esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Random Forest Regression  
The metrics derived from the training, testing, and 10-fold cross-validation (CV) datasets show that the RFR model predicts interface shear strength with excellent accuracy, as shown in Table 5.  
For peak shear stress, in the training phase with 72 observations, the RFR model demonstrates outstanding performance, evidenced by an R\({}^{2}\) value of 0.98 and minimal errors (MAE of 1.28, RMSE of 1.60, RMSLE of 0.07). During the testing phase with 18 observations, there was a slight increase in errors, yet the model maintained high predictability with an R\({}^{2}\) of 0.88 (MAE of 3.74, RMSE of 4.99, RMSLE of 0.19). Finally, in the 10-fold cross-validation with 90 observations, the error metrics increased (MAE of 3.46, RMSE of 4.48, RMSE of 0.20), but the model still achieved a strong R\({}^{2}\) of 0.90. These results are visually represented in Figure 14.  
Figure 13: Comparison of actual and predicted residual interface shear strength using MLR for **(a)** the training dataset, **(b)** the testing dataset, and **(c)** 10-fold cross-validation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training Database**} & \multicolumn{2}{c}{**Testing Database**} & \multicolumn{2}{c}{**10-Fold CV**} \\ \hline \multirow{5}{*}{\
\begin{tabular}{} \end{tabular} } & Observations & 72 & 18 & 90 \\ \cline{2-5}  & MAE & 1.28 & 3.74 & 3.46 \\ \cline{2-5}  & RMSE & 1.60 & 4.99 & 4.48 \\ \cline{2-5}  & RMSLE & 0.07 & 0.19 & 0.20 \\ \cline{2-5}  & R\({}^{2}\) & 0.98 & 0.88 & 0.90 \\ \hline \multirow{5}{*}{
\begin{tabular}{} \end{tabular} } & MAE & 1.53 & 3.77 & 3.97 \\ \cline{2-5}  & RMSE & 2.13 & 4.82 & 5.16 \\ \cline{2-5}  & RMSLE & 0.09 & 0.23 & 0.24 \\ \cline{2-5}  & R\({}^{2}\) & 0.97 & 0.88 & 0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of RFR model accuracy in predicting interface shear strength.  
For residual shear stress, the training phase showed an MAE of 1.53, RMSE of 2.13, RMSLE of 0.09, and an R\({}^{2}\) value of 0.97. During the testing phase, the errors increased (MAE of 3.77, RMSE of 4.82, RMSLE of 0.23), with an R\({}^{2}\) value of 0.88. In the 10-fold cross-validation, the MAE was 3.97, RMSE was 5.16, RMSLE was 0.24, and the R\({}^{2}\) was 0.87. These results are illustrated in Figure 15.
## 2 Literature Review
[34] built an ST-GCN to simulate bike-sharing demand at the city level. They used GCN to explore the spatial connection and GRU to explore the temporal dependency. [PERSON] et al. [35] put forward a relational GCN-based method for the prediction of the station-level demand. [PERSON] et al. [36] developed a multi-Graph Convolutional LSTM (GC-LSTM) network for prediction of bike-flow at the station level by catching the heterogeneous inter-station spatial relationships[PERSON] Recently, attention mechanism has been incorporated in the convolutional network to predict bike-sharing demand, which has been proven to improve the accuracy and interpretability of deep learning algorithms [12; 13; 14][PERSON] [PERSON] et al. [37] divided the entire city into multiple regions and predicted the multi-step city-wide demand of Didi, taxi, and bike-sharing at the region level by applying attention mechanism in neural network models. In conclusion, deep learning has proven to be an effective tool for bike-sharing prediction and numerous research studies have applied combined prediction methods of deep learning to improve the prediction outcome. However, insufficient research has been done regarding incorporating the advantages of GCN, LSTM, and the attention mechanism. Furthermore, none of the aforementioned studies established an attention-based GC-LSTM for the prediction of short-run docked bike-sharing demand at the station level. To fill the aforementioned research gaps, a novel GC-LSTM model with the attention mechanism has been proposed to predict short-run bike-sharing demand based on multi-source data. The station-level shared-bike demand is influenced by multiple complex factors [32, 38, 39, 40]. The data sets considered the multi-source heterogeneous information, including weather conditions, land-use data around the bike station, and users' personal information.
# Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles
## 4 Environment Map Generation Algorithm in a Transparent Obstacle Environment
The current position of the robot is colored blue in Figure 10b, and the eight adjacent cells are colored red. The box with slashes represents the path the robot travels. \begin{table}
\begin{tabular}{c c} \hline \hline
**Terminology** & **Explanation** \\ \hline \(i\) & The cost required to propagate Wavefront \\ \hline \(W_{i}\) & The set of cells for \(i\)th propagation \\ \hline \(X_{G}\) & The set of cells in which the destination is stored (default: The number of destination cells is 1) \\ \hline \(\Phi(x)\) & The function to store the optimized propagation cost of cells that belong to \(W_{i}\) \\ \hline All unexplored neighbors of \(x\) & The non-obstacle cells not yet propagated among adjacent cells with four different directions \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Terminology essential to understanding the Wavefront-propagation algorithm. As the robot travels towards the destination, the robot selects the box with the minimum cost. However, following all the boxes with minimum cost results in awkward movement. Therefore, boxes that are not needed to control the robot's movement are removed. Not only does this contribute to more natural movement, but computing time can also be significantly reduced. The optimized map is shown in Figure 10c. However, in this paper, both the map in Figure 10b,c are used; they represent the original and optimized paths, respectively, for path planning in environments with transparent obstacles. The original path is used to detect the newly found obstacles while the robot is travelling in a dynamic environment. The optimized path is used to direct the robot's motion control. ``` BeginAlgorithm Wavefront-propagation
1 Initialize\(W_{0}\gets X_{G}\), \(i\gets 0\) Initialize\(W_{i+1}\leftarrow\varnothing\) For every x \(\in W_{i}\), assign \(\Phi(\text{x})=i\) and insert All unexplored neighbors of x do
4 If\(W_{i+1}==\varnothing\)then
5 Terminate
6 Else
7\(i\gets i+1\)
8 Goto step 2
9 EndAlgorithm Wavefront-propagation  
Figure 10: Path planning using the Wavefront-propagation algorithm: (**a**) environment map; (**b**) path planning results; and (**c**) optimization results. The time complexity of Wavefront-propagation is O(\(n\)).
# Compact N-Band Tree-Shaped Multiplexer-Based Antenna Structures for 5G/IoT Mobile Devices
## 4 Methods and Structures  
The great advantage of having the prototype composed of \(n\) sections is to guarantee that for each operation frequency, the input impedance is only imposed by the respective resonant element. This leads to a very efficient feeding network, in line with the challenging requirements of 5G, massive IoT and the future satellite communications.  
Nowadays, wireless communications systems are mainly equipped using printed antennas [18; 19] mostly due to their low cost and ease of fabrication. Another important aspect is the ability to produce various structures, with unlimited design shapes and typologies. Bearing in mind the need to ensure communication regardless of direction, monopole and dipole-based structures present interesting advantages.  
In order to verify the practicability of this concept, it was decided to demonstrate it, designing two prototypes, a dual band and a tri-band antenna. Starting with the dual-band model, it is composed by two printed monopoles, each resonant at a different frequency, 28 GHz and 38 GHz respectively. Additionally, confirming the scalability of the concept, a tri-band antenna was developed, and for this, a radiator at 20 GHz was added. Both schemes are presented in Figure 3, clarifying the arrangement and the impedances expected in different parts of the structure.  
The key aspect of these antennas is the feeding structure. The sectioning of this antenna into different parts lies on the existence of the connection points presented in Figure 3, thus, to scale this concept to a \(n\)_-band_ antenna, \(n\)_-1_ connection points are required. These connection points represent the division of the feeding network. In the example, for a dual-band antenna, there is one single connection point, however, for the tri-band structure, two connection points are required, and so on.  
Figure 2: Schematic of the Main Design Concept.  
The resulting parts of the overall antenna have an adequate matching at the resonant frequency of the respective radiating element, while simultaneously showing a close to infinite impedance at the frequency of the opposite radiating element. Therefore, at each resonant frequency, a parallel association of infinite impedances with a characteristic impedance of interest is observable.  
Clarifying with Figure 3a, at the connection point and looking at the 28 GHz element: at the resonant frequency, 28 GHz, an adequate matching is verified ensuring that it works properly, and, at the same time, the feeding network warrants that at 38 GHz a very high impedance appears. These principles are applied symmetrically to the opposite side of the structure.  
Both antennas proposed are designed over a single layer of the dielectric substrate Rogers RO4350B, which main properties include a dielectric constant \(\varepsilon_{\mathrm{r}}=3.48\), thickness \(h=0.254\) mm and dissipation factor of \(\tan(8)=0.0037\) @ 10 GHz. All simulations have been carried out using the electromagnetic simulator software which is the Computer Simulation Technology Microwave Studio Suite (CST-MWS).
# Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery
## 2 Materials and Methods
### Image Classification  
We used a random forest classification algorithm with 200 decision trees [25] to classify the masked study image into the following two land-cover classes: mangrove and non-mangrove. We used our 339 ground control points as the training data and the blue, green, red, and near-infrared spectral bands as the inputs to the classification algorithm. We used only these four bands because they (a) are of proven utility in discriminating mangrove and non-mangrove features [9; 32] and (b) are the only four Sentinel-2 bands with 10 m spatial resolution. To use other, coarser-resolution bands in our analysis, we would have had to resample all bands to the coarsest resolution [36], thereby decreasing the resolution of our analysis. The algorithm collated the blue, green, red, and near-infrared spectral reflectance values at each mangrove and non-mangrove training point to determine the unique spectral signatures of mangrove and non-mangrove features in the study image [36]. Then, the algorithm assessed every other pixel in the study image and assigned it to either the mangrove or non-mangrove land-cover class, based upon which class's spectral signature the pixel more closely matched. After the classification was complete, we masked out the non-mangrove pixels and kept the mangrove pixels to create the preliminary mangrove map.  
Finally, we manually refined the preliminary mangrove map. Referencing our own knowledge of the study area, high-resolution (1 m or finer) Google Earth imagery, and the mangrove maps created by [19] and [22], we visually inspected the preliminary map and manually removed misclassified pixels. These misclassified areas were mainly low-elevation inland areas that were not filtered out by our elevation mask and water pixels that were not filtered out by our NDWI mask. We performed this refinement by drawing polygons around areas we wished to include in the finalized mangrove map. Areas not within these polygons were excluded from the finalized mangrove map. We drew these polygons directly in the Google Earth Engine user interface. The refinement polygons are integrated directly into our algorithm, so future users of our algorithm are not required to make their own further adjustments to achieve the same results. The output of this refinement process was the finalized 2020 Belize mangrove map.
# Can Biochar Improve the Sustainability of Animal Production?
## 2 Summary of Biochar-Production Techniques
### Production Processes
#### 2.1.2 Hydrothermal Carbonization  
Hydrothermal carbonization (HTC) is a highly suitable method to process carbonaceous materials with high moisture content, such as animal and municipal wastes [25; 26]. Briefly, the materials are heated along with water at temperatures between 180 and 300 \({}^{\circ}\)C under autogenous pressure (2-10 MPa) [27; 28]. At these temperatures, water, due to its increased ionization constant, serves as a reactant [29; 30]. In addition, its dielectric constant decreases significantly allowing water to act as a non-polar solvent [31] and to initiate a series of reactions.  
The biomass is first subjected to hydrolysis resulting in the cleaving of ether and ester bonds associated with biomass [32]. Subsequently, the water and oxides of carbon are released via dehydration and decarboxylation, respectively [33]. Finally, the remaining components are recombined into larger molecules via polymerization. Detailed mechanisms of these chemical reactions are summarized by [PERSON] and [PERSON] [34].  
After the completion of all the reactions, the biomass is converted into a solid product called hydrochar, whose physical and chemical properties are somewhat similar to biochar [35]. However, the yield, surface chemistry, and energy content of the hydrochar will directly depend on the processing conditions, including the temperature, residence time, and the presence of catalysts. Numerous results of parametric studies on the optimization of the hydrochar properties are available in the literature [29].  
Considering the advantages HTC offers, several materials have been converted into hydrocarbons. However, considering the theme of the article, only the HTC of animal wastes are summarized here. Cow manure was used as a feedstock for HTC at temperatures 180-260 \({}^{\circ}\)C for 5 and 30 min. [PERSON] et al. [36] tested calcium oxide (CaO) as a catalyst during the HTC of swine manure. Their analyses suggested that CaO enhanced the porosity and hydrophilicity of biochar and was proposed as a suitable soil amendment capable of increased cation exchange capacity (CEC).  
[PERSON] et al. [37] investigated HTC of nitrogen-doped hydrocarbons synthesized from swine manure slurry between 180-260 \({}^{\circ}\)C (24 h). Their results suggested that the HTC process yielded nitrogen-doped chars with points of zero charge between 6.7 and 7.8 making them highly suitable as adsorbents, and precursors for energy-storage devices. [PERSON] et al. [38] suggested the co-processing of materials to enhance the yield and the properties of hydrochar. Swine manure and chicken manure were co-hydrothermally carbonized in various ratios by [PERSON] et al. [39]. It appeared that combining swine manure with chicken manure caused a symbiotic interaction in terms of carbon and nitrogen functionalization in the resulting hydrochar and deoxygenation coupled with higher energy yield.
# Nonlinear Dynamics of a Resonant-Impact Dielectric Elastomer Actuator
## 3 Numerical and Experimental Study Results
### Effects of Constraint Stiffness  
In addition to the constraint gap, the constraint stiffness can cause effects on the dynamics of the resonant-impact DEA system. In this nondimensional numerical study, the nondimensional stiffness \(k\) is varied from 100 to 2.5 \(\times\) 10\({}^{4}\) in 160 steps; the frequency response results are plotted in Figure 13a. It can be noted that varying the value of \(k\) has negligible effects on the peak frequency \(\omega_{p}\) and minor effects on the oscillation amplitude \(|\,amp.\,|\) of the system. However, as shown in Figure 13b, the increase in \(k\) results in the increase of the peak impact force \(|\,f_{max}\,|\). Note that \(|\,f_{max}\,|\) is increased more than ten-fold when \(k\) is increased 100 to 2.5 \(\times\) 10\({}^{4}\).  
To investigate the effects of constraint stiffness in our experiments, four pieces of acrylic beam with thicknesses of 1, 2, 3, and 4 mm were adopted as the constraint. Their equivalent stiffness was measured at \(K\) = 10.6, 51.9, 138.9, and 200.3 N/mm, respectively. The four acrylic beams were mounted such that the constraint gap maintained a constant value of \(G\) = 0.6 mm. Figure 14a shows that the peak frequency \(\Omega_{p}\) remains almost constant against the varying \(K\), while its peak amplitude \(|\,Amp.\,|\) sees a minor reduction as \(K\) increases. The peak impact force \(|\,F_{max}\,|\), on the other hand, increases with increasing \(K\), as shown in Figure 14b, which verifies the findings reported in the nondimensional numerical study in Figure 13b. The deviation between the experimental data and the modelling value in Figure 14b increases as \(K\) becomes larger, which could be due to a stiffer constraint causing a greater number of undesired shifts on the part of the impactor (a steel cap in this design) in the direction perpendicular to the impact.  
Figure 12: Experimental and modelling results of the effects of the constraint gap on (**a**) the peak frequency and oscillation amplitude and (**b**) the peak impact force. The discrete markers indicate experimental data, while the continuous curves are the modelling results.  
Figure 13: (**a**) Effects of constraint stiffness on the frequency responses of the resonant-impact DEA system and (**b**) effects of constraint stiffness on the peak frequency and peak impact force.
# Improving the Robustness of Model Compression by On-Manifold Adversarial Training
## 3 Background  
Our robust model compression method is composed of model compression based on sparse coding and adversarial training based on on-manifold adversarial examples. In this section, we provide background regarding the technologies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Model** & **Off-Manifold** & **On-Manifold** \\  & **Compression** & **Perturbation** & **Perturbation** \\ \hline FGSM-AT [11] & & ✓ & \\ PGD-AT [13] & & ✓ & \\ TRADES [15] & & ✓ & \\ ATMC [18] & ✓ & ✓ & \\ HYDRA [19] & ✓ & ✓ & \\ ANP-VS [45] & ✓ & ✓ & \\ APD [20] & ✓ & ✓ & \\ Stutz et al. [21] & & & ✓ \\ DMAT [42] & & ✓ & ✓ \\ MC-AT(on) (Ours) & ✓ & & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with other robust model training methods. Our method considers both model compression and on-manifold adversarial robustness.
## 2 Analysis of the PCB's Vertical Interconnection Structure
\[Z_{\text{IM}}=\sqrt{\frac{L_{\text{IM}}}{C_{\text{stub}}+C_{\text{IM}}}} \tag{13}\]  
where the value of \(L_{\text{IM}}\) is primarily determined by the length and width of the narrow line, while that of \(C_{\text{IM}}\) is primarily determined by the length and width of the wide line. Although these methods can be used to improve the matching, certain challenges related to traditional PCB processing cannot be overcome by this approach. First, using back drill technology to eliminate the copper stub may damage the top layer antenna structure, affecting both its design and performance. If the remaining residual stub is sufficiently long, its capacitance may be large enough to hinder good impedance matching in LC circuits designed with striplines. The narrow line used to achieve inductance may also be too narrow to be manufactured properly when impedance matching is designed within the inner PCB layer. Therefore, more flexible processing techniques are required in order to adapt this design to more complex PCBs. The copper paste sintering technology, a micro-soldering technique, has better compatibility, higher thermal conductivity, and is lower-cost. In recent years, this process has been gradually expanding from chip applications to PCB-related ones. The technological process of copper paste sintering is relatively complex, but primarily consists of injecting the prepared copper paste into the areas that require interconnection, before transforming the paste into a dense solid copper structure through drying and sintering processes. This process is able to achieve favorable interconnection properties between the upper and lower metal PCB layers. Figure 5 shows three common cases of interconnection using copper paste sintering technology--including line-to-line, via-to-line, and via-to-via. This technology can not only avoid the generation of stubs in the multilayer PCBs, but also reduce their lamination numbers so that more of their layers can be manufactured. Particularly for multi-beam planar arrays, which have multiple feed networks, this technology can effectively reduce the difficulty of both design and manufacturing. A comparison of the copper paste sintering process with the normal PCB process and HDI process can be seen in Table 1.  
Figure 4: LC circuits for impedance matching between the stripline and signal via. (**a**) Impedance matching structure. (**b**) Equivalent \(\pi\) type circuit model. Figure 5: Common cases of interconnection using copper paste sintering technology.