radiation (Rg, MJ m\({}^{-2}\) d\({}^{-1}\)), (**b**) Air temperature (Ta, \({}^{\circ}\)C), (**c**) Air relative humidity (RH, %), (**d**) Precipitation (Rain, mm), (**e**) Wind speed (WS, m s\({}^{-1}\)), (**f**) Potential Evapotranspiration (PET, mm), (**g**) Soil temperature (Ts, \({}^{\circ}\)C), (**h**) Vapor pressure deficit (VPD, kPa), (**i**) Relative extractable water (REW, unitless). Different colors were assigned for each year. Gray-shaded regions are the confidence intervals and the colored line in the grayscale line is the smooth curve. The two vertical broken lines in each panel represent the average day of the year for the beginning and end of the WD period. Monthly sums for precipitation in (**d**) are presented in a bar plot.  
Global radiation (Rg), relative extractable water (REW) and soil temperature (Ts) were the three meteorological parameters that best represented the main climate gradients in our dataset. On an annual basis, Rg appeared to be the best climate predictor for ET (\(R^{2}\) = 0.70; \(p\) < 0.001) and WUE (\(R^{2}\) = 0.40; \(p\) < 0.001) (Table 2). REW and Ts were the second- and third-best predictors for both ET and WUE.
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 2 Additive Manufacturing Technology for Metal Mirrors
### Metal Additive Manufacturing Technology
#### 2.1.3 Selective Laser Melting (SLM)  
SLM based on SLS was proposed by the Institute of Fraunhofer in Germany in 1995 and successfully developed in 2002. It integrates the advantages of SLS, which melts powders layer-by-layer and forms under the protection of inert gas to prevent the reaction between powder and other gas. The main difference between the two is that SLM can achieve full melting, as shown in Figure 10, in a single component, single material powder, and was initially applied only to pure metals [41]. In contrast, SLS processes cannot heat the powders above the melting temperature and form parts with somewhat less than ideal density. Meanwhile, its equipment price and process complexity are above SLS.  
SLM is a technology that has been commonly used to manufacture metal mirrors in recent years, as shown in Table 3. In theory, forming parts via SLM have higher density and mechanical properties than via laser sintering. SLM's products have high dimensional accuracy, a smooth surface, and high density [42]. The laser power of SLM solutions' 3D printer can reach 700 W easily, which is high enough to pre-print mirror blanks. SLMed parts' density is up to 99.0% and above. The above technical advantages have gradually promoted SLM to the mainstream technology for fabricating AM metal mirrors.  
Figure 10: Schematic diagram of laser melting.  
Figure 9: Schematic diagram of laser sintering.The powder composition of SLS is a mixture of high and low melting point powder, mainly nylon, polyamide, and other polymers. For metal SLS, it can be further divided into indirect metal laser sintering and direct metal laser sintering (DMLS) according to powder composition. The binder material for DMLS is a metal powder with a low melting point, while polymer powder is for indirect metal sintering. Researchers began utilizing DMLS to fabricate aluminum mirrors around 2015 when AM metal mirrors were still in the initial exploration stage. However, DMLS has been gradually replaced by SLM in the field of mirror manufacturing due to the progress of AM technologies.
## 3 Case Study  
The study area covered an open-pit mine in Peru (Figure 2) that produces copper concentrates and is developing the expansion of its operations from 100,000 to 14,000 tons per day. The equipment present at the mine is made up of 29 CAT 797F haul trucks, 3 CAT 7495 rope shovels, and 2 LT-2350 FEL. Moreover, all the moved material goes to five destinations, including two waste dumps, a crusher, and three stockpiles.
## 3 Results and Discussion
### Computational Study on Benchmark Instances with CP Solvers
Therefore, there were only small changes between first and second place regarding the makespan metrics. For the 30 s solving time, the difference was 1.31%, and for 300 s the makespan was reduced by 0.3%. Nevertheless, for those instances, the individual solving times deviated widely. Taking the solver with the smaller calculation time saved 77.5% in the calculation time for the 300 s time limit in comparison to the solving time of the second-choice solver and 43.1% for the 30 s maximum calculation time. Figure 5: Makespan differences by algorithm selection per instance for JSSP. \begin{table}
\begin{tabular}{c c c} \hline
**CP Solver** & **30 s** & **300 s** \\ \hline OR-Tools & 148 & 146 \\ CP Optimizer & 155 & 157 \\ \hline \end{tabular}
\end{table} 
Table 5: Scoring points of CP Optimizer and OR-Tools for FJSSP instances. Considering the JSSP instances, there were fewer similar makespan values. Nevertheless, when comparing those instances with the similarly calculated makespan, the time savings by selecting between solvers was high as well. For the 30 s overall time limit, the selection saved 50.3% in the calculation time and 22.9% for a 300 s overall time limit. Although the solvers achieved the same makespan, the difference in calculation time was high, and the algorithm selection could save up to 77.5% in the calculation time. Therefore, especially in industrial environments where time is an expensive factor, an algorithm selection can save costs by reducing the computational time and the production makespan.
# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 1 Introduction
* Multiple ML classifiers, including logistic regression (LR), random forest (RF), decision trees (DT), extreme gradient boosting (XGB), Adaboost, extra trees (ET), and k-nearest neighbors (KNN), are trained and evaluated for KD classification. * The ET classifier achieves state-of-the-art performance using the ACO-selected features: an accuracy of 97.70% (outperforming the previous model by 4.41%), a precision of 97.15%, a recall of 98.37%, an F1-score of 97.73%, and an area under the curve (AUC) of 99.55%. * The integration of XAI techniques such as SHAP and LIME to interpret model predictions bridges the gap between model accuracy and clinical applicability. The remainder of this paper is structured as follows: Section 2 summarizes the previous research, and Section 3 provides background information related to the proposed method. Section 4 describes the proposed methodology, while Section 5 discusses the experimental results and analyses. Section 6 concludes the work.
## 4 Method
### Metrics
The range of the \(SSIM\) value extends from 0 to 1, with a greater value denoting a lesser disparity between the output image and the undistorted image, thereby signifying superior image quality. The calculation of \(SSIM\) relies on three comparative metrics pertaining to the samples \(\alpha\) and \(\beta\)--luminance, contrast, and structure. \[l(\alpha,\beta)=\frac{2\mu_{\alpha}\mu_{\beta}+c_{1}}{\mu_{\alpha}^{2}+\mu_{ \beta}^{2}+c_{1}},c(\alpha,\beta)=\frac{2\sigma_{\alpha}\sigma_{\beta}+c_{2} }{\sigma_{\alpha}^{2}+\sigma_{\beta}^{2}+c_{2}},s(\alpha,\beta)=\frac{\sigma _{\alpha\beta}+c_{3}}{\sigma_{\alpha}\sigma_{\beta}+c_{3}} \tag{23}\]  
where \(\mu_{\alpha}\) and \(\mu_{\beta}\) are the average of \(\alpha\) and \(\beta\). \(\sigma_{\alpha}^{2}\) and \(\sigma_{\beta}^{2}\) are the variance of \(\alpha\) and \(\beta\). \(\sigma_{\alpha\beta}\) is the covariance of \(\alpha\) and \(\beta\). The calculation formula of \(SSIM\) is as follows. \[SSIM(\alpha,\beta)=l(\alpha,\beta)^{\lambda_{1}}\cdot c(\alpha,\beta)^{\lambda_ {2}}\cdot s(\alpha,\beta)^{\lambda_{3}} \tag{24}\]  
The proportion of different features in \(SSIM\) measurement is typically represented by \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\). To avoid instability problems caused when the denominator is 0, we introduce three constants: \(C_{1}\), \(C_{2}\), \(C_{3}\). It is common to take \(\lambda_{1}=\lambda_{2}=\lambda_{3}=1\), and then \(C_{2}=C_{3}/2\), so we can obtain  
\[SSIM(\alpha,\beta)=\frac{\left(2\mu_{x}\mu_{\beta}+C_{1}\right)\left(2\alpha_{ \beta}+C_{2}\right)}{\left(\mu_{\alpha}^{2}+\mu_{\beta}^{2}+C_{1}\right)\left( \sigma_{x}^{2}+\sigma_{\beta}^{2}+C_{2}\right)} \tag{25}\]  
where \(C_{1}=\left(K_{1}L\right)^{2}\) and \(C_{2}=\left(K_{2}L\right)^{2}\).
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 4 System Model for A-MAC
### Assessment Level  
In A-MAC, there is a provision of unique identification and security key corresponding to each vehicle. Let \(ID_{\phi_{0}}\) correspond to ID of the vehicles \(\phi_{0}\) and \(S_{\phi_{0}}\) correspond to security key of the vehicles \(\phi_{0}\). Instead of vehicle's id being regenerated repeatedly by the system, factors are generated using \(ID_{\phi_{0}}\) and \(S_{\phi_{0}}\) as shown in the following Equation.  
\[\rho_{\phi_{0}}=h(ID_{\phi_{0}}\parallel S_{\phi_{0}}) \tag{2}\]  
The vehicle \(\phi_{0}\) compute the factors \(\zeta_{\phi_{0}}\) as shown in the following Equation.  
\[\zeta_{\phi_{0}}=A_{\phi_{0}}\oplus B_{\zeta_{\phi_{0}}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Description** \\ \hline \(\phi_{1}\) & Any vehicle x in the network \\ TA & Trust Authority \\ \(ID_{TA}\) & ID of TA \\ \(ID_{\phi_{1}}\) & ID of any vehicle x \\ \(\theta_{TA}\) & Private key of unit TA \\ \(h(.)\) & Hash function \\ \(\oplus\) & XOR operator \\ \(\parallel\) & Connection symbol \\ \hline \hline \end{tabular}
\end{table}
Table 2: The notation and specific descriptions.  
The factor \(\zeta_{\varrho_{0}}\) is transmitted to the TA with vehicle's \(\varrho_{0}\). When received, the TA generates a random number \(h_{TA}\). The TA factor \(\tau_{TA}\) is shown in the following Equation.  
\[\tau_{TA}=h(\zeta_{\varrho_{0}}\parallel\varphi_{\varrho_{0}})\oplus\theta_{TA} \tag{4}\]  
where \(\varphi_{\varrho_{0}}=h(ID_{\varrho_{0}}\parallel h_{TA})\). Finally, the factors \(\theta_{TA}\) and \(h_{TA}\) are transmitted to the vehicles \(\varrho_{0}\), as shown in Figure 4.  
When a message is received, the vehicles \(\varrho_{0}\) stores these factors in TA and the vehicles \(\varrho_{0}\) factors \(\{\zeta_{\varrho_{0}},\tau_{TA},h_{TA},\varphi_{\varrho_{0}}\}\) are set accordingly.
## 5 Implementation and User Scenario
### Technical Validation and Experimental Results
#### 5.4.4 Results of Simulated User Behavior  
The technical validation conducted thorough assessment of AI-MDD-UX's performance across diverse user sessions. The key findings are summarized as follows:  
* **Evaluation and Comparison of Cluster Quality** To assess the clustering quality of AI-MDD-UX using KMeans++, we compared its performance against three other state-of-the-art algorithms: DBSCAN [16], KMeans [17], and Agglomerative [18]. Figure 11 illustrates the evolution of cluster quality achieved by different algorithms over multiple sessions. Our analysis reveals that KMeans++ provides superior cluster quality with achieving higher silhouette scores. This means that users are effectively segmented into meaningful personas based on their behavioral, demographic, and engagement attributes, enabling more accurate and personalized UI adaptation. In contrast, Agglomerative provides stable and well-formed clusters but lacks scalability to large datasets. DBSCAN offer some robust noise handling but is limited by its sensitivity to parameter tuning and encounters obstacles with dynamic adaptation when user behaviors shift over time.
* **Evaluation and Comparison of Execution Speed** Execution speed was evaluated across multiple test sessions, with up to 2000 sessions analyzed. As shown in Figure 12, the system demonstrated good execution speed, increasing linearly until reaching approximately 20 s for 10,000 sessions. This indicates a  
Figure 11: Cluster quality over sessions.  
strong linear relationship between the number of sessions and execution time. However, DBSCAN struggles with execution speed due to its density-based clustering approach, making it impractical for highly dynamic e-commerce platforms that require real-time adaptability. In contrast, KMeans++ strikes a balance by delivering high clustering quality while maintaining efficient execution times, making it a more suitable choice for such environments.
# Kinetics and Thermodynamics of Efficient Phosphorus Removal by a Composite Fiber
## 3 Results and Discussion
### Adsorption Kinetics at Different Temperatures
#### 3.1.3 Influencing Factors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dosage (g)**} & **Initial Phosphorus** & **Residual Phosphorus** & **Removal Rate (\%)** & **Adsorption** \\  & **Concentration (mg/L)** & **Concentration (mg/L)** & **Concentration (mg/L)** & **Capacity (mg P/g)** \\ \hline
0.5 & 10 & 3.431 & 65.69 & 1.31 \\
1.0 & 10 & 1.960 & 80.40 & 0.81 \\
1.5 & 10 & 1.378 & 86.22 & 0.58 \\
2.0 & 10 & 0.924 & 90.76 & 0.45 \\
2.5 & 10 & 0.697 & 93.03 & 0.37 \\
3.0 & 10 & 0.501 & 94.99 & 0.32 \\
3.5 & 10 & 0.327 & 96.73 & 0.28 \\
4.0 & 10 & 0.196 & 98.04 & 0.25 \\
4.5 & 10 & 0.125 & 98.75 & 0.22 \\
5.0 & 10 & 0.087 & 99.13 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effect of dosage of ZSFB composite fiber on phosphorus removal. Effect of Ammonium on Phosphorus Removal  
The removal rate of phosphorus increased with the increase of ammonium concentration (Figure 7). When the ammonium concentration was lower than 100 mg/L, the removal rate was obviously smaller than 88%. When the ammonium concentration was higher than 100 mg/L, the removal rate increased slowly with the increase of ammonium concentration. When the ammonium concentration exceeded 400 mg/L, the removal rate no longer increased. The reason for this was that the metal cations, such as Fe\({}^{3+}\) and Ca\({}^{2+}\), in the adsorbent substrate can be ion-exchanged with NH\({}_{4}\)\({}^{+}\) and combined with the phosphate to form precipitates [26]. The result indicates that ammonium promotes the adsorption of phosphorus by the composite fiber. Effect of Anions on Phosphorus Removal  
The effects of SO\({}_{4}\)\({}^{2-}\), Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\) and CO\({}_{3}\)\({}^{2-}\) on the phosphorus removal efficiency of ZSFB composite fiber were studied (Figure 8). The figure indicates that SO\({}_{4}\)\({}^{2-}\), Cl\({}^{-}\) and NO\({}_{3}\)\({}^{-}\) had little effect on the phosphorus removal in the concentration range of 0-10 mmol/L. However, CO\({}_{3}\)\({}^{2-}\) had an inhibitory effect on the phosphorus removal. The higher the CO\({}_{3}\)\({}^{2-}\) concentration, the lower the removal rate of phosphorus. The reason is that Ca\({}^{2+}\) can also combine with CO\({}_{3}\)\({}^{2-}\) to form CaCO\({}_{3}\) precipitate, which will compete with the phosphate and cause the removal rate to reduce. Figure 6: Effect of pH level on phosphorus removal efficiency of ZSFB composite fiber. Figure 7: Effect of ammonium on phosphorus removal of ZSFB composite fiber.
# A New Approach to the Allocation of Multidimensional Resources in Production Processes
## 2 Literature Review  
Manufacturing resource allocation is an important problem in manufacturing scheduling, which plays a key role in improving manufacturing processes as well as contributes to reducing costs. Manufacturing scheduling problems exist at various levels: individual machine, workstation, shop floor, supply chain, etc. [9]. To simplify, during the scheduling process, we try to make the best possible allocation of orders/tasks to limited resources (or vice versa), taking into account sequence and time constraints. This is a complicated problem of combinatorial optimization known from the literature and practice. It becomes even more difficult if we take into account modern manufacturing systems, such as e.g., smart manufacturing [10].  
What makes the presented problem different is, on the one hand, taking into account the multidimensionality of resources (Section 3) and, on the other hand, assuming that the schedule is given/imposed at least in terms of time and sequence constraints. Such a situation is frequent in practice, when the contracting authority requires the execution of the order/project according to its own schedule. However, the key issue remains the allocation of the contractor's resources to tasks/projects.  
The presented problem of the allocation of multidimensional resources can be qualified as a certain variant and extension of the generalized assignment problem (GAP). The GAP is defined using the knapsack or the scheduling terminology [11]. Problems related to assignment arise in a number of areas such as transport, education, healthcare, sport, distribution, manufacturing, etc. In practice, this is a well-studied problem in combinatorial optimization with constraints. The GAP refers to research on how to assign n objects (machines, tools, workers, etc.) to m objects (projects, orders, tasks, etc.) in the best possible way (cheapest cost, fastest, etc.) [11; 12].  
There are two components of the GAP: the assignments and the objective function. In the classical approach, the key question in this problem is: How to carry out an assignment with the optimal objective while at the same time fulfilling all the related constraints? Several methods have been proposed in the literature to answer such a question [12]. The most important ones include exact methods, heuristic methods, population search methods,and local search methods [11; 13; 14; 15]. Recently, the Bio-Inspired Hybrid approach seems to be quite promising [16].  
What differentiates our approach is the proposal of an original problem model that allows finding answers not to one general question as in the GAP but many, both general and specific questions. Additionally, the model allows for obtaining feedback on what assignments we fail to make and why, which is crucial in the implementation of the project/order. In addition, we also present a proprietary implementation method using the universal AMPL modeling language, a procedure for reducing the size of the modeled problem and two programming environments (MP and CLP).
## 2 RISs and Their Integration with NTNs  
This section navigates the interplay between RISs and HAPs by understanding the core principles that govern the abilities of RISs to manipulate electromagnetic (EM) waves to enhance the performance of communication systems. We first define RISs and compare their superiority to dynamic switching. We then help to differentiate between RISs with traditional beamforming followed by its comparison with other relevant technologies such as relays, massive multiple-input multiple-output (MIMO) systems, etc. Finally, we highlight the advantages of integrating RISs with NTNs and their applications.