esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions
Additionally, this technique can significantly reduce the cost and time needed for production. The results of this work can serve as a useful starting point for optimizing similar antenna types. The proposed enhanced low-mass short backfire antenna outperforms the C-band standard gain horn antenna and many other short backfire antennas found in the literature in terms of impedance bandwidth and/or gain performance. Hence, it can be used as a superior substitute in many applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Description** & \begin{tabular}{c} **Frequency Range** \\ **(GHz)** \\ \end{tabular} & \
\begin{tabular}{c} **Impedance** \\ **Bandwidth (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Measured Peak Gain** \\ **(dBi)** \\ \end{tabular} \\ \hline This work & Flared SBF antenna & 4–7 & 53.7 & 15.7 \\ \hline
[20] & Waveguide-fed SBF antenna & 4–7 & 27.3 & 15.7 \\ \hline
[22] & Patch-Fed Short Backfire Antenna & 2–2.5 & 15 & 15.2 \\ \hline
[23] &
\begin{tabular}{c} 3D-printed low-cost choke \\ corrugated Gaussian profile horn \\ antenna for Ka-band \\ \end{tabular} & 26.5–40 & 14.3 & 15.2 \\ \hline
[24] &
\begin{tabular}{c} Substrate Integrated Waveguide \\ H-plane Horn Antenna \\ \end{tabular} & 85–103 & 19.6 & 14.5 \\ \hline
[25] &
\begin{tabular}{c} Metamaterial-enabled short \\ backfire antenna \\ \end{tabular} & 1.1–1.75 & 10 & 14.1–15.7 \\ \hline
[26] & K-Band Horn Antenna & 17.5–20.5 & 16.2 & 14.5 \\ \hline
[27] &
\begin{tabular}{c} Horn antenna with integrated \\ metamaterial for beam steering \\ \end{tabular} & 10–11.5 & 4.7 & 13.3 \\ \hline
[28] &
\begin{tabular}{c} Standard Gain C-band Horn \\ Antenna \\ \end{tabular} & 4–7 & 36.0 & 15.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A comparison of the proposed flared SBF antenna with other SBF and horn antennas. **Author Contributions:** Conceptualization, Y.M.A. and D.I. ; methodology, Y.M.A. ; software, Y.M.A. ; validation, Y.M.A. and D.I. ; formal analysis, Y.M.A. and D.I. ; investigation, Y.M.A. and D.I. ; resources, Y.M.A. and D.I. ; data curation, Y.M.A. ; writing--original draft preparation, Y.M.A. and D.I. ; writing--review and editing, Y.M.A. and D.I. ; visualization, Y.M.A. ; supervision, D.I. ; project administration, D.I. ; funding acquisition, D.I. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded in part by the Research Manitoba Innovation Proof of Concept Grant (#5645 IPOC GRANT). The APC was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) (grant number RGPIN-004974-2017). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data available within the article. **Acknowledgments:** The authors would like to thank [PERSON], [PERSON], and [PERSON] for their assistance in antenna fabrication and testing. The authors acknowledge CMC Microsystems for the provision of the CAD tools that facilitated this research. **Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### Pesticide Residues in Apples and Citrus Fruits--Comparison
#### 3.1.1 Apples
Additionally, 16 samples (20%) contained residues of three, four, five and six pesticides, and eight samples (10%) were contaminated with residues of 7 to 14 pesticides [72]. The most frequent two-component combination of pesticides was chlorpyrrifos and cypermethrin. In four-component combinations, the most frequently identified were combinations of insecticides with fungicides--boscalid, chlorpyrrifos, cypermethrin and pyraclostrobin, while in samples with five pesticide residues, the most frequently identified were combinations of insecticides, fungicides and acarticles--boscalid, chlorpyrrifos, cypermethrin, propagtite and pyraclostrobin. The sample with the largest number of identified pesticides (as many as 14) contained three systemic insecticides (acetaniprid, boscalid, lufenuron), seven nonsystemic insecticides (chlorpyrrifos, cypermethrin, delamethrin, lambda-cyhalothrin, profenfos, triazofos, thiazolprid), two systemic fungicides (difeno-conazole, pyraclostrobin), one nonsystemic fungicide (pirymetthanil) and one nonsystemic acaricide (propagtite) [72]. The results obtained in the study are in conformance with the results obtained by [PERSON] and [PERSON] [73], and by [PERSON] et al. [74]. The highest levels of pesticide residues were assayed in apples from Bulgaria, produced in the conventional manner, with residues of chlorpyrrifos and fenitroth [73], and in apples from the Aegon region in Turkey, with residues of propagtite and thiabendazole [74].
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 3 ANN-Based Design Methodology for Low-Power Interconnect
### Proposed Design Methodology  
In this approach, a surrogate model based on ANN is generated using the design requirements. The model serves as a fast and high-accuracy data generation tool in frequency-domain designs. In addition, the objective function and design constraints aid this model to deliver the optimal structure. Finally, the minimum allowable transmitter supply voltage for maximum power saving is determined in time-domain design. The proposed design methodology is illustrated in Figure 5.
## 1 Introduction  
Thermal reflow is an economical and efficient post-processing method [1; 2], which is widely used following many fabrication methods [3; 4] to obtain microlens with high quality surface. Usually, the microstructures tend to reflow to spherical shapes according to the principle of energy minimum. To obtain more complex and well-defined shapes, many approaches have been proposed [5; 6; 7]. [PERSON] et al. [8] proposed a geometry-guided thermal reflow method to guide the reflow along the geometric boundaries of the adjacent thermoset microstructures. [PERSON] et al. [9] used two photoresists with different melting temperatures to fabricate tilted microlens with base constrained. In our previous study [10; 11; 12], by introducing PDMS in a thermal reflow process, it offers a facile and effective way to obtain high quality microstructures with desired shapes. Furthermore, the cured PDMS can be used as a soft mold to transfer micro/nano structures to other materials [13; 14].  
To reveal how the photoresist flow will affect the microlens formation and find out the mechanism of this unique process, a suitable theoretical model is required. Thus far, many calculation models have been introduced to explore the thermal reflow process based on analytical [15; 16] or numerical [17; 18; 19; 20; 21] methods. Solving hydrodynamic equations by numerical methods [17; 18; 19] is a common calculation model for exploring the general shape evolutions of various microstructures during thermal reflow process conducted in the air. In those models, the dynamic substrate wetting is ignored, which is disadvantageous to calculate the thermal reflow process accurately. It is precisely described in an energy-based numerical calculation model by inputting an apparent contact-angle-evolution time constant [20; 21]. However, the method is not suitable for investigating the complex velocity distribution induced by thermal curing of PDMS over temperature and time.  
In this study, we propose an improved theoretical model to analyze the PDMS assisted thermal reflow process and explore the shape formations of photoresist microstructures under curing PDMS. The theoretical model is based on hydrodynamic equations and a continuum surface force model. The reflow mechanism is revealed by analyzing the simulated reflow results of two typical microstructures. Based on the thermal reflow mechanism and the numerical calculation model, the thermal reflow process covered by PDMS can be investigated conveniently.
## 4 Results and Discussion
### Model Performance  
To assess the performance of the trained model, we verified the predictive accuracy of the machine-learning model on unknown data. In addition to validating the overall predictive accuracy using a loss function, we assessed the predictive accuracy of the individual data points. The test set was used for validation and the absolute errors between the predicted and true values were calculated. A parietal chart of the absolute errors and cumulative frequency curves is shown in Figure 5. A scatter plot illustrating the relationship between the predicted and true values is shown in Figure 6. Through these processes, we evaluated the generalization performance of each model. As shown in Figure 5, the XGBoost and LightGBM models exhibit different prediction accuracies. Examining the cumulative frequency for the XGBoost model, 50, 70, and 80% of the test set had absolute errors of \(\pm\)0.14, \(\pm\)0.41, and \(\pm\)0.71 cm, respectively. By contrast, for the LightGBM model, the cumulative frequency indicated absolute errors of \(\pm\)0.26, \(\pm\)0.67, and \(\pm\)1.07 cm for 50, 70, and 80% of the test set, respectively. In this case, the XGBoost model has better accuracy.  
The scatter plot in Figure 6 shows the relationship between the true and predicted values. The XGBoost model appears to fit well with the \(y_{\rm true}=y_{\rm pred}\) line. To quantify  
Figure 5: The parietal chart of absolute errors and cumulative frequency curves: (**a**) XGBoost model; (**b**) LightGBM model.  
Figure 6: The scatter plot depicting the relationship between predicted values and true values. Note that 20,000 samples were randomly extracted from the test data: (**a**) XGBoost model; (**b**) LightGBM model.  
how well the values predicted from the regression model aligned with the true values, the coefficient of determination \(R^{2}\) was defined as below:  
\[R^{2}=1-\frac{\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})}{\sum_{i=1}^{N}(y_{i}-\overline{ y})}, \tag{7}\]  
where \(N\) is the number of test-data points, \(y_{i}\) is the actual value of the test data, \(\hat{y}_{i}\) is the predicted value of the model for the test data, and \(\overline{y}\) is the mean of the actual value. The \(R^{2}\) score ranged from 0 to 1, with values closer to 1 indicating a better fit of the regression model to the actual data. The results show an \(R^{2}\) of 0.97 for the XGBoost model and 0.94 for the LightGBM model. This suggests that the former model provides a better explanation for the amount of ground subsidence. The study uses ground subsidence data based on numerical analysis, which minimizes the effect of outliers. Additionally, missing values were removed by data preprocessing, and an ample amount of data was used. Therefore, this forecasting model has high reliability because it satisfies the data quality and quantity.
# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 3 Methodology
### Prediction Network Module
#### 3.3.3 Output Layer  
The output layer of this model is based on the gated convolution neural network. The output layer consists of two temporal gated convolution layers, normalization layer and fully connection layer. For traffic flow prediction, many researchers use GRU or LSTM to extract time features, but this leads to many difficult training problems such as model parameters and gradient disappearance. In order to simplify the model, we use gated convolution to extract temporal signatures. Unlike RNN, where subsequent time steps must be predicted pending completion by their predecessors, convolution can be performed in parallel because the same filters are used in each layer. The time-gate convolution layer consists of 1D causal convolution and the gated linear unit. The output of 1D causal convolution is divided into two parts, one of which is activated by the Sigmoid function, the other by the addition of input for residual connection, and two parts for GLU by Hadamard multiplication.  
After the multi-graph convolution and spatial-temporal self-attention module, the input of gated convolution is \(X\in\mathbb{R}^{B\times c_{0}\times D\times N}\), the output of fully connected layer \(\hat{Y}\in\mathbb{R}^{B\times p\times N}\), where \(B\) is the batch-size, \(c_{0}\) is the number of channels, \(N\) is the number of metro stations, \(D\) is the sequence length after reshape, \(p\) is the predicted time steps, \(\otimes\) is the dot product, \(W,V,b\) and \(c\) are learnable parameters, \(Z\) is the output of two temporal gated convolution layers. The formula of output layer is as follows:  
\[X=concat(H_{1},H_{2}) \tag{21}\]  
\[H_{3}(X)=(X*W+b)\otimes\sigma(X*V+c) \tag{22}\]  
\[\hat{Y}_{i}=Zw+b \tag{23}\]  
The fully connected layer is used to reduce the data dimension, as well as capture the non-linear correlation between high-level features and outputs. We used only one fully connected layer to reduce the dimension of the flattening layer to the dimension we adopted. The output of the fully connected layer is finally reshaped into the final predicted results as \(\hat{Y}_{i}\), and this paper chose the mean square error (MSE) as the loss function.  
\[\text{Loss}=\text{MSE}=\frac{1}{N}\sum_{i=1}^{N}\bigl{(}\hat{Y}_{i}-\gamma_{i} \bigr{)}^{2} \tag{24}\]
# Exploring a New Adaptive Routing Based on the Dijkstra Algorithm in Optical Networks-on-Chip
## 3 Adaptive Power Control of ONoCs Based on the Dijkstra Algorithm  
### The Dijkstra Algorithm's Principles  
The Dijkstra algorithm is the shortest path algorithm from one vertice to the other vertices. Considering that it is suitable for undirected and directed graphs with positive weights, we take the port-to-port transmission loss of router in ONoCs as weights of the Dijkstra algorithm. The principles of the Dijkstra algorithm are as follows: Firstly, set S contains only the source nodes and set U contains all the remaining nodes. The distances in set U represent the distances between the starting vertice and other vertices. Secondly, it compares the stored distances and finds the node which is the closest to the source node, and then adds it to set S. At the same time, it removes this node from set U and updates the distance from each vertex in U to the current vertex. Thirdly, it traverses again to compare whether the distances thanks to choosing the shortest point as a transit point are more closer or not. If so, it just updates the distance; otherwise, it does not update. Ultimately, it repeats the previous second and third steps until all the vertices are traversed in U.  
In a previous study [28], the shortest path first (SPF) algorithm was applied to select the path with the lowest bit error rate among all shortest available paths. The shortest distance from the starting node to all the nodes could be stored after traversing. The shortest spanning tree algorithm can choose the path with the least sum of weights which is from the starting vertice to other vertices. The Dijkstra algorithm could attain the shortest path of weights from the fixed starting vertice to the fixed ending vertice. Therefore, the Dijkstra algorithm is more suitable to optimized routing paths for network architectures when the source and destination nodes are known. The Dijkstra algorithm is described in detail in Algorithm 1. In this paper, the port-to-port transmission loss of a router serves as the weight of the selection of routing path; the path with minimum transmission loss from the source node to the destination node will be determined by the Dijkstra algorithm.
## 3 The Proposed Method
### Specific Details of the Fault Diagnosis Method (ISCV-ViT)  
#### 3.2.1 The Instantaneous Square Current Value  
Considering the particularity of the current signal, it is necessary to select appropriate signal preprocessing methods. Statistical time-domain characteristics are preferred because of their simple calculation and effective modeling of signal changes and trends [46]. Consequently, to reduce the complexity of signal processing and take the effectiveness of feature extraction into consideration, the two-phase current is coupled based on the idea of the effective value of the current. Finally, the time-domain images are formed as the input to the diagnostic model.  
It is necessary to consider the symmetry of the three-phase current under normal conditions and the current amplitude fluctuation and phase modulation under fault conditions. To fully mine the fault features contained in the current signal, two-phase current sampling values are selected for coupling calculation through Equation (6) according to the theoretical basis for effective value calculation.  
\[I_{n}=\sqrt{\frac{1}{2}(i_{an}^{2}+i_{bn}^{2})} \tag{6}\]  
where \(I_{n}\) is the effective value of the \(n\)th current sampling coupling, and \(i_{an}\) and \(i_{bn}\) are the \(n\)th sampling values of one phase currents.  
The essence of fault diagnosis based on the current signal is to find feature differences. The most obvious and direct one is the fluctuation of current amplitude over time. However, relying solely on one-dimensional time-domain statistical indicators makes it difficult to reflect the process of change over time. We take \(I_{n}\) as the statistical time-domain feature and draw the time-domain images as the subsequent model input, which is shown in Figure 4. Considering the features of the dimensionless data that can accelerate the solution of the model, the obtained current data is subjected to a Min-Max scaling operation after coupling. From the image (b) in Figure 4, it can be seen that the peak part fluctuates more significantly  
Figure 3: The overall architecture of the proposed ISCV-ViT method.  
after ISCV processing due to the integration of two-phase current characteristics with phase difference.
## 2 Materials and Methods
### Descriptive Statistical Analysis  
The dataset under analysis presents 2249 data points with an index-step of 1 m, meaning that for every meter drilled, a sample with DEPTH, RPM, WOB, FLOW, and STICK RATIO is provided, and MSE values were calculated through Equation (1). A dataset description is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \(-\mathbf{1}\) & \(\mathbf{0}\) & \(\mathbf{1}\) \\ \hline - Perfect linear association; & - There is no association between & - Perfect linear association; \\ - Inversely proportional. & variables X and Y. & - Directly proportional. \\ \hline \end{tabular}
\end{table}
Table 2: Pearson and Spearman correlation.  
Since the presented dataset shows more than 50 samples (2249 samples), the Kolmogorov-Smirnov test was employed as per Figure 1[36; 37]. A normal distribution and verification that the Kolmogorov-Smirnov test outcome presented a \(p\)-value less than 0.05 was observed. Thus, it was necessary to perform a Spearman correlation test, as shown in Table 4.  
According to the Spearman correlation test, most drilling mechanic parameters show a significant correlation, with a \(p\)-value < 0.05. FLOW, when compared to MSE, showed no significant correlation. RPM, when compared to STICKRATIO, exhibited a negative correlation, indicating that as RPM increases, the severity of STICKRATIO may decrease. FLOW and WOB showed a positive correlation, suggesting that an increase in these parameters may also increase the severity of STICKRATIO. When analyzing MSE, RPM shows a positive correlation, while WOB and FLOW exhibit a negative correlation. Considering the model demonstrated by [PERSON] [39], all correlations showed weak correlation, with \(-0.3<p<0.30\).  
It is important to note that analyzing the drilling mechanical parameters separately is not ideal; therefore, the values obtained here serve only as a reference to provide guidance and understanding into the isolated behavior of the parameters concerning STICKRATIO and MSE.
# Aerospace
## 9 Additional Mission Concepts
### SmallSat Relay Orbiter for Mars Lander and Venus Balloon Missions  
A small satellite can be inserted into orbit as part of a large Mars lander mission following the same technique described in Section 4. After releasing the small satellite on its aerocapture trajectory, the lander spacecraft would perform a deflection maneuver to target its aim point for atmospheric entry rather than propulsive orbit insertion. The small satellite can serve as a dedicated telecommunications relay for the lander mission, freeing up bandwidth on other large orbiters. Small satellites can also be used as relay orbiters for Venus balloon or lander missions. Unlike Mars, where there exists a network of orbiters that could be used as data relays, Venus missions are constrained by low direct-to-Earth data rates (\(<\)100 bps) through its thick absorbing atmosphere [48]. A small relay orbiter with a deployable high-gain antenna could increase the data rate to several kbps.
# A Simple Alternative Method for Preservation of 2-Methylisoborneol in Water Samples
## 3 Results and Discussion
### Impact of Chlorine on 2-MIB Concentration in Natural Water
For the cases of no chlorine addition, the rate constants were between \(5.04\times 10^{-2}\) day\({}^{-1}\) and \(1.21\times 10^{-1}\) day\({}^{-1}\). The rate constants decreased to \(2.89\times 10^{-2}\) day\({}^{-1}\) to \(6.27\times 10^{-2}\) day\({}^{-1}\), \(9.79\times 10^{-4}\) day\({}^{-1}\) to \(1.95\times 10^{-2}\) day\({}^{-1}\), and \(5.65\times 10^{-3}\) day\({}^{-1}\) to \(1.21\times 10^{-2}\) day\({}^{-1}\), for the cases of chlorine = 2, 5, and 10 mg/L, respectively. The results clearly showed that adding chlorine significantly slowed down the rates of degradation. To test the applicability of filtration as an alternative to rule out biodegradation effects for 2-MIB, another set of experiments was conducted for the samples of NH water. The samples were filtered through 0.3 \(\upmu\)m-fiberglass filters before the preservation experiments, which were used for filtering out the indulgent bacteria in the water. Figure 4 shows that after filtration, the 2-MIB concentration remained almost constant at 4 \({}^{\circ}\)C after 14 days, suggesting that in the samples, the degrading bacteria  
Figure 4: Changes of 2-MIB concentration in natural water preserved with chlorine and stored at 25 \({}^{\circ}\)C, where (**A**) is for the AGD reservoir; (**B**) is for the ZW reservoir; (**C**) is for the NH reservoir; and (**D**) is the residual chlorine concentrations. were filtered out. However, for other samples, including the extracellular samples stored at 25 \({}^{\circ}\)C and the samples with cells present, a reduction in 2-MIB >20% was observed. Because in natural water, 2-MIB may be present in both cell-bound and dissolved phases, filtration is not feasible if both cell-bound and extracellular 2-MIB are to be determined.