esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Agent-Based Simulation  
Agent-based models (ABMs) are computational simulations in which artificial entities interact over time within customized environments [33]. That means, ABMs try to reproduce individual processes of movement, behavior, birth, growth, and death according to a set of information, such as genotype, history, and location of agents (i.e., crucial components of the analysis). Furthermore, ABMs permit an unequaled control and some statistical power by allowing to specify the behavior of any number of agents and observe their interactions over time. In this scenario, an agent is considered an actor who plays a role within a given environment and who functions independently (i.e., according to the nature and behavior of other agents, responses can vary too) from other agents (i.e., settlements, people, political entities or companies, among others). In order to be able to respond to this dynamic system, agents have protocols or mechanisms that describe how they interact with other agents, having themselves their own characteristic idiosyncrasies [34]. Moreover, an agent is an identifiable, discrete individual with a set of characteristics or attributes, behaviors, and decision-making capability which can be considered as a particular individual [35]. Also, agents are adaptive in that they respond to their environment through learning and evolution and are autonomous in that they control their own goals, states, and conducts [36].  
In addition, ABMs present an ideal framework for social simulation that helps to specify causal mechanisms, that is, models that simulate not only individual behavior but also social interaction that characterizes society development and criterion [37]). Considering the aforementioned approach, ABMs are present in the literature in a wide variety of fields, such as economics [38], sociology [39], political science [40], or artificial intelligence [41]. Their use is widespread in modeling human social and organizational behavior and individual decision-making [42; 43], where individual and group behavior, social interaction,and collaboration, among others, are main actors. This trend seems likely to continue in the same line, so their implementation will become frequent in geography and in urban and regional planning [44].  
According to [PERSON] and [PERSON] [45], the main reasons for popularity in the use of agent-based modeling are: _(i)_ the current systems that need to be analyzed and modeled have become in complex systems in terms of interdependencies, and accordingly, traditional modeling tools may not be as applicable as they once were; _(ii)_ ABMs present an answer for those systems that always have been too complex to be represented (i.e., economic models); _(iii)_ the collection process is finer now (i.e., individual-based simulations); and _(iv)_ computational power is advancing rapidly. Nowadays, it is possible to compute large-scale microsimulation models that would not have been plausible just a few years ago.
## 4 Discussion and Conclusions
PSO to find the best coefficients for watermark embedding  
Input: sub band coefficients <c1, c2,... cm>. Output: Particles matrix where row i and column j of the matrix is set to 1 if the corresponding coefficient is selected by the optimization. // Initialize algorithm variables  
pbest = array of size num_swarms x num_of_watermark_bits, initialized to all zeros  
gbest = array of size 1 x num_of_watermark_bits, initialized to all zeros  
pbest_fitness = array of size num_swarms, initialized to all zeros  
gbest_fitness = 0  
max_iterations = user-defined maximum number of iterations  
num_swarms = user-defined number of swarms  
Wmax = user-defined maximum weight factor  
Wmin = user-defined minimum weight factor  
K = user-defined number of selected particles  
iteration = 1// Evaluate initial fitness values For i = 1 to m  F_i = calculate objective function of particle c_i  If F_i is better than F(pbest_i) then  pbest_i = c_i  pbest_fitness(i) = F_i  EndIf  If F_i is better than gbest_fitness then  gbest = c_i  gbest_fitness = F_i  EndIf EndFor // Particle Swarm Optimization loop  while iteration <= max_iterations do  For i = 1 to num_swarms do  // Calculate new particle velocities  W = Wmax - (Wmax - Wmin) * iteration / max_iterations  For j = 1 to num_of_watermark_bits do  r1 = random number between 0 and 1  r2 = random number between 0 and 1  velocity_i_j = W * velocity_i_j + C1 * r1 * (pbest_i_j - particles_i_j) + C2 * r2 * (gbest_j - particles_i_j)  // Update particle position based on sigmoid function  if rand() < 1 / (1 + exp(-velocity_i_j)) then  particles_i_j = 1  else  particles_i_j = 0  EndIf // Select subset of particles based on K  M = sum(particles_i)  if M > K then  index = find(particles_i == 1)  index_to_remove = index(randperm(length(index), M - K))  particles_i(index_to_remove) = 0  else if M < K then  index = find(particles_i == 0)  index_to_add = index(randperm(length(index), K - M))  particles_i(index_to_add) = 1  EndIf
# Practical Grammar Compression Based on Maximal Repeats +
## 2 Preliminaries
### Grammar Compression  
A _context free grammar_ (CFG or simply _grammar_) \(G\) is defined as a four-tuple \(G=\{V,\Sigma,S,R\}\), where \(V\) denotes an ordered finite set of _variables_, \(\Sigma\) denotes an ordered finite alphabet, \(R\) denotes a finite set of binary relations called _production rules_ (or _rules_) between \(V\) and \((V\cup\Sigma)^{*}\), and \(S\in V\) denotes a special variable called _start variable_. A production rule refers to the situation, where a variable is substituted and written in a form of \(v\to w\), with \(v\in V\) and \(w\in(V\cup\Sigma)^{*}\). Let \(X,Y\in(V\cup\Sigma)^{*}\). If there are \(x_{l},x,x_{r},y\in(V\cup\Sigma)^{*}\) such that \(X=x_{l}xx_{r}\), \(Y=x_{l}yx_{r}\), and \(x\to y\in R\), we write \(X\Rightarrow Y\), and denote the reflexive transitive closure of \(\Rightarrow\) as \(\stackrel{{*}}{{\Rightarrow}}\). Let \(val(v)\) be a string derived from \(v\), i.e., \(v\stackrel{{*}}{{\Rightarrow}}val(v)\). We define grammar \(\hat{G}=\{\hat{V},\hat{\Sigma},\hat{S},\hat{R}\}\) as a _subgrammar_ of \(G\) if \(\hat{V}\subseteq V\), \(\hat{\Sigma}\subseteq(V\cup\Sigma)\), and \(\hat{R}\subseteq R\).  
Given a text \(T\), _grammar compression_ is a method for lossless text data compression that constructs a restricted CFG uniquely deriving the text \(T\). For \(G\) to be deterministic, a production rule for each variable \(v\in V\) must be unique. In what follows, we assume that every grammar is deterministic and each production rule is \(v_{i}\to expr_{i}\), where \(expr_{i}\) is an expression either \(expr_{i}=a\) (\(a\in\Sigma\)) or \(expr_{i}=v_{j_{1}}v_{j_{2}}\cdots v_{j_{n}}\) (\(i>j_{k}\) for all \(1\leq k\leq j_{n}\)).  
For estimating the effectiveness of compression, we use the size of the constructed grammar, which is defined as the total length of the right-hand side of all production rules of the grammar.
# Near Infrared Illumination Optimization for Vein Detection: Hardware and Software Approaches
## 2 Methodology
### NIR Projection
#### 2.1.1 LED Wavelength  
Five near-infrared LEDs with different peak wavelengths (720 nm, 760 nm, 850 nm, 900 nm, and 940 nm) were tested in terms of absorption on the vein. The infrared-based sampling mainly utilizes the properties of infrared rays and infrared emission [11]. The wavelength being studied is in the range of near-infrared radiation based on the absorption in human blood. Excellent veins visualization can only be achieved by illuminating the skin with an incident of infrared light radiation of a specific wavelength. Otherwise, the incident light cannot penetrate the skin to reach the deeper veins and could not be reflected. Therefore, in an illumination approach, among the crucial factors of a vein visualization system is wavelength selection. An evaluation of different light sources with a selected range of 700 nm to 1000 nm wavelengths as suggested by [PERSON] and [PERSON] was carried out to assess the best reflection of the vein in the detection process [12].
## 5 Discussion
### Frequency Ratio Analysis Results  
Through FR analysis, the NDVI emerged as the most influential factor for LSP in this study area. It was also the primary criterion for selecting non-landslide points in Scenario 3. When the NDVI values ranged between 0.32 and 0.64 in this region, the FR value peaked at 3.7. While moderate vegetation cover enhances soil stability, landslides may still occur due to water infiltration. Shallow landslides, typically involving soil masses less than 2 m thick [57], dominate the dataset in this study, which may explain why the NDVI emerged as the most influential factor for LSP. Shallow landslides are sensitive to vegetation cover (expressed as both below-ground and above-ground vegetation) [58; 59]. Therefore, the results of the FR analysis should be interpreted considering the local geological context, particularly the prevalence of shallow landslides, and should not be generalized to other regions without careful consideration.
# Research on High-Speed Vibration and Structure Optimization of Multiwire Sawing Machine
###### Abstract  
To improve the sawing stability and slicing quality of an existing multi-wire saw under severe vibrations using a high wire speed, its structure is optimized to improve its structural stiffness, so that it can still maintain good operation stability under higher wire speed. First, a finite element simulation model is established based on the shape, structural and material characteristics of the multi-wire saw, and then a transient dynamic simulation calculation is carried out using finite element software to obtain the vibration characteristics of the machine, which is verified with the measured data. The results show that the error between the calculated and the measured value is less than 11.46%, which verifies the accuracy of the simulation. On this basis, a multi-objective optimization design method based on the response surface is used to optimize the structure of the whole machine, and the optimized model is simulated again to evaluate its optimization effect. The results show that the amplitude of the optimized machine at a 2400 m/min wire speed is equivalent to that of the original machine at a 1500 m/min wire speed, and the wire speed is increased by 60% year-on-year with the same operation stability, which can guide research on high-speed multi-wire saws and machine tool vibration control strategies.  
multi-wire saw; high wire speed; operation stability; multi-objective optimization +
Footnote †: journal: Applied sciences
# Genetic Programming to Remove Impulse Noise in Color Images
## 5 Experimental Results
### Performance of the Proposed Filter
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Filter**} & \multicolumn{2}{c}{\(\mathbf{p=0.05}\)} & \multicolumn{2}{c}{\(\mathbf{p=0.10}\)} & \multicolumn{2}{c}{\(\mathbf{p=0.15}\)} & \multicolumn{2}{c}{\(\mathbf{p=0.20}\)} \\ \cline{3-11}  & & **MAE** & **PSNR** & **MAE** & **PSNR** & **MAE** & **PSNR** & **MAE** & **PSNR** \\ \hline \multirow{4}{*}{**FMM**} & IDCNN [33] & 0.7903 & **38.1716** & 1.4747 & **34.4618** & 2.4699 & 29.4046 & 4.9519 & 23.0100 \\  & & GP & 1.0939 & 35.0095 & 1.5978 & 31.5771 & 2.5213 & 27.6317 & 4.1993 & 23.6414 \\  & DBAIN [25] & **0.4772** & **38.4290** & **0.7630** & **36.0019** & **1.1004** & **34.0328** & **1.5002** & **32.3204** \\  & NARSM [22] & **0.6070** & 36.1685 & **0.9462** & 34.2428 & **1.2883** & **32.9280** & **1.6345** & **31.8994** \\  & GSAPA [17] & 2.6156 & 31.7966 & 2.9893 & 31.0663 & 3.5351 & 30.1346 & 4.2661 & 29.2487 \\  & FMPGF [20] & 2.2195 & 28.8003 & 3.8605 & 24.0488 & 6.5878 & 20.4174 & 10.4068 & 17.8075 \\  & VMF [16] & 3.1961 & 30.5483 & 3.8062 & 27.4112 & 5.1515 & 23.2530 & 8.0343 & 19.2650 \\ \hline \multirow{4}{*}{**FMM**} & IDCNN [33] & 0.7684 & 38.3377 & **1.3842** & 35.3368 & **2.0003** & 33.3050 & 2.7118 & **31.1671** \\  & GP & 1.0140 & 34.4532 & 1.3874 & 32.1703 & **1.9043** & 30.1309 & **2.6113** & 28.2127 \\  & DBAIN [25] & 4.8939 & 20.3859 & 9.1438 & 17.5642 & 13.3550 & 15.8917 & 17.5735 & 14.6853 \\  & NARSM [22] & 4.8221 & 20.4132 & 9.2765 & 17.5368 & 13.6620 & 15.8390 & 18.0242 & 14.6200 \\  & GSAPA [17] & 2.6536 & 31.7040 & 2.9988 & 31.0339 & 3.4723 & **30.2954** & 4.2545 & **29.2325** \\  & FMPGF [20] & 2.2267 & 29.4335 & 3.2508 & 27.5745 & 4.3313 & 26.0814 & 5.5097 & 24.7540 \\  & VMF [16] & 3.1275 & 31.6515 & 3.4545 & 30.3364 & 3.8811 & 28.9711 & 4.4963 & 27.3815 \\ \hline \multirow{4}{*}{**FMM**} & IDCNN [33] & **0.3695** & **42.2402** & **0.6119** & **39.4709** & **0.8544** & **37.7503** & **1.0627** & **36.6582** \\  & GP & **0.9586** & **34.9322** & **1.2799** & **32.9630** & **1.7195** & **30.9907** & **2.3007** & 29.0910 \\  & DBAIN [25] & 4.8995 & 20.3836 & 9.1560 & 17.5635 & 13.3640 & 15.8837 & 17.6076 & 14.6789 \\  & NARSM [22] & 4.8599 & 20.3924 & 9.2837 & 17.5376 & 13.6712 & 15.8314 & 18.0630 & 14.6114 \\  & GSAPA [17] & 2.6684 & 31.6861 & 3.0830 & 30.9442 & 3.7525 & 29.8858 & 4.7601 & 28.4749 \\  & FMPGF [20] & 1.5161 & 31.8201 & 1.8178 & 31.0714 & 2.1347 & 30.3423 & 2.4708 & **29.6420** \\  & VMF [16] & 3.1635 & 31.5580 & 3.4802 & 30.3551 & 3.8537 & 29.1292 & 4.3345 & 27.7417 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the average MAE and PSNR values of the filters applied to the test images perturbed by salt-and-pepper, uniform, and correlated noise. The best, second-best, and third-best values are indicated in purple, teal, and brown, respectively.
## 3 Evaluation Methods of the Performance of GSP  
### Common Laboratory Testing Methods  
Common traditional laboratory tests are used for the evaluation of GSP, including: the Marshall test and the rutting test to assess high-temperature performance, splitting test, semi-circular bending test, small beam bending test at \(-10\)\({}^{\circ}\)C to measure low-temperature performance, and Immersion Marshall Residual Stability test to estimate moisture resistance, as shown in Table 10.
## 6 Conclusions and Future Perspectives
### Directions for Future Research  
#### 6.3.1 Reducing Interpolation-Induced Errors  
Several promising directions can be explored to mitigate the negative impacts of interpolation-induced errors. First, improving data quality control should be a priority. Ensuring high-quality and well-distributed input data is essential for accurate interpolation. Preprocessing steps such as outlier detection and filtering are recommended before interpolation to avoid accumulating errors in the interpolated outputs. Second, interpolation methods should be selected based on the physical characteristics of the region and data. While simple methods like nearest-neighbor or linear interpolation are computationally efficient, they may fail to capture the nonlinear dynamics in complex environments such as oceanic frontal zones. More adaptive methods (e.g., spline or kriging) may perform better in such cases. Besides, interpolated outputs should be validated against other measurements to assess their accuracy. Finally, exploring alternative approaches beyond traditional interpolation is encouraged. For instance, DL-based gap-filling methods or hybrid physics-informed models could provide more accurate and robust solutions. One example is the MAESSTRO framework [67], which leverages an MAE to reconstruct cloud-covered SST data. It outperformed cubic radial basis interpolation, highlighting the potential of DL-based methods to preserve spatial structures and reduce interpolation-induced errors.
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 6 Resin Adhesive Systems  
### Composition of the Adhesive Systems  
A dental adhesive system's fundamental parts are as follows:  
Etchant, presently H3 PO4 at a concentration ranging between 30% and 40%. The common H3 PO4 gels are thickened using silica microparticles, while some incorporate alternative thickeners like xanthan gum. To enhance the application precision and guarantee that all gel is rinsed out, a color dye should be integrated. Glycol is frequently used to increase wettability and lessen viscosity. The etchant is constantly washed from the dental surface [3; 96; 145].  
A hydrophilic solution including resin monomers, organic solvent (alcohol or acetone), water, and stabilizers is used as a primer [9]. The hydrophilic groups enhance the dentin wettability, which is located in a humid setting. Primers have a similar role in dentin adhesives as they do in paints [3]. The primer binds to surfaces and generates a binding layer that prepares the surface for the paint, in this case, the bonding resin [3; 9]. Because of its high hydrophilicity and solvent-like properties, HEMA is the popular primer used [33]. Primers are often not washed or cured after being applied to the tooth surface; they are just air-dried [3].  
The bonding resin is a low-viscosity solvent-free (hydrophobic) resin that is placed over the primer and then polymerized [9]. The hydrophobic groups engage and copolymerize with the restorative material, increasing the stability and durability of dentin bonding by sealing the bonded interface against NL [3]. The hydrophobic resin enhances both the primer's polymerization rate and the mechanical assets of both the adhesive and the HL [3; 146]. Adhesive systems with this independent bonding phase produce better results. Most adhesive resins are made up of hydrophobic dimethacrylates such as Bis-GMA, tri-ethylene glycol dimethacrylate (TEGDMA), and urethane dimethacrylates (UDMA) [3; 17].  
Additional components were present in the composition of the adhesive systems including initiators [for a self-cure resin system with an initiator such as benzoyl peroxide, the polymerization process can be triggered using a photo-initiator system consisting of the photosensitizer (e.g., camphorquinone) and an activator (e.g., tertiary amine)], fillers (silica particles), and other ingredients (paraben used as antimicrobials, glutaraldehyde (GA) used as desensitizer, fluoride for prevention of secondary caries, and chlorhexidine (CHX) used to inhibit collagen breakdown) [15; 33].  
All in all, the chemistry of an adhesive system is clarified by the following formula M-R-X, where \"M\" is a methacrylate group that combines with the resin matrix, \"R\" is a spacer, and \"X\" is a functional group for adherence to dental surfaces [107].
# Experimental Study on the Workability and Stability of Steel Slag Self-Compacting Concrete
## 3 Experimental Results and Workability Evaluation
### Dynamic Stability Test of SSCC (Visual Stability Index, VSI)  
The visual stability index method was used to analyze and evaluate the dynamic stability of fresh SSCC. According to the experimental study and analysis, the VSI value of the SSCC with different VPS after adjustment is 0, that is, highly stable. The VSI results of SSCC are shown in Figure 5.  
According to the evaluation criteria of the visual stability index and the performance of the above graphs, we preliminarily determined that the VSI of each group of SSCC was 0, so its dynamic stability  
Figure 4: Effect of steel slag sand on the basic workability of SSCC after mix proportion adjustment. (**a**) slump flow (SF) and T500; (**b**) passing ability (PA) and \(\Delta\)h.  
Figure 5: VSI evaluation of SSCC after adjustment. (**a**) S\({}_{0}\)SSCC (VSI = 0, SF = 690 mm); (**c**) S\({}_{40}\)SSCC (VSI = 0, SF = 705 mm); (**d**) S\({}_{60}\)SSCC (VSI = 0, SF = 710 mm); (**e**) S\({}_{80}\)SCC (VSI = 0, SF = 655 mm); (**f**) S\({}_{100}\)SCC (VSI = 0, SF = 660 mm).  
(segregation resistance performance) met the test requirements. This test method is relatively simple and easy to judge, but most of the test results are determined by the subjective performance of the test personnel, so whether it can meet the requirements of construction still needs further test verification.
## 2 Methods
### Separation of Cells and Plasma from Peripheral Blood  
Blood samples were collected between 8:30 and 11:00 a.m. and transported to the laboratory within an hour for the separation of blood cells and plasma. The separation was performed using Ficoll, in accordance with the manufacturer's protocol (GE Healthcare  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **Control** & **Control (\(\geq\)58 years)** & **AMI** & **CHF** \\ \hline Study participants (\(n\)) & 86 & 46 & 25 & 18 \\ Gender (male/female) & 44/42 & 20/26 & 20/5 & 10/8 \\ Age (median [percellie 25–75]) & 59 (47–65.3) & 63.5 (61–69.4) & 58 (49–71.5) & 69.5 (63.7–77.2) *** \\ NSTELMI/STEM (\(n\)) & & & 5/15 & \\ HFEF/HFmref/HFpEF (\%) & & 16.7/42/79.2 & 88.9/5.6/5.6 \\ Heart rate (beats per minute) & & 70 (65–80) & 85 (84.5–91) \\ Sistolic blood pressure (mm Hg) & & 120 (100–128.75) & 130 (125–137.5) \\ Dastolic blood pressure (mm Hg) & & 70 (60–70) & 750–77.5 \\ LVEF (\%) & & 53.5 (50–60) & 29 (23.75–32.25) \\ Left atria size (mm) & & 38.85 (34.7–41.6) & 45 (42–50) \\ septum (mm) & & & 11.5 (10–13) \\ NT-probeNP (pg/mL) & & & 2049.5 (1094.75–3203.25) \\ creatine kinase (U/L) & & 840 (352.5–1752.75) & \\ Troponin (mg/mL) & & 1903.5 (537.25–5240) & \\ LDH (UI/L) & & 400 (155-593) & \\ Aspartate AT (U/L) & & 149 (64–244) & \\ Alanine AT (UI/L) & & 45.5 (29–75.25) & \\ Aspartate AT/Alanine AT & & 2.61 (1.67–3.78) & \\ eGFR (mL/mm/1.73 m\({}^{2}\)) & & & 75 (57.5–80) \\ Glucose (mg/dL) & & 106 (96–127) & \\ Urea (mg/dL) & & 33.5 (27.5–46.25) & \\ Creatinine (mg/dL) & & 0.82 (0.74–0.98) & \\ Sodium (mEq/L) & & 139 (136–141) & \\ Potassium (mEq/L) & & 4 (3.8–4.4) & \\ \hline \end{tabular}
\begin{tabular}{l c c} \hline AMI: acute myocardial infarction; AT: aminotransferase; CHF: chronic heart failure; eGFR: estimated glomerular filtration rate; HFrEF: heart failure with reduced ejection fraction (<40\%); HFmref: heart failure with mildly reduced ejection fraction (40–49%); HFpEF: heart failure with preserved ejection fraction (>50%); LDH: lactate dehydrogenase; LVEF: left ventricular ejection fraction; \(n\): number of cases; NSTELMI: non-ST-elevation myocardial infarction; STEMI: ST-elevation myocardial infarction; NT-proBNP: N-terminal pro-B-type natriuretic peptide. *** \(p<0.001\) compared to the control group using the Kruskal–Wallis test. \\ \end{tabular}
\end{table}
Table 1: Characteristics and clinical and analytical data of participants. The quantitative variables are expressed as medians (with percentiles 25–75).  
Ficoll-Paque PLUS, from Sigma-Aldrich, Madrid, Spain). Cell counts were determined using a Neubauer counting chamber. Subsequently, aliquots of mononuclear cells, erythrocytes, and plasma were frozen in liquid nitrogen and stored at \(-80\)\({}^{\circ}\)C until they were needed to determine the amines.