esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
The HOMO (the outermost orbital containing electrons) is related to the ability to donate electrons, while the LUMO (the lowest unoccupied molecular orbital) represents the ability to accept electrons. These frontier molecular orbitals are crucial in illustrating the electronic properties of a substance and have significant roles in identifying active sites, determining kinetic stability, and influencing the chemical reactivity of molecules. These electronic orbitals are fundamental to the reactivity of chemical compounds. The formation of transition states in reactions occurs through the interaction of the HOMO and LUMO border orbitals of reacting species [38].  
Quantitative structure-property relationship (QSPR) data describe the structural and physicochemical information. Energy values of these orbitals, along with other molecular properties derived from simulations, are presented in Figure 3. for QPhNO\({}_{2}\) and structures with six repeating units, which are representative of the polymer. This approach assumes that the interaction of QPhNO\({}_{2}\) effectively occurs only with those segments closest to it, making the length of the polymer chain less significant if the polymer chain has a few or hundreds of repeating units. Since the QPhNO\({}_{2}\) molecule has a chiral center (see Figure S1), which allows the formation of two enantiomorphic forms, it was not possible to observe significant differences between the values obtained from our simulations (data shown in Supporting Information). This result is important because, in our simulations, QPhNO\({}_{2}\) is flexible, adopting any configuration that allows anchoring and minimizes interaction energy.  
The lipophilicity of substances is linked to the octanol-water partition coefficient (logP), which evaluates the ability of compounds, such as QPhNO\({}_{2}\) and PCL, PLGA, or PMMA polymers, to traverse the intestinal epithelium (logP > 0 indicates lipophilic substances, but logP < 0 indicates hydrophilic substances). The method of [PERSON], [PERSON], and [PERSON] [40] is used to determine log P values [41]. [PERSON]'s Rule of Five [42] stipulates that the logP for drug molecules should be less than 5, based on the observation that the majority of orally absorbed compounds exhibit logP < 5. LogP and PSA can be correlated when assessing potential drugs using the criteria stated by [PERSON] et al. [43], which include log P < 4 and PSA > 75 A2, as can be seen for QPhNO\({}_{2}\)[44].  
Polarizability provides information about electrostatic interactions that arise when an ion or dipole induces a temporary dipole in an adjacent molecule. Those values can be observed in Table 1. While polarizability is defined by the electronic distribution throughout the molecule, the dipole moment depends on the spatial configuration of the molecule. These results emphasize the importance of understanding molecular properties and their implications in drug development and optimization.
## 2 Materials and Methods
### Method Validation  
The consideration of matrix effects is crucial during the development of a method, as they can have an impact on both reproducibility and accuracy. To assess matrix effects, the results obtained from using the developed HND-SBME-HPLC-DAD method for extracting selected steroids spiked at 1, 100, and 10\({}^{4}\) ug L\({}^{-1}\) from ultrapure water were compared to those obtained from groundwater and tap water samples spiked at the exact quantities using the same method. The absolute matrix effect was determined by calculating the following [28]:  
\[\text{Matrix Effect (\%)}=\text{C}_{\text{matrix}}/\text{C}_{\text{ ultrapure water}}\times 100 \tag{1}\]  
where C\({}_{\text{matrix}}\) is the measured concentration of each steroid in the groundwater and tap water sample, and C\({}_{\text{ ultrapure water}}\) is the measured concentration of each steroid in ultrapure water. In particular, when the matrix effect value is closer to 100%, the interference caused by the matrix is minimal.  
The proposed method was validated for accuracy, precision, and robustness. This included testing its linear dynamic range, correlation coefficient, limit of detection (LOD), quantification (LOQ), reproducibility, and recovery for three analytes in ultrapure water and steroids-free urine samples. The method calibration curves were evaluated for linearity in the 0.5-10\({}^{4}\) ug L\({}^{-1}\) range for each analyte. The limits of detection (LOD) and quantification (LOQ) were determined based on three times and ten times the standard deviation of the blank divided by the slope, respectively [29]. Inter- and intra-precision were estimated by measuring the recoveries of selected steroids spiked at three concentrations (1, 100, and 1000 ug L\({}^{-1}\)) in water and steroid-free urine over one and three days, respectively. The method's robustness was evaluated in ultrapure water and steroid-free urine samples (spiked at 100 ug L\({}^{-1}\) for each analyte) by varying four parameters of the HDE-SBME method: pH, extraction time, elution volume, and ultrasonication time. The ranges for these parameters were 6.8-7.2 for pH, 28-32 min for extraction time, 190-210 \(\upmu\)L for elution volume, and 4-6 min for ultrasonication time. Each experiment, including these variation parameters, was triplicated to ensure consistency in the results.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 5 The Proposed Security Scheme
### Surveillance UAV Network Operation Phase  
This phase organizes the connection between the drones and \(CS\) to guarantee the efficiency of rescue operations in the disaster area.
# Computational Characterization of Activities and Learners in a Learning System
## 3 Proposal
### Characterization of Learners and Activities
#### 3.2.1 Definition of a Feature Vector  
In a learning system, a learner model is a resource that stores all the learner's features, such as their preferences or their behavior along the learning process [49]. It is the same for the activity model, which stores all the features of an activity, such as learning style or difficulty level. Both models will be processed by the system and interpreted by the selection engine to perform the corresponding selection at any time. Therefore, these models must be prepared to be correctly read and computed; that is why they are stored as a collection of features in the form of a vector. Each of these features is a measurable variable used to characterize a concrete aspect of an individual or a phenomenon. So, a feature vector will end up being a set of explanatory variables of a phenomenon being observed [50].  
As feature vectors are used to characterize the individuals and make decisions by the corresponding algorithm depending on their values, each of those features must be meaningful, discriminating, independent and, as we mentioned before, measurable, so that they will have an interpretable value for the algorithms, which can be quantitative, such as integer or real, or qualitative, also named categorical, containing a value inside a predefined range [51]. A feature is meaningful if it makes sense to a human observer, that is, if the mathematical representation it assumes is interpretable by a person and if it corresponds to what it actually represents. In this case, the features presented are based on models proposed by researchers to explain the complex learning process. Although a mathematical representation is necessary, the basis on which this representation is developed is solid from the point of view of educational research. Those features must also be discriminating to be correctly differentiated into categories, and so to classify the learner. They must also be independent, which means that their values do not depend on other variables, eliminating redundant information and simplifying the task from a computational point of view.  
In our model the variables are neither binary nor excluding, meaning that they are configured in the form of weights, so that you can have more or less of each feature, and having one feature does not imply no longer having another. These weights have been normalized and all of them have a value in an interval _[0,100]_, where 0 indicates a total absence of that characteristic, and 100 a maximum value for it.
## 3 Integrated Adaptive Security Framework for IoT (IASF-IoT)
### Adaptive Content Authentication  
Content authentication is enhanced using an adaptive hash-based signature scheme, serving as the final layer of security in the IASF-IoT framework. This layer integrates with and complements the previous layers by providing a flexible and scalable method for verifying the authenticity and integrity of data processed through the system. The number of hash function evaluations is dynamically adjusted based on the current security requirements:  
\[k=g(\text{security\_level},\text{computational\_resources}), \tag{8}\]  
where the security level can be influenced by the threat assessments from the AI-driven security-orchestration layer and the computational\(\_\)resources factor considers the constraints identified in the edge-centric security layer. This adaptive approach ensures that authentication strength can be tailored to the specific needs of different IoT devices and network segments. The signature is then generated as  
\[\text{sig}=(R,\sigma,\text{AUTH}(PK)), \tag{9}\]  
where \(R\) is a random value, \(\sigma\) is the actual signature, and \(\text{AUTH}(PK)\) is an authentication path in the hash tree. This scheme complements the blockchain-based identity management by providing an additional layer of verification for individual data points or transactions. The use of hash-based signatures also aligns with the quantum-resistant cryptography integration, offering a future-proof method for content authentication.This study conceptually models the signature scheme using lightweight hash functions such as SHA-256 or SHA3-256, which are widely supported and suitable for integration with quantum-safe signature mechanisms like LMS or XMSS.  
Furthermore, this adaptive authentication can be fine-tuned based on insights gained from the Digital Twin simulations and federated learning models, ensuring that it remains effective and efficient across various IoT deployment scenarios while respecting the privacy-preserving data processing protocols established in earlier layers.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 5 Research Opportunities and Future Directions
### Distributed Antenna System  
The relatively harsh environment in industries (as compared to homes, offices, etc.) demands robust communication solutions. Traditional co-located multi-antenna systems reduce path loss, but they are insufficient in demanding real-world industrial applications where NLOS conditions occur often [189; 190; 37]. URLLC in Industry 4.0 and beyond for cyber-physical systems is a crucial challenge that is anticipated to be served by distributed antenna systems at mmWave bands [191; 192; 193; 194]. The potential of mmWave-over-fiber distributed antenna systems for high-data wireless communication was demonstrated in [191]. Measurements in a controlled anechoic chamber and a realistic environment resembling an Industry 4.0 setting revealed the potential of distributed antenna system for establishing and maintaining mGbps wireless communication while overcoming self-blocking and LOS blockage issues [195; 191].  
The link blockage problem must be carefully addressed in order to transmit mmWave signals through LOS pathways to the required users. This will result in effective coverage and smooth network connectivity for mmWave communication systems [139]. Distributed antenna systems can serve as a potential solution by adding the properties of mmWave communication systems [192; 196].
###### Abstract  
This study investigated the antioxidant, antiaging, and antibacterial properties of _Gracilaria verrucosa_ (GV) based on 95% methanol (GVM), ethanol (GVE), and hot water (GVW) extractions. Antioxidant activity assays revealed the total polyphenol and flavonoid contents were highest in GVM and GVE. The 2,2-diphenyl-l-picryl-hydrazyl-hydrate (DPPH) and 2,2'-Azinobis(3-ethylbenzothizo line-6-sulfonic acid) (ABTS) activities were highest in GVE and GVM. Furthermore, GVE exhibited the highest ferric-redeting antioxidant power (FRAP) value. In comparison, superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) activities were highest in GVM. Collectively, GVE and GVM had stronger antioxidant activities than GVM. Additionally, collagenase, elastase, and tyrosinase inhibition assays showed that GVM exhibited the strongest anti-wrinkle and skin-whitening activities. Liquid chromatography-tandem mass spectrometer (LC-MS/MS) revealed that GVW had the highest 4-hydroxy benzoic acid content, whereas GVE had the highest naringenin and naringin contents. Additionally, GVE exhibited the strongest antimicrobial activity against six foodborne bacteria, with minimum inhibitory and bactericidal concentrations of 0.06-0.3 and 0.1-0.5 \(\upmu\)g/\(\upmu\)L. Correlation analysis of the GV extracts indicated a strong positive relationship between TPC and ABTS, SOD, and CAT activities (r = 0.760-0.982, \(p\) = 0-0.018). Overall, GVE and GVM can be applied to the development of functional agents across diverse industries.  
Gracilaria verrucosa; antioxidant; antiaging; antibacterial; phenolic content +
Footnote †: journal:  
marine organisms such as abalone and crustaceans and are utilized as a primary source of agarose and bioethanol [15; 16]. It is also consumed raw or added to salads in South Korea and used in salads and jelly in France, Malaysia, Vietnam, and Indonesia [16].  
Various edible seaweeds, such as _Sargassum fisiforme_, _Undaria pimmatifida_, _Saccharina japonica_, _Uliva prolifera_, _Perplynura tenera_, and GV, are globally recognized [17]. In 2020, these seaweeds were cultured to approximately 35 million tons globally, with _Gracilaria_ species contributing approximately 14.8% (5.18 million tons) of the total seaweed production, showing a consistent increase in production since 2020 [18]. GV, which thrives in all coastal areas of Korea, has been studied for its carpospore release induction [19], effects of growth conditions (light intensity and temperature) on early growth [20], phylogeny [14], and mineral content [21]. Although previous studies have investigated its antiproliferative [22], skin improvement [23], antioxidant [24], and antimicrobial [25] activities, studies exploring its various biological activities in different extraction solvents are scarce. Consequently, this study investigated the antioxidant, antiaging, and antifungal activities of GV extracts in different extraction solvents (ethanol, methanol, and water), with the findings intended to serve as foundational data for the development of functional products using GV extracts.
## 4 Analysis of the Results
### Qualitative Insights of the Results  
Of the 352 teachers who responded to the pre-course survey, 174 answered the question \"Here you can write down what you expect from the course\". Most participants expect that the FTP course will help them improve and optimize the creation of quizzes in Moodle, especially in terms of efficiency, agility, and ease of managing large question banks. Many highlighted the importance of automating the process of creating and varying questions, which would save time when preparing quizzes for multiple groups or exams. Others focused on gaining more control over the questions, particularly in how to organize, store, reuse, and modify question banks, as well as generating questions with automatic variants.  
Of the 150 teachers who completed the post-course survey, 65 responded to the question \"Here you can write down what you think of the FTP application, whether you would recommend it, and whether it was what you expected\". Most participants provided positive feedback on FTP, emphasizing its usefulness in simplifying the creation and management of quizzes in Moodle. The course has generally exceeded expectations, with many participants stating they would recommend the tool to colleagues. The application's intuitive nature, ability to streamline quiz creation, and the capacity for organizing and managing large question banks were frequently highlighted. However, a few users mentioned challenges, such as initial learning curves, bugs, or limitations with specific operating systems, particularly MacOS.  
In Appendix A, the most representative comments from the pre- and post-training surveys are grouped.
# Design and Testing of a Gasoline Engine Muffler Based on the Principle of Split-Stream Rushing
## 2 Materials and Methods
### Scheme of Orthogonal Tests  
In order to determine the final structural size parameters of the split-stream rushing muffler, simulations of models with different structural sizes were required to pick the optimal model. The method used to select the optimal model in this article was orthogonal testing. Orthogonal test design is a reliable multi-factor test design method based on scientific research verification. It uses orthogonal tables to achieve efficient and reliable results by systematically arranging and analyzing tests. Orthogonal experimental design is one of the most widely used experimental design methods [21].  
The split-stream rushing muffler was divided into two parts in total in terms of composition: the front section is the split-stream rushing muffler unit, whereas the rear section is the traditional reactive muffler unit. Building on the group's previous detailed research, this orthogonal test focused only on the back section of the reactive muffler units of some important structural dimensions to study, discuss, and select the structure that had the greatest impact on the acoustic performance and aerodynamic performance as a factor. A three-factor, three-level orthogonal test with the perforation aperture of the perforated pipe (A), the perforation rate of the perforated pipe (B), and the perforation rate of the perforated plate (C) as the test factors and Transmission Loss and Pressure Loss as the test indexes was established.  
According to the principle of orthogonal test design, using the standard orthogonal test table L9 (3\({}^{3}\)), a total of nine simulation test groups were designed, with three repetitions of the simulation analysis, and the average value of each group of tests was used as the test results. The test scheme was shown in Table 2.  
Figure 8: Structure diagram of split-stream rushing muffler.  
For the selection of the level in each factor, it was found that when the perforation rate of the muffler unit was too low, the Pressure Loss increased significantly. Because the perforation rate was too small, the actual passing area of the airflow was much smaller than the cross-sectional area, and the circulation of the airflow was subjected to excessive resistance; therefore, the Pressure Loss became larger. If the perforation rate was too high, the plate or the tube provided limited obstruction to the airflow, and the muffling performance did not change significantly. In this case, the muffler with the perforated unit could be equivalent to an ordinary expansive muffler; therefore, it should be selected in the appropriate range. The muffler with a perforated unit could be equated to an ordinary expansive muffler, so the perforation rate should be selected within a suitable range [22]. After reviewing the literature, it was finally decided to use the perforation rates of 5%, 10%, and 15% for the perforated pipe, 7%, 9%, and 15% for the perforated plate, and the perforation aperture of 6 mm, 8 mm, and 10 mm for the perforated pipe as the factor levels for this orthogonal test.
# Electronic Beam Steering Metamaterial Antenna with Dual-Tuned Mode of Liquid Crystal Material
## 3 Design, Simulation and Analysis of Dual-Tuned CRLH Metamaterial Antenna Based on LC
### Theoretical Analysis of CRLH Structure with Dual-Tuned Mode  
According to the antenna theory [36], the beam direction of the antenna \(\theta\) is determined by the phase constant, \(\beta\), and wave number in free space, \(k_{0}\), as follows:  
\[\theta=\arccos\frac{\beta}{k_{0}} \tag{1}\]  
In the CRLH metamaterial antenna, the phase constant, \(\beta\), can be expressed as follows [37]:  
\[\beta=\omega\sqrt{L_{R}C_{R}}-\frac{1}{\omega\sqrt{L_{L}C_{L}}} \tag{2}\]  
where \(\omega\) is the angular frequency of the antenna, \(L_{R}\) and \(C_{R}\) are the right-handed inductance and capacitance, and \(L_{L}\) and \(C_{L}\) are the left-handed inductance and capacitance. When the LC is incorporated into the electronic beam-steering metamaterial antenna, the \(L_{R}\), \(C_{R}\), \(L_{L}\) and \(C_{L}\) can be transformed with the tuning of the LC state. When the LC material is under the alignment state and the saturation bias voltage, sequentially, the dispersion functions of the CRLH unit cell are described as follows:  
\[\beta_{0}(\omega)=\omega\sqrt{L_{R0}C_{R0}}-\frac{1}{\omega\sqrt{L_{L0}C_{L0}}} \tag{3}\]  
\[\beta_{V}(\omega)=\omega\sqrt{L_{RV}C_{RV}}-\frac{1}{\omega\sqrt{L_{LV}C_{LV}}} \tag{4}\]  
where \(\beta_{0}\) is the dispersion of the CRLH unit cell; \(L_{R0}\) and \(C_{R0}\) are the right-handed inductance and capacitance of the CRLH unit cell; \(L_{L0}\) and \(C_{L0}\) are the left-handed inductance and capacitance of the CRLH unit cell when LC is under the alignment state; \(\beta_{V}\) is the dispersion of the CRLH unit cell; \(L_{RV}\) and \(C_{RV}\) are the right-handed inductance and capacitance of the CRLH unit cell; and \(L_{LV}\) and \(C_{LV}\) are the left-handed inductance and capacitance of the CRLH unit cell when the LC is loaded with saturated bias voltage. Then the beam-scanning angle of the CRLH unit cell, \(\Delta\theta\), can be expressed as follows:  
\[\Delta\theta=\arcsin\biggl{(}\frac{\omega}{k_{0}}\sqrt{L_{RV}C_{RV}}-\frac{1 }{\omega k_{0}\sqrt{L_{LV}C_{LV}}}\biggr{)}-\arcsin\biggl{(}\frac{\omega}{k_{ 0}}\sqrt{L_{R0}C_{R0}}-\frac{1}{\omega k_{0}\sqrt{L_{L0}C_{L0}}}\biggr{)} \tag{5}\]  
Equation (5) is written as follows:  
\[k_{0}(\sin\theta_{V}-\sin\theta_{0})=\omega\biggl{(}\sqrt{L_{RV}C_{RV}}-\sqrt{ L_{R0}C_{R0}}\biggr{)}+\frac{\sqrt{L_{LV}C_{LV}}-\sqrt{L_{L0}C_{L0}}}{\omega \sqrt{L_{LV}C_{LV}L_{L0}C_{L0}}} \tag{6}\]  
According to the dual-tuned mode of the LC in Section 2, when the bias voltage is loaded onto different LC layers, the dual-tuned mode shows four different states, thus causing the metamaterial antenna based on the LC to form beam steering.