esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### MOORA Method
#### 2.3.1 Ratio Approach  
In this approach, initially, a decision matrix A is constructed, wherein m alternatives are evaluated based on n criteria. Each element of matrix A, xij, denotes the value of criterion i for alternative j. Here, i = 1,2,...,n and j = 1,2,...,m, and the decision matrix A is represented as shown in Equation (24).  
\[\text{A}=\begin{bmatrix}x_{11}&x_{12}&\ldots&x_{1n}\\ x_{21}&x_{22}&\ldots&x_{2n}\\ \cdot&&&\cdot\\ \cdot&&&\cdot\\ \cdot&&&\cdot\\ x_{m1}&x_{m2}&\ldots&x_{mn}\end{bmatrix} \tag{24}\]  
In the second step, each xij value in the decision matrix is normalized within the range [0,1]. Normalization is performed using the formula provided in Equation (25). During the normalization process, each xij value is divided by the square root of the sum of the squares of the alternatives in the i. column, resulting in the normalized value xij*.  
\[x_{\text{ij}}*=\frac{x_{ij}}{\sqrt{\sum_{j=1}^{m}x_{ij}^{2}}} \tag{25}\]  
In the subsequent step, the values of criteria to be maximized and those to be minimized are separately summed for each alternative in the decision matrix comprising normalized xij*'s. The sum of the maximized values is then subtracted from the sum of the minimized values to derive the ranking values for each alternative. The calculation of the resulting ranking values, \(y_{j}\), is illustrated in Equation (26) [21].  
\[y_{j}=\sum_{i=1}^{g}x_{ij}^{*}-\sum_{i=g+1}^{n}x_{ij}^{*} \tag{26}\]  
Here, the values of the criteria to be maximized are expressed as i = 1,2,...,g, and the values to be minimized as i = g + 1,g + 2,..,n.  
Finally, ranking is performed based on the obtained \(y_{j}\) values. The alternative with the highest \(y_{j}\) value is considered as the best alternative, while the alternative with the lowest \(y_{j}\) value is considered as the worst alternative.
## 4 On-Site Monitoring Results Analysis
### Engineering Countermeasures  
Given that the stress value of the steel arch at the E point fluctuated immensely, the pressing action on this point was apparent. To improve the stability of the weak link, the locking foot catheter was set at the arch foot (\(\Phi\)42, \(L\) = 4.5 m, quantity: 2). It was demonstrated that when the angle of the locking foot catheter was set at 40\({}^{\circ}\)-50\({}^{\circ}\), the combined bearing effect with the steel arch was most effective [29]. The ends were firmly welded to the steel arch and finished with grouting (Figure 28) to ensure that most of the surrounding rock loads transmitted by the upper steel arches could be withstood before the lower steel arches were lowered. The principle of the joint action of the locking foot catheter, steel arch, and surrounding rock was that under the stress of surrounding rock load, the locking foot catheter and steel arch formed a cohesive structural unit. The steel arch was supported by the end of the locking foot catheter, and the surrounding rock load was transferred to the locking foot catheter. The locking foot catheter was subjected to the lateral and axial support reaction force. Correspondingly, the locking foot catheter was deflected by the lateral load (bending moment and shear force) at the end, while the end load diffused into the surrounding rock. Under the action of the axial load (main dynamic) at the end, the surrounding rock would provide the support reaction force and inhibit the axial displacement of the lock tube.  
In the process of interaction between the surrounding rock and the support, the steel arch was affected by the stress fluctuation of the surrounding rock, which led to periodic tensile and plastic deformation. This process could be approximately regarded as the  
Figure 28: Locking foot catheter arrangement.  
cyclic fatigue of steel. Therefore, given that the tunnel had high stress properties and weak surrounding rock, when designing the support, it was necessary to fully consider cracks caused by fatigue, and at the same time pay attention to the fatigue life of steel when selecting materials, so as to prolong the service life of the steel structure and ensure the safety of the project.
## Abbreviations
### Nomenclature
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## 3 Design Method of MCRWPT System Based on Simulation
### Design Circuit and Excitation Frequency  
#### 3.2.1 Design Circuit  
In practical applications, the load resistance changes during the operation of the RMINS, resulting in a change in voltage gain. When the input voltage is fixed, the output voltage will vary and cannot provide a constant voltage for the IMU. A common method to stabilize the output voltage is to use a DC-DC boost-buck converter to adjust the output voltage of the MCRPWT system to a fixed value, such as 5 V, to power the load [21]. Therefore, the output voltage of MCRPWT needs to be properly designed to ensure that the voltage is within the input voltage range of the DC-DC converter under the condition of fixed input voltage. In addition, the introduction of a DC-DC converter reduces the total  
Figure 7: Magnetic flux density (maximum value) simulated under the condition of constant load power along red measurement lines.  
transmission efficiency (TTE) of the power supply system, where the TTE is the product of the MCRWPT system's efficiency and the DC-DC converter's efficiency. Therefore, the optimization of TTE is also a problem to be solved in this paper.  
The block diagram of the MCRWPT system is demonstrated in Figure 8. The transmitting circuit and receiving circuit form an MCRWPT circuit. The transmitting circuit comprises DC power \(U_{IN}\), a full-bridge inverter circuit, primary compensation capacitors, and a primary winding. The receiving circuit consists of a secondary winding, secondary compensation capacitors, a full-wave rectifier, a filter circuit, and equivalent load resistance. \(R_{L}\) represents the equivalent load resistance here, including a DC-DC converter and load. The primary compensation capacitor is connected in series with the primary winding, and the secondary circuit has the same structure as the primary circuit. The DC-DC converter stabilizes the output voltage and reduces the ripple of the output voltage supplied to the load, which is the IMU. Here, \(V_{WPT}\) represents the output voltage of the MCRWPT system, and \(V_{OUT}\) represents the load voltage output by the DC-DC converter.  
For the buck DC-DC converter, the symbols \(V_{WPT}\), \(I_{WPT}\), and \(R_{WPT}\) are defined here as the input voltage, input current, and equivalent input resistance of the DC-DC converter, respectively. Symbols \(V_{OUT}\), \(I_{OUT}\), and \(R_{OUT}\) are the output voltage, output current, and output load resistance, respectively. When the output power of the DC-DC converter is equal to its input power, the following formula exists.  
\[V_{WPT}I_{WPT}=V_{OUT}I_{OUT} \tag{10}\]  
The following relationship exists between a DC-DC converter's the equivalent input resistance \(R_{WPT}\) and output resistance \(R_{OUT}\).  
\[R_{WPT}=\left(\frac{V_{WPT}}{V_{OUT}}\right)^{2}R_{OUT} \tag{11}\]  
By analyzing Equation (11), it can be known that the change of the load resistance \(R_{OUT}\) will affect the equivalent load resistance \(R_{L}\) of the MCRWPT system, where \(R_{L}\) is \(R_{WPT}\).
# Passive Intermodulation at Contacts of Rough Conductors
## 3 Thermal Nonlinearities at Contacts of Good Conductors  
Thermal effects at conductor joints are associated with the main processes listed in Figure 1: (i) self-heating due to RF power dissipation in imperfect conductors; (ii) electro  
Figure 4: Bias dependence of normalised tunnelling conductance, calculated for the barrier mean thickness \(\delta=15\) Å, barrier height \(\phi=1.0\) eV, and roughness \(\sigma\) varying in the range of 5%–20% of \(\delta\) in steps of 2.5% (blue points), and single-thickness (\(\sigma\equiv 0\)) conductance for 2–15 Å in 1 Å steps (solid green lines). Reproduced from [65] with the permission of AIP Publishing.  
thermal coupling in contact joints; and (iii) thermal expansion of the contact areas. They are linked intrinsically and influence other types of nonlinearities despite significant differences in the time scales of the underlying physical processes. Ambient temperature also has a notable impact on the effect of thermal nonlinearities [69]. The main properties and distinctive features of the thermal effects at contacts of conductors with rough surfaces are discussed below.
## 4 Results  
Regarding costs, results for a 500 MW floating offshore wind farm located in Figueira da Foz (Portugal) and composed of 50 SATH(r) platforms are: 1.2% for the \(C_{1}\), 0.3% for the \(C_{2}\), 42.3% for the \(C_{3}\), 2.9% for the \(C_{4}\), 51.5% for \(C_{5}\) and 1.8% for \(C_{6}\). Therefore, as Figure 6 is shown, the most significant costs of the SATH(r) platform are the manufacturing cost (C3) and the exploitation cost (C5). The total cost can be disaggregated in the sub-costs previously defined in the present paper, whose values are defined in Table 4.  
Regarding the economic parameters, Internal Rate of Return (IRR) of the project has a value of 0.12% in Scenarios 1 and 2 and 17.64% in Scenarios 3 and 4. This is because the IRR does not depend on the capital cost of the project. In addition, the discounted pay-back period of the project is higher than 28 years for Scenarios 1 and 2 and 9 years for Scenarios 3 and 4. Accordingly, the project is not economically feasible for Scenarios 1 and 2 because IRR is lower than the capital cost considered and the number of years to recover the investment of the project is very high. This is because the electric tariff is very low. Alternatively, the project would be economically feasible for Scenarios 3 and 4 because the IRR is superior than the capital cost considered, and the payback period is low comparing it with the life of the project (20 years). In these last scenarios the electric tariff is high and this is the main reason why the project would be feasible. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Electric Tariff (\(\epsilon\)/MWh)** & **Capital Cost (\%)** \\ \hline
1 & 50 & 6 \\
2 & 50 & 8 \\
3 & 150 & 6 \\
4 & 150 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Scenarios analyzed for the SATH® platform. Figure 6: SATH® platform costs. Nevertheless, the Net Present Value (NPV) varies depending on the electric tariff and the capital cost taken into account. Results of the farm considered analyzing the SATH(r) platform are shown in Figure 7. Scenario 2 has the worst value for NPV (\(-\)531 ME), followed by Scenario 1 (\(-\)479 ME). However, Scenarios 3 and 4 have better values for NPV: 1657 ME and 1365 ME, respectively. A project is economically feasible if its NPV is higher than zero. Therefore, regarding NPV Scenarios 1 and 2 are not economically feasible and Scenarios 3 and 4 are economically feasible because their value is higher than zero. These steel structures make floating offshore wind energy more economically viable. If we compare the material costs of a steel platform with a concrete one, we find a reduction of 37% in the case of the concrete platform. These costs do not include the cost of installation, transportation, wind turbine or mooring lines. It is assumed that the costs of these elements that have not been included are similar for both cases according to [25; 38].
## 4 Results and Discussion
### Influence Factor Investigation  
A parametric study was conducted to evaluate the influence of the inputs on the amplitude of GPR signals from rebar of concrete structures. With the interaction of multiple input variables to the estimation value of the output, the couple considered input parameters were changed from the lowest to the highest range while the rest input variables stayed at their mean value. It is worth noting that the symbols Low, ML, Mean, MH, and High in Table 3 are the abbreviation of the smallest, middle-low, mean, middle-high, and largest values of each input parameter.  
Figure 5: Input and output relationship of the experimental data: (**a**) Temperature; (**b**) Relative humidity; (**c**) Chloride contamination level; (**d**) Rebar corrosion status.  
Figure 6a,b shows the influence of changing the temperature parameter (i.e., T) coupled with a variation of other input variables (i.e., CL, and CS, respectively) on the output. During the variation of temperature, other variables were also changed to estimate the interaction effects between temperature variables and other parameters on the reflection amplitude of GPR signals. It should be noted that up to two-order interactions are considered in this study. The influence of the relative humidity parameter coupled with other input variables on the output is presented in Figure 6d,e.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Low** & **ML** & **Mean** & **MH** & **High** \\ \hline T & 5 & 13.8 & 22.5 & 31.3 & 40 \\ RH & 55 & 62.5 & 70 & 77.5 & 85 \\ CL & 0 & 0.025 & 0.05 & 0.075 & 0.1 \\ CS & 0 & 0.25 & 0.5 & 0.75 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantities of four input variables.  
Figure 6: _Cont._  
By the same token, the influence of changing chloride contamination level parameters (i.e., CL) and varying rebar corrosion status variables (i.e., CS) coupled with changing other input parameters to the output is presented in Figure 7. While Figure 6(a)-c represents the changing chloride contamination level, Figure 6(d)-f presents the effects of variation of rebar corrosion status on the amplitude of GPR signals from rebar.  
Figure 6: Effect of temperature (**a**,**b**) and relative humidity (**c**–**e**).  
Figure 7: _Cont._  
Sensitivity analysis was conducted to examine the effect of input factors on the output using a one-at-a-time (OAT) method. In order to do that, the value of one factor was selected from the Low, ML, Mean, MH, and High, while the value of the other factors remain at the mean. Measure the output and then repeat the process for the rest of the parameters. The results are depicted in Figure 8. As can be seen, the temperature variable (T) was the most influential variable on the output of the predictive model, followed by the chloride contamination level parameter (CL). In the meantime, the relative humidity variable (RH) was found to have limited influence on the prediction capacity of the proposed DBN model. The finding was in line with the previous study [25]. It is noted that the impact of uncertainty of the input parameters was not considered in this study.
## 3 Performance Analysis of NOMA-Enabled HSTRNs
### Ergodic Capacity  
We now provide detailed derivations of ergodic capacity for DF and AF relaying protocols. It is worth mentioning that ergodic capacity of a wireless network is an important performance metric to determine the average link capacity.
# A Novel Hybrid Genetic-Wale Optimization Model for Ontology Learning from Arabic Text
## 5 Experimental Results
### Corpora  
The Arabic corpora tested in this work are automatic content extraction (ACE) [72; 73] corpora, ANERcorp [74; 75] dataset, and a private corpus of Arabic biomedical texts. In the previously published computational linguistic work, the ACE and ANERcorp were frequently utilized for the purposes of evaluation and comparison with the existing systems. Three ACE corpora were investigated in this study: ACE 2003 (Broadcast News (BN), and Newswire (NW)), as well as ACE 2004 (NW). They are publicly available and were all tested by the proposed algorithm. For each dataset, the types of concepts (named entities) and their representation are demonstrated in Table 3. With the goal of identifying certain types of Arabic biomedical named entities in this work, we created a private corpus for evaluating the proposed approach of Arabic ontology learning. This task was accomplished by collecting a number of the Arabic open source texts in the biomedical domain, which were assessed by expert physicians. The private corpora information was illustrated in Table 3, where we represent each class in each Arabic domain by a number of documents that contained the number of unique words the concept mining and ontology learning algorithms will operate on.  
Furthermore, the non-Arabic corpora tested in this work include two publicly available ones that belong to the biomedical domain and are related to the protein-protein interactions. These corpora are Learning Language in Logic (LLL) [76] and the Interaction Extraction Performance Assessment (IEPA) [77]. The LLL corpus presents the task of gene interaction from a group of sentences related to _Bacillus subtilis_ transcription. The IEPA dataset comprises 303 abstracts obtained from the repository of PubMed, each one including a particular pair of co-occurring chemicals.
# Configurational Effects on Strain and Doping at Graphene-Silver Nanowire Interfaces
## 4 Material and Methods  
Sample fabrication: Gr/AgNW/ITO: ITO substrate (2 cm \(\times\) 1 cm) is cut and cleaned with ethanol and IPA, before air-spraying of 2 mL (0.25 mg/mL) AgNW/water solution, which is diluted from the stock solution from Sigma (5 mg/mL). Graphene, initially sandwiched between PMMA and polymer, is commercially obtained from ACS Material, which is then transferred to AgNW/ITO by Trivial Transfer Graphene (TTG) technique under the following procedure. The PMMA/Gr/polymer hybrid is immersed in deionised water to release the polymer, and the remaining PMMA/Gr is left in water for 2 hours before transferring to AgNW/ITO substrate to form PMMA/Gr/AgNW/ITO. After the transfer, the sample is dried naturally for 30 minutes and then baked in oven at 100 \({}^{\circ}\)C for 30 minutes. The sample is then immersed in acetone bath for 30 minutes to dissolve the surface layer PMMA and dry in oven at 40 \({}^{\circ}\)C for 20 minutes to assemble the final Gr/AgNW/ITO hybrid. AgNW/Gr/ITO: The sample is similarly fabricated as in Gr/AgNW/ITO but the transfer of graphene into ITO takes place before the air-spraying of AgNW.  
UV-vis characterisation is performed by Shimadzu UV3600 Plus UV\(-\)vis\(-\)NIR at wavelength between 300 nm and 800 nm. AFM and KPFM characterisation is carried out by Bruker Dimension Icon with advanced operation mode of PF-QNM (PeakForce-Quantitative nanomechanical) and PF-KPFM (PeakForce-Kelvin Probe Force Microscopy), respectively, to measure the topography, adhesion, and surface potential. Work function is measured at the lift height of 15 nm. Raman spectroscopy is performed by Renishaw inViaTM confocal Raman microscope with 0.8 cm\({}^{-1}\) spectral resolution. 532 nm laser (type: solid state, model: RL53250) with 5 mW laser power and 1800 mm\({}^{-1}\) grating is used. The peak position and intensity are then estimated by Lorentz fitting.  
Formal analysis, P.L.; Investigation, F.L.; Project administration, A.B.D.; Supervision, M.T. and A.B.D. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge for funding support from Advanced Material Development Ltd.in carrying out the work.  
MT would like to acknowledge University of Sussex strategic development fund.  
The authors declare no conflict of interest.
## 3 Data Analysis and Processing Method
### Spectrum Normalization and Energy Conversion  
Frequency analysis shows the distribution of energy converted through normalized output. Figure 12 shows the frequency distribution for each channel, separated by color, for the average frequency intensity of an area of 0-20 Hz. The order of the right and left sides from top to bottom, and the four left diagrams of each row, represent the output of the left regions of the brain and the other four right regions of the brain, respectively. Where the signal strength of the CSP filtering data collected from each set of experiments is high, it is depicted in red; where the signal strength has a lower intensity, it is depicted in blue. The effective range of the output frequency is from 1 to 20 Hz, and the horizontal axis refers to the time (1 s) of the sampled interval.  
Figure 12: Outputs by frequency of raw EEG and CSP EEG for channels 1–8. (**a**) RAW EEG output for channels 1–8 right (upper) and left (lower); (**b**) CSP EEG output for channels 1–8 right (upper) and left (lower).  
Figure 13 shows the composition of the frequency component distribution of generalized outputs.  
Figure 13: Outputs by frequency of Diff raw EEG and Diff CSP EEGs for channels 1–8. (**a**) Right-hand (upper) and left-hand (lower) channel Diff raw EEG outputs; (**b**) right-hand (upper) and left-hand (lower) channel Diff CSP EEG outputs.