esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Approaches to Source Code Transformation into Vector-Based Representations
### Markov Chains for Abstract Syntax Trees  
AST-based Markov chain models are described in [15; 16; 19]. In [15; 19], only first-order Markov chains are considered and used in classification and clustering tasks. In this study we describe a general algorithm which transforms an AST into a higher-order Markov chain. Our aim is to investigate if there is an increase in classifier quality when program vectors are based on Markov chains of an increased order.  
A discrete-time first-order Markov chain is a sequence of dependent discrete random variables \(x_{0},x_{1},x_{2},\ldots,x_{t}\) if the probability distribution of \(x_{t+1}\) only depends on the value of the current variable \(x_{t}\):  
\[\text{P}(x_{t+1}|x_{t},x_{t-1},x_{t-2},\ldots,x_{0})=\text{P}(x_{t+1}|x_{t}), \tag{4}\]  
where \(x_{t}\) and \(x_{t+1}\) are random variables following each other, \(t\) is the time moment.  
A discrete-time \(n\)-th order Markov chain is a sequence of dependent discrete random variables \(x_{0},x_{1},x_{2},\ldots,x_{t}\) if the probability distribution of \(x_{t+1}\) depends on the values of the \(n\) preceding variables \(x_{t},x_{t-1},x_{t-2},\ldots,x_{t+1-n}\):  
\[\text{P}(x_{t+1}|x_{t},x_{t-1},x_{t-2},\ldots,x_{0})=\text{P}(x_{t+1}|x_{t},x _{t-1},x_{t-2},\ldots,x_{t+1-n}), \tag{5}\]  
where \(x_{t}\) and \(x_{t+1}\) are random variables following each other, \(t\) is the time moment, and \(n\) is the order of the Markov chain.  
The range of values of random variables \(x_{0},x_{1},x_{2},\ldots,x_{t}\) is called the state space of a Markov chain. The state space \(H\) is given by \(\{h_{1},h_{2},\ldots,h_{m}\}\), where \(m\) is the count of all possible different states of a Markov chain, \(m=|H|\). The transitions between every pair of states from \(H\) are given by the state transition matrix \(\textbf{P}\in\mathbb{R}^{m\times m}\). For a first-order Markov chain, the elements of a matrix **P** are given by:  
\[p_{ij}=\text{P}\big{(}x_{t+1}=h_{j}\big{|}x_{t}=h_{i}\big{)},\ p_{ij}\geq 0, \forall\ i:\sum_{j=1}^{m}p_{ij}=1, \tag{6}\]  
where \(x_{t}\) and \(x_{t+1}\) are random variables following each other, \(t\) is the time moment, \(h_{i}\in H\) and \(h_{j}\in H\), \(p_{ij}\) is the cell of the state transition matrix **P**, denoting the probability of the transition from state \(h_{i}\) to state \(h_{j}\), \(m\) is the count of all possible states, and the matrix **P** is the weighted adjacency matrix of the state transition graph of a Markov chain.  
When building an AST-based Markov chain, the state space \(H\) contains types of all nodes that occur in the AST, and the **P** matrix contains the probabilities of the existence of links between every couple of AST node types.  
After building an \(n\)-th order Markov chain state transition graph from an AST using Algorithm 2, a weighted adjacency matrix is constructed based on the state space \(H\) containing all node types that occur in a vectorized dataset [19]. Figure 7 summarizes the process of Markov-chain-based embedding of programs into vector space.
This latency risks rendering anti-jamming decisions obsolete before adapting to rapidly evolving jamming. * Poor transferability: Methods leveraging transfer learning or knowledge graphs perform better when confronting identical or similar jamming styles, whereas the traditional DQN still demands extensive retraining. To address the above challenges, this paper investigates how Generative Adversarial Networks (GANs) can enhance the convergence speed of DQN anti-jamming algorithms based on the traditional DQN framework. As a generative model, GANs can solve the problem of insufficient samples by generating similar samples with the same distribution as real samples, as has been widely applied in fields such as image processing [11; 12], target detection [13; 14], and audio generation [15; 16; 17]. This paper applies GANs to the field of communication anti-jamming and proposes a small-sample anti-jamming decision method based on a GAN-enhanced Deep Q-Network (GA-DQN). The method uses a GAN to independently learn from collected jamming signals, capture the latent distribution of jamming signals, and generate training samples that approximate the jamming signals distribution to construct a mixed experience replay buffer. Then, a stochastic gradient descent method is adopted to train the policy network, which significantly improves the number and diversity of samples obtained in each algorithm iteration and accelerates the training process of the policy network.
## 3 Results and Discussion
### Investigation of the Theoretical Maximum Bandwidth
#### 3.2.2 Non-Equidistant Gaps \(\Delta f_{\mathrm{i}}\)  
In the previous section, different bandwidths with equidistant gaps of the resonant frequencies were analyzed. In the present subsection, the gaps between the resonant frequencies are not equidistant. All possible combinations of frequency gaps with 2, 3 and 4 cantilevers have been simulated with the resolution of 0.01 Hz. The combinations with the maximum theoretical bandwidth are shown in Table 2. The individual frequencies of the cantilevers do not vary significantly when increasing the number of cantilevers. This behavior is advantageous in the calculations of the optimum arrangements  
Figure 8: Simulation of bandwidth dependence on the number of cantilevers for \(\Delta f\) = 1.5 Hz. The lines connecting theoretical points serve as a guide for the eye.  
Figure 9: (**a**) Simulated dependence of the number of cantilevers on the frequency detuning \(\Delta f\); (**b**) Dependence of the maximum bandwidth on the frequency detuning \(\Delta f\). The lines connecting theoretical points serve as a guide for the eye.  
of more cantilevers, because the simulation of all possible combinations of more than four cantilevers is time consuming. By using already identified frequencies for the simulation of a larger number of cantilevers, the computational complexity has been significantly reduced.  
Table 3 compares the theoretical maximum bandwidth for non-equidistant and equidistant gaps. The frequency bandwidth is larger for non-equidistant frequency detuning. However, the difference to the equidistant case is small. The reason for this is that, for a small number of cantilevers, the optimum frequency gaps of non-equidistant and equidistant cases are very similar. The advantage of non-equidistant frequency gaps over equidistant gaps increases with the growing total number of PECs.
# Microbiota and Metabolite Profiling of Spoiled Spanish-Style Green Table Olives
## Results and Discussion  
### Physicochemical Characteristics of Brine Samples  
The evolution of pH and the titratable acidity of brine samples from Gordal and Manzanilla cultivars at up to 11 months of brining are shown in Figure 1.  
Figure 1: \(Cont.\)  
The initial increase in tittatable acidity due to lactic acid fermentation occurred faster in the Gordal samples compared to the Manzanilla samples. In all the samples but the control ones, the tittatable acidity reached its maximum value during the fermentation step, and progressively decreased afterward according to the measured pH increase. The final values for pH ranged between 4.66 and 5.10. It is worth noting that in the control samples from both cultivars tittatable acidity increased with a concomitant decreased in pH as a result of adding lactic acid after 8 months of brining, but then the tittatable acidity decreased during the post-fermentation stage (Figure 1). This could be attributed to oxidative yeasts growing on the surface of brine, which partially metabolized the lactic and acetic acids (Figure S1). The final physicochemical characteristics of all samples are shown in Table 1. The different values of combined acidity and total phenols were indicative of the different alkaline treatments and washing steps applied. All samples with low salt concentration (35-39 g L\({}^{-1}\) NaCl in brine) were detected by smell as spoiled to a greater or lesser extent. On the contrary, all the control brine samples (with 87-88 g L\({}^{-1}\) NaCl) had normal odors. The abnormal odors of samples with low salt were confirmed by a quantitative descriptive analysis, as discussed later.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample a**} & \multirow{2}{*}{**pH**} & **Tittatable acidity** & **Combined Aidity** & **Salt** & **Total Phenols** \\  & & **(g L\({}^{-1}\)** & **Lactic Acid)** & **(g L\({}^{-1}\))** & **(g L\({}^{-1}\)** & **NaCl)** & **(g L\({}^{-1}\)** \\ \hline \multicolumn{6}{c}{**Gordal Cultivar**} \\ \hline G1 (control) & 4.53 & 3.6 & 0.082 & 87 & 1.39 \\ G2 (control) & 4.67 & 3.6 & 0.080 & 88 & 1.39 \\ GFC1 & 4.99 & 4.3 & 0.130 & 38 & 1.72 \\ GFC2 & 5.05 & 4.4 & 0.127 & 37 & 2.17 \\ GFL1 & 5.10 & 2.4 & 0.086 & 37 & 1.57 \\ GFL2 & 4.86 & 4.5 & 0.097 & 37 & 1.50 \\ GCC1 & 5.06 & 3.9 & 0.121 & 38 & 2.48 \\ GCC2 & 4.95 & 4.8 & 0.119 & 38 & 2.54 \\ GCL1 & 4.83 & 3.8 & 0.076 & 35 & 1.74 \\ GCL2 & 4.84 & 4.0 & 0.081 & 39 & 1.64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The physicochemical analysis of olive brines after 11 months of brining.  
Figure 1: The changes in the pH and tittatable acidity of Gordal and Manzanilla samples during fermentation. The points are the means of duplicate fermentations. Error bars show the range of the data (n = 2). Where error bars are not visible, the values were within the range of the symbols. See Table 5 for the meanings of samples abbreviations.
## 2 ADCS System Model
### Attitude Determination Algorithm
However, to predict this matrix it can be computed through the Triad method:  
\[\hat{\mathbf{t}}_{r}=\frac{\mathbf{r}}{|\mathbf{r}|},\quad\hat{\mathbf{t}}_{\theta}=\hat{\mathbf{ t}}_{h}\times\hat{\mathbf{t}}_{r},\quad\hat{\mathbf{t}}_{h}=\frac{\mathbf{r}\times\hat{\mathbf{r}} }{|\mathbf{r}\times\hat{\mathbf{r}}|} \tag{19}\]  
\[[HN]=\left[\hat{\mathbf{t}}_{r}\quad\hat{\mathbf{t}}_{\theta}\quad\hat{\mathbf{t}}_{h}\right] \tag{20}\]  
where \(\mathbf{r}\) is estimated by an orbit propagator or GNSS receiver; for this work, just the orbit propagator case is considered. Otherwise, for applying the EKF, the dynamics should be expressed as follows:  
\[\dot{\mathbf{x}}=f_{d}(\mathbf{x},\mathbf{L}_{u})+\mathbf{w} \tag{21}\] \[\mathbf{y}=h_{d}(\mathbf{x},\mathbf{L}_{u})+\mathbf{v} \tag{22}\]  
where \(\mathbf{w}\) and \(\mathbf{v}\) are disturbances. Next, the required equations for EKF are presented:  
\[ \begin{bmatrix}\dot{\mathbf{q}}\\ \dot{\mathbf{\omega}}\end{bmatrix} = \begin{bmatrix}\frac{1}{2}\mathbf{\Omega}(\mathbf{ \omega})\mathbf{q}\\ [I]^{-1}(-[\ddot{\mathbf{\omega}}][I]\mathbf{\omega}+\mathbf{L}_{u})\end{bmatrix} + \begin{bmatrix} \mathbf{0}\\ \mathbf{\omega}_{\text{drift}}+\mathcal{N}(0,(l_{min}^{-1}L_{d-max})^{2})\cdot\mathbf{ 1}_{3\times 1}\end{bmatrix}  \tag{23}\]  
Notice that the discrete EKF will be implemented. By discretizing this system, it will also introduce a discretization error, which in turn will introduce process noise to \(\mathbf{q}_{k}\) and \(\mathbf{\omega}_{k}\).
# A Low-Profile Wideband Monocone Antenna Using Bent Shorting Strips
## 3 Antenna Analysis
This phenomenon can be regarded as the main cause of the input impedance matching improvement in the mid- and upper frequency bands. Figure 3: Simulated reflection coefficients of reference 1, reference 2, reference 3, and the proposed antennas. Figure 5 shows the simulated reflection coefficients when the radius of the inner circular sleeve (\(R_{2}\)) varies from 38 mm to 40 mm. Figure 6 shows the simulated reflection coefficients when the height of the inner circular sleeve (\(H_{2}\)) varies from 8 mm to 10 mm. It can be seen that the inner sleeve radius and height mainly affect the impedance matching characteristics in the mid- and upper-frequency bands. Figure 7 shows the simulated reflection coefficients when the height of the outer circular sleeve (\(H_{3}\)) varies from 3 mm to 7 mm. It can be seen that the magnitude of the sleeve height mainly affects the impedance matching characteristics in the mid- and upper-frequency bands. Figure 8 depicts the simulated reflection coefficients when the height of the parasitic sleeve (\(H_{4}\)) for the proposed antenna varies from 8 mm to 12 mm. The results show that the impedance matching characteristics are mainly affected in the mid and upper bands and that the high-frequency band is expanded by shifting the resonance point of the upper band to the high-frequency side. The improvement of the impedance matching characteristics of the mid and upper bands by the parasitic and circular sleeves is due to capacitance coupling between the sleeves and the monocone antenna. Figure 9 illustrates the effect of the width of the bent shorting strips (\(W_{2}\)) on the impedance matching as it changes from 0.7 mm to 1.3 mm. It is shown that the width of the bent shorting strips affects the impedance matching characteristics in mid- and upper-frequency bands. By properly choosing the width of the bent shorting strips, the bandwidth of the proposed antenna is enhanced by shifting the resonance point of the upper band to the high-frequency side. Figure 4: The current distributions for (**a**) reference 1, (**b**) reference 2, and (**c**) proposed antenna. Figure 5: Simulated reflection coefficients for different radius (\(R_{2}\)) values of the inner circular sleeve. Figure 6: Simulated reflection coefficients for different height (\(H_{2}\)) values of the inner circular sleeve. Figure 7: Simulated reflection coefficients for different height (\(H_{3}\)) values of the outer circular sleeve.
# A Denoising Method for Seismic Data Based on SVD and Deep Learning
###### Abstract  
When reconstructing seismic data, the traditional singular value decomposition (SVD) denoising method has the challenge of difficult rank selection. Therefore, we propose a seismic data denoising method that combines SVD and deep learning. In this method, seismic data with different signal-to-noise ratios (SNRs) are processed by SVD. Data sets are created from the decomposed right singular vectors and data sets divided into two categories: effective signal and noise. The lightweight MobileNetV2 network was chosen for training because of its quick response speed and great accuracy. We forecasted and categorized the right singular vectors by SVD using the trained MobileNetV2 network. The right singular vector (RSV) corresponding to the noise in the seismic data was removed during reconstruction, but the effective signal was kept. The effective signal was projected to smooth the RSV. Finally, the goal of low SNR denoising of two-dimensional seismic data was accomplished. This approach addresses issues with deep learning in seismic data processing, including the challenge of gathering sample data and the weak generalizability of the training model. Compared with the traditional denoising method, the improved denoising method performs well at removing Gaussian and irregular noise with strong amplitudes.  
MobileNetV2; seismic data; denoising; deep learning; SVD +
Footnote †: journal: Applied Sciences  
1
# Computed Tomography-Driven Design and Fused Filament Fabrication of Graded Density Bone Scaffolds
## 2 Materials and Methods
### Cad-CAM Integration
#### 2.3.3 G-Code Editing  
With the CAD and CAM models properly aligned, the G-code can be modified wisely. Since a G-code file consists primarily of motion instructions in the format of G1 Xx Yy Zz Ee, where X, Y, and Z represent the Cartesian coordinates and E specifies the extrusion length in mm for the filament feed, each \(i-th\) position along the toolpath in the infill section is considered:  
\[\Delta E_{e,i}=\Delta E_{s,i}\frac{\rho_{e,i}}{\rho_{s}}, \tag{6}\]  
where \(\Delta E_{e,i}\) represents the required material amount at the \(i-th\) point of the toolpath in the G-code editing phase; \(\Delta E_{s,i}\) is the material amount calculated by the slicer software, while \(\rho_{slicer}\) denotes the infill density from the slicing process. Additionally, \(\rho_{e,i}\) is the required  
Figure 6: Representation of the linear equation that maps HU values to \(P\) values.  
relative density at the \(i-th\) point obtained through trilinear interpolation of the \(\rho\) 3D scalar field onto the mid-point of each toolpath segment [53]. It must be noted that the voxel structure or volumetric model is employed as an interpolation grid, making the CT voxel size (vSX, vSY, and vSZ) independent of the scaffold's cell size \(L_{cell}\).  
The computation of \(\Delta E_{e,i}\) incorporates the minimum relative density \(\rho_{e,min}=1-P_{E_{w,min}}\) for a consistent part, considering the layer thickness \(l_{h}\) among the various parameters. To simplify the G-code editing and avoid calculating \(\Delta E_{s,i}\), relative E coordinates are used instead of absolute coordinates. A custom Python script was developed to implement the G-code editing. The pseudocode is reported below:  
_for each line in the G-code file:_  
_if available, extract X, Y, Z, E_  
_if line is infill:_  
_calculate interpolated value \(\rho_{e,I}\) at X, Y, Z_  
_if \(\rho_{e,I}<\rho_{emin}\):_  
_\(\rho_{e,I}=\rho_{emin}\)_  
_compute new value for E (Equation (6))_  
_substitute new value for E in G-code line in E position_
# Arsenic Removal from Water by Adsorption onto Iron Oxide/Nano-Porous Carbon Magnetic Composite
## 4 Conclusions  
The removal of As (III) from aqueous solution by adsorption onto magnetic Fe\({}_{3}\)O\({}_{4}\)/activated carbon composite prepared from sugarcane bagasse was studied. The performance of the composite was compared to those of plain AC. The percentage adsorption of the composite was much higher than the plain AC. The adsorption capacity of the composite for As (III) was dependent on the pH, adsorbent dose, and contact time. The equilibrium data obtained from this study was fitted more to Langmuir isotherm than Freundlich isotherm. It indicated that the uptake of arsenic occurred on a homogenous surface of the composite by monolayer adsorption. The comparisons of maximum adsorption capacity of composite with earlier investigated adsorbents showed that the magnetic composite prepared in this work was an efficient adsorbent for the adsorptive removal of As (III) from aqueous solution. Thus,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
** adsorbents** & **Maximum Adsorption Capacity (\(q_{\rm m}\))** & **References** \\ \hline Iron impregnated AC from & & \\ Lapsi seed stone & 2 mg/g & [PERSON]. [48] \\ Magnetic nanoparticles & & \\ obtained from metallic wool & 2.2 mg/g. & [PERSON] [PERSON] and [PERSON] [PERSON]. [49] \\ Magnetic-maghemite & & \\ nanoparticles & 3.69 mg/g & [PERSON] and [PERSON]. [50] \\ Iron oxide/commercial & & \\ activated carbon composite & 7.5 mg/g & Previous study [51] \\ Magnetic Fe\({}_{3}\)O\({}_{4}\)/sugarcane & & \\ Bagasse activated & 6.69 mg/g & Present study \\ carbon Composite & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Langmuir and Freundlich parameters for the adsorption of As (III) onto the composite.  
Figure 6: Freundlich isotherm for the adsorption of As (III) onto composite with error bars.  
this magnetic composite adsorbent prepared from sugarcane bagasse showed significant potentiality towards the adsorptive removal of As (III) from aqueous solution.  
Conceptualization, B.S. and S.S.; methodology, S.J., M.S., and A.K.; formal analysis, B.S. and S.J.; writing--review and editing, B.S., S.J., and S.S.  
This research received no external funding.  
The authors gratefully acknowledge University Grants Commission (UGC) CRG 73/74-Engg-02, Nepal for financial support for this work. Also, the present research has been conducted by the Research Grant of Kwangwoon University in 2019.  
The authors declare no conflict of interest.
## 3 Results
### Principal Component Analysis  
Principal component analysis (PCA) was done to estimate the relationships between the different fermentation conditions (methods, as well as duration) and 10 parameters listed in Tables 1-3. The results of PCA are shown in Figure 1. The PCA indicated that the first two components explained 44.26% (PC1) and 39.35% (PC2) of the total variance (Figure 1). Eigenvalues of the PC1 and PC2 were higher than one (4.43 and 3.94, respectively). Vitamin C, dehydroacorbic acid, L-ascorbic acid, chlorophyll \(b\), and sucrose were highly positively associated with the first factor (PC1). The second factor (PC2) was positively associated with chlorophyll (total), chlorophyll \(a\), sugars (total), glucose, and fructose.  
As shown in Figure 1, the NF (control) and AEF 24 h were associated with higher amounts of vitamin C, L-ascorbic acid, chlorophyll \(b\), and sucrose. ANEF 24 h and ANEF 72 h were grouped closely due to the higher amounts of sugars (total), fructose, glucose, chlorophyll (total), and chlorophyll \(a\).  
Figure 1: Principal component analysis of vitamin C, dehydroacorbic acid, L-ascorbic acid, total chlorophyll, chlorophyll \(a\), chlorophyll \(b\), total sugars, glucose, fructose, and sucrose in willowerb leaves (NF (control)—unfermented leaves (control), AEF 24 h—aerobic fermentation 24 h, AEF 48 h —aerobic fermentation 48 h, AEF 72 h—aerobic fermentation 72 h, ANEF 24 h—a anaerobic fermentation 24 h, ANEF 48 h—a anaerobic fermentation 48 h, ANEF 72 h—a anaerobic fermentation 72 h).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fermentation**} & \multirow{2}{*}{**Fermentation**} & \multicolumn{2}{c}{**Total Sugars**} & \multicolumn{2}{c}{**Fructose**} & \multicolumn{2}{c}{**Glucose**} & \multicolumn{2}{c}{**Sucrose**} \\ \cline{3-6}  & & & & **g 100 g\({}^{-1}\) DW** & & \\ \hline Control (unfermented leaves) & 5.30 \(\pm\) 0.03 c 1  & 1.42 \(\pm\) 0.04 e & 1.26 \(\pm\) 0.02 d & 2.62 \(\pm\) 0.06 a \\ Aerobic & 24 h & 5.53 \(\pm\) 0.21 c & 1.75 \(\pm\) 0.09 c & 2.18 \(\pm\) 0.11 c & 1.59 \(\pm\) 0.05 c \\ Aerobic & 48 h & 3.03 \(\pm\) 0.04 d & 0.83 \(\pm\) 0.00 f & 0.86 \(\pm\) 0.00 e & 1.34 \(\pm\) 0.04 d \\ Aerobic & 72 h & 2.79 \(\pm\) 0.04 d & 0.75 \(\pm\) 0.00 f & 0.83 \(\pm\) 0.00 e & 1.21 \(\pm\) 0.04 e \\ Anaerobic & 24 h & 6.24 \(\pm\) 0.08 b & 2.15 \(\pm\) 0.02 b & 2.78 \(\pm\) 0.05 b & 1.32 \(\pm\) 0.04 d \\ Anaerobic & 48 h & 5.46 \(\pm\) 0.07 c & 1.59 \(\pm\) 0.02 d & 2.10 \(\pm\) 0.04 c & 1.77 \(\pm\) 0.09 b \\ Anaerobic & 72 h & 8.06 \(\pm\) 0.27 a & 2.66 \(\pm\) 0.10 a & 3.58 \(\pm\) 0.10 a & 1.82 \(\pm\) 0.07 b \\ \(p\)-values (fermentation method \(\times\) & & & & & \\  & & & & & \\