esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Phase Field Methods
### Galerkin Approximation  
The strong form of the coupled governing Equations (78) and (82) are set in a discrete form following standard Galerkin approximation. In this setting, the trial solution spaces are defined as  
\[S_{u}=\left\{\left.\mathbf{u}\in\left(H^{1}\left(\Omega\right)\right)^{d} \right|\mathbf{u}=\mathbf{\bar{u}}\text{ on }\partial\Omega_{b}\right\} \tag{86}\]  
and  
\[S_{c}=\left\{c\in H^{1}\left(\Omega\right)\right\} \tag{87}\]  
for the displacement field and the phase field respectively. Corresponding weighting functions spaces are further defined as  
\[W_{u}=\left\{\left.\mathbf{w}^{u}\in\left(H^{1}\left(\Omega\right)\right)^{d} \right|\mathbf{w}^{u}=\mathbf{\bar{w}}^{u}\text{ on }\partial\Omega_{b}\right\} \tag{88}\]  
and  
\[W_{c}=\left\{w^{c}\in H^{1}\left(\Omega\right)\right\} \tag{89}\]Multiplying Equation (71) with the weighting functions (88) and performing the necessary integration by parts leads to the standard weak form of the equilibrium equation  
\[\int\limits_{\Omega}\mathbf{\sigma}\cdot\
abla\mathbf{w}^{u}d\Omega-\int\limits_{ \Omega}\mathbf{b}\cdot\mathbf{w}^{u}d\Omega-\int\limits_{\partial\Omega_{\Omega}} \mathbf{\tilde{t}}\cdot\mathbf{w}^{u}d\partial\Omega_{\mathbf{t}}=0 \tag{90}\]  
Multiplying Equation (78) with the weighting functions (89) and performing the necessary algebraic manipulation gives rise to the phase field weak form employed in [229]  
\[\int\limits_{\Omega}\left(\left[\frac{4l_{0}\left(1-k\right)\mathcal{H}}{ \mathcal{G}_{c}}+1\right]c,w^{c}\right)d\Omega+\int\limits_{\Omega}\left(4l_{0 }^{2}\
abla c,\
abla w^{c}\right)d\Omega-\int\limits_{\Omega}\left(1,w^{c} \right)d\Omega=0 \tag{91}\]  
Similarly, the cohesive phase field weak form derived from Equation (82) assumes the following form  
\[\int\limits_{\Omega}\left(g^{\prime}(c)\tilde{D},w^{c}\right)d \Omega+\int\limits_{\Omega}\frac{3\mathcal{G}_{c}}{8l_{0}}\Bigg{[}-(1,w^{c})+ \left(2l_{0}^{2}\
abla c,\
abla w^{c}\right)\Bigg{]}\,d\Omega \tag{92}\] \[+\int\limits_{\Omega}\left(\langle\lambda+\gamma(c-c^{n-1}) \rangle_{+},w^{c}\right)d\Omega=0\]  
The weak forms introduced in Equation (91) or (92) can be further discretised employing either mesh-based, i.e., the FEM, mesh-less methods, see, e.g., [275] or MPM [32]. The resulting discrete equations are then solved in an incremental fashion. Due to the nonlinear nature of \(g(c)\), the resulting discrete problem is a nonlinear one, even for the case of elastic fracture, hence necessitating the use of iterative solvers.
## 3 Geopolymer Concrete (GPC)
### Microstructural Properties of Hardened GPC
#### 3.3.2 Scanning Electron Micrograph (SEM) and Energy Dispersive X-ray (EDX)  
SEM involves examining and scanning the material's surface with a focused beam of electrons through image analysis to measure and assess fine details. When electrons contact sample atoms, they provide multiple surface topography and composition signals, highlighting component failures, detecting particles, and analysing the interactions between substances and substrates.  
Some studies have investigated the SEM and EDX test of GPC [88; 96; 97; 98; 98]. The microstructure of geopolymer-treated concrete appears more refined and denser than untreated concrete (Figure 20). Calcium hydroxide was not detected using EDX, but particles of non-reactant lime were present at levels similar to the control specimen. The main component of geopolymer cement is lime, which explains why it has these properties. Calcium, silicon, magnesium, and potassium peaks increase dramatically in the EDX elemental analysis of the geopolymer-modified PC concrete, which can be attributed to the high concentrations of these elements in the geopolymer cement (except potassium, which originates mostly from the alkaline activator).  
[PERSON] et al. [88] showed that scanning electron microscopy (SEM) pictures of the failed specimens were taken to investigate the geopolymer concrete microstructure. Figure 21 shows the micrographs of geopolymer concrete. The microstructure revealed that several phases exist in the matrix GPC. According to [PERSON] et al. [90], the microstructure investigation is absolute, thick, and unbroken, with a highly reactive microstructure. The polymerisation results were in good condition, with no breakdown or crystalline water.  
Figure 20: SEM and EDX curve showing the chemical composition of geopolymer concrete [99].
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 2 Materials and Methods
### Data
#### 2.2.3 Meteorological  
Meteorological products were acquired from OpenWeatherMap ([[https://openweathe.rmap.org/](https://openweathe.rmap.org/)]([https://openweathe.rmap.org/](https://openweathe.rmap.org/)) (accessed on 20 April 2023)) API. The weather data collection we used is free of charge and provides current weather and 5-day forecast for any location on the globe with a 3-h step. From the available weather data, we included temperature, humidity, pressure, wind speed and direction, wind gusts, and precipitation. OpenWeatherMap provides many kinds of weather maps and graphs.
## 4 Joint Task Offloading, Resource Allocation, and Security Assurance Algorithm
### Overall Algorithm  
In this paper, we propose an efficient iterative algorithm by jointly considering the task offloading, the resource allocation, and the security assurance for UAV-assisted VANETs, named the LBTO algorithm. The proposed LBTO algorithm is summarized in Algorithm 1. In detail, we first decompose the optimization problem P1 into two subproblems, i.e., the MEC server selection problem and the task offloading problem. Then,given \(\mathbf{f}\) and \(\lambda\), we use the constraint relaxation scheme and the relax-and-rounding method to solve the optimization problem P3. Afterward, based on the selected MEC server on UAV, we adopt the method of Lagrangian dual decomposition to solve the optimization problem. Finally, we repeat this process until convergence.  
```
1:Initialization
2: Set iterations \(k=0\).
3: Set \(\mathbf{x}^{k}\), \(\mathbf{f}^{k}\), and \(\lambda^{k}\).
4:repeat
5:\(k=k+1\).
6: Based on \(\mathbf{f}^{k-1}\) and \(\lambda^{k-1}\), we can solve the optimization problem P3 by the constraint relaxation scheme and the relax-and-rounding method.
7:until Obtain the feasible solution \(x_{i,j}^{*}\).
8:repeat
9: According to the Lagrangian dual decomposition, we can solve the optimization problem P7 and P8.
10: Calculate the optimal solution \(f_{i,j}^{*}\) and \(\lambda_{i,j}^{*}\).
11:until Convergence.
```  
**Algorithm 1** The Proposed LBTO Algorithm.
# Building Information Modeling Impact on Cost Overrun Risk Factors and Interrelationships
## 2 Literature Review
### Pls-Sem  
PLS-SEM is an effective technique that may be used to diagnose complicated models with several latent groups and observable indicators. It makes it easier to estimate the inner model, which evaluates relationships between various latent groups, and the outer model, which captures associations between latent groups and their indicators. Figure 1 displays the components of a simple PLS-SEM model, and also shows a PLS-SEM model with two latent groups (A and B). Group A has three indicators (A1, A2, and A3). At the same time, group B has two indicators (B1 and B2). In this example, group A impacts group B (A\(\rightarrow\)B). Therefore, group A is exogenous, and group B is indigenous. The relationship between group A with its indicators (A1, A2, and A3) and the relationship between group B with its indicators (B1 and B2) represent the outer model. At the same time, the relationship between group A and group B is the inner model, as shown in Figure 1. This adaptability is particularly useful when examining complicated causal interdependencies across different groups [28]. PLS-SEM also enables researchers to find significant factors, propose new ideas for investigation, and examine interactions and patterns within data. Adaptability and handling small sample sizes make it suitable for exploratory research [29]. Various studies extensively utilize PLS-SEM to assess risks across different sectors. For instance, [PERSON] et al. [30] employed PLS-SEM to explore the relationship between safety risks and mitigation steps in the Malaysian construction sector. Other previous studies have not utilized PLS-SEM to assess risk factors of cost overrun in construction projects that incorporate BIM during construction. More research is needed to examine interdependencies and influences among significant risk factors in this context.  
Figure 1: Components of a simple PLS–SEM model.
## 4 Tuning Parameters and Results Assessment
### Algorithms Performance Assessment via K-Fold CV  
Two undesirable phenomena that may appear after predicting unseen datasets using ML algorithms are underfitting or overfitting. Underfitting occurs when the model cannot account for the data, and this leads to inaccuracy in the estimated results. Overfitting occurs when there is excellent performance with the available training data, but poor performance with an unknown dataset [34]. To identify these problems, the K-fold CV model evaluation method is a simple and effective approach. In this method, the dataset is divided into K groups and each group is a test set one time, to check and evaluate the performance of the executing algorithm (Figure 10). In this study, K = 10 was used.  
\[CV_{K-Fold}=\frac{1}{K}\sum_{j=1}^{K}MSE_{j} \tag{11}\]
# Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks
## 4 Methodology
### Artificial Neural Network (ANN)  
A supervised feedforward artificial neural network (ANN) is adopted to predict the occurrence of a traffic breakdown in the next time interval. The typical structure of an ANN consists of an input layer, one or more hidden layers, and an output layer.  
The input layer serves as the first stage of neural network processing to receive training data. Typically, this layer includes a bias neuron, which provides the network with the ability to horizontally shift the activation function. This enables the modeling of a diverse range of input-output relationships. As a result, the number of neurons in this layer is equal to the number of input features plus one.  
The hidden layers consist of multiple neurons, each of which receives inputs from the previous layer and applies a non-linear activation function to generate its output. In the hidden layer, there is also a neuron that serves as a bias. Its function is similar to that of the bias in the input layer and introduces a shift in the data. The number of hidden layers can vary depending on the complexity of the problem, but usually should not exceed two. In this paper, we purposed an ANN model that contains one hidden layer with Rectified Linear Unit (ReLU) activation function. ReLU applies the function f(x) = max (x, 0) to the input x, resulting in an output of 0 for all negative inputs and an output equal to the input for all nonnegative inputs.  
The primary function of the output layer is to produce the final output of the network, which is then compared with the desired output to compute the error signal. The number of neurons and the activation function in the output layer are determined by the problem-specific characteristics. As the intended model is for binary classification, the model architecture comprises a single neuron with sigmoid activation function. The sigmoid function is commonly utilized for binary classification purposes. The mathematical expression for the sigmoid function is as follows.  
\[\sigma(\mathrm{x})= \frac{1}{1+e^{-x}}\]  
The sigmoid function is an S-shaped curve and maps the input value from 0 to 1.  
In the backpropagation algorithm, the error signal from the output layer is propagated backward through the network. The weights of the connections between neurons are iteratively adjusted to minimize the difference between the predicted and actual output. The proposed design of the ANN model is shown in Figure 4.
# Optical Boundaries for LED-Based Indoor Positioning System
## 5 Results and Discussions
### Defined Threshold Angle to Reduce for Positioning Delay  
In this section, the effect of a defined threshold angle is presented in terms of positioning time. This is because the positioning time presents information on the practicability of the positioning system. Given that the average walking rate of a person is about 1 m/s [89], the desired range of positioning time will be below 1 s.  
For a single LED transmitting packets where bits are encoded with a pulse length \(L\) between 0 to 1 ms, the APTs are presented in Figure 12. It shows the APT when optical boundaries are defined at the threshold angle and the APT when they are defined at the full angle as explained in Section 4.2 using 4-bit and 12-bit packets in (17). The results show that the APT generally increases with an increase in encoding pulse duration. However, for the 12-bit packet, the APT is initially very high as a result of the high BER when the pulse duration is low. At \(L=600\)\(\upmu\)s, the APT for the threshold angle defined optical boundary system is 11 ms for 12-bit packets and 3 ms for 4-bits packets, and for the conventional system it is 2.5 s for 12-bit packets and 40 ms for 4-bit packets.  
When a two-LED overlap region is considered, for a cycle time of 72 ms where the minimum APT occurs, the boundary defined receiver maintains the positioning time of the 4-bit packets at 0.45 ms instead of 5.39 ms, and for the 12-bit packets it is maintained at 1.35 s instead of 388 s as presented in Figures 13 and 14. The implication of this is that the conventional full angle cannot be used to define boundaries for the overlap based system. Delays of over 1 s (of about 5 s and 388 s) renders the positioning technique unusable. Therefore, a receiver based threshold angle must be implemented with the IPS. This is because the use of threshold angle prevents the receiver from persistent delays caused by high BER, where PDR falls below the acceptable rate \(\mathcal{P}\).  
Figure 12: Reduction of the average positioning time (APT) by the use of receiver defined threshold angle (TA) instead of the conventional full angle (FA) in (17).
# Evaluating LoRaWAN Connectivity in a Marine Scenario
## 3 Results and Discussion
This conclusion may also be consistent considering the presence of obstacles (buildings, hills, and mounts) along the terminal part of the radio path from the off-shore end-nodes to the gateways on the mountains. As the distance between end-nodes and gateways increases, the curves based on the equations by Friis and by Hata (with semi-urban conditions) tend to be similar for the gateway located at an altitude of about 700 m (Mount Fasce, Mountain pass Faiallo, Mount Figogna) whereas, when the altitude is greater than 1700 m (i.e., Mount Moro) the difference between the two models is of about 20 dBm. Results demonstrate that a distance between gateways and end-nodes of more than 110 km can be reached in a marine scenario under the hypothesis of LOS and that although considerable, the obtained maximum distance of 113.22 km cannot be considered the upper limit of reliable LoRa transmission towards the sea, since the lowest received power detectable for LoRa transmission is considered \(-\)140 dB and, in the test, it was about \(-\)128 dB.
# Fault Detection in Active Magnetic Bearings Using Digital Twin Technology
## 1 Introduction
A digital twin can identify possible problems, forecast when failures might happen, and suggest steps to prevent or reduce them by doing real-time data analysis. Digital twin modeling is one of the leading technologies propelling the development of AMBs [13]. The purpose of a digital twin is to imitate the behavior and performance of a physical asset or system under various operating situations [14]. A digital twin may be used to simulate the behavior of the rotating equipment, the control system, and the magnetic field in the context of AMBs [15]. This makes it possible to enhance the system's performance and design before it is developed. AMB system behavior may be predicted via digital twin modeling under various operating situations, such as loads, speeds, and vibration levels [16]. This shortens the time and expense of development by enabling the identification of possible problems and improving the system before it is developed. Digital twin modeling may be used to monitor system performance in real-time and improve the design of AMB systems [17]. The system's behavior may be tracked and modified to improve its performance by connecting sensors and control algorithms with the digital twin. This might increase productivity, save downtime, and enhance system lifetime. [PERSON], [PERSON], et al. [18] presented a Digital Twin reference model for rotating machinery fault diagnosis. They designed a model to be adaptable and updatable, and the researchers propose a model updating scheme based on parameter sensitivity analysis to improve its accuracy. AMBs and digital twin modeling together provide a powerful new technique for enhancing the efficiency and dependability of rotating equipment. AMB systems may be created and refined to operate at their peak in various operating situations by using modern control systems and virtual modeling. However, this study aims to investigate and present a new methodology for identifying malfunctions in magnetic bearings through the utilization of digital twin technology. The objective of this study is to examine the necessity for efficient failure detection techniques in magnetic bearings in order to minimize system malfunctions, enhance reliability, and reduce downtime.
## 4 Results
### Results from Artificial Neural Network
This is due to the fact that there are very few data in the double-layer forest for training and testing the MLP-ANN model, and \(\sim\)62.3% of the double-layer structure has been misclassified as a triple-layer structure due to the similar canopy characteristics of the double-layer and triple-layer forests. Therefore, it could be mentioned that 62.3% of double-layer forests are very similar to the pattern of triple-layer forests. This result in this study means that a double-layer structured area cannot be completely separated from a triple-layer structure forest. The user and producer accuracy in triple-layer structure forest is about 79.85% and 73.92%, respectively. Triple-layer structure accuracy was higher than the accuracy of single- and double-structure. It can be interpreted that this is due to the well-trained MLP ANN model parameters in the triple-layer structure. Nevertheless, about 12.3% and 13.8% of the triple-layer structure were misclassified as single-and as double-layer forests, respectively. To analyze the cause of the misclassification, the forest map and the original Sentinel-2 RGB image were compared and analyzed by visual interpretation. As a result, it was confirmed that forest trees were artificially removed or newly formed in the single-, double-, and triple-layer structure, as shown in Figure 13. This type of misclassification is occurred due to the time difference between the acquisition time of satellite images and the field survey, which could lead to the learning and output of ANN algorithm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ANN Reference** & **Single Layer** & **Double Layer** & **Triple Layer** & **Total** & **User Accuracy (\%)** \\ \hline Single Layer & 71,952 & 9,904 & 59,110 & 140,966 & 51.04\% \\ Double Layer & 15,421 & 20,170 & 66,624 & 102,215 & 19.73\% \\ Triple Layer & 40,069 & 49,886 & 356,398 & 446,353 & 79.85\% \\ Total & 127,442 & 79,960 & 482,132 & 689,534 & \\ \hline Producer accuracy (\%) & 56.46\% & 25.23\% & 73.92\% & & \\ \hline Overall accuracy (\%) & & & 65.06\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Validation of forest vertical structure classified by using the ANN (unit: pixel).
# Nanocarriers-Assisted Nose-to-Brain Delivery of Levodopa: Current Progress and Prospects
## 3 Biopharmaceutical Aspects for the Drug Delivery of L-DOPA  
The molecule of L-DOPA is characterized by low molecular mass (Mm 197.2 g/mol) and high hydrophilicity (log P \(-\)2.9) but only slight solubility in water (1.65 mg/mL at 20 \({}^{\circ}\)C) [45; 46]. This is explained by the fact that in the pH range of 2.3-8.1, the compound is in a zwitterion state, and the arising intermolecular bonds between the deprotonated carboxyl group and the protonated amino group hinder its dissolution in polar solvents [47; 48; 49]. However, in acidic media (pH < 2.3), the solubility increases; for example, up to 10-fold in HCL 0.1 M (pH 1)--the approximate pH of the stomach fluid, and up to 100-fold in HCL 1 M (pH 0) [46]. Untraditionally for these physicochemical properties, but because of a specific carrier-mediated absorption across the biological membranes, L-DOPA could unofficially be attributed to class I of the Biopharmaceutical Classification System (BCS)--a class normally intended for drugs of high solubility and high mucosal penetrability [50].  
Apart from what was already discussed about L-DOPA's in vivo chemical instability due to enzymatic degradation, the drug is also susceptible to oxidation and aggregation in vitro, which should be considered when formulating it into dosage forms. In the presence of oxidative triggers such as oxygen, light, etc., L-DOPA decomposes to toxic derivatives (quinones) with the final product of the oxidation cascade being melanin [51; 52]. The oxidative degradation of L-DOPA occurs through deprotonation and is thus dependable on the pH; acidic pH (ensuring abundance of hydrogen ions) and low temperatures are considered most favorable for the compound's isolation and storage of its preparations [48; 49; 51].
## 2 Results and Discussion
### Scanning Electron Microscopy  
Figure 10 displays SEM images of both untreated and treated FFs. The untreated fibers had uneven surfaces with larger overall fiber diameters, while the alkaline-treated fibers revealed a smoother fiber surface. This trend indicates that alkaline treatment contributes to the removal of non-cellulosic components (e.g., hemicellulose, lignins, and pectins) and impurities such as wax, which contribute to smoother surface features [43]. Fenton treatment resulted in improved surface characteristics of the fibers by reducing the average diameter of FFs and the non-cellulose biomass fractions. The F-T15 and F-T15-ALK samples showed the most notable reduction in fiber diameter, with a smoother surface appearance. These results concur with those obtained from optical microscopy, NMR, and FT-IR analysis, revealing that Fenton treatment effectively removed the non-cellulosic content of the fibers and defibrilated the fiber bundles into fibers with reduced diameter.  
Figure 9: Residual MB dye in aqueous solution after exposure of a fixed concentration of MB dye with raw and treated fibers for 48 h, along with their respective standard deviations. The reader is referred to Table 2 for definitions of the sample ID codes used in this figure.