esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Induction Heating and Numerical Simulations
### Finite Element Modeling  
The numerically simulated induction heating involved an induction coil with 23 windings and a working frequency of 15 kHz, which was implemented in both the models. The relatively high number of windings was selected based on the relatively high workpiece length (approximately 330 mm). The geometrical dimensions and material parameters were identical for both the simulations, although the FEM computational methods differed. The input boundary conditions used for the simulation were the following: modulus of elasticity = 125.45 MPa; thermal conductivity = 174 KJ/ms \({}^{\circ}\)C; Poisson's ratio = 0.337; density = 19.3 g/cm\({}^{-3}\); specific heat = 0.75 KJ/t \({}^{\circ}\)C; and linear expansion coefficient = 1.88 K\({}^{-1}\). To optimise the heating process, three individual target heating temperatures were investigated in three individual simulations. These final deformation temperatures were 700 \({}^{\circ}\)C, 900 \({}^{\circ}\)C and 1100 \({}^{\circ}\)C. The electric current defined in the three simulations was I = 70, 90, and 120 A, respectively. The heated WNiCo workpiece was defined by the following material parameters, acquired based on experimental stress-strain testing at an elevated temperature: ultimate tensile strength = 1067 MPa; elongation to failure = 21.7%. In both the 2D and 3D predictions, the geometry of the coil was determined by the number of windings. Meshing of the investigated area was performed in accordance with the required calculation accuracy. The meshes were optimized according to the Global mesh model (3D space for magnetic field computations) in both the simulations in order to maintain reasonable computational times. For this reason, we used anisotropic meshes featuring the finest elements within the workpiece and its closest vicinity (see Figure 3a,b for the generated mesh and the details, respectively, of the 3D model, Figure 3c,d for the generated mesh and the detail, respectively, of the 2D model, and Figure 3e,f for the details of the 3D models of the heated workpiece and inductor coil, respectively). In the 3D model, the inductor consisted of 29,878 elements in total, whereas the workpiece consisted of 39,351 elements in total. The total global mesh of the 1/24 symmetry part contained 273,975 elements in total. The electromagnetic characteristics and heat transfer parameters were investigated for a pre-sintered WNiCo workpiece with the diameter of 25 mm and length of 330 mm. The analyzed material exhibits a paramagnetic behavior, which means that the permeability in the calculations was set to the value of 1 (\(\mu_{r}\) = 1).  
Figure 3: \(Cont\).
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 3 Applications
### Materials and Mechanical Applications  
The properties of nanomaterials differ greatly from the respective material in bulk form, which make them useful functional materials. Thus, the advantages of nanoparticles depend upon the ability to be able to customise the geometries of materials at increasingly smaller dimensions to obtain the necessary properties, therefore adding to the growing scientific portfolio. It is possible to produce materials with defined characteristics that yield electrical, physical, optical and imaging properties that are suitable for particular applications [93].  
Nanoparticles find applications in the textile industry, where they can be added to or used to treat fabric surfaces, which result in enhanced ballistic protection properties, and reduced wrinkling and bacterial growth. Nanomaterials are emerging to allow wash able, resilient \"smart fabrics\" that are incorporated with nanoscale sensors and electronic components allowing functions such as health tracking, solar energy capture and energy harvesting by movement [94; 95].  
Nanoparticle based consumer products include new electronics and computers, health trackers and other miscellaneous home products. It is predicted that nanotechnology will eventually be the next breakthrough across several sectors, including food manufacturing and packaging. Resonant energy transfer (RET) systems incorporating noble metal nanoparticles are growing in fields of optics and material science. So that NPs can be used in future commercial products.  
Nanoparticles possess excellent mechanical properties such as Young's modulus, stress and strain; mechanically stronger nanodevices can be developed [3]. For mechanical industries, their applications could be useful for coatings and lubricants. They can be embedded in the metal and polymer matrices to enhance the tribological properties. That is because the rolling contact area between the lubricated nanoparticles and the substrate will be greatly reduced, resulting in very low surface friction and wear. Furthermore, NPs provide strong sliding and delamination properties, which could also enhance wear resistance and decrease friction [94].  
Coatings can increase hardness and wear resistance by increasing a material's resistance. Alumina, titania and carbon-based NPs have shown to be effective coating agents in enhancing mechanical properties [7].
# Taguchi Method-Based Synthesis of a Circular Antenna Array for Enhanced IoT Applications
## 2 Synthesis of Radiation Patterns of a Concentric Ring Antenna Array Using the Taguchi Method
### Amplitude Synthesis
For the 36-element configuration, the HPBW is reduced to 8.3\({}^{\circ}\), indicating a tighter and more concentrated radiation pattern. In conclusion, the results clearly demonstrate that increasing the number of elements, through the addition of concentric rings, leads to a noticeable improvement in antenna performance, particularly in reducing side-lobe radiation and enhancing the main lobe focus.
## 4 Comparative Analysis of the E-Government Acts in Korea and the United States
### E-Government Fund  
As mentioned earlier, the United States has been operating e-government funds since 2001 as one of its e-government implementation strategies. These e-government funds are designed to support e-government projects related to ministries rather than single-agency projects. In particular, the General Services Administration (GSA) is responsible for the management of the fund, but the E-Government Office has secured control over e-government projects since the OMB's E-Government Office is in practice responsible for all processes of the e-government project, ranging from proposal review to enforcement.  
In contrast, there is no separate e-government business fund under the E-Government Act of Korea. Previous e-government laws provided support through the Information Promotion Fund; however, this provision was removed in the recent amendment and it no longer exists. Currently, in Korea, the fund is formulated in Article 41 (Establishment of Information and Communications Promotion Fund) of the Information and Communications Technology Industry Promotion Act as follows:  
_Information and Communications Technology Industry Promotion Act. Article 41 (Establishment of Fund for Promotion of Information and Communications)_  
_The Government shall establish a fund for the promotion of information and communications (hereinafter referred to as \"Fund\") in order to support the promotion of information and communications._  
In the case of Korea, a number of remarkable projects in the field of information and communications have been promoted within a short period of time due to stable financing through the Information and Communications Promotion Fund. In order to accelerate the pace of technological development and to successfully implement long-term e-government projects, stable procurement of resources as well as flexible management and operations are essential. Therefore, it would be efficient to procure funds in the form of funds rather than in the form of an annual budget.  
Today, digital government implementation projects are expanding rapidly and demand for information project budgets is also increasing. Therefore, it is necessary to realize a large amount of funding by reallocating funds from the information promotion fund to a new e-government fund.
## 5 FSK Modulation and Demodulation
### FSK Modulation for General Implants  
Much research has been focused on various types of biomedical implant devices. In this regards, [PERSON] and [PERSON] [57] developed an FSK protocol and compared it with the ASK protocol. The developed high rate data transfer protocol receives the serial bit from the input clock, extracted from 2 MHz to 20 MHz FSK carrier signal. The extracted FSK signal is as follows:  
\[f(t)=f_{0}(t)\sin(2\pi f_{0}t+\phi)+f_{1}(t)\sin(2\pi f_{1}t+\phi) \tag{8}\]  
A novel 4-FSK demodulator technique was used to improve the bit error rate (BER) performance and to increase the decision accuracy of the system by generating additional zero-crossing using a modified ZIFZCO with clocks [58]. However, this device has relative high power consumption. [PERSON] and [PERSON] [59] further developed this work in [57] using the same carrier frequency to reduce the power consumption and chip area. The demodulator circuit of the system is developed with a data rate to carrier frequency ratio up to 67% to require a much lesser power consumption of 0.38 mW. Again, [PERSON] and [PERSON] [60] developed a three FSK demodulator circuit with data-rate up to carrier-frequency ratio up to 67% in 5-10 MHz. This novel method was used to send data inductively to a wireless biomedical implant with 4 Mbps data rates. The power consumption and the chip area of the implanted devices are comparatively less than that of the previous system.  
CMOS technology is very popular for designing an effective modulator. Accordingly, [PERSON] and and [PERSON] [61] reviewed many of the implanted circuits and devices that used standard CMOS technologies. [PERSON] _et al_. [62] have developed a low power FSK modulator/demodulator for an MICSband transceiver to reduce the CMOS size and chip area. [PERSON] _et al._[63] developed two different types of demodulators such as an analogue FSK (AFSK) and a digital FSK (DFSK) for general implanted devices. The AFSK consists of a signal balance mixer and a comparator, however, the DFSK consists of only one block. Both demodulators are implemented in 0.18 \(\upmu\)m CMOS technology with a power supply of 1 V. The AFSK demodulator occupies a chip area of 0.016 mm\({}^{2}\) and the data rate is 2.5 Mbps. The DFSK occupies a chip area of 0.004 mm\({}^{2}\) and the data rate is up to 5 Mbps. The power consumption of ASFK and DFSK demodulators are 0.47 mW and 0.022 mW, respectively.
## 4 Generation Scheme
### Generation Complexity  
Due to the frequency-hopping subcarrier, the generation of ACE-FHBOC generally requires more resources than ACE-BOC. For the sake of clarity, we selected Type-I ACE-FHBOC(\(\alpha_{M-1}\):\(d\):\(\alpha_{0}\), 1) in comparison with Type-I ACE-BOC(\(\alpha\), \(\beta\)). Table 2 lists their generation complexity comparison summary. The minimum number of bits of direct subcarriers generators is three for ACE-FHBOC and ACE-BOC. If the phase rotation generation method is adopted, ACE-BOC, as a special case of ACE-FHBOC, has one subcarrier frequency, which requires \(12\times 16\) LUT. However, ACE-FHBOC has \(M\) subcarrier frequencies, and the resource consumption of ACE-FHBOC is \(M\) times that of ACE-BOC.
## 1 Introduction
### Supercritical Carbon Dioxide (scCO\({}_{2}\)) Dissolved in Brine  
Most experimental and modelling studies concerning mineral transformations in carbon storage reservoirs focus solely on scCO\({}_{2}\) dissolved in brine [21]. CO\({}_{2}\) can bond with water to produce carbonic acid (H\({}_{2}\)CO\({}_{3}\)), lowering the pH level:  
\[\text{CO}_{2}+\text{H}_{2}\text{O}\Leftrightarrow\text{H}_{2}\text{CO}_{3} \tag{1}\]  
As the carbonic acid adsorbs to the shale surface, it can dissociate into H\({}^{+}\) and HCO\({}_{3}\)\({}^{-}\). In the presence of H\({}^{+}\), some carbonate and/or silicate mineral components in the rock will react further, causing dissolution and corrosion. These reactions increase the pH level again (this is known as pH buffering). Mineral dissolution can increase the pore space volume; however, precipitation of new clays and secondary carbonates can occur, thereby reducing this again. The specific pore size ranges, which increase and then decrease in volume, do not necessarily overlap, and can potentially decrease reservoir permeability [22; 23]. The magnitude of these processes, as well as the pore size ranges affected, depends on the mineral content of the shale [24; 25]. This mineralisation of injected CO\({}_{2}\) is desirable as a long-term trapping mechanism.  
The reactivity of the system also depends on the maximum concentration of CO\({}_{2}\) in the solution. The solubility is dependent on temperature and pressure. When modelling reaction networks, corrections are needed for the activity for dissolved species and density through the use of Pitzer's formalism [26]. A model constructed by [PERSON] and [PERSON] [27] showed that solubility increases with temperature and pressure, ranging from 1-2 mols CO\({}_{2}\)/kg water for typical reservoir conditions. The rate of dissolution also varies with temperature, pressure, salinity, and the impurities present. The largest uncertainty comes from how much brine is in contact with the CO\({}_{2}\) plume. This depends on four factors, as laid out by [PERSON] and [PERSON] [24]:  
* The geometry of the plume front and saturation within the plume (contact surface area);
* Plume migration before becoming stable;
* The presence of any hydrological flows within the reservoir; and  
Figure 2: Summary of potential CO\({}_{2}\) injection schemes and various CO\({}_{2}\)-rock interactions. Reprinted from Gaus [19]. Copyright (2010), with permission from Elsevier.  
The development of convection currents due to brine densification when scCO\({}_{2}\) is dissolved.  
Field case studies have provided some information on the relative importance of these factors, but the research is incomplete [26].
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 3 The Software-Defined Media Ontology
### Description of Class Definitions
#### 3.3.4 **Processor** Class  
The **Processor** class represents a device or system that analyzes, processes, edits, and generates spatio-temporal media objects. The **Analyzer** subclass is a **Processor** subclass with only **Media** object input. The **Converter** subclass has both **Media** object input and output, and the **Generator** subclass has only **Media** object output. The **inputFrom** property represents an input **Media** object. The **outputTo** property represents an output **Media** object. Similar to the **Recorder** class, the **Processor** class also has the **CompositeProcessor** subclass, which can recursively describe a composite of **Processor** objects.
## 3 Results and Discussion  
### Carbonization of Palm Kernel Shell (PKS)  
Figure 2 shows the instrumentals for the pyrolytic carbonization of palm kernel shell (PKS) in an inert tubular nitrogen gas chamber of a furnace, which produced a black carbonaceous char with an average yield of 30.96% as tabulated in Table 1. The result agrees with other research groups [42; 56]. Their analysis shows that the obtained char was about 30%, the char results from the thermal decomposition of cellulose, lignin, and hemicellulose in the PKS precursor. The average of 30.96% of char obtained in this work produced from the PKS consists of 20.8-27.7% cellulose, 21.6-22.3% hemicellulose, and 44.0-50.7% lignin [56; 73]. Previous studies affirmed that cellulose, hemicellulose, and lignin are the most abundant components of crude biomass materials, including PKS [7; 22; 55; 58]. The studies postulated that the composition of biomass influences the derived carbonaceous material properties, and hence show the possible transformation of cellulose, hemicellulose, and lignin to graphitic materials by the dehydration condensation process. The dehydration condensation process is one way to form carbonaceous materials or CDs when biomass, precisely, cellulose, hemicellulose, and lignin subjected to pyrolysis. The authors showed that the individual percentage conversion of the constituents of biomass was as follows: cellulose (9%), hemicellulose (0%), and lignin (39%) at 900 \({}^{\circ}\)C and 21%, 4.6% and 52% at 400 \({}^{\circ}\)C calcination temperature, respectively [7; 22; 55; 58]. [PERSON] et al. and [PERSON] et al. have characterized the graphitic material obtained from the char of carbonized PKS to graphene oxide and reduced graphene oxide. The study showed that the highest percentage conversion of PKS to carbonaceous material was 32% at 400 \({}^{\circ}\)C [57; 58]. The type of graphitic material produced after the carbonization depends on the material source, heating rate, and temperature condition of pyrolysis [56]. This study showed that the average percentage conversion of PKS to carbonaceous material was approximately 30.96% at 600 \({}^{\circ}\)C.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Class** & **No. of Pixels** & **No. of** **Landslide** & **Frequency** & **Normalized Frequency** \\  & & **in Domains** & **Pixels** & **Ratio** & **Ratio** \\ \hline Distance to & [0.00, 167.16] & 8947 & 3 & 0.61 & 0.28 \\  & [167.16, 281.13] & 12,553 & 16 & 1.46 & 0.81 \\ Distance to & [281.13, 391.30] & 13,430 & 41 & 0.77 & 0.38 \\ streams (m) & [391.30, 501.48] & 12,372 & 20 & 1.28 & 0.69 \\  & [504.48, 623.05] & 9369 & 25 & 1.77 & 1.00 \\  & [623.05, 763.61] & 6205 & 23 & 1.39 & 0.76 \\  & [\(>\)763.61] & 4129 & 12 & 0.63 & 0.50 \\ \hline  & [\(-\)7.19, \(-\)5.25] & 8350 & 11 & 0.63 & 0.50 \\  & [\(-\)5.25, \(-\)1.73] & 3148 & 2 & 0.30 & 0.24 \\  & [\(-\)1.73, 1.70] & 13,316 & 24 & 0.86 & 0.69 \\ Topographic & [1.70, 2.94] & 21,415 & 56 & 1.25 & 1.00 \\ wetness index & [2.94, 4.43] & 14,843 & 38 & 1.23 & 0.98 \\  & [4.43, 6.72] & 4652 & 9 & 0.93 & 0.74 \\  & [\(>\)6.72] & 1281 & 0 & 0.00 & 0.00 \\ \hline Stream power & [\(-\)13.82, \(-\)9.79] & 487 & 0 & 0.00 & 0.00 \\ Stream power & [\(-\)9.79, \(-\)8.40] & 3850 & 2 & 0.25 & 0.17 \\ index & [\(-\)8.40, \(-\)4.54] & 7251 & 11 & 0.73 & 0.48 \\  & [\(-\)4.54, \(-\)0.35] & 13,013 & 16 & 0.59 & 0.39 \\ \hline Stream power & [\(-\)0.35, 0.88] & 22,507 & 51 & 1.08 & 0.72 \\  & [0.88, 2.60] & 16,613 & 52 & 1.50 & 1.00 \\ index & [\(>\)2.60] & 3284 & 8 & 1.17 & 0.78 \\ \hline  & [0.00, 4.35] & 15,486 & 17 & 0.53 & 0.20 \\  & [4.35, 10.88] & 21,160 & 31 & 0.70 & 0.27 \\ Sediment & [10.88, 16.87] & 16,659 & 44 & 1.26 & 0.48 \\ transport index & [16.87, 24.49] & 9054 & 29 & 1.53 & 0.58 \\  & [24.49, 37.01] & 3267 & 18 & 2.64 & 1.00 \\  & [37.01, 58.23] & 1065 & 0 & 0.00 & 0.00 \\  & [\(>\)58.23] & 314 & 1 & 1.52 & 0.58 \\ \hline  & [0.00, 25.24] & 6302 & 0 & 0.00 & 0.00 \\  & [25.24, 33.92] & 13,887 & 14 & 0.48 & 0.20 \\ Terrain & [33.92, 42.20] & 14,614 & 23 & 0.75 & 0.31 \\ roughness & [42.20, 51.30] & 12,675 & 33 & 1.25 & 0.52 \\ index & [51.30, 61.64] & 9415 & 27 & 1.37 & 0.57 \\  & [61.64, 73.23] & 6747 & 34 & 2.41 & 1.00 \\  & [\(>\)73.23] & 3365 & 9 & 1.28 & 0.53 \\ \hline  & Non-forest & 1027 & 0 & 0.00 & 0.00 \\  & Pine & 143 & 0 & 0.00 & 0.00 \\  & Nut pine & 2319 & 3 & 0.62 & 0.24 \\  & Larch & 1389 & 2 & 0.69 & 0.27 \\  & Pitch pine & 431 & 0 & 0.00 & 0.00 \\  & Sawtooth oak & 11,482 & 18 & 0.75 & 0.30 \\ Timper type & Mongolian oak & 1183 & 2 & 0.81 & 0.32 \\  & Oriental oak & 565 & 3 & 2.54 & 1.00 \\  & Other oak & 25,845 & 63 & 1.17 & 0.46 \\  & Poplar & 1419 & 6 & 2.02 & 0.80 \\  & False acasia & 15,809 & 30 & 0.91 & 0.36 \\  & Other broadleaf & 3707 & 12 & 1.55 & 0.61 \\  & forest & 1686 & 1 & 0.28 & 0.11 \\  & Mixed forest of soft & & & & \\  & and hardwood & 1027 & 0 & 0.00 & 0.00 \\ \hline Timber & Non-forest & 1027 & 0 & 0.00 & 0.00 \\ diameter (cm) & [6; 16] & 2124 & 3 & 0.68 & 0.66 \\  & [16; 28] & 63,854 & 137 & 1.03 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 3 Discussion
### Data Fusion Techniques  
The variety of data fusion approaches found in the literature indicates that there is no technique that fits all (or even the majority) of the possible applications. [PERSON] et al. [92] argued that the best data fusion approach depends on the application and attributes considered, and that the selection of an appropriate method should be conducted using independent sample subsets (independent validations) to avoid bias. It is worth noting that, depending on the problem being addressed, experiments sometimes reveal that data fusion is simply not effective [41]. It is important to keep in mind that data fusion is not always the best approach to prevent the practice of attempting to force the results to fit the original hypothesis, which often leads to biased and unrealistic claims.  
The use of conventional regression models (proximal and aerial scales) and well-established spatiotemporal methods (orbital scale) is still prevalent. These types of methods have been exhaustively tested and have consistently yielded good results. Because different studies usually employ different datasets, it is not possible to make a direct comparison, but the general trend seems to indicate a lack of progress and a certain level of redundancy between studies using this type of approach. Machine learning techniques have also been applied for some time, but their full potential has yet to be realized. There are a few reasons for this. First, practicing data fusion using machine learning and artificial intelligence models is far from straightforward. Different types of data need to be handled properly in order to make them compatible within the confines of the model. Since many machine learning techniques have poor interpretability [83], it is often difficult to determine how to achieve an acceptable degree of compatibility. Second, there are a lack of databases that properly cover the entire variability associated to a given application. Although this is a problem for any type of technique [48], the effects of data gaps become more evident when machine learning techniques are applied. Third, machine learning techniques (deep learning in particular) require large amounts of computational power for model training. This problem has become much less damaging in recent years as the computational power availability increases [1]. This, combined with the fact that deep leaning has shown remarkable potential for combining and extracting information from different types of images [21; 45; 81], will probably lead to this type of technique being increasingly used in the future.