esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### System Development
#### 3.3.1 KG Construction  
As shown in Table 1, the KG construction in the Multi-Examiner system takes [PERSON]'s taxonomy as its theoretical foundation, achieving deep integration of educational theory and technical architecture through systematic knowledge representation structures. At the knowledge representation level, the research develops based on two dimensions of [PERSON]'s taxonomy: the knowledge dimension and the cognitive process dimension. The knowledge dimension is reflected through core attributes of entities, with each knowledge point entity containing detailed knowledge descriptions and cognitive type annotations. The knowledge description attribute not only provides basic definitions and application scopes of entities but also, more importantly, structures knowledge content based on [PERSON]'s taxonomy framework. The cognitive type attribute strictly follows [PERSON]'s four-level knowledge classification: factual knowledge (e.g., professional terminology, technical details), conceptual knowledge (e.g., principles, method classifications), procedural knowledge (e.g.,  
Figure 2: System architecture processes.  
operational procedures, problem solving), and metacognitive knowledge (e.g., learning strategies, cognitive monitoring). Specifically, the design is based on the K-12 Computer Science Standards [58].  
In the cognitive process dimension, the knowledge graph implements support for different cognitive levels through relationship type design. For example, the Contains relationship primarily serves knowledge expression at the remembering and understanding levels, supporting the cultivation of basic cognitive abilities through explicit concept hierarchies. The Belongs to relationship focuses on supporting cognitive processes at the application and analysis levels, helping learners construct knowledge classification systems. The Prerequisite relationship plays an important role at the evaluation level, promoting critical thinking development by revealing knowledge dependencies. The Related relationship mainly serves the creation level, supporting innovative thinking through knowledge associations. This relationship design based on cognitive theory ensures that the knowledge graph can provide theoretical guidance and knowledge support for generating questions at different cognitive levels.  
Through this systematic theoretical integration, the knowledge graph not only achieves structured knowledge representation but also, more importantly, constructs a knowledge framework supporting cognitive development. When the system needs to generate questions at specific cognitive levels, it can conduct knowledge retrieval and reasoning based on corresponding entity attributes and relationship types, thereby ensuring that generated questions both meet knowledge content requirements and accurately match target cognitive levels.
## 3 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\omega_{i}\) & & \multicolumn{8}{c}{**Effect of Strain Energy on the Natural Frequency [Hz]**} \\ \cline{3-10} \(\omega_{i}\) & & \multicolumn{2}{c}{_L/Ih = 10_} & \multicolumn{2}{c}{_L/Ih = 12_} & \multicolumn{2}{c}{_L/Ih = 14_} & \multicolumn{2}{c}{_L/Ih = 16_} & \multicolumn{2}{c}{_L/Ih = 20_} & \multicolumn{2}{c}{_L/Ih = 22_} & \multicolumn{2}{c}{_L/Ih = 24_} \\ \hline
1 & TMM & 3665.7 & 2728.6 & 2138.8 & 1737.3 & 1448.2 & 1230.8 & 1062.0 & 927.61 \\  & C\({}_{f,a}\) & 0.0493 & 0.0780 & 0.1102 & 0.1439 & 0.1777 & 0.2104 & 0.2415 & 0.2706 \\  & C\({}_{f,b}\) & 0.5957 & 0.5187 & 0.4562 & 0.4062 & 0.3665 & 0.3350 & 0.3102 & 0.2907 \\  & C\({}_{c}\) & 0.3550 & 0.4033 & 0.4336 & 0.4499 & 0.4558 & 0.4545 & 0.4482 & 0.4386 \\  & Equation (36) & 3665.8 & 2728.6 & 2138.8 & 1737.4 & 1448.2 & 1230.8 & 1062.1 & 927.63 \\ \hline
2 & TMM & 12,209.0 & 8709.6 & 6580.6 & 5183.5 & 4212.9 & 3508.2 & 2978.1 & 2567.7 \\  & C\({}_{f,a}\) & 0.0211 & 0.0290 & 0.0395 & 0.0521 & 0.0664 & 0.0822 & 0.0990 & 0.1166 \\  & C\({}_{f,b}\) & 0.8544 & 0.8104 & 0.7667 & 0.7246 & 0.6851 & 0.6485 & 0.6150 & 0.5846 \\  & C\({}_{c}\) & 0.1245 & 0.1606 & 0.1938 & 0.2233 & 0.2485 & 0.2693 & 0.2860 & 0.2988 \\  & Equation (36) & 12,209.1 & 8709.6 & 6580.6 & 5183.5 & 4213.0 & 3508.3 & 2978.2 & 2567.7 \\ \hline
3 & TMM & 24,878.8 & 18,625.3 & 13,901.8 & 10,822.3 & 8699.2 & 7170.5 & 6030.6 & 5156.2 \\  & C\({}_{f,a}\) & 0.5580 & 0.0142 & 0.0195 & 0.0262 & 0.0343 & 0.0435 & 0.0536 & 0.0645 \\  & C\({}_{f,b}\) & 0.0014 & 0.8981 & 0.8702 & 0.8417 & 0.8132 & 0.7852 & 0.7581 & 0.7321 \\  & C\({}_{c}\) & 0.4406 & 0.0877 & 0.1103 & 0.1321 & 0.1525 & 0.1713 & 0.1883 & 0.2033 \\  & Equation (36) & 24,878.9 & 18,625.3 & 13,901.8 & 10,822.3 & 86 Based on the results computed in Tables 5-12, the effect of variations in the normal and shear strain energies on the natural frequencies of such beams, as well as the effect of change in axial, bending, and shear strain energies on the natural frequencies of three layered beams with different end conditions, are illustrated in Figures 4-7. In the dynamic behaviors of such beams, as shown in Figures 4-7, the bending modes for all boundary conditions are significantly affected by the normal strain energy of the face layer, but the effect of the shear strain energy of the core is also sufficiently large that it cannot be ignored. By observing the variations in the axial, bending, and shear strain energies represented in Figures 4-7, it can be concluded that the axial strain energy has a significant effect, even in the bending-dominated modes.
## 4 Data Descriptions  
### Location and Road Geometry Data Set  
To analyze traffic accidents on an expressway, various types of geometric conditions are required. After reviewing several highways in Seoul, the Naebu Expressway, one of six motorways, was selected as the most appropriate site for this study (Figure 4). Its length is 17.5 km (10.9 mi) (excluding the tunnel), and it is the motorway with the highest severity rate under wet conditions. The available information about the expressway includes geometric characteristics such as the vertical grade, super-elevation, curve radius, road type (tangent or curve), and curve length.
## 4 Simulation Verification of the Method  
First, the effectiveness of the proposed method was verified by simulation. For this purpose, a series of Monte Carlo experiments [33; 43] were conducted, the results of which are discussed later in the article. Each experiment involved computing the variance of the dynamic error signal 100,000 times, for a vector \(x(i)\) consisting of \(N\) samples taken at time intervals \(T_{s}=\frac{1}{f_{s}}\). The initial phase of the processed signal \(s(t)\) was randomly selected for each iteration. The actual variance \(\sigma_{s}^{2}\) of the dynamic error signal was determined based on Equation (13), taking into account the dependencies described by Equations (7) and (19), while the estimated variance \(\sigma_{e}^{2}\) was determined based on Equation (28). For the obtained results, the relative error in estimating the variance was determined:  
\[\delta=\frac{\sigma_{e}^{2}-\sigma_{s}^{2}}{\sigma_{s}^{2}}\cdot 100\%, \tag{34}\]  
along with statistical parameters of this value: the mean \(\overline{\delta}\), standard deviation \(\sigma_{\delta}\), and expanded uncertainty \(U_{\delta}\) at a 95% confidence level [17]. The effectiveness of the proposed method for the experimental conditions was assessed based on these parameters.  
During the experiments, the measurement chain converting the physical quantity \(s(t)\) into its voltage representation \(y(t)\) and then into its discrete form \(x(i)\), which is the input for the D/D processing algorithm, was analyzed. The subsequent \(N\) values of \(x(i)\) were taken with a sampling frequency \(f_{s}=48\) kHz. The fragment converting \(s(t)\) to \(y(t)\) was characterized by the transfer function \(G_{y}(j\omega)\), assuming the following:  
\[\hat{G_{y}}\big{(}j\omega\big{)}=1, \tag{35}\] \[\hat{G_{y}}\big{(}j\omega\big{)}=\frac{1}{1+j\frac{\omega}{2\pi f_ {c}}}=\frac{1}{\frac{\omega^{2}}{4\pi^{2}f_{c}^{2}}+1}-j\frac{\omega}{2\pi f_{ c}\left(\frac{\omega^{2}}{4\pi^{2}f_{c}^{2}}+1\right)}, \tag{36}\]  
where \(f_{c}=320\) kHz is the cutoff frequency of the analog processing part (A/A). Depending on the experimental conditions, the A/D converter introduced a quantization error to \(x(i)\), and the measured quantity \(s(t)\) was disturbed by a white noise signal with constant power spectral density and normally distributed values [44; 45].  
The experiments were performed using GNU Octave version 9.4.0 [46]. Equation (20) was implemented using the program's built-in FFT function. The source code, which allows reproducing all experiments, is publicly available in the repository [47].
## 3 Stationary Localization Algorithms
### Distributed Localization Algorithms  
In this section, seven distributed localization schemes are summarized: (1) Node Discovery and Localization Protocol (NDLP), (2) Large-Scale Hierarchical Localization (LSHL) approach, (3) Reactive Localization Algorithm (RLA), (4) Underwater Positioning Scheme (UPS), (5) Underwater Sensor Positioning (USP), (6) Localization Scheme for Large Scale underwater networks (LSLS) and (7) Ray Bending based Localization (RBL).
# The Progress in Treatment of \(\beta\)-Thalassemia and Research on Animal Models of the Disease
## 3 Treatment Approaches for \(\beta\)-Thalassemia
### Reduction of Ineffective Erythropiesis  
In \(\beta\)-thalssemia, the \(\beta\)-globin gene can exhibit either partial expression (\(\beta^{+}\)) or complete repression (\(\beta^{0}\)), leading to a relative excess of \(\alpha\)-globin peptide chains. This imbalance results in the formation of unstable \(\alpha\)-globin tetramers due to the self-association of the excess \(\alpha\)-globin chains [11]. Furthermore, free \(\alpha\)-globin is prone to oxidative aggregation, which causes inclusions to form and accumulate on the red blood cell membrane. This accumulation diminishes the deformability of red blood cells, leading to membrane damage and a consequent reduction in their lifespan. Consequently, a significant number of ineffective red blood cells are produced, ultimately resulting in severe hemolytic anemia [12].  
Ineffective erythropoiesis is a critical pathophysiological mechanism that contributes to chronic anemia and complex morbidity in patients with \(\beta\)-thalssemia. Consequently, numerous agents aimed at reducing ineffective erythropoiesis are currently under investigation. Among the commonly used clinical medications are hydroxyurea, decidabine, thalidomide, and histone deacetylase inhibitors [13; 14; 15]. These drugs induce \(\gamma\)-globin gene expression, which inhibits the aggregation of \(\beta^{5}\)-globin. As a result, they improve the \(\alpha/\beta\)-globin imbalance and alleviate elevated \(\alpha\)-globin levels in patients, ultimately mitigating the clinical phenotype of \(\beta\)-hemoglobinopathies. Furthermore, recent studies suggest that Luspatercept, a clinically utilized drug, can reduce the transfusion requirements in patients by promoting late-stage erythroid maturation, increasing the hemoglobin levels, and mitigating iron overload [16; 17].  
Furthermore, a deficiency of ATP in red blood cells leads to ineffective erythropoopoiesis. One of the key rate-limiting enzymes in the final stage of glycolysis is pyruvate kinase in red blood cells (PK-R). Mitapivat (AG-348) constitutes a small-molecule allosteric activator of PK-R. By enhancing the enzyme's affinity for its substrates, this compound restores the activity of the glycolytic pathway in patients with PK-R deficiency [18; 19; 20]. Research has shown that Mitapivat reduces oxidative stress, increases hemoglobin levels, and alleviates symptoms of anemia and hepatic iron overload [21]. In addition to Mitapivat, Etavopivat (FT-4202) is another promising oral PK-R activator. Studies indicate that Etavopivat effectively lowers the levels of 2,3-bisphosphoglycerate while increasing ATP levels. This dual action improves the oxygen affinity of hemoglobin, suggesting its potential as a therapeutic option for managing \(\beta\)-thalassemia [22].
## 2 Method
### Search Strategy  
The search started in two multidisciplinary databases most likely to yield the largest number of relevant papers: Web of Science and PubMed/Medline. Subsequently, further investigations to collect grey literature were performed using two databases that collated dissertations, theses and unpublished material: ProQuest and BASE. The key terms in the search string were established based on the review question. The terms were combined using Boolean operators and wildcards to gather all relevant studies (sensitivity) and to minimise irrelevant researches (specificity) [31]. The use of the Boolean operator \"OR\" for synonyms and the application of the wildcard asterisk \"*\" increased the number of similar results, whilst the Boolean operator \"AND\" was used to improve the sensitivity of the search. Moreover, the sparing use of the Boolean operator \"NOT\" only for the title research enhanced the specificity of the search considerably.  
Search string: (occurrence OR presence OR level * OR distribution OR determination) AND (pharmaceutical * OR antibiotic * OR antimicrobial *) AND (wastewater OR WW OR (\"waste water\" OR \"waste waters\") OR effluent * OR influent * OR sewage *) AND (\"anti * resistance\") AND (\"wastewater treatment plant \"*\" OR WWTP *) NOT ti(review) NOT ti(gene *) NOT ti(resistant) NOT ti(dairy OR swine)  
(Supplementary Material, Table S3 for the strings used for each database) A clarification may be required regarding the use of the Boolean operator \"NOT\" in this context. A preliminary scan of the literature revealed that research of PRs in water environments could be approximately subdivided into three study areas: one concerning the presence of antimicrobials in water bodies, another dealing with removal solutions and the third investigating resistant organisms and related genes in aquatic environments. Therefore, the Boolean operator \"NOT\" was used to exclude all the literature belonging to the third research area. Furthermore, the use of this operator enabled the rejection of review papers and all studies that make use of dairy and swine effluents that are commonly studied separately from municipal or hospital WW. Regarding the restriction on articles about removal treatments, a cautious approach was followed since these studies often analyse also the presence of PRs.
# Design and Modeling of a Parent Big STAR Robot Platform That Carries a Child RSTAR
## 3 Kinematic and Dynamic Analysis
### Working Range
#### 3.4.2 BSTAR as a Charging Point for Scanning Operations  
If the robots' mission is to scan/scout a specific area, the scanning area can be substantially increased if the BSTAR, which can carry up to three RSTARs, acts as a mobile charger for the smaller robots. Using BSTAR's battery (which can charge an RSTAR's battery 20 times), the RSTAR robots can travel a total cumulative distance of 105 km. Assuming that scouting takes place in an open area implementing a spiral path and the scanning range is 2 m on either side (the distance between two lines of the spiral is \(h=4\) m), the area the robots can scan, assuming that the BSTAR is at the center of the spiral, can be calculated.  
The length of the spiral, from point \(S_{i}\) to point \(S_{i+1}\) is:  
\[\begin{array}{rl}L_{Si}(\varphi_{i},\varphi_{i+1})=\frac{h}{4\pi}&\left( \varphi_{i+1}\sqrt{\varphi_{i+1}{}^{2}+1}+\ln\!\left(\varphi_{i+1}+\sqrt{ \varphi_{i+1}{}^{2}+1}\right)\right)\\ &-\frac{h}{4\pi}\!\left(\varphi_{i}\sqrt{\varphi_{i}{}^{2}+1}+\ln\!\left( \varphi_{i}+\sqrt{\varphi_{i}{}^{2}+1}\right)\right)\end{array} \tag{14}\]  
where \(L_{Si}\) is the spiral length between points \(S_{i}(\varphi_{i})\) and \(S_{i+1}(\varphi_{i+1})\), \(h\) is the distance between two consecutive spiral lines (\(h=4\) m in our example) and \(\varphi_{i}\) is the angular position of point \(i\) (see Figure 14). Starting from \(S_{0}\), the RSTAR travels a distance \(L_{S0}\) to point \(S_{1}\) and returns a radial distance \(R_{I}\) to \(S_{0}\) to recharge its battery on BSTAR. In the second run after recharging, the robot travels back a distance of \(R_{I}\) to \(S_{1}\), rotates a distance \(L_{S1}\) along the spiral and returns a radial distance \(R_{2}\) back to BSTAR). The length of the radius \(R_{i}\) is:  
\[R_{i}=\frac{\varphi_{i}}{2\pi}h. \tag{15}\]  
Figure 14: The area that the robots can scout, where the BSTAR acts as a recharging platform for the RSTAR robots. S0 is the recharging point, Si is the stop before returning to recharge.  
The maximum distance the RSTAR can travel without recharging \(d_{R}\) is equal to the sum of the distance travelled along the spiral plus the radial distance back and forth:  
\[d_{R}=L_{Si}(\varphi_{i},\varphi_{i+1})+\frac{\varphi_{i}+\varphi_{i+1}}{2\pi}h. \tag{16}\]  
Given that the range of the RSTAR \(d_{R}\) and the location of the previous stop \(S_{i}(\varphi_{i+1})\) are known, Equation (16) must be numerically solved to calculate the location of the next stop \(S_{i+1}(\varphi_{i+1})\). Since the RSTAR can be recharged 20 times using the BSTAR battery, the total area that can be explored is 0.375 km\({}^{2}\). Note that as the area and the radii increase, some of the travelling distance is lost on the return trip to the previous stop and back to the charging base. The ratio of the scanned distance to the total distance travelled is 90.6%, 86.7% and 81.4% respectively for 10, 20 and 40 recharging times.
## 1 Introduction
By bridging the divide between theoretical research and practical implementation, this research enhances predictability and, consequently, the feasibility of employing UHPC in future construction projects. Predicting the strength of UHPC is a complex task that involves multiple variables and intricate nonlinear relationships [9]. Traditional predictive approaches, such as linear regression and empirical equations, often rely on simplified assumptions and linear processes, making them inadequate for accurately capturing and describing the dynamic interactions within these complex nonlinear systems [10]. The complexity further increases when considering projections of carbon emissions [11]. To address these limitations and enhance overall accuracy, the arithmetic optimization algorithm (AOA) was introduced. AOA is a novel optimization technique that improves both global and local search capabilities of the solution space through straightforward yet effective arithmetic operations [12]. This method is inspired by basic arithmetic principles in nature, employing multiplication and division operations to explore various regions of the solution space and refining the search direction and step size using addition and subtraction operations. This approach aims to identify hyperparameter configurations that minimize network errors. By integrating AOA into the hyperparameter optimization process, the search for optimal hyperparameters can be both automated and expedited. This approach enhances the likelihood of identifying superior hyperparameter combinations, as AOA effectively balances exploration (global search) and development (local search). This balance ensures that the algorithm avoids local optima and progresses towards a global optimal solution [13]. Furthermore, employing AOA reduces the dependence on manual expertise, thereby making the model training process more efficient and automated [14]. This study introduces a novel approach in materials engineering by leveraging the vector regression (SVR) model for predicting the compressive strength of ultra-high-performance concrete (UHPC). The integration of AOA with SVR significantly improves both prediction accuracy and computational efficiency, setting a new benchmark for predictive modeling in material science. The systematic analysis of key factors and their correlations with prediction results provides valuable insights, paving the way for the broader application of optimized renewable concrete in sustainable building environments. By demonstrating the superior performance of the AOA-SVR model, this research contributes to the development of next-generation construction materials with enhanced performance characteristics. The study's findings have far-reaching implications for advancing the field of sustainable materials engineering, addressing global challenges related to resource efficiency and environmental sustainability, and pushing the boundaries of high-performance building materials.
## 3 Problem Solution
### Multiobjective Large Neighborhood Search
#### Removal Operators  
In this section, five different removal operators are described for selecting the customers which will be removed from their routes. Most operators are adapted from [PERSON] and [PERSON] (2006) and [PERSON] et al. (2017), while a new one (distant and waiting-time removal) is presented. From all removal operators, we manage to select \(s\) customers and remove them from their routes.  
Route Removal  
The specific operator removes an entire route from the solution. In each iteration, the route is randomly selected and \"destroyed\" from the solution. In a later phase, and since all customers must be served, the customers of the \"destroyed\" route are reinserted according to the insertion operator.  
Random Customers Removal  
In this operator, a specific number of customers (s) that will be removed from the existing solution is initially determined. Consequently, an empty list of size \(s\) is created and filled with customers who are randomly selected. The random nature of this operator increases the diversity, as well as the searching space. As shown in Figure 2, three customers are randomly selected (\(s=3\)) and removed from the route that each one belongs.  
Distant Customers Removal  
In this operator, as with the case of random customers removal, the number of customers (s) that will be removed from the solution is determined. Consequently, for each customer k, we calculate the metric \(c_{k}\ =\ d_{ik}\ +\ d_{kj}\), where \(i\) is the previous delivered customer and \(j\) is the next delivered customer, according to \(k\), while d indicates the distance between two customers. We then sort all customers according to the metric ck and select the s \"worst\" customers (highest \(c_{k}\)).  
Waiting-Time Removal  
For each customer to be served, the metric \(wt\) calculates the difference between the start of the time window and the time of arrival. More specifically, the metric \(wt\) for customer \(i\) is calculated by \(wt_{i}\ =\ \max\{0,\ e_{i}\ -\ t_{i}\}\), where \(t_{i}\) is the time of arrival at customer \(i\). Consequently, customers are sorted according to metric \(wt_{k}\) and \(s\) number of customers that have the highest waiting times (\(wt_{i}\)) are selected.  
This operator combines in a single metric both the distant customers removal and the waiting-time removal operators, by assigning weights to both metrics. Similar to the previous operators, a specific number of customers (\(\mathbf{s}\)) with the worst metrics is selected.  
Regardless of the removal operator, the initial solution is presented as an \(I\) list containing all customers. The sequence of the elements of I determines the order of customer visitations. When a constraint is violated (capacity or time window), then a new route begins, as shown in Figure 1(a). In addition, after the removal operator is applied, the customers that no longer belong to the route are saved in another list \(L\), as shown in Figure 1(b).