esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Optimization Algorithms
#### 2.2.3 Grey Wolf Optimization (GWO)  
The Grey Wolf Optimization (GWO) algorithm is a bio-inspired approach developed by [PERSON] et al. in 2014 [52], and it simulates the leadership hierarchy within a pack of grey wolves to find optimal solutions to various problems. In this algorithm, the positions of the grey wolves are initialized randomly in the search space. There are three primary wolves: Alpha (\(\alpha\)), Beta (\(\beta\)), and Delta (\(\delta\)). These three types of wolves represent the best solutions found so far. All other solutions are classified as Omega (\(\omega\)) wolves.  
Based on the fitness score of each grey wolf (or solution), each grey wolf is assigned a leadership role. The positions of the Alpha, Beta, and Delta wolves are updated to reflect the best solutions in the pack. The update rules are identical to the whale algorithm (see Equations (2) and (3)).  
The positions of the wolves are then updated based on the positions of the alpha (\(\mathbf{x}_{\mathrm{s}}\)), beta (\(\mathbf{x}_{\beta}\)), and delta (\(\mathbf{x}_{\mathrm{s}}\)) wolves (see Equations (6)-(8)) [53; 54; 55; 56; 57].  
\[\mathbf{D}_{\mathrm{a}}(t) =|C\mathbf{x}_{\mathrm{a}}(t)-\mathbf{x}_{i}(t)|\] \[\mathbf{D}_{\beta}(t) =|C\mathbf{x}_{\beta}(t)-\mathbf{x}_{i}(t)|\] \[\mathbf{D}_{\delta}(t) =|C\mathbf{x}_{\delta}(t)-\mathbf{x}_{i}(t)| \tag{6}\]  
\[\mathbf{x}_{\mathrm{a}}(t+1) =\mathbf{x}_{\mathrm{a}}(t)-A_{A}\mathbf{D}_{\mathrm{a}}(t)\] \[\mathbf{x}_{\beta}(t+1) =\mathbf{x}_{\beta}(t)-A_{\beta}\mathbf{D}_{\beta}(t)\] \[\mathbf{x}_{\delta}(t+1) =\mathbf{x}_{\delta}(t)-A_{\delta}\mathbf{D}_{\delta}(t) \tag{7}\] \[\mathbf{x}_{i}(t+1) =\frac{\mathbf{x}_{\mathrm{a}}(t+1)+\mathbf{x}_{\beta}(t+1)+ \mathbf{x}_{\delta}(t+1)}{3} \tag{8}\]  
where the parameters \(A\) and \(C\) are identical to those defined in the WOA algorithm in the previous section (see Figure 1).  
From the equations presented, unlike PSO and WOA, which utilize a single candidate to guide all others, GWO employs the top three candidates to guide the rest. This approach is beneficial when one of these top positions is at a local optimum, as the other two candidates can assist both that candidate and the remaining candidates in escaping local optimal solutions [52].
# State-of-All-the-Art and Prospective Hydrogel-Based Transdermal Drug Delivery Systems
## 6 Some Important Applications of Hydrogels in Transdermal Drug Delivery Systems
### Cardiovascular Treatment  
Reduced cardiac ejection fraction in heart failure patients may result in frequent alteration of pharmacokinetics (P.K.) and pharmacodynamics (P.D.), resulting in low drug absorption. In such scenarios, transdermal drug delivery presents itself as a viable alternative to conventional drug administration. For instance, propranolol is a nonselective beta-adrenergic blocker prescribed to patients with cardiovascular issues. An animal study involving rabbits demonstrated that oral propranolol could reach a peak plasma concentration of 56.4 ng/mL within 13.2 min, with 12% bioavailability. In contrast, the transdermal propranolol patch maintained a steady-state plasma concentration of 9.3 ng/mL after an initial lag time of 8 h, with a significantly higher bioavailability of 74.8% [10]. An additional illustration of hydrogel-based transdermal drug delivery is the application of sicdnafil citrate (S.C.) in treating pulmonary hypertension (P.H.). P.H., a cardiovascular disease, significantly reduces functional capacity, diminishes quality of life, and substantially shortens life expectancy [86]. Microneedles with hydrogel-forming capabilities that penetrate the stratum corneum were used to distribute the drug via the transdermal route.
# Low-Profile Antenna System for Cognitive Radio in IoST CubeSat Applications
## 2 Antenna System Structure
### Narrowband Reconfigurable Antenna  
The narrowband antenna is a semi-ellipse-meander-shaped slot antenna; the meander shape is employed to reduce the antenna's physical size. Similar to the sensing antenna, the NB antenna is loaded with various capacitance values from 0.84 pF to 5.08 pF. A varactor diode is connected to the antenna to reconfigure its operating frequency based on the bias voltage value, which changes the capacitor value. The proposed slot antenna has a total length of 86 mm and a width of 32 mm. Two NB antennas are printed on the same view with the sensing antenna. They are printed in two different directions to achieve pattern diversity. Five capacitance values (0.84, 0.9, 1.24, 2.09, and 5.08 pF) are used to tune the antenna's resonant frequency over the wide frequency band. Figure 6 depicts the reflection coefficients of different capacitance values for NB P-3 and NB P-4. The results show a good match in all cases, and the resonant frequency decreases with increasing the capacitance value. High isolations between the NB P-3/NB P-4, NB P-3/WB-P1, and NB P-4/WB-P1 have been achieved thanks to the different orientations of the antennas (see Figure 7).  
Figure 5: Current distribution for phases at 0\({}^{\circ}\), 45\({}^{\circ}\), 135\({}^{\circ}\), and 180\({}^{\circ}\), when the antenna is excited from (a) P-1 and (b) P-2.  
Figure 6: Simulated reflection coefficients of the NB antennas with different values of the capacitor: (a) \(S_{33}\) and (b) \(S_{44}\).  
The biasing circuitry of the reconfigurable antenna, as shown in Figure 2, consisted of the varactor diode, RF choke (L\({}_{1}\), L\({}_{2}\) = 1 \(\mu\)H), and current-limiting resistor (R\({}_{1}\), R\({}_{2}\) = 2.1 K\(\Omega\)). The varactor diode (SMV 1233) is connected to the biasing circuitry through shorting posts. RF chokes are used to separate the radiating structure from the DC power source while the reverse-biased varactor diode serves as a DC blocking capacitor to ensure that the DC biasing component and the RF radiating structure are well isolated.
# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 5 Results
### Vacation Leave Prediction
#### 5.1.6. Attribute Importance with Random Forests for Vacation Leave Prediction  
In Table 8, we outline the most important attributes for predicting vacation leave using Random Forests on three different prediction horizons: one-week, two-weeks, and one month. The attributes are ordered according to their importance based on impurity reduction. The results suggest a clear pattern: For shorter prediction horizons (one week), longer-term historical data (up to 12 months) are more significant, reflecting recurring patterns in individual absence behaviors. However, as the prediction horizon extends to two weeks and beyond, more recent data and seasonal influences play a more prominent role. This indicates that, while longer-term historical data remains relevant, the relative importance of seasonal and short-term features increases with the prediction period.
## 3 Results
### Properties  
#### 3.2.1 Density  
The comparison of simulated and experimental densities for A-2, A-4, A-6, and A-8 systems are plotted in Figure 6. The experimental results were 6.3%, 8.1%, 3.8%, and 4.5% larger than the simulated results from A-2 to A-8. The discrepancies can be contributed to the selected force field and experimental error. Despite many efforts to keep the samples pure before testing, the LiCl should be hydrated and this affected the results. The BMHFT potential underestimated the density by about 5.3%, 6.3%, and 9.0% for pure molten LiCl, NaCl, and KCl, respectively [26]. Thus, it seems that the BMH pair potential, Larsen combination rule, and correction factors do a reasonable job of capturing molten phase properties.  
Densities across the whole composition and temperature range are shown in Figure 7. It decreased linearly with the ascending temperature and Li\({}^{+}\) addition. A database has been built for the ternary system across the full concentration, and the fitting expression is shown below:  
\[\begin{array}{c}\rho_{density}=\begin{array}{c}-0.00164x\end{array}-\begin{array} []{c}8.14236\end{array}\times\begin{array}{c}10^{-4}T\end{array}-\begin{array} []{c}1.51351\end{array}\times\begin{array}{c}10^{-6}x^{2}\end{array}+\begin{array} []{c}1.94672\end{array}\times\begin{array}{c}10^{-7}T^{2}\end{array}-\begin{array} []{c}1.94938\end{array}\times\begin{array}{c}10^{-9}x^{3}\\ -4.86205\end{array}\times\begin{array}{c}10^{-11}T^{3}\end{array}+\begin{array} []{c}1.24708\end{array}\times\begin{array}{c}10^{-6}xT\end{array}+\begin{array} []{c}4.17543\end{array}\times\begin{array}{c}10^{-13}x^{2}T^{2}\end{array}+ \begin{array}{c}2.13573\end{array} \tag{5}\]  
where \(x\) is the concentration of LiCl. Neglecting the minor terms and fitting again:  
\[\begin{array}{c}\rho_{density}=\begin{array}{c}-0.00172x\end{array}- \begin{array}{c}6.58567\end{array}\times\begin{array}{c}10^{-4}T\end{array}- \begin{array}{c}1.34192\end{array}\times\begin{array}{c}10^{-6}x^{2}\end{array}+ \begin{array}{c}4.29788\end{array}\times\begin{array}{c}10^{-8}T^{2}\end{array}+ \begin{array}{c}1.33476\end{array}\times\begin{array}{c}10^{-6}xT\end{array}+ \begin{array}{c}2.08316\end{array} \tag{6}\]  
Figure 6: Comparison of simulated and experimental liquid densities with the linear fit formula of simulated density appending.  
As listed in Table 4, the absolute values of the slopes of fitted lines become smaller with the addition of lithium ions, which indicated that the slope decreased slower with ascending temperature as the LiCl addition increased. Derived from [PERSON] et al.'s [15] work, pure LiCl has the smallest slope in LiCl, NaCl, and KCl. Therefore, the higher LiCl concentration was, the more slowly the density changed along the temperature.
# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 3 Proposed Name Disambiguation Scheme
### Author Name Disambiguation
#### 3.4.2 Deep Learning-Based Scheme  
The deep learning-based analysis, similar to rule-based analysis schemes, employs major attributes from the metadata that can act as distinguishing factors for individuals with the same name. These attributes were represented in a graph, and the GCN was utilized to learn the latent features of the paper.  
Deep learning analysis uses various attributes, such as the title, keywords, abstract, co-authors, publication year, and journal data. As the document title and abstract were used as attributes, major keywords from these attributes were extracted using natural language processing packages, such as konlpy and NLTK. The extracted keywords were then converted into a vector form using FastText. This converted vector was used as the input vector for the deep learning model. In this study, the converted vectors were constructed in the form of a triplet network. The triplet network structure, as shown in Figure 5, represents vectors by placing vectors with similar and dissimilar values close to each other and further apart, respectively. In the (A) triplet network, anchor refers to a document of a unique author, positive refers to another document of that unique author, and negative refers to a document of an author with the same name but not the unique author. With the anchor document as a reference, the objective of the triplet network is to bring the documents corresponding to the positive closer and drive those corresponding to the negative far apart. As shown in Figure 5b, a plot triplet transformed the vector value of the converted paper into an optimal vector value using the vector value of another paper. Initially, \(P_{pj}\) referred to a paper similar to the plot paper (with author, title, publication year, keywords, journal info, etc.), and \(N_{pk}\) referred to a paper dissimilar to the plot paper. The aim of the triplet was to calculate the distance between vectors, bringing similar vectors closer and pushing dissimilar vectors further apart. Therefore, based on the similarity in the information of the papers, the vector values of all the input papers were converted into the form of a triplet network, as shown in Figure 5a.  
Figure 4: Example of name disambiguation rules.
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 2 Design Space
### Geometric Design Space and Parametrization Techniques
The method adopted in this study is based on the Bezier-GAN (BGAN) parameterization technique, developed by [PERSON], [PERSON], and [PERSON] from the University of Maryland [29]. This deep learning model, based on Info-GAN structures, specifically designed for aerodynamic shape generation, leverages the smooth, continuous properties of Bezier curves within a GAN framework to produce realistic airfoil shapes. These networks utilize latent codes and noise variables as inputs to the network, to effectively capture both major and minor shape variations in airfoils, enabling the synthesis of smooth, aerodynamic designs. The latent codes form a low-dimensional representation that serves as the primary mechanism for representing major shape features, such as thickness, camber, reflex, and overall curvature. Alongside the latent codes, noise variables can be introduced to handle finer, more detailed aspects of the airfoil shape. These noise variables provide an additional level of granularity, allowing for subtle adjustments that add diversity to the generated shapes. Following the training methodologies proposed by [PERSON] et al., the parameters depicted in Table 3 are selected to train the airfoil generator. Thus, with only eight design variables, it is possible to accurately represent an airfoil that belongs in the original UIUC database (training dataset), as well as to generate new novel configurations that enhance the exploration of the design space. It is important to note that while the UIUC database primarily contains airfoils analyzed at low Reynolds numbers, the BGAN training dataset utilizes only the geometry of the airfoils and not the results of the corresponding analyses. Moreover, many of the airfoils in the database are widely used in operating aircrafts, where they perform effectively at significantly higher Reynolds numbers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Parameter** & **Value** & **Description** \\ \hline Bezier degree & 32 & The number of Bézier control points \\ Latent codes dimension & 8 & Number of latent codes \\ Noise dimension & 0 & Number of components in noise vector \\ Train steps/epochs & 150,000 & - \\ Batch size & 32 & Number of samples per network pass \\ Bounds & [0, 1] & Bounds of latent codes and noise components \\ Latent code sampling PDF & Uniform & Probability distribution \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training parameters for the BGAN model.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 2 Application Scenarios
### UAV-Based Software-Defined Networks  
Network function virtualization (NFV) is commonly used to virtualize network functions on servers with general-purpose, standard storage devices, and switches, thus allowing a programmable network architecture, which is particularly useful to seamlessly integrate UAVs into existing networks. Moreover, by separating the control and data planes, software-defined networking (SDN) provides centralized control, a comprehensive view of the network, ease of reconfiguration, and arrangement of network functions through flow-based networking.  
In a cellular network, a centralized SDN controller performs efficient radio resource and mobility management, which is particularly important to enable U-RANs [40]. For instance, SDN-based load balancing can be very useful for multi-tier U-RANs, so as to suitably weigh up the load of each UAV-mounted BS and terrestrial BS. Moreover, SDN allows faster switching between RANs, enabling the use of different RANs in multi-tier UAV-based cell networks.  
U-RANs can be particularly useful in cloud computing architectures (see Figure 3), since they can perform remote processing and computing, either to reduce the load of the main cloud center, or to substitute damaged parts of the terrestrial networks in case of natural disasters. Furthermore, network architectures based on hierarchical SDN controllers are able to allow granular management of flows through UAV cells to implement efficient handoff and routing procedures [38; 48; 49].
# Recent Advances in Efficient Dynamic Graph Processing
## 6 Future Work
### Parallel and Distributed Approaches for Large-Scale Graphs  
Further optimized algorithms for different update scenarios such as in-memory updates, parallel updates, and distributed updates play a crucial role in various application contexts. For instance, parallel Processing utilizes multi-core processors and GPUs to perform multiple graph operations simultaneously, which can significantly accelerate processing speeds. Besides, distributed computing on graphs leverages frameworks like Pregel to distribute the graph processing task across a cluster of machines, enabling the handling of extremely large datasets.
# In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice
## 3 Results
### Effect of Sliding Velocity  
Figures 8 and 9 show the general trend that \(\mu_{\mathrm{k}}\) increases with increasing sliding velocity. With data averaged under different test conditions, Figure 10 depicts the variation of \(\mu_{\mathrm{k}}\) with sliding velocity in logarithmic coordinates. The error bars represent the standard deviation. \(\mu_{\mathrm{k}}\) exhibited an increasing trend over the whole investigated velocity range for each contact. A similar velocity strengthening of \(\mu_{\mathrm{k}}\) was also observed by [PERSON] and [PERSON] [21] in their ice-ice friction experiments at temperatures above \(-5\,\mathrm{\SIUnitSymbolCelsius}\) and over a velocity range of 0.5-3.0 m\(\cdot\)s\({}^{-1}\). Most previous experiments detected a decrease in \(\mu_{\mathrm{k}}\) with increasing sliding velocity, which was due to the low sliding velocity [22; 37] and the low ambient temperatures [23; 25; 32] under the test conditions. Under these conditions, thin water films, which act as a lubricant, are produced at the sliding interface by frictional heat [24]. However, at high air temperatures in the present study, frictional heat is dominated by melting of ice and therefore the friction was dominated by the drag force of water and increased with increasing velocity [21; 24].  
Figure 9: Variations of \(\mu_{\mathrm{k}}\) with velocity \(V\) of friction cases of SS, CS, and ice bottom sliding on the Bottom track. The data were averaged over all normal stresses, and the error bars represent the standard deviation. Also shown are the test air temperature, standard deviations of all datasets, and the significance of the rank sum test.
## 3 Preliminaries
### The Attention Mechanism  
The attention mechanism, drawing inspiration from the selective concentration observed in human cognition [30], functions as a weighted message-passing algorithm among nodes in a graph. It has demonstrated impressive performance in the domain of Natural Language Processing (NLP) and Transformer Models. Figure 1 illustrates the fundamental principle of the attention mechanism. It involves the mapping of a query \(q\) and a collection of key-value (k-v) pairs, which results in an output obtained through a weighted summation of the values. These weights not only represent the similarity between the query and the corresponding key, but also serve as the weights assigned to the message value \(v\) received by a node from its neighboring nodes.  
To be more specific, we define the query \(q_{i}\in\mathbb{R}^{d_{k}}\), key \(k_{i}\in\mathbb{R}^{d_{k}}\), and value \(v_{i}\in\mathbb{R}^{d_{v}}\), where \(d_{k}\) denotes the dimensionality of the query \(q_{i}\) and key \(k_{i}\), and \(d_{v}\) represents the dimensionality of the value \(v_{i}\). These values are computed by projecting the embedding \(h_{i}\) using the following equations:  
\[q_{i}=W^{Q}h_{i} \tag{5}\]  
\[k_{i}=W^{K}h_{i} \tag{6}\]  
\[v_{i}=W^{V}h_{i} \tag{7}\]  
where \(W^{Q}\), \(W^{K}\), and \(W^{V}\) are matrices of dimensions \(d_{k}\times d_{h}\), \(d_{k}\times d_{h}\), and \(d_{v}\times d_{h}\), respectively.  
Figure 1: Schematic diagram of the multi-head attention mechanism.  
Subsequently, the compatibility \(u_{ij}\in\mathbb{R}\) is calculated through dot product [30] as follows:  
\[u_{ij}=\begin{cases}\frac{q_{i}^{T}k_{j}}{\sqrt{d_{k}}}&\text{if $i$ is adjacent to $j$}\\ -\infty,&\text{otherwise}\end{cases} \tag{8}\]  
The attention weights \(a_{ij}\) are obtained by applying a softmax function:  
\[a_{ij}=\frac{e^{u_{ij}}}{\sum_{j}^{t}e^{u_{ij}}} \tag{9}\]  
Consequently, the message node \(i\) receives, denoted as \(h_{i^{\prime}}^{\prime}\), is the convex combination of the message values \(v_{j}\):  
\[h_{j}^{\prime}=\sum_{j}s_{ij}v_{j} \tag{10}\]  
Multi-head Attention: In scenarios involving large and complex datasets, multi-head attention has been observed to deliver improved performance [30]. This approach allows each node in the graph to receive different types of messages from different neighbors. Let \(M\) denote the number of attention heads, and \(h_{im}^{\prime}\) denote the resultant vectors for \(m\in 1,\ldots,M\). These vectors are then projected back to a single \(d_{h}\)-dimensional vector using parameter matrices \(W_{m}^{O}\) of dimensions \(d_{h}\times d_{v}\). Therefore, the final multi-head attention value for node \(i\) is a function of \(h_{1},\ldots,h_{n}\) through \(h_{im}^{\prime}\):  
\[MHA_{i}(h_{1},\ldots,h_{m})=\sum_{m=1}^{M}W_{m}^{O}h_{im}^{\prime} \tag{11}\]
## 2 Materials and Methods
### Module Development
#### 2.2.2 Long Short-Term Memory (LSTM)  
LSTM is a special kind of Recurrent Neural Network (RNN) with additional features to memorize the sequence of data. They were explicitly designed to avoid long-term dependency issues, which is the cause of the gradient problem in normal RNNs. The ability to learn long-term dependencies is due to the structure of LSTM units, which incorporate gates that regulate the learning process. The structure of the LSTM unit contains forget gate \(f_{t}\), candidate layer \(\overline{C}_{t}\), input gate \(I_{t}\), output gate \(O_{t}\), and memory state \(C_{t}\). The relevant mathematical formulas are as follows [31]:  
\[f_{t}=\sigma\Big{(}X_{t}*\mathbf{W_{f}}+H_{t-1}*\mathbf{W_{f}}+b_{f}\Big{)} \tag{2}\]  
\[\overline{C}_{t}=\tanh\left(X_{t}*\mathbf{W_{\overline{C}}}+H_{t-1}*\mathbf{W_{ \overline{C}}}+b_{\overline{C}}\right) \tag{3}\]  
\[I_{t}=\sigma(X_{t}*\mathbf{W_{i}}+H_{t-1}*\mathbf{Wi}+bi) \tag{4}\]  
\[O_{t}=\sigma(X_{t}*\mathbf{W_{o}}+H_{t-1}*\mathbf{W_{o}}) \tag{5}\]  
\[C_{t}=f_{t}*C_{t-1}+I_{t}*\overline{C}_{t} \tag{6}\]  
\[H_{t}=O_{t}*tanh(C_{t}) \tag{7}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Values** \\ \hline Optimizer & [Adam, RMSProp, Nadam] \\ \hline Batch size & [32–512 with a step of 32] \\ \hline Layers number & [1, 2, 3, 4] \\ \hline Number of Neurons in each layer & [8–256 with step of 8] \\ \hline Dropout rate & [0.1, 0.2] \\ \hline Epoch & 120 \\ \hline Activation function & ReLu \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters values.  
where \(X_{t}\) is the input vectors, \(H_{t-1}\) is the previous output cells, \(C_{t-1}\) are the previous memory cells, \(H_{t}\) are the current output cells, \(C_{t}\) are the current memory cells, \(\mathbf{W}\), \(\mathbf{b}\) are the weight matrices, and the bias vectors, respectively. Using the combination of these equations, the LSTM unit can be expressed as follows:  
\[h_{t}=f(h_{t-1},X_{t},\mathbf{\psi}) \tag{8}\]  
where \(f\) is the LSTM function and \(\mathbf{\psi}\) is the vector parameter in the LSTM.  
The input gate determines whether or not to let the new input in. An output gate decides what information to output, and memory gate chooses which new data must be stored in the cell. These gates are analog gates based on the sigmoid function, which works in the range 0-1.