esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Wideband, Planar, Bow-Tie Antenna
The main operating component of the proposed bow-tie antenna at each of the previously mentioned frequencies is determined based on the featured strong currents shown in black circles in Figure 8. At 1.26 GHz, the strongest currents are highlighted on bow-tie-86 in Figure 8a. Bow-tie-130 has strong currents on the majority of its area at 1.84 GHz, shown in Figure 8b. Finally, the entire surface area of the single bow-tie-166 is covered in  
Figure 8: Current distributions at (**a**) 1.26 GHz, (**b**) 1.84 GHz, (**c**) 2.41 GHz, and (**d**) 2.97 GHz (left to right). strong currents at 2.41 GHz and 2.97 GHz, shown in Figure 8c,d, respectively. This demonstrates that bow-tie-86 is the main operating component at 1.26 GHz (lower frequency range), bow-tie-130 at 1.84 GHz (the middle frequency range), and bow-tie-166 at 2.41 GHz and 2.97 GHz (the higher frequency range), confirming the operating principle that each bow tie contributes to the wide bandwidth, through the superposition of their individual impedance bandwidths. It is also observed that the first null (indicated by the blue circle) on bow-tie-86 (Figure 8a,b) moves towards the feed gradually as the frequency increases, and that the second null (indicated by the red circle) appears from 2.41 GHz, demonstrating the harmonics of bow-tie-86. At 2.41 GHz and 2.97 GHz, the grey circles in Figure 8c,d highlight the emergence of strong currents traveling in the opposite direction on the top and bottom edges of bow-tie-130, due to the harmonics of bow-tie-86 and bow-tie-130 at this frequency. This results in a minor alteration in the radiation pattern at 2.41 GHz and 2.97 GHz, which is shown in Section 4.
## 4 Methodology
### Data Analysis
#### 4.2.3 Classification  
The whole data set contains 200 structure-borne sound signals at 20 different combinations of radial feed and tool tumble. Seven structure-borne sound signals of each of the ten repetitions per parameter setting are used as labeled learning data sets for the model training (140 structure-borne sound signals). Three signals per parameter setting (60 structure-borne sound signals) are randomly selected, excluded from model training, and later classified with the trained model in order to validate the models found. All data are also variance normalized.  
For the classification of the process parameters and quality parameters, three different classification methods are tested with the selected features. The chosen methods, the Bayes classifier, the support vector machine, and the nearest-k-neighbor classifier, are selected since they are commonly used in literature and based on different principles. While in the Bayes classifier, the decision is made probability based and in the SVM the best separation plane to separate the different classes is searched in an optimization problem. In the KNN classifier, unknown data points are compared with similar (small-distance or large-correlation coefficient) training objects.  
The models of the classification methods are trained using the learning data set in fivefold cross validation. The standard hyperparameters of the different models (see Table 4) are varied in 30 iterations. In an automatic optimization, the classification model with the minimum classification error is searched for.  
The model found is then used to predict the output variables of the validation data set. With the evaluation measure, accuracy, the models trained per feature combination and classification method are evaluated with respect to their suitability for the prediction of the respective output variable. The calculation of the accuracy can be seen in Equation (1). The evaluation allows statements about which combination of feature extraction and selection, as well as classification methods for the prediction of process parameters and quality variables, are potentially promising in the practical application.  
\[\text{accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}}\text{ with }\begin{array}{l}\text{TP}:\text{Truepositive}\\ \text{FP}:\text{Falsepositive}\\ \text{TN}:\text{Truenegative}\\ \text{FN}:\text{Falsenegative}\\ \end{array} \tag{1}\]
## 3 Results and Discussion  
### X-ray Diffraction Analysis  
Figure 2a represents the XRD patterns of the Ni wires synthesized in the absence and presence of external magnetic fields. The patterns show three sharp peaks, representing the polycrystalline nature of the samples. Indexing of the pattern was performed using the High Score Plus software, which assigns peaks to the (111), (200) and (220) reflections corresponding to the face-centered-cube (fcc) structure of the nickel. Peak positions correspond to the JCPDS card number 040850 [12]. The full-width-at-half maximum (FWHM), calculated from the XRD patterns, is displayed in Figure 2b, and was found to be lowest (0.18\({}^{\circ}\)) for the Ni-Non-Magnetic sample and highest (0.48\({}^{\circ}\)) for the Ni-Single sample. Further, the lattice constant was calculated using Bragg's Law. The value of the lattice constant was found to be in the range of 3.506-3.522 A, which is comparable to the reported values of the lattice constant for Ni in the literature. The value of the lattice parameter for fcc-Ni, reported by [PERSON] et al., was 3.523 A [12]. Therefore, the values of the lattice parameter found in the present case are well in agreement with the reported literature. Figure 2c, showing the average diameters determined using HR-FESEM images, is discussed in next section.
## 3 Results and Discussions
### Comparative Study  
The adsorption capacity of MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite toward removing Pb\({}^{2+}\) ions and NR dye from the water was compared with earlier synthesized adsorbents even the same adsorbent synthesized by other methods as shown in Table 2. The information in the table indicates that the nanocomposite synthesized in this study is suitable for removing Pb\({}^{2+}\) ions and NR dye from water. Also, many reviews reported the application of different materials for the removal of Pb\({}^{2+}\) ions and dyes [58; 59; 60]. According to Table 2, the similar nanocomposite synthesized by [PERSON] et al. [40] via a different method, conditions, and different percentages of composite phases showed a higher adsorption capacity toward Pb\({}^{2+}\) ions than that synthesized in the present study. This indicates that MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite synthesized in the literature has a higher effective surface area and larger functional groups for the chelation of Pb\({}^{2+}\) ions. However, the most effective adsorbent for Pb2+ removal is few-layered graphene oxide synthesized by [PERSON] et al. [61]. This high adsorption capacity is attributed to the existence of a large number of oxygen-containing functional groups over its surfaces due to the higher surface site density of few-layered graphene oxide that is about two times more than other graphene materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**adsorbent** & **Pollutant** & **Isotherm** & \(q_{m}\) **(mg/g)** & **Ref.** \\ \hline Banana and orange peels & NR dye & Langmuir & 18.0 and 14.0 & [62] \\ \hline Halloysite nanotubes & NR dye & Langmuir & 54.85 & [63] \\ \hline Peanut husk & NR dye & Langmuir & 37.5 & [64] \\ \hline GO-Fe\({}_{3}\)O\({}_{4}\) & NR dye & - & 171.3 & [47] \\ \hline GO & Pb\({}^{2+}\) & Langmuir & 488.0 & [65] \\ \hline Carboxylated chitosan magnetic submicron spheres & Pb\({}^{2+}\) & Langmuir & 142.0 & [66] \\ \hline Tetrahylenepentamine modified CS/CoFe\({}_{2}\)O\({}_{4}\) & Pb\({}^{2+}\) & Langmuir & 228.0 & [67] \\ \hline GO-EDTA & Pb\({}^{2+}\) & Langmuir & 455.0 & [68] \\ \hline Pinecome activated carbon & Pb\({}^{2+}\) & Langmuir & 27.6 & [69] \\ \hline EDTA-GO & Pb\({}^{2+}\) & Freundlich & 509.0 & [70] \\ \hline GO-MnFe\({}_{2}\)O\({}_{4}\) nanohybrids & Pb\({}^{2+}\) & Langmuir & 673 & [40] \\ \hline Few-layered graphene oxide & Pb\({}^{2+}\) & Langmuir & 842 & [61] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reported adsorbents for adsorption of Pb\({}^{2+}\) ions and NR dye.  
Figure 9: Regeneration of MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite for Pb\({}^{2+}\) and NR dye removal up to five cycles.
# Cutting Performance and Wear/Damage Characteristics of PCBN Tool in Hard Milling
## 3 Experimental Analysis
### Surface Quality  
The surface topography of hardened steel with the carbide tool and PCBN tool under the same cutting parameters (Test 4) is shown in Figure 6. The surface texture of the workpiece machined by the PCBN tool was clear, and the ridge and the scallop of the surface texture caused by the feed rate were relatively low. Therefore, better surface topography could be obtained by machining hardened steel with the PCBN cutter.  
Figure 5: The effect of milling speed on milling forces: (**a**) Fx; (**b**) Fy; (**c**) Fz.  
Figure 6: The machined surface topography (Test 4): (**a**) carbide cutting tool; (**b**) PCBN tool.  
The new PCBN tool and carbide tool were selected to cut the workpiece. The total cutting distance was 20,000 mm (the tool flank wear was less than 0.10 mm). Surface roughness was measured once every 2500 mm. Five different positions were selected for each measurement, and the average value of five surface roughness values was taken as the final result. The surface roughness values obtained at the same cutting distance are shown in Figure 7.  
It was found that the surface quality of hardened steel machined by the cemented carbide tool was significantly higher than that machined by the PCBN tool. The surface roughness of hardened steel obtained by the carbide tool finishing was between 0.71 \(\upmu\)m and 2.19 \(\upmu\)m. The surface roughness of hardened steel obtained by the PCBN tool finishing was between 0.21 \(\upmu\)m and 0.55 \(\upmu\)m. The reasons for the difference in surface roughness were as follows: (1) the friction coefficient of the PCBN tool was relatively small; (2) the wear of the PCBN tool was relatively small with the processing.
## 3 Results and Discussion
### Impact of pH to Urease Activity  
Figure 4 represents urease activity with different pH conditions at a certain temperature (30 \({}^{\circ}\)C) for G1, G2, and G3 species. The results exposed a nearly bell-shaped outline showcasing the maximum activity around pH 7 for all of the three species.  
Following the same conditions, the G1 species showed the highest urease activity compared to the G2 and G3 species. Previous research showed that the MICP process favored an initial pH environment of 6.5-9.3 (neutral to basic condition). Additionally, it was revealed that, in the case of some species such as _Pararhodobacter_ sp., considerable loss of urease activity was observed with acidic (lower than pH 6) and alkaline (higher than pH 9) conditions attributed to denaturation of protein [24]. It was also reported that the urease activity of some ureolytic bacterial species favored lightly alkaline conditions [25; 26; 27]. Thus the pH value played an important role for the bacteria transportation and adhesion to obtain homogeneously improved strength of treated soils. Therefore, the results of this study could also be useful information for obtaining a successful sand solidification process for identifying the optimum pH condition for native species.
# A Compact Sub-GHz Wide Tunable Antenna Design for IoT Applications
###### Abstract  
This work presents a compact meandered loop slot-line 5G antenna for Internet of Things (IoT) applications. Recently, sub-gigahertz (sub-GHz) IoT technology is widely spreading. It enables long-range communications with low power consumption. The proposed antenna structure is optimized to operate at sub-GHz bands without any additional complex biasing circuitry or antenna structure. A miniaturized design was achieved by a meandered structured loop slot-line that is loaded reactively with a varactor diode. Wideband frequency reconfigurability (FR) was achieved by the use of the varactor diode. The proposed antenna resonates over the frequency band of 758-1034 MHz with a minimum bandwidth of 17 MHz over the entire frequency band. The RO4350 substrate with dimensions of 0.18\(\lambda_{g}\times\) 0.13\(\lambda_{g}\) mm\({}^{2}\) is used to design the proposed antenna design. The efficiency and gain values varied from 54-67% and 0.86-1.8 dBi. Compact planar structure, narrow-band operation (suitable for NB-IoT) and simple biasing circuitry, which allows for sub-GHz operation, are unique and attractive features of the design.  
sub-GHz; meandered loop; frequency agile; IoT antenna; 5G antenna 202211074. [[https://doi.org/10.3390/electronics11071074](https://doi.org/10.3390/electronics11071074)]([https://doi.org/10.3390/electronics11071074](https://doi.org/10.3390/electronics11071074))
## 2 Materials and Methods
### CWRU's Experimental Setup Overview  
The test stand layout is depicted in Figure 1, and the different REBs geometries and faults' characteristic frequencies are detailed in [14].  
In summary, the test rig consisted of a two-hp induction motor, a torque transducer and encoder and a dynamometer. Faults were implanted into the different REBs' parts with an electro-discharge machine. Outer race faults were seeded at different positions relative to the REB load zone, since they are stationary, with the fault's position having an essential effect on the motor/REB system's vibration response. Vibration signals were captured with accelerometers. During each test, acceleration was measured at the drive-end REB housing (Drive End Accelerometer (DEA)), the fan end REB housing (Fan End Accelerometer (FEA)), and the motor support base plate (Base Accelerometer (BA)). Data were captured at 12 kHz and 48 kHz sampling rates using a 16-channel data acquisition card.  
The faulty REBs vibration signals to be classified belong to the drive and fan end REBs tables [14], accompanied by healthy REBs vibration signals. These vibration signals have a commonality: they were collected at a sample rate of 12 kHz. Thus, four groups are derived from the dataset: healthy, inner race fault, outer race fault and ball fault. If vibration signals collected at 48 kHz had wanted to be included, they would have formed four different groups, taking into account the difference between sample rates. For simplification purposes, they have not been included in the dataset. As previously mentioned, each file contains FEA, DEA and BA data. Overall, there are 307 vibration  
Figure 1: CWRU experimental set-up layout.  
signals, 8 corresponding to healthy REBs, 76 to inner race fault REBs, 76 to ball fault REBs and 147 to outer race fault REBs, as summarised in Table 1.
###### Abstract  
Triggering the interaction of nanomaterials with molecules by means of electrical potentials in aqueous media remains challenging, especially if 3D through-flow systems are used as electrodes, as in potential-controlled liquid chromatography (PCC). In this paper, multi-walled carbon nanotubes (MWCNTs) function as a particulate packed bed electrode in order to study the system's response to various applied potentials and electrolyte compositions. The process principle was analyzed using chronoamperometry and cyclic voltammetry. Applying an electrical potential to the hydrophilic MWCNTs induces the presence of both capacitive and faradaic currents. This leads, over time, to a degradation of the electrode due to structural changes of the MWCNT matrix and an increase in redox reactions on the surface. The role of the electrochemical double layer (EDL) is highlighted as a main player in the process, directly influencing the adsorption capability of the electrode. The EDL rearrangement time and coverage radius depend on the composition of the mobile phase and on the potential applied. The capacity of the electrode for the target (maleic acid) increases at high positive potentials (+800 mV vs. Ag/AgCl), while the presence of electrolytes leads to a capacity decrease. Our research enhances the understanding of capacitive through-flow cells.  
Keywords:multi-walled carbon nanotubes; aqueous system; chronoamperometry; cyclic voltammetry; electrochemical double layer; potential-controlled chromatography +
Footnote †: journal: Applied Physics Letters B
###### Abstract  
This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future.  
energy management strategy; rule-based strategies; optimization-based strategies; data-driven strategies; hybrid electric vehicles +
Footnote †: journal: Applied Sciences
## 4 Results
### Analysis of the 365 W PV Panel  
Table 5 presents the power output of the 365 W PV panel measured before and after different failure types. From the table, it can be seen that before failure, the output of the 365 W panel is 355.79 W, and after failure, it decreased by at least 10% in all cases.  
Figure 5 shows photographs and thermal images of the 365 W PV panel after different types of failure. From the figure, it can be seen that overheating occurs in a certain part of the panel in the case of the hotspot problem. Furthermore, sporadic heat generation is observed in the case of diode malfunction. In the case of connector failure, heat generation is observed at the edges of the panel. In the case of cell overheating, heat generation is observed throughout the damaged areas. Lastly, in the case of busbar failure, overheating is observed in the area where the busbar is located.  
Figure 4: Analysis results of the main effects of the signal-to-noise ratio.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Failure Type**} & **Output before** & **Output after** & **Voltage after** & **Current after** \\  & **Failure (W)** & **Failure (V)** & **Failure (A)** \\ \hline Hot Spot & 355.76 & 316.96 & 39 & 8.135 \\ \hline Diode & 355.79 & 199.95 & 25 & 7.99 \\ \hline Connector breakage & 355.79 & 0 & 0 & 0 \\ \hline Panel breakage & 355.79 & 110 & 39 & 2.846 \\ \hline Busbar & 355.79 & 214.37 & 25 & 8.575 \\ \hline Cell overheating & 355.79 & 239.47 & 25 & 9.589 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Power output of the 365 W PV panel after different failure types.
## A CR Spectrum Allocation Algorithm in Smart Grid Wireless Sensor Network  
**[PERSON], [PERSON] *, [PERSON] and [PERSON]**  
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China; E-Mails: [EMAIL_ADDRESS] (W.H.);  
[EMAIL_ADDRESS] (Q.Z.); [EMAIL_ADDRESS] (S.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +86-23-6510-5242.  
External editor: [PERSON]  
_Received: 7 May 2014; in revised form: 21 September 2014 / Accepted: 23 September 2014 / Published: 13 October 2014_  
Cognitive radio (CR) method was introduced in smart grid communication systems to resolve potential maladies such as the coexistence of heterogeneous networks, overloaded data flow, diversity in data structures, and unstable quality of service (QOS). In this paper, a cognitive spectrum allocation algorithm based on non-cooperative game theory is proposed. The CR spectrum allocation model was developed by modifying the traditional game model via the insertion of a time variable and a critical function. The computing simulation result shows that the improved spectrum allocation algorithm can achieve stable spectrum allocation strategies and avoid the appearance of multi-Nash equilibrium at the expense of certain sacrifices in the system utility. It is suitable for application in distributed cognitive networks in power grids, thus contributing to the improvement of the isomerism and data capacity of power communication systems.  
cognitive radio; game theory; smart grid; spectrum allocation; simulation
# Performance Analysis of Cache Based on Popularity and Class in Named Data Network
## 6 Conclusions  
In conclusion, this research explains the basic idea of Cache Based on Popularity and Class (CAPIC) with two cache determination proportions, that are static and dynamic. The mathematical formula was derived to analyze and optimize the cache hit ratio for the content store, with the Static-CAPIC described. The Static-CAPIC algorithm fulfills its purpose to improve the total cache hit ratio on the network. It provides a simple cache technique, however, it was not perfect at controlling the cache hit ratio for priority class. The Dynamic-CAPIC, as an improved algorithm of Static-CAPIC, provides flexibility to change the cache proportion based on the frequency of requests in real-time. The formula of Dynamic-CAPIC involves consideration of the consumer request all the time. It gives the appropriate cache hit ratio that is fit for each content class's character. This algorithm also outperforms the Static-CAPIC, and the LCD+sharing scheme in total network cache hit ratio parameter. The average path stretch of the Dynamic-CAPIC is relatively the same compared to the Static-CAPIC scheme.