esa-sceva/satcom-chunk-collection huggingface dataset:
# Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
## 5 Conclusions  
Nowadays, the application of machine learning (ML) in fabrication technologies is increasing. In this study, the raster angle, an important parameter in the fabrication process, was examined using fused deposition modeling (FDM), an additive manufacturing (AM) method. The estimation process for this parameter was performed using ML algorithms. Training data were collected on the basis of material and time consumption values on 3D models with different geometries using 3D printer's slicing software. Performance analysis was conducted to evaluate the performance of the ML algorithms. Among these algorithms, the RFR model predicted the raster angle in the FDM process with high accuracy: R\({}^{2}\) = 0.92, EVS = 0.92, MAE = 0.012, RMSE = 0.056, and MSE = 0.0032. The other methods provided controllable responses within the requirements of the error. The ML method successfully verified and predicted raster angles in the FDM process. This suggests the potential application of similar prediction models to different printing parameters in future research. The method shows promise, outperforming existing techniques according to state-of-the-art classifiers. This indicates the classifiers' capability to predict raster angles effectively in FDM. Implementing advanced ML tools to predict the optimal raster angle for various geometries conclusively decreases fabrication costs without compromising quality. The  
Figure 8: Cross-validation of the testing dataset employing RFR for (**a**) MSE, (**b**) MAE, (**c**) RMSE, and (**d**) R\({}^{2}\).  
novelty of this study is that the implementation of ML for FDM raster angle prediction has never been implemented before. This study's findings contribute to the growth of the AM industry by reducing product lead times and costs without sacrificing accuracy. Future studies could focus on developing a web application utilizing deep neural networks to predict the optimal raster angle for any given 3D image, offering a promising avenue for further exploration.  
Conceptualization, O.U., M.S.B. and M.K.; methodology, O.U. and M.S.B.; validation, O.U. and M.K.; formal analysis, O.U., M.S.B. and M.K.; investigation, O.U. and M.S.B.; resources, M.S.B. and M.K.; data curation O.U. and M.S.B.; writing--original draft preparation, O.U. and M.K.; writing--review and editing, O.U., M.S.B. and M.K.; visualization, O.U. and M.S.B.; supervision, O.U. and M.K. All authors have read and agreed to the published version of the manuscript. plantation.  
The author received no financial support for the research, authorship, and/or publication of this article.  
The author declares that this study does not require ethical approval.  
Not applicable.  
All data underlying the results are available as part of the article and no additional source data are required.  
The author declares no conflicts of interest.
## 4 Experiments and Results
### The Comparison of the Execution Time of Charger Deployment and Modified Deployment Using SMRU  
In the experiment, we measured the execution time it takes for the SMUR algorithm to adjust the position of chargers after sensor mobility. After the SMRU selects the charger position, this experiment randomly moves the sensors from 0 to 2 units to simulate the movement of the sensors. A comparison of the execution time of charger deployment and modified deployment using SMRU is shown in Figure 10.
# Dual-Polarization Conversion and Coding Metasurface for Wideband Radar Cross-Section Reduction
## 2 Metasurface Design
Figure 3a demonstrates that linear-to-circular conversion is achievable over the frequency bands of 11.80-12.29 GHz, 13.17-18.44 GHz, and 33.33-40.35 GHz with equal amplitudes (\(\left|r_{xy}\right|/\left|r_{yy}\right|\cong 1\)), fulfilling the first criterion. Furthermore, as illustrated in Figure 3a, the phase difference between these two components is also close to \(\pm 90^{\circ}\) or an odd multiple of \(\pm 90^{\circ}\). Thus, both criteria for LP-CP conversion have been met within the frequency range of 11.80-12.29 GHz, 13.17-18.44 GHz, and 33.33-40.35 GHz. Moreover, the conversion from linear to circular polarization is deemed valid even when the axial ratio (AR) of the co- and cross-polarized components remains below 3 dB. The AR can be expressed as follows for a normal \(y\)-polarized incident wave:  
\[AR=\left(\frac{{{r_{xy}}^{2}+{r_{yy}}^{2}+\sqrt{\beta}}}{{{r_{xy}}^{2}+{r_{yy}} ^{2}-\sqrt{\beta}}}\right) \tag{4}\]  
where '\(\beta\)' can be expressed as  
\[\beta={{r_{xy}}^{4}+{r_{yy}}^{4}+2{r_{xy}}^{2}r_{yy}^{2}\cos\bigl{(}2(\angle r _{yy}-\angle r_{xy})\bigr{)}} \tag{5}\]  
Figure 2: The simulated result of proposed unit cell: (**a**) reflection coefficient in magnitude (\(\left|r_{xy}\right|,\left|r_{yy}\right|\)) and (**b**) polarization conversion efficiency (%). Also, for the \(x\)-polarized incident wave, subscripts \(y\) and \(x\) will be replaced in Equations (4) and (5). The operating bandwidth for circular polarization is defined as the AR curve with less than 3 dB, as shown in Figure 3b. Thus, Figure 3b illustrates that linear-to-circular conversion can be achieved within the frequency range of 11.80-12.29, 13.17-18.44, and 33.33-40.35 GHz, with a 4.06%, 33.35%, and 19.05% fractional bandwidth.
## Appendix B Supplementary Results and Algorithm  
### Algorithm  
```
Input :Multi-scale features \(\{F_{i}\}_{i=1}^{N}\) Output :Fused feature \(F_{fused}\)
1 FunctionFrequencyAdaptiveFusion.Initialize(in_channels_,num_scales):
2 learnable_wavelets \(\leftarrow\varnothing\);
3 for\(i=1\)to\(num\_scales\)do
4 learnable_wavelets.append(Conv2d(in_channels,in_channels,3));
5\(\alpha\leftarrow\) Parameter(ones(1)) ; // HF weight initialization
6\(\beta\leftarrow\) Parameter(ones(1)) ; // LF weight initialization
7 FunctionEstimatellumination(x):
8 intensity \(\leftarrow\) mean(x);
9 lux \(\leftarrow\) clamp(intensity \(\times\) 255.0, 0.1, 15.0);
10 returnlux;
11 FunctionAdaptiveDecomposition(x):
12 lux \(\leftarrow\) EstimateIllumination(x);
13\(\sigma_{t}\leftarrow\alpha\cdot e^{-\beta flux}\) ; // Adaptive threshold
14 HF_components, LF_components \(\leftarrow\varnothing,\varnothing\);
15 forachwavelet in learnable_waveletsdo
16 feature \(\leftarrow\) wavelet(x);
17 HF \(\leftarrow\) LoG(feature,\(\sigma_{t}\)) ; // High - frequency extraction
18 LF \(\leftarrow\) GaussianBlur(feature,\(\sigma_{t}\times 2\)) ; // Low - frequency extraction
19 HF_components.append(HF);
20 LF_components.append(LF);
21 returnHF_components, LF_components;
22 FunctionScaleFrequencyAttention(HF,LF):
23\(F_{base}\leftarrow\Gamma_{intra}(HF)\oplus\Gamma_{inter}(LF)\) ; // Base fusion
24 for\(s\in\{HF,MF,LF\}\)do
25\(A_{s}\leftarrow\frac{\exp((W_{s}F_{base}+b_{s})+b_{s})}{\sum_{i^{\prime}}\exp(W_{s }^{\prime}+b_{s}^{\prime}+b_{s^{\prime}})}\) ; // Attention calculation
26 return\(A_{HF},A_{MF},A_{LF}\);
27 FunctionNonlinearBandFusion(HF,LF,A_{HF},A_{MF},A_{LF}\):
28 lux \(\leftarrow\) EstimatedIllumination(concat(HF,LF));
29 for\(s\in\{HF,LF\}\)do
30\(M_{s}\leftarrow\) DenseBlocks(\(F_{\text{\emph{Huse}}}\)) ; // Frequency - specific processing
31\(\Phi_{s}\leftarrow\) SwitchedBatchNorm(\(M_{s}\), lux) ; // Illumination - adaptive normalization
32\(F_{fused}\leftarrow\sum_{s}A_{s}\odot\Phi_{s}(M_{s}(F_{base}))\) ; // Weighted fusion
33 return\(F_{fused}\);
34 FunctionForward(multi_scale_features):
35 fused outputs \(\leftarrow\varnothing\);
36 forachfeat in multi_scale_featuresdo
37 HF, LF \(\leftarrow\) AdaptiveDecomposition(feat);
38\(A_{HF},A_{MF},A_{LF}\leftarrow\) ScaleFrequencyAttention(HF,LF);
39 fused \(\leftarrow\) NonlinearBandFusion(HF,LF,A_{HF},A_{MF},A_{LF}\));
40 fused_outputs.append(fused);
41 result \(\leftarrow\) HierarchicalMerge(fused_outputs);
42 returnresult;
43 FunctionHierarchicalMerge(features):
44\(\sigma_{t}\leftarrow\Gamma_{intra}(\alpha_{t}\cdot\psi_{t}^{HF}+\beta_{t}\cdot \psi_{t}^{LF})\oplus\Gamma_{inter}(UpSample(\sigma_{t+1}))\);
45 return\(\sigma_{t}\);
```  
**Algorithm 1**Frequency-Adaptive Fusion (FAF).
## 3 Model and Methodologies
### Preparation Phase  
#### 3.1.1 DeepAR for Demand Forecasting  
This study adopts the autoregressive recurrent network framework proposed by [PERSON] et al. [3], which draws inspiration from the DeepAR model. Their innovative approach to predicting demand distribution in bike-sharing systems builds upon the work of [PERSON] et al. [2], utilizing an LSTM-based encoder-decoder structure. This framework is designed to capture the conditional distribution of future time series data based on historical patterns.  
The model's architecture formulates distribution as a product of likelihood values, with outputs generated through LSTM cells. This design effectively integrates past observations with previous function outputs, allowing the simultaneous addressing of conditioning and prediction challenges within a sequence-to-sequence paradigm. To enhance versatility, the framework accommodates both normal and Poisson distributions, deriving parameters through carefully selected functions: affine and softplus for the normal distribution, and a softplus activation function for the Poisson distribution to ensure positive mean values.  
The training process optimizes the log-likelihood of time series data, incorporating data standardization to improve computational efficiency. This comprehensive approach enables the accurate estimation of demand distribution quantiles for future periods, providing a robust foundation for forecasting in complex bike-sharing scenarios. [PERSON] et al. [3] demonstrated the efficacy of this sophisticated method through comparative analysis with traditional non-neural network techniques, establishing its potential as an advanced tool for demand forecasting in bike-sharing systems. Given its proven effectiveness, we have chosen to implement this framework in our study to address the unique challenges of bike-sharing demand prediction.
## 3 Results
### Effects of Scale on Burial/ISA Relationships
Several additional areas in the suburbs of Maryland and Virginia appear at this analysis scale, largely centered on more recent suburban and commercial development around significant transportation corridors (Figure 8), and exhibiting a wide range in deviation from the 1:1 line. Figure 7: Relationship between estimated stream burial and percent ISA [22] for analysis units of different scales. Black dots represent individual analysis units. Blue, dashed line is 1:1 line. Red, solid line is the regression line for the data. Vertical, dashed line is the 30% ISA threshold. Colored ovals are referenced in ‘Results’ section of the text. One grid cell, centering on Hagerstown, MD (Figure 8), falls just above the 30% ISA threshold, with recent and rapid development around the intersection of Interstates 70 and 81 leading to a very high ratio of burial per unit ISA (Figure 7D, yellow oval). Additional cells, including Springfield, VA (I-95 corridor), Manassas, VA (I-66 corridor), and in Prince George's County, MD (I-495 corridor), fall on the 1: line, having managed to maintain significant natural land cover (Figure 8) despite centering on significant transportation corridors. A grid cell in Prince William County, VA, adjacent to Manassas, and one cell centered on the I-270 corridor in Montgomery County, MD, exhibit burial/ISA ratios just below 1:1. Two additional grid cells with >30% ISA fall well below the 1:1 line (Figure 7D, green oval), and include the areas around Herndon, VA, and suburban Prince George's County, MD, that exhibited similarly low ratios at the 45 km\({}^{2}\) scale, as discussed above. Figure 8: Maps of the study area showing the distribution of analysis units with >30% ISA, the threshold beyond which units exhibit a burial/ISA ratio above 1:1. Land cover data is the 2006 NLCD [22].
# Shear-Wave Velocity Model from Site Amplification Using Microtremors on Jeju Island
## 1 Introduction
Quite possibly, this LVL plays a crucial role in controlling both the site amplitude and site attenuation of ground motion for seismic microzonation in the Jeju region [26; 27; 28; 29; 30; 31; 32]. The vicinity of Jeju Island is characterized by relatively low seismic activity compared to other regions in Korea. Nevertheless, seismic design remains imperative for significant national infrastructures and utilities within the Jeju region, including the construction of new airports and potential nuclear power plants. The objective of this study is to furnish more dependable characteristics of seismic microzonation for Jeju Island by describing the significant LVLs or clusters of LVLs, employing the HVSR of environmental noises and their regional distribution. For the analysis of HVSR curves, we utilized a substantial quantity of environmental noise data preceding the first P-phase arrival, which were recorded by the Korean National Seismic Network in the Jeju region, originating from the 9 Pohang earthquake series and the 15 Kumamoto earthquake series (main event: ML 7.3, JMA intensity 7). These datasets were selected owing to their comprehensive coverage of the Jeju area and the magnitude of the earthquakes, which ensured their detectability at most seismic stations in the region. The present findings are compared with those reported by preceding studies employing diverse geophysical methods, including borehole analysis [26], gravity and magnetic surveys [27], gravity studies [28], paleomagnetic research [29], seismic investigations [30], magneto-telluric (MT) assessments [31], and deep resistivity soundings [32], to ascertain the LVL of the Jeju region. We anticipate that our results will align with those of previous studies and potentially reveal new systematic LVLs that have not been documented previously. Furthermore, a novel hypothesis has been proposed regarding the origin of the LVL at the UDO site [26; 27; 28; 29; 30; 31; 32]. This research significantly advances the geological examination of the distinct geological units in the Jeju region, including the sea-origin U-formation and Seoguipo strata, with a focus on stratigraphic information directly or indirectly pertinent to the LVL. Additionally, it contributes to the field of seismic design engineering by facilitating the analysis of seismic hazards associated with seismic microzonation. This is achievedthrough the examination of notable site amplification and attenuation phenomena in the Jeju region.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Design
#### 4.5.2 Application Scenario  
The proposed covert channel can be used in a variety of application scenarios associated with 5G-based communication. The hiding method involves exactly one covert sender and one covert receiver. The covert sender is located in the gNodeB, while the covert receiver is residing in the UE. The overt communication link between the gNodeB and the UE is bidirectional, but the covert data are only transferred from the covert sender to the covert receiver. Thus, a backward channel would be feasible. A covert sender and receiver can also be located the other way around depending on the respective application scenario. It may also be possible to use the proposed hiding method for the transmission of covert data among the participants of a group communication, but this depends on the message types and the associated logical channels. Since the covert channel is based on the PDCP, the messages that are exploited for the covert data transmission must also be processed by the PDCP layer. Otherwise, the covert channel cannot be applied.
## 4 Discussion
Hence, understanding virome dynamics could eventually help to design microbiome-based phage therapies aimed at treating metabolic disorders, such as obesity and T2 DM. In this work, we identified biomarkers for individuals with obesity and post-obesity treatment. Comparing this information with the viral taxa identified in this research, we noticed differences between each condition. _Escherichia_ phage, _Geobacillus_ phage, and _Lactobacillus_ phage had the relatively largest abundance in the obese group. On the other hand, we identified three viruses in the obesity condition: _Uncultured phage crAssphage_, _Enterobacteria phage fAA91_ss_, and _Enterobacteria phage P88_. In contrast, we identified a higher abundance of _Escherichia phage pro483_ and _Escherichia phage pro147_ in post-obese patients. This difference could be the result of the direct contribution of the gut microbiota post-surgery and must be related to the differences observed in the core virome of pre- and post-obesity patients reported in this paper. Although the nature of all viruses and their role in the health or dysbiosis states of the intestinal microbiome are not currently known, the evidence indicates that their role may be fundamental for bacterial dynamics, pointing to the need to perform deep gut virome studies to elucidate their ecological role in this niche. Given the increase in viral genomes that have been included in the International Committee on Taxonomy of Viruses (ICTV) [39; 40; 41], we can predict that the gut virome will be a hot topic within a short time. A weakness of this study was that only 49 samples were retrieved from public databases to perform the quick bioinformatic comparison of the viral content in both conditions, namely obesity and post-obesity treatment. In the literature, some works use shotgun metagenomics to study the gut microbiome in that context. However, few of these publish their raw data sequences or the metadata associated with the raw data sequences, which is needed to understand and re-analyze the metagenomic sequences. Another weakness was the quick description performed using a bioinformatics tool based on viral recognition by the stored information in public databases. Altogether, these limitations could be complemented by carrying out a meta-study with a high sample number and bioinformatics tools that recognize and assemble viral genomes without the need to use database recognition. Moreover, new advances in functional metagenomics will be needed besides the development of new bioinformatics tools that integrate all the metadata that microbiome studies collect. Finally, these observations suggest that, at present, it is not possible to understand the whole complex virome dynamics and their implication on the human gut microbiome, but it is a topic that needs a great deal of attention.
## 5 Simulated Jamming Results Analysis
### Low jamming Scenario
#### 5.1.2 Second Trajectory
The maximum horizontal positioning error dropped from 9.882 m by using the GPS DF signals to 2.596 m by using the GPS TF + Galileo TF + GLONASS DF signals, whereas the maximum vertical positioning error dropped from 17.911 m by using the GPS TF signals to 6.393 m by using the GPS TF + Galileo TF + GLONASS DF signals. This is an indicator that using multi-frequency, multi-constellation GNSS signals significantly improves positioning performance in terms of accuracy and reliability. Figure 11: (**a**) Horizontal positioning error for different GNSS signal combinations [second trajectory]. (**b**) Zoomed view of horizontal positioning error for different GNSS signal combinations [second trajectory]. Figure 12: (a) Vertical positioning error for different GNSS signal combinations [second trajectory]. (b) Zoomed view of vertical positioning error for different GNSS signal combinations [second trajectory]. (second trajectory). Figure 13: Number of visible satellites for different GNSS signal combinations [second trajectory].
## 3 Results
### Accuracy versus Costs  
In the following numerical experiments, the performance of both polynomial approximations is measured by the respective error-cost relation. In most engineering applications, the cost of running simulations for different sets of parameter values typically outweighs any other numerical operation necessary for the construction of the approximation. In this context, the cost of a method refers solely to the number of simulations, equivalently, solver calls or model evaluations, that are needed until the sought approximation accuracy is reached. This notion of cost is also used in this section, despite the fact that computationally inexpensive models are employed. In particular, in each numerical experiment and for both methods, we compute 19 polynomial approximations corresponding to increasing simulation budgets, i.e., for \(B=10,20,\ldots,100,200,\ldots,1000\), and compare the errors of Equations (14) and (15) for the same costs.  
We should note that regression-based gPC methods, such as the LAR-gPC [9] method employed here for comparison purposes, are significantly more expensive than interpolation-based methods in terms of the neglected costs. This is due to the fact that the solution of possibly expensive LSproblems is necessary. On the other hand, regression-based gPC methods allow to pre-compute the QoI for different parameter values, i.e., run the required simulations offline before the algorithm for the construction of the gPC approximation is used. Moreover, this offline task is embarrassingly parallelizable. On the contrary, as can easily be observed from Algorithm 1, dimension-adaptive sparse interpolation is based on sequential computations and therefore does not allow the use of parallelization to that extent. Despite it not being addressed here, those differences must be taken into account by practitioners, considering the problem and the computational resources at hand.
# The Use of Rapeseed Husks to Remove Acidic and Basic Dyes from Aquatic Solutions
## 2 Materials and Methods
### Computation Methods  
The amount of dye adsorbed on RH was determined from formula (1).  
\[Qs=(C_{0}-C_{S})\times\frac{V}{m} \tag{1}\]  
\(Q_{S}\)--mass of sorbed dye [mg/g]  
\(C_{0}\)--initial concentration of dye [mg/L]  
\(C_{S}\)--concentration of dye after sorption [mg/L]  
\(V\)--volume of the solution [L]\(m\)--mass of the sorbent [g].  
The kinetics of dye sorption onto RH was described using the pseudo-first order model (2), the pseudo-second order model (3), and the intraparticular diffusion model (4).  
\[Q=q_{e}\times\left(1-e^{(-k_{1}\times t)}\right) \tag{2}\]  
\[Q=\frac{\left(k_{2}\times q_{e}{}^{2}\times t\right)}{\left(1+k_{2}\times q_{e} \times t\right)} \tag{3}\]  
\[Q=k_{id}\times t^{0.5} \tag{4}\]  
Q--instantaneous value of the sorbed dye [mg/g]  
\(q_{e}\)--the amount of dye sorbed at the equilibrium state [mg/g]  
\(t\)--time of sorption [min]  
\(k_{1}\)--pseudo-first order adsorption rate constant [1/min]  
\(k_{2}\)--pseudo-second order adsorption rate constant [g/(mg-min)]  
\(k_{id}\)--intraparticular diffusion model adsorption rate constant [mg/(g\(\cdot\)min\({}^{0.5}\))].  
The experimental data obtained from studies on the maximum sorption capacity of RH were described using three popular sorption models: the Langmuir 1 isotherm (5), the Langmuir 2 isotherm (Langmuir double isotherm) (6), and the Freundlich isotherm (7). The Langmuir 2 isotherm, also known as the \"dual-site Langmuir model\", describes the adsorption of one type of sorbate on two types of sorption centers. It allows each active site to be described with a separate Langmuir equation [25].  
\[Qs=\frac{\left(Q_{max}\times K_{\rm C}\times C\right)}{\left(1+K_{\rm C}\times C \right)} \tag{5}\]  
\[Qs=\frac{\left(b_{1}\times K_{1}\times C\right)}{\left(1+K_{1}\times C\right) }+\frac{\left(b_{2}\times K_{2}\times C\right)}{\left(1+K_{2}\times C\right)} \tag{6}\]  
\[Qs=K\times C^{\frac{1}{2}} \tag{7}\]  
\(Qs\)--mass of the sorbed dye [mg/g]  
\(Q_{max}\)--maximum sorption capacity in Langmuir equation [mg/g]  
\(b_{1}\)--maximum sorption capacity of sorbent (type I active sites) [mg/g]  
\(b_{2}\)--maximum sorption capacity of sorbent (type II active sites) [mg/g]  
\(K_{\rm C}\)--constant in Langmuir equation [L/mg]  
\(K_{1}\), \(K_{2}\)--constants in Langmuir 2 equation [L/mg]  
\(K\)--the equilibrium sorption constant in Freundlich model  
\(n\)--Freundlich equilibrium constant  
\(C\)--concentration of dye remaining in the solution [mg/L]  
\(n\)--constant in the Freundlich model.