esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions  
In conclusion, we have designed a mm-wave 1 \(\times\) 4 linear MIMO array antenna featuring a slotted triangular radiating patch and a defected ground plane. This MIMO antenna effectively covers multiple resonance frequencies (26.82, 28.34, 32.68, and 38.11 GHz) through precise positioning of the slots in the radiating elements and careful tuning of the dimensions in the ground plane. Additionally, the antenna achieves isolation of over 24 dB between adjacent ports without the need for additional decoupling techniques. The analysis of ECC, DG, MEG, TARC, CCL, and bending characteristics and its ultra-thin, flexible design confirms the antenna's suitability for high data rate (100 Mbps up to a distance of 70 m at 28.0/38.0 GHz) 5G and wearable biomedical communication technologies. The SAR evaluation on a multilayer phantom reveals values well below critical limits for both 1 gm and 10 gm of tissue, ensuring safe use for wearable biomedical applications.  
Conceptualization, R.N.T., K.G.M., G.N., P.B.N., D.S., P.S. and P.K.; methodology, R.N.T., K.G.M., G.N., P.B.N., D.S., P.S. and P.K.; formal analysis, R.N.T.; measurements, R.N.T.; software, R.N.T., K.G.M., G.N. and P.B.N.; writing--original draft preparation, R.N.T.; writing--review and editing, R.N.T., D.S., P.S. and P.K.; supervision, R.N.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Complete data is available in the research paper.  
The authors declare no conflicts of interest.  
Figure 22: Link margin versus distance for the proposed MIMO antenna.
## 3 Results and Discussion
### FTIR Results  
FTIR was performed to inspect the functional groups found in pure PCL and _I. gravelens_/PCL nanofibers scaffolds which were clarified in (Figure 2). The FTIR spectrum presents a number of peaks at curve regions from about 3400 to 650 cm\({}^{-1}\). Areas on the curve of _I. gravelens lead_ to the O-H stretching, stretching (CH, CH2, and CH3), C=O stretching, C-C intraloop stretching, C-H bending, C-O-C stretching, C-O stretching, carboxylic acid bending O-H, and C-H bending, respectively. On the PCL curve, which corresponds to stretching CH2, stretching C-H, stretching C=O, stretching C=C, bending C-H C-H rock, C-H way, C-O of carboxylic acid, bending OH of carboxylic acid, and aromatic C-H, respectively, some peaks can also be found in 2900 to 740 cm\({}^{-1}\) region. We can observe peaks corresponding to PCL when analyzing the _I. gravelens_/PCL curve, which is indicative of the lack of any chemical reactions between _I. gravelens_ and PCL.
# Quantum Selection for Genetic Algorithms Applied to Electromagnetic Design Problems
## 5 Electromagnetic Design Problems Considered
### Multilayer EM Absorber  
A multilayer EM absorber is a stack of \(N\) materials as shown in Figure 4. The materials that are used are lossy, dissipating the power transported by the EM wave into thermal energy, thereby reducing the reflected power. The thickness of each layer plays an important role not only in the effective impedance perceived by the incoming EM wave. However, in many applications, the total thickness of the absorber has to be as low as possible to reduce the overall occupied space or to keep its weight below a given constraint.  
For this problem, normal incidence is considered for the incoming EM wave; additionally, all the materials are considered purely dielectric, lossy, and non-frequency selective (\(\mu_{r}=1\), \(\tan\delta>0\), and \(\epsilon_{r}(f)=\epsilon_{r}\)). The reflection coefficient for normal incidence [19] can be approximated as  
\[\Gamma\approx\Gamma_{0}+\Gamma_{1}\;e^{-2j\beta_{1}d_{1}}+\Gamma_{2}\;e^{-2j( \beta_{1}d_{1}+\beta_{2}d_{2})}+\cdots+\Gamma_{N}\;e^{-2j(\beta_{1}d_{1}+\cdots +\beta_{N}d_{N})} \tag{3}\]  
Figure 4: A schematic representation of the multilayer electromagnetic absorber is presented. Each layer is modeled as a lossy dielectric slab contributing to power dissipation and reflection reduction.  
Considering the values of \(|\Gamma_{i}|\) in the first layers to be low, \(|\Gamma_{i}|\) and \(\beta_{i}\) are computed using the following equations:  
\[\Gamma_{i}=\frac{\eta_{i+1}-\eta_{i}}{\eta_{i+1}+\eta_{i}}\qquad\qquad\beta_{i} =\frac{2\pi f}{c}\sqrt{\epsilon_{r,i}\;\mu_{r,i}}\qquad\qquad\eta_{i}=\sqrt{ \frac{\mu_{0}\;\mu_{r,i}}{\varepsilon_{0}\;\epsilon_{r,i}}} \tag{4}\]  
The fitness function is computed as a weighted sum between the mean value of the reflection coefficient in the target band \((f_{min},f_{max})\) and the total thickness of the absorber. The final design is the combination of slab thicknesses that achieves the trade-off between total thickness and absorption dictated by the weight \(w\).  
\[\text{Fitness }=\text{ mean}(|\Gamma|)_{f_{min}\leq f\leq f_{max}}+w\sum d_{i} \tag{5}\]
## 3 Purpose of This Study  
From the existing literature review, it can be found that the current rockburst prediction research generally adopts the correlation analysis between indicators as the main basis for feature selection, aiming to construct a set of weakly correlated evaluation indicator systems as model input. However, empirical studies have shown that the statistical correlation between indicators has no significant effect on the performance of prediction models. It is worth noting that there is still a lack of comparative studies on different indicator systems in rockburst prediction, and there is a serious lack of empirical analyses to systematically evaluate the predictive efficacy of each indicator system. In addition, existing studies rarely consider the correlation between the indicators and the rockburst level as the feature selection criterion, and this approach has not yet been fully explored in related fields of research.  
In order to deeply explore the influence of the selection of evaluation indexes on the performance of the prediction model, this study uses the index-indicator correlation and index-rockburst grade correlation as the selection criterion and the maximum tangential stress (\(\sigma_{\rm{0}}\)), the uniaxial compressive strength (\(\sigma_{\rm{c}}\)), the tensile strength (\(\sigma_{\rm{t}}\)), the elastic strain energy index (\(W_{\rm{et}}\)), the stress concentration factor (SCF), the rock brittleness index B\({}_{1}\) (B\({}_{1}\) = \(\sigma_{\rm{c}}/\sigma_{\rm{t}}\)), the rock fragility index B\({}_{2}\) (B\({}_{2}\) = \((\sigma_{\rm{c}}-\sigma_{\rm{t}})/(\sigma_{\rm{c}}+\sigma_{\rm{t}})\)), and seven other indices as the basis. Different numbers of evaluation indices (seven, six, five, four, and three indices) are used as input variables, combined with integrated learning algorithms (XGBoost, CatBoost, LightGBM, and random forest (RF)) and six mainstream algorithms, such as traditional algorithms (support vector machine (SVM) and multilayer perceptron machine (MLP)) with the Optuna hyperparameter optimization algorithm, to carry out a comparative analysis. By comparing the prediction effect of each model under different judging index systems, we aim to assess the specific impact of each index combination on the model performance and provide a theoretical basis for optimizing the rockburst prediction model.
## 3 Results and Discussion
### Analysis of Droplet Spreading over Cu-SiC Surfaces
#### 3.3.1 Analysis of Droplet Spreading over Cu-SiC Surfaces Processed by Abrasive Materials
As noted earlier in Section 3.2, the contact angle hysteresis on the Cu-SiC surfaces is mainly due to the non-uniformity of the elemental composition (Table 2). As can be seen from Figure 7, the difference between CAH measured at 5 mL/min and 100 mL/min is not significant. However, according to the recommendation [47], the CAH values are obtained at very low fluid flow rates, therefore, measurements at 5 mL/min are taken as CAH values. Thus, the CAHs on Cu-SiC surfaces processed by abrasive materials are 10.1-14.9\({}^{\circ}\). Figure 6: Time dependences of the dynamic contact angles for a 10-mL water droplet spreading over the surfaces of Cu-SiC composite materials processed by abrasive materials. Liquid flow rate: (**a**)—5 mL/min; (**b**)—100 mL/min. I is the liquid supplying (advancing DCAs); II is the liquid pumping out (receding DCAs).
# Satellite Network Task Deployment Method Based on SDN and ICN
## 5 The Simulation Result
### Imulation Scenario and Parameter Design
The sensor detection half-cone Angle of the three GEO satellites was set to 10\({}^{\circ}\). We created a LEO intersatellite link to connect the ground cloud computing center to areas without ground network. Mission deployment requires stable intersatellite link support. A single satellite is set to have four neighborhood communication links, two of which are used to establish fixed intersatellite links with front and back satellites on the same orbit. The other two links are used to establish unstable links with LEO satellites on the adjacent orbital plane. Figure 6 shows the 2D view of LEO intersatellite links after setting. We selected one of the satellites and tested the status of the satellite's four communication links within 24 h, as shown in Figure 6.
# A Reconfigurable Single-/Dual-Bandstop Filter with Controllable Equal-Ripple Performance
## 2 Design and Analysis of the Proposed Reconfigurable BSF
### State I: S-BSF
#### 2.1.3 Design Examples of State I  
To verify the proposed theory for controlling equal-ripple levels of \(SR_{S}\) and \(RL_{S}\), \(SR_{S}\), and \(RL_{S}\) are selected as 20 dB and 25 dB. The variation curves of \(Z_{1}\), \(Z_{2}\), and \(Z_{4}\) versus \(Z_{3}\) are exhibited in Figure 4. The realizable range is marked by the gray area in Figure 4. It can be seen that \(Z_{1}\) and \(Z_{2}\) decrease, and \(Z_{4}\) increases, as \(Z_{3}\) increases.  
Figure 4: The achievable range of the proposed reconfigurable BSF at State I. (The gray area represents the realizable range for the value of characteristic impedance).  
To verify that \(Z_{3}\) can be given arbitrarily with \(SR_{S}\) and \(RL_{S}\) fixed at 20 dB and 25 dB, three design examples are selected from Figure 4. The detailed design parameters of three examples are summarized in Table 1, which verifies the changing trend of \(Z_{1}\), \(Z_{2}\), and \(Z_{4}\) mentioned above. Figure 5 exhibits the circuit simulated results for three design examples. It can be seen that \(SR_{S}\) and \(RL_{S}\) are fixed at 20 dB and 25 dB, and the BW of the stopband becomes narrower with the increase of \(Z_{3}\).
## 3 Mixing and Casting  
The self-compactness of the geopolymer concrete is dependent on the water/binder ratio; as a result, the authors optimised the water/binder ratio to 0.45 in the previous studies [3; 28; 29]. Similar to previously reported studies, the mixing of the self-compacting geopolymer is performed by adding the weighed binder materials (fly ash, ground granulated blast furnace slag, micro fly ash), along with the alkali activator in the dry form and the coarse and fine aggregates in a pan mixer for four minutes [3; 28; 29]. Then, the water was added to the mix slowly and the mixing was continued for 8 min with a rest period of two minutes between [3; 28; 29]. After mixing, the workability tests, including slump flow and T500, were carried out as per the EFNARC guidelines [31]. Finally, the self-compacting geopolymer concrete was poured into cylindrical moulds of 100 mm diameter and 200 mm height, and subjected to ambient curing for 28 days prior to three-month exposure to ambient and marine conditions. Ambient curing in this study refers to the air curing of the samples at a temperature of 23 \(\pm\) 2 \({}^{\circ}\)C in the laboratory facility of Deakin University-Waurn Ponds Campus, Geelong [3; 28; 29]. The mix proportion and workability properties of the SCGC mix used in this study are given in Table 3 below.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material \({}^{*}\)**} & \multicolumn{8}{c}{**Material Properties**} \\ \cline{2-10}  & **Fineness (Passing** & **Loss on** & **Moisture** & **SO\({}_{3}\)** & **Available** & **Relative** & **Chloride Ion** \\  & **through 45 μm Sieve** & **Ignition** & **Content** & **Content** & **Alkali** & **Density** & **Content** \\ \hline Fly ash & 88\% & 1.0\% & 0.1\% & 0.2\% & - & - & - \\ \hline Micro fly ash & 99\% & 0.7\% & 0.01\% & 0.2\% & 0.2\% & 2.35 & 0.001\% \\ \hline Ground Granulated & 97\% & - & - & - & - & 2.86 & - \\ Blast Furnace Slag & \multicolumn{8}{c}{} \\ \hline \hline \end{tabular}  
* \({}^{*}\)Sourced from-1. Fly Ash—Cement Australia, 2. Micro Fly Ash—Fly Ash Australia, 3. Ground Granulated Blast Furnace Slag—Independent Cements.  
\end{table}
Table 1: Material properties of binder materials [3].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
\begin{tabular}{c} **Chemical** \\ **Composition** \\ \end{tabular} & **SiO\({}_{2}\)** & **CaO** & **Al\({}_{2}\)O\({}_{3}\)** & **MgO** & **K\({}_{2}\)O** & **SO\({}_{3}\)** & **V\({}_{2}\)O\({}_{5}\)** & **TiO\({}_{2}\)** & **Na\({}_{2}\)O** & **P\({}_{2}\)O\({}_{5}\)** & **FeO** \\ \hline Fly Ash (\%) & 65.75 & - & 32.87 & - & - & - & - & 1.38 & - & - & - \\ \hline Slag (\%) & 35.19 & 41.47 & 13.66 & 6.32 & - & 2.43 & 0.20 & 0.73 & - & - & - \\ \hline Micro Fly Ash (\%) & 63.09 & - & 32.26 & - & 0.83 & - & - & 1.67 & 0.41 & 0.62 & 1.12 \\ \hline \hline \end{tabular}  
\end{table}
Table 2: Chemical composition (percentage by weight) of fly ash, slag, and micro fly ash [3].
## 3 Results and Discussion  
### Drying  
Traditional convection drying and hybrid microwave-convection drying were analyzed. Figure 3 shows the changes in the reduced water content (MR) as a function of the duration of the convection drying and the microwave-convection drying of the Moldavian dragonhead leaves. In the case of convection drying, a reduction in the drying time was observed with increasing temperature. The convection drying time ranged from 184 min at 40 \({}^{\circ}\)C to 75 min at 70 \({}^{\circ}\)C and was similar to that obtained in the work of [PERSON] et al. [2]. The microwave support resulted in a radical shortening of the process. The microwave drying time ranged from 33 min (MW-CD 100W 30C) to 7 min (MW-CD 300W) (Table 1).  
The combined microwave-convection drying time depended on both the air temperature and the microwave power. Similar observations were made by many authors using microwave-assisted drying [17; 18; 19; 20; 33; 34; 37]. During microwave drying, the drying speed decreases as the amount of water dipoles decreases. In Figure 3 this phenomenon was visible in the drying curves that correspond to higher microwave power (200 and 300 W). Similar results were obtained in the case of drying many leaf materials [22; 29; 30]. In some works, the opposite effect was observed, consisting of a sudden acceleration of the drying rate at MR = 0.4. This is explained by the formation of stresses causing tissue disruption and the leakage of cellular contents [29; 38]. The intensity of the drying process can also be analyzed based on the values of the effective water diffusion coefficient. Microwave radiation increases the intensity of the process, which is reflected in the increasing D\({}_{\text{eff}}\) coefficient. The water diffusion coefficients obtained in this study are presented in Table 1. In the case of convection drying, the D\({}_{\text{eff}}\) was in the range of 7.103:10\({}^{-12}\)-9.103:10\({}^{-11}\), and in the case of microwave-convection drying, the D\({}_{\text{eff}}\) was in the range of 9.783:10\({}^{-11}\)-1.103:10\({}^{-9}\) m\({}^{2}\).s\({}^{-1}\). The increases in both the temperature and the microwave power resulted in an increase in the D\({}_{\text{eff}}\) values (Table 1). The obtained dried samples slightly differed in water activity, corresponding to the same water content (MR).  
Similarly, low D\({}_{\text{eff}}\) values, obtained during the microwave-convection drying of leaves, were observed by other researchers [25; 38; 39].
## 5 Discussion and Conclusions  
The determination of the location, distribution, direction, and depth of the potential slip zone on the slope is the key to the slope condition assessment. In this paper, the high-density resistivity method, numerical simulation, and FBG deformation monitoring technology are combined to provide a reliable method for slope stability evaluation. The main conclusions of this study can be summarized as follows:  
(1) According to the geological environment of the slope, the corresponding high-density resistivity survey line layout scheme is proposed. Based on the conversion and analysis of the detection results, the engineering geological structure map of the slope is obtained, and the potential slip surface is delimited preliminarily.  
(2) The comprehensive analysis shows that the slope residual layer on the north side of the slope is thick, which makes it easy to slide under the influence of external factors. On this basis, the section of the north side of the slope is extracted and analyzed.  
(3) The 2D finite element analysis model of the slope is established by ANSYS. The position and shape of the potential sliding surface are determined by the simulation results, which are consistent with the results of the high-density resistivity method.  
(4) The FBG monitoring system is set up in the selected monitoring area, and the deformation and change trend of the slope are understood by analyzing the longitudinal strain monitoring data of the sensor.  
(5) During the monitoring period, the longitudinal strain of IN 2\(\sim\)IN 4 fluctuated in a small amplitude, and the strain in the shallow area of IN 1 and IN 5 varied more, with the maximum values reaching 426 \(\upmu\)e and \(-\)182 \(\upmu\)e, respectively. The monitoring and analysis of these two areas should be strengthened in later stages.  
Based on this paper, further research can be carried out. For example, the cross-section and longitudinal section of the slope can be combined to establish a three-dimensional model of the slope for stability analysis. However, this needs to be supported by more detailed geological data on the slope.  
Conceptualization, C.L. and L.W.; methodology, C.L., L.W. and Q.X.; software, L.W. and J.L.; validation, J.L. and L.W.; formal analysis, C.L. and L.Y.; investigation, Q.X.; resources, L.Y and X.W.; data curation, Q.X.; writing--original draft preparation, L.W.; writing--review and editing, C.L.; visualization, J.L.; supervision, L.Y.; project administration, Q.X. and X.W.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This work is partly supported by the National Natural Science Foundation of China (Grant No. 62263015).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Yunnan Aerospace Engineering Geophysical Detecting Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 1 Introduction
This would provide a 3D representation of porosity from ultrasonic data and improve the information on the impact of porosity on manufacturing, increasing quality and facilitating efficiency. To do this, phased array (PA) UT was used to obtain the best porosity images, XCT inspections were performed, and a labeled dataset of porosity defects was built and applied to train a convolutional neural network to segment the voids using UT images as input and XCT data as the ground truth. The dataset consists of several C-scans with different cases of void structures. The idea was to train a CNN on 2D cases, evaluated on a 2D test set, and use the trained network to infer ultrasonic 3D data as a sequence of C-scans, long the lines of the approach using B-scans reported in [15]. Experiments were performed on two 150 \(\times\) 40 \(\times\) 5 mm\({}^{3}\) CFRP coupons. The main contributions of the research are:  
* A methodology to use ultrasonic and XCT data and its application for porosity assessment, which is a use-case where UT data would not enable the formation of a supervised dataset;
* the construction of a dataset of ultrasonic porosity images;
* the optimization of phased array ultrasonic inspections enhances the detail level in the analysis of porosity, and void shape, size and distribution;
* the evaluation of model performance depending on which data were used for training or testing, equal to an F1 of 0.6-0.7 and a IoU of 0.4-0.5 for the test data. Furthermore, the results proved to be robust, since the segmentation was equivalent for data that were part of the training and the test datasets in different training processes.
# Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
## 3 Notation and Problem Statement
### Notations  
The proposed model is based on the following notations:  
\begin{tabular}{l l} \(x(t)\) & Inventory level at time \(t\) \\ \(a(t)\) & Age of the machine at time \(t\) \\ \(u(t)\) & Production rate at time \(t\) \\ \(\xi(t)\) & Stochastic process \\ \(u_{max}\) & Maximal production rate \\ \(u_{i}\) & Productivities of the machine \\ \(w(t)\) & Control variable for the repair/major maintenance \\  & policy at time \(t\) \\ \(\beta(\cdot)\) & Rate of defectives \\ \(d\) & Constant demand rate of products \\ \(\Omega\) & Set of states of the machine \\ \(\rho\) & Discount rate \\ \(\pi_{i}\) & Limiting probability at mode \(i\) \\ \(\lambda_{max}(\cdot)\) & Transition rate from mode \(\alpha\) to mode \(\alpha^{\prime}\) \\ \(g(\cdot)\) & Instantaneous cost function \\ \(J(\cdot)\) & Expected discounted cost function \\ \(v(\cdot)\) & Value function \\ \(\tau\) & Jump time of \(\xi(t)\) \\ \(c^{+}\) & Inventory holding cost/units/time units \\ \(c^{-}\) & Backlog cost/units/time units \\ \(c_{r}\) & Repair cost \\ \(c_{m}\) & Major maintenance cost \\ \(c_{d}\) & Cost of defectives \\ \(c_{pro}\) & Cost of production per unit of produced parts \\ \(\theta\) & Adjustment parameter for the rate of defectives \\ \end{tabular}
# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
## 5 Experimental Results
### Comparison Experiment of Modified Differential Evolution Algorithm
Therefore, the modified differential evolution algorithm proposed in this paper has the advantages of global optimality and fast convergence speed, which improves the convergence speed and convergence effect of the algorithm, and the overall performance is better than other existing algorithms. For the optimal path problem of space robots for maintenance tasks of large space solar-power stations, in order to better prove the performance of the modified DE proposed in this paper, we take the space-robot cluster with a total energy of 6000 as an example, the genetic algorithm (GA), ant colony optimization algorithm (ACO), artificial bee colony algorithm (ABC) and modified DE are, respectively used to solve the optimal path of maintenance task of large space solar-power station. The test results are shown in Table 8.  
By comparing the different optimization algorithms in Table 8, we can see that in the space maintenance task, the modified differential evolution algorithm is better than the GA, ACO and ABC in solving the optimal solution of the maximum benefit, the number of maintenance points and the objective function value of the space-robot cluster. Therefore, this paper chooses to use the modified DE for the optimal path planning of the space robot, so that it can better complete the space maintenance task. Figure 8: Value of the objective function when the space-robot cluster energy is 6000. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Objective Function Value** & **Maximum Income** & **Maintenance Points** \\ \hline GA & 5794.99 & 6470 & 321 \\ ACO & 7049.01 & 7790 & 396 \\ ABC & 10,246.18 & 11,200 & 561 \\ modified DE & 11,688.33 & 12,640 & 632 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of other algorithms.