esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Metamaterial-Integrated Dipole Array Antenna for Enhanced Gain in 5G Millimeter-Wave Bands
## 3 Antenna Results and Discussion
### Gain and Radiation Efficiency  
The gain and radiation characteristics of the proposed antenna are demonstrated in Figure 11, while the gain and the radiation efficiency are presented in Figure 12. It can be seen from Figure 12a that without the metamaterial structure, the antenna gain is quite low, especially at 28 GHz around 5 dBi, while the proposed metamaterial-integrated antenna offers a stable high gain of around 8 dBi within the operating frequency band, as presented in Figure 12b. Additionally, with the proposed metamaterial structure, the antenna provides a high maximum gain of 11.21 dBi at 41 GHz. The proposed metamaterial  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Freq.** & \multicolumn{2}{c}{**HPBW (\({}^{\circ}\))**} & **Freq.** & \multicolumn{2}{c}{**HPBW (\({}^{\circ}\))**} & **Freq.** & \multicolumn{2}{c}{**HPBW (\({}^{\circ}\))**} & **Freq.** & \multicolumn{2}{c}{**HPBW (\({}^{\circ}\))**} \\ (**GHz**) & **E Plane** & **H Plane** & **(GHz)** & **E Plane** & **H Plane** & **(GHz)** & **E Plane** & **H Plane** & **(GHz)** & **E Plane** & **H Plane** \\ \hline
22 & 73 & 87.2 & 28 & 54.1 & 74.4 & 34 & 42.4 & 47.6 & 40 & 31.6 & 45 \\
24 & 54.6 & 75.9 & 30 & 60.3 & 72.8 & 36 & 38.3 & 45.2 & 42 & 28.1 & 42.9 \\
26 & 51.7 & 76.8 & 32 & 50.7 & 59.1 & 38 & 35.2 & 50.2 & 44 & 24.9 & 43.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The HPBW of the proposed antenna at different operating frequencies.  
Figure 11: The radiation pattern of the developed metamaterial-integrated dipole array antenna: (**a**) simulated 3D directivity at different frequencies and (**b**) 2D polar radiation pattern with gain scale.  
structure enhances the directivity of the antenna's radiation, resulting in a gain increase of more than 3 dBi compared to the dipole array alone. Due to antenna performance tradeoffs, the metamaterial structure shows a discontinuous result at higher frequencies (34-36 GHz), as depicted in Figure 6, which results in slightly lower radiation efficiency at that region, as can be seen in Figure 12. The proposed metamaterial structure configuration is finalized as it offers a significant gain enhancement. Overall, the proposed antenna also offers a good radiation efficiency of more than 64% within the operating frequency band, as shown in Figure 12b.
# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 6 Discussion
The results shown in Table 6 and Figures 9 and 10 emphasize the benefits of the proposed DLS, especially its advantage of being shallow (unlike the deep pre-trained models) with a low training burden (10 epochs). The comparative results with other deep learning systems, tested on different COVID datasets, highlighted that although the proposed model is shallow, it provides high accuracy in a short time when tested on CXR images. The above results document that the proposed DLS system can be helpful in healthcare systems in several ways, such as decreasing consultation-associated costs, enhancing detection consistency, and thus lowering the risk of disease spread. Despite the promising results, our analysis pipeline has some limitations. Firstly, we trained and tested our approach from a single benchmark dataset. Thus, using external test datasets from other different centers should further enhance the robustness of our automated DLS. Secondly, our DLS only utilized a single input data type: chest X-ray images. In clinical practice, multiple data sources are usually used, such as clinical biomarkers and, sometimes, chest CT scans, which can provide more localized views of the affected lungs. Recent studies, e.g., [28; 29; 30; 31; 32], explored the various DL methods available to diagnose COVID-19 based on chest CT images. The studies have documented high accuracy rates for both binary and multi-class classification tasks. Therefore, an update to the proposed model could integrate multiple inputs and give a more robust prediction at the patient level. Finally, our system concentrated on two activation functions without further investigation of the effect of \(\alpha\) or other activation functions.
## 6 Instrument description
### Instrument characteristics
#### In terms of the tracking mode:  
Because the satellite altitude can change rapidly (1 meter in 50 ms) as well as the backscatter coefficient (due to varying sea state, wind conditions,...), which leads to a change in the return power, echoes have to be \"power tracked\" and \"range tracked\". The tracking algorithm computes estimates based on 25 ms accumulated waveforms. Indeed, in order to reduce the speckle noise, waveforms are accumulated before their processing and transmitting to the ground. About 100 elementary return pulses are accumulated, which corresponds to a cycle of 25 ms.  
In order to maintain the echo power level at a constant level after accumulation and FFT, an AGC (Automatic Gain Control) loop (1\({}^{\text{st}}\) order loop) is first used.  
Then, in order to remain correctly positioned within the frequency analysis window and to make a correct summation for each range gate from one pulse to the next, a 2 nd order range tracking loop is used to maintain the waveform front slope at a determined range gate. In addition to a classical POSEIDON type algorithm, an experimental tracking mode using the DIODE information can be used. In the latter case, the range instruction is processed by using the DIODE altitude and an additional on-board data set containing the elevation of the surface. This means that to perform this \"open\" tracking process, a DEM providing the height above the geoid at the nadir point along the whole orbit has to be stored on board the altimeter.  
Last characteristic to mention is that a high data rate mode will be available on AltiKa. It will provide complex in phase and in quadrature waveforms at the PRF rhythm and will be used to analyze speckle and echo characteristics in transition regions.
## 3 Results  
### Gene Cloning and Sequence Assay  
A \(\beta\)-mannanase gene was successfully amplified with the PCR method from _Enterobacter aerogenes_ B19. The ORF of the \(\beta\)-mannanase gene contained 1047 bp encoding 348 amino acids. The molecular mass (Mw) and isoelectric point (pI) of the enzyme were predicted by ProtParam to be 38.61 kDa and 5.15, respectively. SignalP predicted that no signal peptide existed in the amino acid sequence. BLASTp result showed that Man26E shared high identity in the amino acid sequence (above 97%, Query cover 100%) with many _Enterobacter_ bacteria strains, which included a _Pantoea agglomerans_ A021 strain, _Enterobacter ludwigii_ strains and _Enterobacter cloace_ strains. However, up to now, there are few reports on the identification, structure analysis, purification and heterologous expression of these \(\beta\)-mannanases. Multiple sequence alignment of Man26E (GenBank accession no. ANA84055.1) and other bacterial \(\beta\)-mannanases from _Enterobacter ludwigii_ (GenBank accession no. WP_078773900.1), _Bacillus subtilis_ strain 168 (PDB ID: 3 CBW_A), _Bacillus subtilis_ strain 168 (PDB ID. 2 WHK_A), _Pantoea agglomerans_ (GenBank accession no. ACN30272.1) and _Bacillus subtilis_ Z-2 (PDB ID. 2 QHA_A) were done by Clustal Omega ([[https://www.ebi.ac.uk/Tools/msa/clustalo/](https://www.ebi.ac.uk/Tools/msa/clustalo/)]([https://www.ebi.ac.uk/Tools/msa/clustalo/](https://www.ebi.ac.uk/Tools/msa/clustalo/))). The result showed that Man26E had 98.85% sequence identity with WP_078773900.1, 98.28% with WP_108648449.1, 47.97% with 2 WHK_A, 97.70% with ACN30272.1, 47.97% with 2 QHA_A and 47.41% with 3 CBW_A. Although the similarity between three _Enterobacter_ strains and three _Bacillus subtilis_ strains was less than 50%, a large number of highly conserved amino acid residues were found in their sequences. The residues which have been considered to be involved in the catalysis of GH26 enzymes were conserved, and they were Glu183 (putative catalytic acid-base) and Glu282 (putative catalytic nucleophile) in Man26E (Figure 1).
# Mechanical Analysis of Ground Sleeve
## 5 Mechanical Analysis of Grouted Sleeve Lapping Connector
### Strain Analysis of the Sleeve
#### 5.4.1 Analysis of Hoop Compressive Strain for the Sleeve  
Figure 11(a) shows the load-strain curves of SG1 (as shown in Figure 4) installed on specimens with lap lengths of 100 mm, 200 mm, and 300 mm. Generally, the curves could be classified into three similar regimes (as shown in Figure 11(b)). In the first stage, the strain was compressive and increased with the load. The compressive strain reached a maximum (\(\varepsilon_{\mathrm{c}}^{max}\)) when the load reached a certain value (\(P_{\mathrm{SG1}}^{\prime}\)). In the second stage, the strain was still compressive, but decreased as the load increased. At the moment that the load reached \(P_{\mathrm{SG1}}^{\prime\prime}\), the strain decreased to 0. In the last stage, the strain was tensile and increased with the load until a maximum was reached.  
The ultimate compressive strain values of SG1 (i.e., \(\varepsilon_{\mathrm{c}}^{\mathrm{max}}\)), and the corresponding loads (\(P_{\mathrm{SG1}}^{\prime}\)) are shown in Figure 22. To reduce the scatter caused by the heterogeneity of the grout and the sleeve deflection, the average values of specimens with the same lap length were used. As the lap length  
Figure 21: Load-strain curves of SG1 in a grouted sleeve lapping connector.  
Figure 20: Load-strain curves of SG5 and SG6 in a grouted sleeve lapping connector.  
increased, the average ultimate compressive strain value of SG1 and the corresponding average load increased. As we can see from Figure 18a, at the same load, the longer the lap length was, the larger \(P_{\text{re}}\) and the axial tensile force of the sleeve were. Thus, the axial tensile stress of the sleeve wall increased with respect to the lap length, which led to a larger hoop compressive strain in the sleeve. In addition, an increment in the lap length decreased the radial stress and the expansion degree of the sleeve in the transverse direction [27], which resulted in a smaller hoop tensile strain in the sleeve. Therefore, the longer the sleeve was, the larger the ultimate hoop compressive strain of the sleeve and the corresponding load were.
## 3 Results and Discussion
### Assessment of Temperature-Corrected Shear Viscosity
The increase in shear viscosity after performing the viscous heating correction is evident from the increased \(n\) value (see Table 2 for \(n\) vs. corrected \(n\)) and is reflected in larger \(\Delta P_{\rm cap}\), in accordance with Equation (13). Using the equivalent viscosity \(\overline{\eta}\) in the simulation, in Figure 7, we see a close agreement between experiment and model prediction (solid lines) over the entire range of shear rates. Thus, the proposed methodology in Figure 3a permits a reliable temperature correction for melts in capillary rheometry. Note once again that we have evaluated \(\overline{\eta}\) in Equation (13) using the average fluid temperature at the wall \(\overline{T}_{\rm w}\) instead of the bulk fluid temperature, as previously assumed. Among the samples studied, viscous heating occurs to a significant degree for ABS and filled PA66, and the correction becomes clearly significant at the lowest measurement temperature. This can be easily understood to stem from the large fluid temperature rises at the wall as shown in Figure 8a-e (symbols), where it can be seen that viscous heating can result in pronounced deviations from the inlet temperature by 10-25 \({}^{\circ}\)C.  
Figure 8: _Cont._  
Figure 7: Measured capillary pressure drop \(\Delta P_{\rm cap}\) (symbols, \(L/D=30\)) vs. viscous Cross-WLF model prediction based on \(\eta\) (\(\dot{\gamma}\)) with and without temperature correction (lines) as a function of \(\dot{\gamma}_{\rm a}\) for (**a**) HIPS, (**b**) GPPS, (**c**) ABS, (**d**) PP, and (**e**) filled PA66. At this point, one might reasonably wonder whether or not the high-pressure effect on viscosity plays a minor role under the pressure conditions of these experiments. According to the Barus equation, \(\eta_{0,p}=\eta_{0}\exp\left(\beta_{p}p\right)\)[58; 59], the reciprocal of the pressure coefficient \(\beta_{p}\) may indicate a critical pressure \(p_{\rm c}\), above which the pressure dependence of viscosity cannot be neglected. Reported \(\beta_{p}\) values are \(10<\beta_{p}\) (\(\rm GPa^{-1}\)) \(<\) 43 for PS, \(24<\beta_{p}\) (\(\rm GPa^{-1}\)) \(<\) 33 for ABS, and \(16<\beta_{p}\) (\(\rm GPa^{-1}\)) \(<\) 24 for PP [33; 60; 61; 62], corresponding to pressure ranges of \(23<p_{\rm c}\) (\(\rm MPa\)) \(<\) 100 for PS, \(30<p_{\rm c}\) (\(\rm MPa\)) \(<\) 42 for ABS, and \(42<p_{\rm c}\) (\(\rm MPa\)) \(<\) 63 for PP. For the materials studied in Figure 7, the measured \(\Delta P_{\rm cap}\) are comparatively small, so that the pressure dependence of viscosity may be safely neglected in the present analysis.
# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
## 3 Results
### Direct Method  
In our approach, the deep power forecasting model using the direct method allows us to exploit the differences with the indirect method proposed in power forecasting. The data in Table 11 allow us to estimate an ideal performance that could have the direct or indirect methodology of having GHI and GTI forecasts with zero error, which further underlines the importance of these variables.  
Table 13 shows that the model trained with the variables Calibration\({}_{\text{MAE}}\) and Skartveit obtain an MAE of 0.896 MW, RMSE of 1.677 MW and \(R^{2}\) of 0.908, representing the \"optimal case\". Furthermore, different combinations of variables barely affect the metrics, but all outperform conventional methods, validating the effectiveness of the direct method. Contrasting the errors by station shows considerable deviations in stations 4 and 5 as in the results of the GHI block, which suggests problems with the pyranometer or, failing that, with the NWP forecasts. Station 0, however, validates the great effectiveness of the Transformer Bi-LSTM model in improving forecasts.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Predictors**} & \multirow{2}{*}{**Metric**} & \multicolumn{5}{c}{**Station**} & \multicolumn{5}{c}{**Mean**} \\  & & **0** & **1** & **2** & **3** & **4** & **5** & **8** \\ \hline \multirow{2}{*}{\(\text{LMD}_{\text{GHI}}\)} & MAE & 0.094 & 0.482 & 0.324 & 0.395 & 0.717 & 0.676 & 0.364 & 0.436 \\ \multirow{2}{*}{\(\text{LMD}_{\text{GTI}}\)} & RMSE & 0.187 & 0.970 & 0.630 & 0.729 & 1.447 & 1.274 & 0.708 & 0.849 \\  & \(R^{2}\) & 0.982 & 0.973 & 0.977 & 0.981 & 0.957 & 0.981 & 0.981 & 0.976 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Training results of Transformer Bi-LSTM for photovoltaic power estimation with GHI derived from sensors and \(\text{LMD}_{\text{GTI}}\). RMSE and MAE are measured in \([MW]\), and \(R^{2}\) is unitless.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Predictors**} & \multirow{2}{*}{**Metric**} & \multicolumn{5}{c}{**Station**} & \multicolumn{5}{c}{**Mean**} \\  & & **0** & **1** & **2** & **3** & **4** & **5** & **8** \\ \hline \multirow{2}{*}{Best\({}_{\text{Ipoa}}\)} & MAE & 44 & 50 & 59 & 57 & 97 & 63 & 70 & 63 \\  & RMSE & 82 & 103 & 106 & 106 & 187 & 122 & 131 & 120 \\  & \(R^{2}\) & 0.93 & 0.90 & 0.87 & 0.92 & 0.83 & 0.85 & 0.89 & 0.88 \\ \hline \multirow{2}{*}{NWP\({}_{\text{Ipoa}}\)} & MAE & 45 & 50 & 60 & 57 & 96 & 64 & 70 & 63 \\  & RMSE & 81 & 103 & 107 & 104 & 186 & 126 & 132 & 120 \\  & \(R^{2}\) & 0.93 & 0.90 & 0.87 & 0.92 & 0.83 & 0.84 & 0.89 & 0.88 \\ \hline \multirow{2}{*}{NWP\({}_{\text{Ipoa}}\)} & MAE & 45 & 50 & 59 & 55 & 96 & 64 & 68 & 62 \\  & RMSE & 82 & 103 & 105 & 102 & 187 & 125 & 129 & 119 \\ \multirow{2}{*}{Best\({}_{\text{Ipoa}}\)} & \(R^{2}\) & 0.93 & 0.90 & 0.87 & 0.92 & 0.83 & 0.84 & 0.89 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Results of deep transposition model block. RMSE and MAE are measured in [W/m\({}^{2}\)], and \(R^{2}\) is unitless.
# A Multi-Objective Harmony Search Algorithm for Sustainable Design of Floating Settlements +
## 2 Problem Definition  
As mentioned above, the problem in this study is concerned with the establishment of a floating settlement between four islands in the studied region. We consider four islands (Akca, Yassuca, Incirli, and Prnarli), with their locations shown in Figure 1. In establishing such a settlement, our aims include architectural and urban planning related ones, such as accessibility, as well as technical ones, such as location in suitable sea water depths, etc.  
Figure 1: A top view of the islands of Urla [7].  
In particular, we consider three main objectives for our problem definition. The first one concerns the accessibility of our different functions. By accessibility in this study we refer to the walking distance between the functions. We wish to minimize this distance, so that users of the settlement can easily move on foot within the settlement. As such, the need for additional transportation is eliminated. The second objective concerns the protection of our settlement from wind currents present in the area. The third one is maximization of visibility for making commercial places such as houses, a yacht marina, and a yacht dub noticeable from outside the settlement.  
After the abstract establishment of our functions is complete, we consider how to connect functions, most efficiently with a minimal pedestrian network, which will be realized using pontoons. In other words, this constitutes a form-finding problem. This is a second phase of our problem definition, and takes place after the multi-objective optimization.  
The following notations are used in the formulation of the problem:  
\(\begin{array}{l}\mathit{ltx}=x\;\mathit{coordinate\;of\;houses}\\ \mathit{hy}=y\;\mathit{coordinate\;of\;houses}\\ \mathit{mx}=x\;\mathit{coordinate\;of\;marina}\\ \mathit{my}=y\;\mathit{coordinate\;of\;marina}\\ \mathit{yx}=x\;\mathit{coordinate\;of\;yacht\;club}\\ \mathit{yy}=y\;\mathit{coordinate\;of\;yacht\;club}\\ \mathit{px}=x\;\mathit{coordinate\;of\;public\;area}\\ \mathit{py}=y\;\mathit{coordinate\;of\;public\;area}\\ \mathit{pix}=x\;\mathit{coordinate\;of\;protected\;region}\\ \mathit{piy}=y\;\mathit{coordinate\;of\;protected\;region}\\ \mathit{db_{h,p}}=\mathit{distance\;between\;houses\;and\;public\;functions}\\ \mathit{db_{y,p}}=\mathit{distance\;between\;yacht\;club}\\ \mathit{db_{m,p}}=\mathit{distance\;between\;marina\;and\;public\;functions}\\ \mathit{db_{m,y}}=\mathit{distance\;between\;marina\;and\;yacht\;club}\\ \mathit{db_{h,p_{i}}}=\mathit{distance\;between\;houses\;and\;protected\;area} \left(i\right),\;i=1,2\\ \mathit{hoi_{h}}=\mathit{number\;of\;intersected\;lines\;for\;houses}\\ \mathit{hoi_{y}}=\mathit{number\;of\;intersected\;lines\;for\;yacht\;club}\\ \mathit{hoi_{m}}=\mathit{number\;of\;intersected\;lines\;for\;marina}\\ \mathit{wdy}=\mathit{water\;depth\;for\;yacht\;club}\\ \mathit{z_{1}}=\mathit{accessibility}\\ \mathit{z_{2}}=\mathit{wind\;protection}\\ \mathit{z_{3}}=\mathit{visibility}\end{array}\)
## 3 Controller Design Approach
### Proposed GA-Fuzzy-Based Control Schemes
#### 3.3.2 Pitch Angle Control System
For the sake of applying GA in the intended pitch angle control approach to enhance its performance, the MATLAB(r) (Release 2016a, The MathWorks, Inc., Natick, MA, USA) environment is utilized with the following fitness function:  
\[\text{Fitness}\quad\text{Function}=\int_{0}^{t}|\Delta P_{\text{W}}tg|\,dt. \tag{5}\]  
Following the usage of the GA, the optimal values for membership functions parameters acquired are then scored in Table 4. In Table 4, the unit of the input membership functions parameters (\(B_{1}\)-\(B_{12}\)) is pu.Mw. Meanwhile, the units of the output membership functions parameters (\(B_{13}\)-\(B_{27}\)) are in degrees. The flowchart of the complete design process for the proposed two GA-fuzzy based control schemes is presented in Figure 7.
###### Abstract  
As an intrinsic part of the Internet of Things (IoT) ecosystem, machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity between machines. Millimeter-wave (mmWave) communication is another promising technology for the future communication systems to alleviate the pressure of scarce spectrum resources. For this reason, in this paper, we consider multi-hop M2M communications, where a machine-type communication (MTC) device with the limited transmit power relays to help other devices using mmWave. To be specific, we focus on hop distance statistics and their impacts on system performances in multi-hop wireless networks (MWNs) with directional antenna arrays in mmWave for M2M communications. Different from microwave systems, in mmWave communications, wireless channel suffers from blockage by obstacles that heavily attenuate line-of-sight signals, which may result in limited per-hop progress in MWNs. We consider two routing strategies aiming at different types of applications and derive the probability distributions of their hop distances. Moreover, we provide their baseline statistics assuming the blockage-free scenario to quantify the impact of blockages. Based on the hop distance analysis, we propose a method to estimate the end-to-end performances (e.g., outage probability, hop count, and transmit energy) of the mmWave MWNs, which provides important insights into mmWave MWN design without time-consuming and repetitive end-to-end simulation.  
Internet-of-Things (IoT); machine-to-machine (M2M) communications; multi-hop networks; hop distance +
Footnote †: journal: Sensors