esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Radiation Pressure-Driven PhC Cavity Sensors
### Future Perspectives for Optomechanical Sensing Based on PhC Cavities
#### 4.3.2 Two-Dimensional Materials Integrated with Optomechanical Systems  
Besides conventional mechanical resonators, two dimensional materials [169], are also extensively explored to construct novel optomechanical systems. For instance, a simple patterned graphene layer can be engineered to control light confinement compared with that of dielectric materials. Graphene, a single layer of hexagonally packed carbon atoms, endows extraordinary electronic, optical, thermal, and mechanical properties [170; 171; 172]. Optical conductivity of graphene can be effectively modified by electrically controlling its Fermi level, which enables the optical absorption of graphene-based reconfigurable photonic devices. Besides, mechanical resonators based on tensed graphene membranes with a low mass, \(\rho\approx 0.75\)mg/m\({}^{2}\), have been widely studied for force and mass sensing with a significant improvement in sensitivity. Therefore, feasible electrical integrability, and strong optical interaction, ultra-small mass and high operating frequencies of graphene makes it an attractive functional material to be integrated with micro-/nano-photonic devices [173; 174; 175]. Here, the conceptual proposal focuses on integration of graphene and on-chip PCN cavity for optomechanical applications.  
Based on the optomechanical principles and graphene properties, the optomechanical graphene integrated PCN resonator is proposed in Figure 28. In order to maximize the optomechanical coupling between the silicon nanobeam and graphene in the graphene drum areas, an air-mode nanobeam is designed to confine optical resonant modes within the central air holes. The optical absorption of graphene covered on the silicon waveguide causes absorption loss. Hence it leads to a dramatical reduction of Q-factors [176] and severely affects the optomechanical coupling strength. One feasible solution is to define the geometry of graphene pattern (length \(L_{S}\)) and location of graphene coverage on nanobeam. In the reported graphene optomechanical systems [177; 178], graphene itself acts as mechanical oscillator and it will interplay with the optical resonator. In this way, they two generate optomechanical phenomena by laser detuning. Another electro-opto-mechanical graphene-nanobeam resonator [179; 180] can also be conceptually designed by integrating electronic circuits. Under an electrostatic force generated by the electronic circuits, the deflection of graphene gives rise to tension and strain to modulate optomechanics in the graphene integrated PCN resonator.  
In addition, the concept of Fermi level modulation [181] for tuning of optical absorption and graphene micro-heater [182] can also be explored and applied to potential sensing applications based on opto-mechanical interaction.
# Investigation of Biochars in Terms of Vitamin E Adsorption Capacity
## 3 Results and Discussions
### Characteristics of Biochars
#### 3.1.1 Influence of Processing  
Even if the pyrolysis process is similar for different biochars (Table 1), the pyrolysis retention time, the pyrolysis temperature, and the post-treatment (data not available, see Section 2.1) will differ, resulting in different particle sizes. This assumption is based on the statement that biochar's particle size distribution is influenced by the biomass and its particle size distribution, and the production processes, including the pyrolysis temperature and retention time, as well as the post-processing, such as grinding [58]. In general, the particle size is reduced at a higher pyrolysis temperature [62], and a higher pyrolysis temperature increases the elemental nutrient concentrations and decreases acidic surface groups, which increases the pH [63]. In general, the surface groups and their kinetics also determine the adsorption capacity [46]. However, as the particle size distribution prior to pyrolysis is unclear, the effect of the pyrolysis temperature on the particle size could not be investigated. [PERSON] et al. [64] separated the pyrolysis process into fast, intermediate, and slow, which were further separated into batch or continuous processes. Batch processes are conducted with a kiln or retorts, whereas continuous processes are conducted in a drum or screw pyrolyzer or a rotary kiln. For the biochars analysed, the retention time of the pyrolysis process is unknown. However, using a screw pyrolyzer, such as the 'PYREG', the retention time can be adjusted [44]. When using a kiln, the retention time depends on the dimensions of the kiln, the pyrolysis temperature, and the biomass used. Another difference is the setting of the pyrolysis temperature: in a kiln, temperatures between 400 and 600 \({}^{\circ}\)C are used, whereas the incline is 50 \({}^{\circ}\)C [44; 65]. A screw-type pyrolyzer can operate up to 900 \({}^{\circ}\)C [66], and the lowest temperature should be 350 to 400 \({}^{\circ}\)C, with a heating rate of 20 to 40 \({}^{\circ}\)C/min [67]. [PERSON] and [PERSON] [44] classified processes based on analysis of 66 procured biochars. They showed that even if the biochars were produced with a similar process, their properties varied. This was also the case for biochars made with the 'PYREG'. Still, based on the properties for the use of biochar as a soil amendment, they preferred 'PYREG' over a kiln [44].  
Figure 1: Conf.  
Figure 1: Visual particle size distribution of biochars A to j: (**A1**-**J1**) photos, where bar indicates 10 mm, and (**A2**-**J2**) scanning electron microscopy, where bar indicates 500 μm.
## 4 Calculation Method of Shear Carrying Capacity of UHPFRC Beam-Column Joint
### Components of Shear Carrying Capacity
#### 4.2.2 Shear Force Carried by Steel Fiber  
The test results indicate that the majority of steel fibers bridging across the cracks constantly carry the diagonal tensile stress like the shear reinforcement, until the third stage of the failure process. Therefore, steel fibers can be regarded as dispersed reinforcement bars. Furthermore, owing to the fact that the length of steel fibers incorporated into UHPFRC is less than the critical length of fibers when they are broken, steel fibers are not broken, but rather pulled out from the UHPFRC matrix. The tensile capacity of the steel fibers \(F_{f}\) is mainly dependent on the adhesion between the fibers and UHPFRC matrix, which can be expressed as:  
\[F_{f}=2\tau_{f}\frac{l_{f}}{d_{f}}\rho_{f}A_{f} \tag{8}\]In Equation (8), \(\tau_{f}\) is the adhesive stress between the steel fiber and UHPFRC matrix; \(l_{f}/d_{f}\) is the length-diameter ratio of the steel fiber; \(\rho_{f}\) is the volume percentage of steel fiber to total UHPFRC volume; and \(A_{f}\) is the area of crack interface.  
However, the positions of random fibers incorporated into UHPFRC are not only vertical to the crack interface, and the lengths embedded in the UHPFRC across cracks are not equal, resulting in fibers being more easily pulled out from the shorter end and the distribution of fibers in specimens not being symmetrical. These factors have adverse effects on \(F_{f}\) and cause the actual value of \(F_{f}\) to be less than the calculated value from Equation (8). Therefore, a coefficient \(\beta\) is introduced to Equation (8) to account for the adverse effects of these factors, and can be expressed as  
\[F_{f}=2\beta\tau_{f}\frac{l_{f}}{d_{f}}\rho_{f}A_{f} \tag{9}\]  
We define \(\lambda_{f}=\left(l_{f}/d_{f}\right)V_{f}\), with \(\lambda_{f}\) representing the characteristic coefficient of steel fibers and \(\eta=2\beta\tau_{f}\) representing the pulled-out total effective coefficient of steel fibers. The relation of \(\eta\) versus \(\lambda_{f}\) (as shown in Figure 9) following cracking is acquired through regressive analysis on the experimental results of UHPFRC conducted in [28; 29]. When the length-diameter ratio varies from 56 to 75 and the volume percentage of steel fiber varies from 0% to 5%, then  
\[\eta=16.447-14.30\lambda_{f}^{0.061} \tag{10}\]  
The main crack interface is approximated as the diagonal of joints, so the crack interface area \(A_{f}=\sqrt{h_{c}^{2}+h_{b}^{2}}\times b_{c}\), and \(A_{f}\sin\theta\approx h_{b}\times b_{c}\). Hence, the horizontal shear carrying capacity of steel fiber can be calculated by the expression  
\[V_{f}=\eta\lambda_{f}A_{f}\sin\theta=(16.447-14.30\lambda_{f}^{0.061})\lambda_ {f}h_{b}b_{c} \tag{11}\]
## 3 Research Methodology
### Quality Assessment and Data Extraction  
A second filtering stage was carried out to refine the process by targeting publications that demonstrated clear technical or conceptual contribution to the research goals. During this stage, a set of inclusion criteria was used to ensure the quality and depth of the studies selected. First, articles that were not available in full-text form were eliminated, because they could not allow the formulation of a detailed analysis. Secondly, papers that did not offer deep discussions pertaining to implementation frameworks, architectures, or conceptual models that targeted TPM specifically in the digital context were eliminated from the process. After the application of the criteria, a list of **33 articles** was left and was subjected to a **systematic content analysis**.  
Figure 1: Analysis methodology inspired by [33].
# Metamaterial Integrated Folded Dipole Antenna with Low SAR for 4G, 5G and NB-IoT Applications
## 2 DNG Metamaterial Design  
The latest development in wireless communication systems has resulted in high demand for compact-size antennas that can handle the hazardous impacts of radiation on the human body [22]. Metamaterial integrated antennas have the potential to be an effective option for addressing these requirements [23]. The metamaterial can be designed using CRLH (Composite Right Left Hand) theory by implementing the circuit elements' equivalences [24; 25].  
Circuit components can be realized by implementing the inductance and capacitance equivalence using the microstrip line technique. The metamaterial is designed using the modified complementary metallic cross (CMC) for the inductance and capacitance equivalent of CRLH metamaterial [26]. The metallic structure is printed on the face side of a 5 \(\times\) 5 mm\({}^{2}\) FR4 substrate. The FR4 substrate has a thickness of 1.574 mm and a dielectric constant of 4.3. The conductivity and thickness of the metallic print on the FR4 substrate are considered in correlation to the actual data characteristic of annealed copper, and were chosen as 5.8 \(\times\) 10\({}^{7}\) S/m and 35 \(\upmu\)m, respectively. The CMC behavior is analogous to that of inductance and capacitance equivalency. The structure's LC resonator behavior may be rationalized by considering the CMC length and gap width for inductance and capacitance equivalency, respectively (Figure 1). As the value of L2 increases, the capacitance equivalency diminishes, and more tweaking is available through the usage of W1. In contrast, raising L1 and W1 results in a rise in the sectional area, resulting in the escalation of inductance, although W1 has an influence on both procedures. The design was simulated using CST Microwave Studio to evaluate the refractive index, permittivity, and permeability using the approach described in [27].  
The simulated results for central gap width (w1) of 0.5 mm, and outer (L1) and inner (L2) width of 3 and 1.5 mm, respectively, are illustrated in Figures 2-4. The permittivity, permeability and refractive index for the CMC is zero at 17.23 GHz. As a result, the material is capable of sustaining forward waves greater than 17.23 GHz and backward waves less than 17.23 GHz. Additionally, the material transitions seamlessly from the Double Negative to the Double Positive spectrum. In general, a DNG metamaterial with negative index properties up to 17.23 GHz was designed and can be used for negative index applications.  
Figure 1: DNG metamaterial unit cell illustration.  
Figure 3: Retrieved permeability for the structure.  
Figure 2: Retrieved permittivity for the structure.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 2 Kalman Filtering GNSS/INS Fusion Navigation Basic Principles
### Basic Principles of Kalman Filtering
#### 2.1.2 Tightly Coupled  
In contrast, tightly coupled integration [23] requires the utilization of more raw and comprehensive data for computation, such as pseudorange data from GNSS. In tightly coupled integration, it is common to compare the pseudorange and pseudorange rate estimates computed from INS outputs with the pseudorange and pseudorange rate measurements from GNSS receivers. The difference obtained serves as the measurement input for the filter. Through combined navigation filtering, error estimates of the INS are generated, and these estimates can be used to correct the system through measurement updates, thereby enhancing accuracy.  
The tightly coupled structure is relatively more complex but can utilize raw data for computation, thereby enhancing system accuracy. Its architecture is depicted in Figure 2.  
Figure 1: Architecture of loosely coupled closed-loop system.  
Figure 2: Architecture of tightly coupled system.
## 2 Methodology
### Multi-Dimensional Joint Distribution Model
#### 2.1.2 Marginal Distribution Test  
The point estimation method commonly used in engineering has a limited capacity to evaluate the error between the estimated result and the true value, making it difficult to assess the reliability and accuracy of the estimate. In contrast, interval estimation not only determines the range within which parameters may lie but also provides the probability that the true parameters are contained within that range. During parameter interval estimation based on bootstrap sampling, \(N\) bootstrap samples were first generated to obtain parameter estimation values \(\hat{\theta}_{i}^{*}=\hat{\theta}(x_{1}^{si},x_{2}^{si},\ldots,x_{n}^{si})\), \(i=1,2,\ldots,N\) of each sample, and then the parameters \(\hat{\theta}_{1}^{*},\hat{\theta}_{2}^{*},\ldots,\hat{\theta}_{n}^{*}\) were ranked from small to large [28].  
Figure 1: Procedure of the bootstrap method.  
The Kolmogorov-Smirnov (K-S) and Anderson-Darling (A-D) tests can be used to estimate the marginal distribution of parameters. However, the results may vary depending on which test method is used, and some parameters may fit more than one distribution. As a result, the marginal distribution remains unclear for certain parameters. To address this, the Akaike information criterion (AIC) can be employed as a measure of the fitness of a statistical model. The AIC evaluates the goodness of fit based on the entropy principle [29; 30], as shown in Equation (2),  
\[AIC=-2\sum_{i=1}^{N}\ln f(x_{i};\varepsilon)+2k \tag{2}\]  
where \(f\left(x_{i};\varepsilon\right)\) is an alternative marginal distribution probability density function for the material parameter; \(x_{i}\) is the sample value of the material parameters to be tested; \(\varepsilon\) is the distribution parameter vector of the alternative marginal distribution probability density function; and \(k\) is the number of material parameters in the probability density function.  
Based on the marginal distribution test, if a soil parameter fits multiple distribution types, the distribution with the smallest AIC value is considered the optimal fit for that parameter.
# Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
## 4 System Modeling
### Communication Model  
We introduce the M/M/s model for the proposed IoT network. The average response of a computing device, either a fog or _MEC_ node, is measured based on the arrival rate of tasks, as follows [8; 26]:  
\[T_{j-ave}^{f_{j}}(\lambda)=\frac{E_{C}\big{(}s_{j},\frac{\lambda_{i}}{\mu_{j}} \big{)}}{s_{j}\mu_{j}-\lambda_{j}}+\frac{1}{\mu_{j}}\forall f_{j}\in FN, \tag{6}\]  
\[T_{i-ave}^{MEC_{i}}(\lambda)=\frac{E_{C}\big{(}s_{i},\frac{\lambda_{i}}{\mu_{i} }\big{)}}{s_{i}\mu_{i}-\lambda_{i}}+\frac{1}{\mu_{i}}\forall MEC_{i}\in MECN, \tag{7}\]  
\[E_{C}(n,\varphi)=\frac{(\frac{(sp)^{n}}{n!})(\frac{1}{1-\varphi})}{\sum_{k=0}^ {n-1}\frac{(np)^{k}}{k!}+(\frac{(np)^{n}}{n!})(\frac{1}{1-\varphi})} \tag{8}\]  
where \(T_{j-ave}^{f_{j}}\), and \(T_{i-ave}^{MEC_{i}}\) are the average responses of the \(j\)th fog and \(i\)th _MEC_, respectively; \(\lambda_{j}\) is the task rate at the fog node; \(\mu_{j}\) is the service rate of the fog server; \(\lambda_{i}\) is the arrival rate at the _MEC_ node; and \(\mu_{i}\) is the service rate of the _MEC_ server.
## 2 Experiment
### Development of the Dispersive Solid-Phase Extraction onto the Biomass-Derived Adsorbent  
The adsorption capacity for Cr(III), Fe(III), Co(II) and Ni(II) of the prepared biomass-derived adsorbent was determined by investigating various masses including 0.01, 0.02, 0.04, 0.06, 0.08 and 0.1 g.L\({}^{-1}\) mixed with 20 mL of 25 mg.L\({}^{-1}\) heavy metal ion solution. A water bath was then used to shake the mixture for 1 h at 150 rpm. Then, the phases were separated by centrifugation, and the remaining Cr(III), Fe(III), Co(II) and Ni(II) were analyzed by ICP-MS (the operating conditions are described in Tabls S1). The adsorption capacity was calculated from Equation (1):  
\[q_{e}=\frac{(C_{0}-C_{e})*V}{M} \tag{1}\]  
where \(C_{0}\) represents the initial concentration of metal ions in the solution, \(C_{e}\) is the equilibrium concentration of metal ions in the solution, \(V\) is volume of the solution (\(L\)) and \(M\) is the mass of the adsorbent (g).  
For the dispersive solid-phase extraction optimization, a 50 mL of sample solution including a mixed heavy metal standard of Cr(III), Fe(III), Co(II) and Ni(II) was adjusted to the desired pH by adding 2 mL of phosphate buffer and then adding drops of diluted hydrochloric acid (0.01 M) or sodium hydroxide (0.01 M). The biomass-derived adsorbent was then added with a mass of 0.1 g. A water bath was used to shake the mixtures at 150 rpm for 10 min, and then the biomass-derived adsorbent was separated by centrifugation at 3000 rpm for 4 min. For recovery of adsorbed heavy metal ions from the biomass surface, 1 mL of 0.5 M nitric acid was added and hand-shaken for 1 min, and the phases were separated by centrifugation at 3000 rpm for 4 min. The recovered heavy metal mixture including Cr(III), Fe(III), Co(II) and Ni(II) was analyzed by ICP-MS (the condition for analysis is available in the Supplementary Materials). The recovery was calculated using Equation (2):  
\[Recovery\%=\left(\frac{C_{f}}{C_{0}}\right)*100, \tag{2}\]  
where \(C_{f}\) is the final concentration and \(C_{0}\) is the initial concentration.  
The same procedures were repeated to investigate the rule of pH, eluent type, sample volume, shaking time, heavy metal concentration and coexisting ions. All treatments were applied in triplicate, and blank experiments were conducted in all cases. The intraday and interday precision was estimated by analyzing 0.4 mg.L\({}^{-1}\) of Cr(III), Fe(III), Co(II) and Ni(II) with 8 replicates at different time intervals over three consecutive days [30].
# Investigation of Phishing Susceptibility with Explainable Artificial Intelligence
## 4 Results and Discussion
### Deep Learning Predictor  
To prepare the data for the neural network model, the dataset was initially divided into training and testing sets in a 4:1 ratio, with 80% of the data allocated to training and 20% to testing. Due to the highly imbalanced nature of the data, where the number of non-click users was considerably higher than the number of click users in the training set, a technique called NearMiss was applied. NearMiss is an undersampling method that uses a K-nearest neighbors algorithm to reduce the number of majority class instances by selecting the samples closest to the minority class [52]. By using this technique, the imbalance in the training set was addressed, and a more balanced training set was obtained.  
In addition, the dataset contains categorical and binary features such as gender and position, which cannot be used directly as input for the neural network model due to their nominal nature. To address this challenge, we employ a technique known as one-hot encoding to transform these non-ordinal features into numerical values that can be effectively utilized by the model. One-hot encoding represents each category as a binary vector with a length equal to the number of categories in the feature. Each category in the feature is then represented by a unique binary vector, with a value of 1 in the position corresponding to that category and 0 s in all other positions. This allows the neural network to learn the relationship between each category and the target variable by treating each category as a separate feature with a numerical value.  
The performance of the trained neural network predictor was evaluated using the testing set, and the receiver operating characteristic (ROC) curve is displayed in Figure 2.  
Figure 2: ROC Curve for the Learned Deep Learning Model. The aera under the ROC curve (AUC) is 0.78.  
The model yielded an accuracy of 0.78, with a recall of 0.71, a precision of 0.57 and a F-1 score of 0.64. In contrast, [PERSON] et al.'s study [51] on the same population employed a linear logistic regression model achieving 0.71 accuracy, a recall of 0.17 and a precision of 0.11 on the IT dataset. These models are not directly comparable because [PERSON], et al. [10] reported predictive accuracy only for a model that did not make use of any behavioral factors. These results indicate that our model exhibits reasonable predictive capabilities and can be valuable in identifying potential click risks in real-world scenarios. Table 5 summarizes the model performance results.
## 2 Materials and Methods
### Selection of a Lactic Acid Bacteria for the Fermentation of Onion  
Fresh onion (100 g) was mixed with 200 g of water. After boiling for 2 h at 90 \({}^{\circ}\)C, the mixture was inoculated with 109 cells of 16 different lactic acid bacteria (_Lactococcus lactis subsp. Cremoris_ (KCCM-40699), _Leuconostoc mesenteroides_ (KCCM-13374), _Lactobacillus brevis_ (KCCM-40017), _Lactobacillus cellobiosus_ (KCCM-40983), _Lactobacillus plantarum_ _subsp. Plantarum_ (KCCM-13093), _Lactobacillus plantarum_ (KCCM-11322), _Lactobacillus casei_ (KCCM-12452), _Lactobacillus rhamnosus_ (KCCM-32405), _Lactobacillus acidophilus_ (KCCM-32820), _Lactobacillus delbrueckii subsp. Bulgictus_ (KCCM-35465), _Lactobacillus casei subsp. Casei_ (KCCM-35465), _Lactococcus lactis subsp. Lactis_ (KCCM-40104), _Lactobacillus acidophilus_ (KCCM-40265), _Lactobacillus heleticus_ (KCCM-40989), _Lactobacillus paracasei subsp. Paracasei_ (KCCM-41276), _Streptococcus salivarius_ (KCCM-41580)), which were obtained from Korea Culture Center of Microorganisms (KCCM). The growth of lactic acid was monitored by counting the number of lactic acid bacteria in fermented onion using a 3M Petri film lactic acid bacteria count plate (3M Korea Co., Seoul, Republic of Korea). Sensory evaluation of fermented onion was also carried out.  
Based on the growth of lactic acid bacteria and sensory evaluation (data not shown), _Lactobacillus casei_ (KCCM-12452) was selected for the fermentation of onion. Furthermore, _L. casei_ has been known as a probiotic that has been reported to balance the gut microbiota, relieve gastrointestinal dysfunction, prevent infection, modulate inflammatory and immunological responses, and alleviate IBS symptoms [12; 13].
## 2 Basic Theory
### Convolutional Neural Network (CNN)  
CNN is a deep feedforward neural network, which consists of convolutional layers, pooling layers, and fully connected layers. It processes 1D time series and 2D image data through convolution operations and can learn features from nonlinear and non-stationary signals. Image and pattern recognition works well. The fundamental theory of convolutional neural networks (CNN) can refer to the relative investigations [24; 26; 28; 29; 40].