esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Datasets
#### 3.2.2 Exam Questions and Processing  
Table 1 shows the distribution of the original exam questions, a wide range of open-ended (essay-style) questions spanned across different cognitive levels according to [PERSON]'s taxonomy [8] and categorized by question design into: single-step questions--generally require students to address one primary concept or task; and multi-step questions--involve multiple, interlinked sub-tasks or a sequence of reasoning steps. These items reflect theory-oriented or application-oriented course content. Instructors contributed an initial set of questions, particularly single-step ones, which were further expanded through generative strategies. Using a fine-tuned LLM (detailed in Section 3.4), three variant forms of each original single-step item were created, each emphasizing different alignment aspects of the course materials. A second cycle then added multi-step questions by integrating vector-based and graph-based content, producing two new multi-step variations per original question. These added variations exhibited deliberately asymmetric alignments (one strongly matched vector-based content but loosely aligned with graph-based materials, and the other the reverse), thus probing how each LLM architecture handles partially overlapping or conflicting retrieval cues.  
Once aggregated, these original and generatively varied items served as inputs for the three tasks executed by each LLM-based agent. In Tasks 2 and 3, the agent itself generates two new questions (an improved question and an entirely new one), effectively increasing the total number of question-based responses well beyond the original items shown in Table 1. After discarding 37 invalid responses that did not meet minimum quality or consistency checks, the final dataset for this study included 3605 valid agent responses (covering Tasks 1, 2, and 3 across all conditions). The final exam-question dataset therefore encompassed both human-curated items and partially augmented versions. This balanced dataset supported robust evaluation of each LLM agent's ability to score, refine, and generate questions. Example questions are given in the Appendix A.2.
# SDN-Based Secure Common Emergency Service for Railway and Road Co-Existence Scenarios
## 6 Validation of the Developed Application to Avoid DDoS
### When a New/Malicious Node Sends Emergency Messages  
When a new node sends emergency messages, the data packet is sent to the controller and secondary servers, as shown in Figure 14. While the new node sends messages within the allowed limit it is served by the secondary server. The secondary server initially handles the new node, monitoring its messaging behavior for a specified duration. In the context of this application, the new node's behavior is observed for one hour. It is important to note that this observation period is merely for demonstration purposes within this application, and the actual duration would depend upon the specific application and its functional requirements. Following the observation period, if the new node's behavior is deemed acceptable, the IP and MAC addresses of the new node are incorporated into the ONOS application/database. This action authenticates the new host.  
Suppose the new node is malicious and it starts flooding the system by sending emergency messages, in this case, the ONOS application marks the new node as malicious, drops all the packets, and blocks it, as shown in Figure 15.
# Effectiveness of LRB in Curved Bridge Isolation:
###### Abstract  
Lead Rubber Bearings (LRBs) represent one of the most widely employed devices for the seismic protection of structures. However, the effectiveness of the same in the case of curved bridges has not been judged well because of the complexity involved in curved bridges, especially in controlling torsional moments. This study investigates the performance of an LRB-isolated horizontally curved continuous bridge under various seismic loadings. The effectiveness of LRBs on the bridge response control was determined by considering various aspects, such as the changes in ground motion characteristics, multidirectional effects, the degree of seismic motion, and the variation of incident angles. Three recorded ground motions were considered in this study, representing historical earthquakes with near-field, far-field, and forward directivity effects. The effectiveness of the bi-directional behavior considering the interaction effect of the bearing and pier was also studied. The finite element method was adopted. A sensitivity study of the bridge response related to the bearing design parameters was carried out for the considered ground motions. The importance of non-linearity and critical design parameters of LRBs were assessed. It was found that LRBs resulted in a significant increase in deck displacement for Turkey ground motion, which might be due to the forward directivity effect. The bi-directional effect is crucial for the curved bridge as it enhances the displacement significantly compared to uni-directional motion.  
curved bridge; lead rubber bearing; bi-directional effect Article
## 3 Results and Discussion  
### Main Components of Steel Slag Leading Solution  
Since it is difficult to distinguish Fe compounds from Cr compounds in suspended metal solution after alkaline-fusion, the Fe compound and Cr compound in converter slag and EAF slag used in this experiment were called Fe\({}_{2}\)O\({}_{3}\) or Cr\({}_{2}\)O\({}_{3}\). According to the data in Table 2, the converter slag used in this experiment contained CaO, Fe\({}_{2}\)O\({}_{3}\), P\({}_{2}\)O\({}_{5}\), and V\({}_{2}\)O\({}_{5}\), higher than that in EAF slag. In particular, for CaO, Fe\({}_{2}\)O\({}_{3}\), and P\({}_{2}\)O\({}_{5}\), the existing quantity in converter slag was much higher than that in EAF slag. Contents of SiO, MnO, Al\({}_{2}\)O\({}_{3}\), Cr\({}_{2}\)O\({}_{3}\), and TiO\({}_{2}\) in converter slag were lower than that in EAF slag. Especially for SiO\({}_{2}\) and Cr\({}_{2}\)O\({}_{3}\), the existing quantity in converter slag was much lower than that in EAF slag. The presence of MgO in converter slag was not significantly different from that in EAF slag.  
Table 6 lists the concentrations of main elements in converter slag and EAF slag leaching solutions. Concentration of Total Ca in converter slag and EAF slag leaching solutions were relatively high (5.63 and 2.22 mg/L, respectively), the reason of which was that in these two kinds of leaching solutions, the content of CaO was relatively high, reaching to 46.60 and 27.89 wt%, respectively. Content of Fe\({}_{2}\)O\({}_{3}\) in converter slag was also relatively high (31.52 wt%). However, in converter slag leaching solution, the concentrationof Total Fe was very low, only 0.8 \(\times\) 10\({}^{-3}\) mg/L, the reason for which was the different solubility of different elements in water.  
In order to discuss the influence of converter slag and EAF slag leaching solutions on the water environment, the concentrations of main elements in converter slag and EAF slag leaching solutions were compared with the emission standard of the national integrated wastewater discharge standard (GB 8978-1996) and the national sanitary standard of drinking water for life (GB 5749-2006). After comparing the concentration of main elements in the leaching solutions of converter slag and electric arc furnace slag in Table 6 with the national integrated wastewater discharge standard (GB 8978-1996) and the national sanitary standard of drinking water for life (GB 5749-2006), it was found that concentrations of main elements in these two leaching solutions were lower than the two standards.  
In this research, a steel slag leaching experiment was carried out using pH adjusted ultrapure water, which differs from the natural water in that it contains a variety of substances.  
The experimental design might be too simple to elucidate the leaching phenomenon of steel slag in a natural aquatic environment. To prevent steel slag from polluting the natural aquatic environment, additional leaching experiments with natural water are required.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 6 Biofilm Formation Enhances Antibiotic Resistance
### The Barrier of Extracellular Polysaccharide  
Research has shown that exopolysaccharides play a crucial role in the biofilm matrix, providing structural stability to biofilms and shielding bacteria from antimicrobial substances [89; 90]. Exopolysaccharides seen in biofilms have been associated with increased antibiotic resistance. They achieve this by obstructing the entry of antibiotics and forming a barrier that prevents phagocytosis by the host's immune cells [49]. Exopolysaccharides play a dual role in biofilm formation by providing structural support and limiting the entry of antibiotics, therefore contributing to antibiotic resistance [91]. Gradually introducing antibiotics may allow for adaptive phenotypic responses, potentially enhancing tolerance [24; 88; 92].
# Measurement of Unit Weight of Dry Sand Using Piezoelectric Sensor
## 3 Results of EMI Sensing  
Table 2 summarizes all testing cases for the three sand types in terms of the resonant frequency (peak frequency) and the corresponding value of conductance at resonant frequency (peak conductance). The initial dry unit weights of the three sand types were determined to be 14.45, 15.74, and 16.83 kN/m\({}^{3}\) without surcharge. The unit weights of sand after application of different surcharge levels were determined via consideration of the settled surface. The calculated dry unit weights of the three sand types are compared in Table 2 and shown in Figure 7. With an increase in surcharge levels, the unit weight of sand was found to have gradually increased. The variation in unit weight was most dominant for small-sized sand grains, its value ranging from 14.45-17.49 kN/m\({}^{3}\)- as shown in Figure 5.
## 3 Results and Discussion
### Effective Boundary Slip
#### 3.3.1 Superoleophilic Surfaces
We assumed that each zone was divided into two areas: (i) the no-slip area \(S^{\varepsilon}\), corresponding to a cross-section of the pitch of the rough surface, containing a circle of radius \(\alpha\) centered in the origin, which is relative to the area of \(S^{\varepsilon}\); and (ii) the slip area, corresponding to a cross-section of the slippery part of fluid flow. We assume that the average no-slip area \(S^{\varepsilon}\) contains a circle of radius \(a_{\varepsilon}\alpha\), via \(a_{\varepsilon}\alpha=\frac{1}{m}\sum_{i-1}^{m}\alpha_{i}\), where \(\alpha_{\varepsilon}\) is a numerical prefactor. The no-slip area \(S^{\varepsilon}\) is defined by:  
\[S^{\varepsilon}=\left( \begin{array}{c}\varepsilon k_{1}+\frac{\varepsilon }{2}\\ \varepsilon k_{2}+\frac{\varepsilon}{2}\end{array} \right)+a_{\varepsilon}S, \tag{12}\]  
where \(S^{\varepsilon}\) is expected to depend on the underlying geometry of the surface, and \((\varepsilon k_{1}+\frac{\varepsilon}{2},\varepsilon k_{2}+\frac{ \varepsilon}{2})\) is the center of the average no-slip area \(S^{\varepsilon}\). The slip length can be estimated by the solid friction \(\Phi_{s}\) between solid and liquid, which is defined by the relative area of the no-slip area \(S^{\varepsilon}\) in the square of \(\left(\varepsilon k,\ \varepsilon k+1\right)^{2}\). It can be found that solid friction at the no-slip area \(S^{\varepsilon}\) is \(\Phi_{s}\)-\(\infty\). The expression of solid friction \(\Phi_{s}\) is given by [PERSON] et al. [31]:  
\[\Phi_{s}=\Big{(}\frac{a_{\varepsilon}}{\varepsilon}\Big{)}^{2}|S^{ \varepsilon}|,\]  
where \(|S^{\varepsilon}|\) is the area of the average no-slip area \(S^{\varepsilon}\). Since \(\underset{\varepsilon=0}{\lim}\Phi_{s}=0\), the slip length can be obtained by:  
\[b=\frac{\alpha}{\sqrt{\phi_{s}}}=\frac{\alpha}{\big{(}\frac{a_{\varepsilon}}{ \varepsilon}\big{)}\sqrt{|S^{\varepsilon}|}}=\frac{\alpha}{a_{\varepsilon}} \Bigg{(}\frac{\varepsilon}{\sqrt{|S^{\varepsilon}|}}\Bigg{)}, \tag{13}\]  
Figure 8: Mechanism of effect of spacing roughness on effective slip length on superoleophilic and superoleophobic surfaces with varied \(Rsm\) roughness. The cross-section of the surface was obtained by cutting at a height of reference surface \(h=R_{q}\) in the horizontal direction. (**a**) \(S\) is the no-slip area; and (**b**) \(S^{\varepsilon}\) is the average no-slip area. where \(\frac{\kappa}{a_{e}}\) is the constant and remains unchanged. Thus, the effect of spacing roughness on the effective slip length depends on the value of \(\frac{\varepsilon}{\sqrt{|S^{i}|}}\). With increased \(Rsm\), the values of \(\frac{\varepsilon}{\sqrt{|S^{i}|}}\) remain constant, therefore the results of effective slip length remain constant.
# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 2 Method
### The Proposed RFKNN-MPA Methodology  
A combined model is useful when the predictions of different ensemble members do not match up or when errors of the different members are not closely linked. The primary concerns when using a hybrid model are the construction or selection of component models and the manner of combining their outputs. A hybrid model's ensemble members should be chosen so their predictions do not match up [50].  
A hybrid ensemble network combines the results of several different models to make a single prediction. The subcomponents of the hybrid model work in parallel, but each component is self-sufficient in making predictions. This can improve system performance. The random forest and k-nearest neighbors hybrid algorithm with the marine predators optimization algorithm (MPA) is a strategy that improves the accuracy of predictions by combining the strengths of both RF and KNN as the MPA algorithm.  
The method that combines random forest (RF) and k-nearest neighbors (KNN) with MPA is a three-stage procedure that optimizes data. Figure 1 depicts the algorithm's stages.  
In the first stage, predictions are made using RF and KNN. The original RF and KNN algorithms are trained on the training data and then evaluated on the validation data. In the second stage, the parameters of the RF and KNN models are optimized using the MPA method. In this case, MPA aims to find the set of parameters that minimizes the mean squared error (MSE) between the predicted and actual ore grades. In the third stage, the final prediction is made by combining the predictions from the improved RF and KNN models. The predictions from the RF and KNN models are weighted according to their performance on the validation data. The final prediction is then computed as the sum of the weighted predictions.  
Specifically, the RF regressor produces Pred 1, and the KNN regressor produces Pred 2. These two predictions are then optimized so that the strengths of both regressions can be exploited. An optimization algorithm is used to find the optimal weights for Pred 1 and Pred 2. The optimal weights are then used to compute the final prediction, P. The MPA optimizer employs a population of solutions with two parameters (W1 and W2) that are initialized randomly. Throughout the optimization operation's iterations, these generated solutions evolve towards the promising solution to reach a proper objective value; in our case, the objective function is the final prediction obtained with a minimized MSE error from the corresponding actual regression value. Compared to other algorithms, the hybrid RFKNN-MPA algorithm is superior in several respects, making it an effective tool for both machine learning and data analysis. It uses the Random Forest, KNN, and Marine Predators Optimization Algorithm (MPA) to create more accurate predictions than individual models. In addition to this, it can deal with high-dimensional data, which is a typical obstacle in machine learning. It is also adaptable and can be applied to classification and regression issues.
## 2 Materials and Methods
### Automated Field Spectroscopy System and the Measurements of SIF
Before SIF retrieval, data quality was checked to further reject the data affected by short-term changes in illumination conditions, which was similar to the quality evaluation in [PERSON], et al. [25]. First, those data between 8:00 and 17:00 (sun zenith angle was smaller than \(\approx\)60\({}^{\lx@math@degree}\)) were selected to conduct the next process. Second, quick-changing solar light was considered as the fraction of variation between the corrected irradiance in one plot (E1) and the irradiance in the next plot (E2), i.e., \(|\)E2 \(-\) E1\(|\)/E1. Those acquired data were rejected when their \(|\)E2 \(-\) E1\(|\)/E1 value was above 0.2. Third, the raw data greater than 15,000 DN were also rejected because of the saturation that occurred in HR2000+. The rapid increase in illumination was also assessed using the ratio of canopy radiance (L) and the corresponding solar irradiance (E/\(\pi\)). The data was also rejected when L/(E/\(\pi\)) was greater than 1. Only those data that satisfied all these quality indicators were used to estimate SIF values at 760 nm (F760). The bands covering the range of 755.6-765.6 nm (242 bands) were used to retrieve SIF by means of spectral fitting methods (SFM) [3]. Both reflectance and SIF were assumed to be linear functions of wavelength. Those SIF data were selected only when the fitting algorithm achieved R\({}^{2}\) > 0.99.
## 3 Results and Discussion
### MIMO Decoupling Structure  
The KAYI-shaped \(3\times 1\) port MIMO antenna is designed with band notching elements. The antenna size is compact and has weak coupling between all three antennas. The coupling between the antennas is further reduced by placing the antennas at a distance of \(120^{\circ}\) from each other. Because of the symmetrical circular configuration, the MIMO antennas are sufficiently isolated and less interactive with each other. Figure 9 shows that the S\({}_{21}\), S\({}_{31}\), and S\({}_{23}\) have low mutual coupling between the elements of the antenna. The simulated and measured results are found to be in good comparison with each other.
# Sulfonation Treatment of Polyether-Ether-Ketone for Dental Implant Uses
## 1 Introduction  
A new thermoplastic engineering plastic is polyether-ether-ketone (PEEK) [1]. Its mechanical, chemical, and biological qualities are outstanding [2, 3]. Its main biomedical use is as an orthopedic implant in vivo. PEEK's biomechanical qualities are similar to those of human bones, reducing the danger of bone resorption and osteolysis induced by implant stress shearing. Medical PEEK is subject to fundamental performance criteria. The human body is tough and caustic [3, 4].  
Body fluids include several electrolyte ions and complex chemical substances. Chemical erosion is inevitable if metal implants are exposed in human fluids for a long period of time [5, 6]. Therefore, orthopedic implants should resist rusting. The majority of corrosion is electrochemical. Electrochemical changes cause metal orthopedic implant pitting corrosion in humans. Function-based implant criteria are also important [7]. As sterile orthopedic implants, PEEK is stable, radiation-resistant, and mature. Ethylene oxide, gamma rays, and hot steam disinfect it [8].  
PEEK's linear aromatic backbone contains functional ether and ketone groups [9]. PEEK is biocompatible, compatible with reinforcing materials, and has low solubility andwater absorption. Its corrosion resistance, fatigue resistance, high-temperature stability, sterilization stability, radiolucent, and ease of machinability are improved by these qualities.  
Fixed crowns, fixed and detachable bridges, removable dentures, implant abutments, dental implants, and more are made from PEEK. Because of its better elastic and esthetic modulus, which are similar to those of human bones, PEEK has been regarded as a good contender to replace titanium in dental implants [10].  
Ion-exchangeable charged groups are introduced to PEEK polymer chains with sulfuric acid to improve hydrophilicity. Sulfonation promotes polymer hydrophilicity and cation transport [11]. Acid etching modifies surface topography and creates a microinterlocking structure [12; 13]. Etching using 98% sulfuric acid creates a porous surface where adhesives can penetrate, improving bond strength [14]. There is unanimity that alumina sandblasting and sulfuric acid etching improve PEEK adhesion [15].  
Sulfonated PEEK may be prepared by understanding the optimal sulfonation duration and post-treatment procedure. This work examined the characteristics of sulfonated PEEK with five sulfonation periods (5, 10, 30, 60, and 120 s) and three post-treatment procedures (NaOH immersion, acetone washing, and H\({}_{2}\)O hydrothermal treatment). Finding the optimal sulfonation period and post-treatment procedure was the aim.  
We hypothesized that pre-treatment with either mechanical and/or chemical means would result in the possible bonding of composite resin to PEEK. Secondly, as the time of immersion extended, the porous structure increasingly became more evident as well as more sophisticated.
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
## 2 Distributed GEP Algorithm and Its Extensional Applications  
In this section, the GEP is represented as a trace optimization subjecting to fixed quadratic constraints. Then, a one-shot distributed GEP algorithm (DGEP) is proposed. We are seeking the generalized eigenvectors of the symmetric empirical data covariance in distributed settings with one-shot communication.
# Marine Gelatine from Rest Raw Materials
## 3 Production Strategies for Gelatine
### Pre-Treatment and Extraction Strategies
#### Extraction of Gelatine  
After pre-treatment of fish skins, extraction of gelatine with water at various temperatures and time lengths is the universally applied approach for obtaining gelatine. [PERSON] and [PERSON] [17] and [PERSON] and [PERSON] [21] have reported on the various procedures employed for gelatine pre-treatment and extraction. Most commonly, distilled water was used and the temperatures and lengths of extraction show a high variability between different authors. The most often used extraction temperature in various research papers is around 45 \({}^{\circ}\)C, with the time of the extraction varying from 12 to 18 h (or \"overnight\") [43; 48; 51; 54]. Multi-stage extractions and different temperatures have also been reported [63; 64; 65; 32]. [PERSON] and [PERSON] [63] have developed an optimized method for pre-treatment and extraction of gelatine from Pollock skin. They concluded that an extraction temperature of 50 \({}^{\circ}\)C was optimal regarding the extraction yield. Besides from pure water, some authors have reported successful gelatine extraction using mild acidic conditions [66] and also with addition of mixtures of protease inhibitors [53]. Due to the low denaturation temperature of fish collagen, the extraction temperature and time can have a significant influence on the properties of the extracted gelatine, especially on the gel strength. Gel properties of gelatine from clown featherback skin under different extraction temperatures (45, 65 and 85 \({}^{\circ}\)C) and times (6 and 12 h) were investigated by [PERSON] et al. [47]. Their results indicated that, although yield was highest at the highest extraction temperatures, by increasing temperature and prolonging extraction time, band intensity of \(\alpha\)-, \(\beta\)- and \(\gamma\)-chains decreased in the extracted gelatine. Similar findings were reported by [PERSON] et al. [42], where temperature, extraction time, and concentration of acid during pre-treatment were used to assemble a central composite rotational design (CCRD) in order to elucidate its effect on gelatine viscosity. The strong influence of pre-treatment and extraction conditions on the yield and properties of fish gelatine need to be taken into consideration in an industrial setting, and usually a compromise between yield, desired properties, and energy efficiency needs to be considered for optimal production.