esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Typical Applications of IoT in Smart Agriculture
### Greenhouse Production  
A greenhouse consists of walls and a roof, which are usually made from transparent materials, such as plastic or glass. In a greenhouse, plants are grown in a controlled environment, including controlling for moisture, nutrient ingredients of the soil, light, temperature, etc. Consequently, greenhouse technology makes it possible for humans to grow any plant, at any time, by providing suitable environmental conditions [108]. Figure 7 illustrates a smart agriculture IoT system for monitoring greenhouse farming factors based on IoT ecosystems.  
In [109], the authors introduced an IoT-based greenhouse environmental monitoring system for multipoint monitoring in large greenhouses. Instead of using multiple sensors at different locations, this solution involves a drive system that allows the sensor system to move to different locations in the greenhouse. The experimental results show that the proposed system can effectively monitor multiple points in large greenhouses. In [110], the  
Figure 7: An illustration of IoT application for monitoring farming conditions in a greenhouse.  
authors introduced an energy-saving temperature control technology for smart greenhouses. This study proposed two intelligent control methods: active disturbance rejection control and fuzzy active disturbance rejection control. The experimental results demonstrate that the proposed technology saves over 15% of the total energy consumption of the greenhouse. In [111], the authors designed an intelligent IoT system to monitor and control greenhouse temperature for energy efficiency and improve crop productivity. The experimental results for the Kingdom of Saudi Arabia, where daytime temperatures can be above 50 \({}^{\circ}\)C, demonstrate the efficiency of the proposed solution, including saving energy and predicting the rate of plant growth.  
Recent studies indicated that solutions integrating IoT, big data processing, and artificial intelligence could be applied in greenhouses to reduce labour and energy efficiency. Moreover, it also provides direct connections between the greenhouse farms and the customer [112; 113; 114; 115].
## 3 Theoretical Foundation
### Selection Criteria and Application Methods of Phase Change Materials  
The use of phase change materials depends mainly on the desired operating temperature since, during the physical state transition process, the material presents slight temperature variation, transforming the transferred energy into enthalpy variation. This way, the material selection starts from determining this temperature [24]. Other characteristics of PCM must be observed to extract the most significant potential from its implementation, for example, its thermodynamic, chemical, kinetic, and economic properties [7; 14], summarized below:  
* Thermodynamics: adequate phase change temperature, low, phase segregation, high latent heat of fusion per unit volume, high specific heat, high thermal conductivity, and low volume variation between physical states.
* Chemicals: non-toxic, low flammability, non-explosive, low crossivity of construction materials, long-term chemical stability, complete melting and solidification cycles.
* Kinetics: high nucleation rate, absence of subcooling, high crystalline growth rate.
* Economical: low cost, commercial availability.
* Others: low environmental impact, non-polluting, recyclable, compatibility with the capsule material.  
The application of PCM in civil construction can be carried out through different methods [7]: direct incorporation, immersion, encapsulation, and shape stabilization. Among these, encapsulation is the most promising method for buildings. The form of encapsulation can also be divided according to the scale of the capsule: nanoencapsulated (1 to 1000 nm), microencapsulated (1 to 1000 \(\upmu\)m), and macroencapsulated (\(>\)1000 \(\upmu\)m) [25]. Techniques involvinglow capsule scales (\(\approx\)1000 \(\upmu\)m) incorporate the materials directly into the construction elements, changing the thermal properties of the final system. Macroencapsulation is the most promising application in the buildings' context [7].
## 3 Results
### Fluorogenic Optical Properties  
After synthesizing acetylated trifluoromethyl diboronic anthracenes (AcTDAs) and trifluoromethyl diboronic anthracenes (TDAs), we characterized the optical properties of the analogs by measuring and comparing the emission and excitation spectra. Figure 1 shows the spectra for both molecules. AcTDA, which has trifluoromethyl and acetyl groups, exhibited excitation and emission wavelengths at 420 nm and 510 nm, respectively (Figure 1a). In contrast, TDA, the counter-diboronic anthracene with no acetyl group, demonstrated the longest excitation and emission wavelengths at 405 nm and 430 nm, respectively. These results clearly demonstrated that the addition of an acetyl group resulted in \(\sim\)80 nm and 15 nm red-shifted maximum emission and excitation wavelengths, respectively. Table 1 shows the excitation and emission wavelengths of the reported diboronic anthracene derivatives. As shown in Table 1, diboronic anthracene with an acetyl group on the anthracene produced a relatively large Stokes shift with a long excitation wavelength. This analysis confirms the increase in the Stokes shift and excitation wavelength by the acetyl group on the anthracene. Among the diboronic anthracene derivatives reported in Table 1, AcTDA exhibited the largest Stokes shift with the longest excitation wavelength [14; 23; 24; 25; 26]. Compared with similar acetylated derivatives [23], AcTDA produced excitation and emission fluorescence that was red-shifted by at least 15 and 22 nm, respectively. It has been reported that the substitution of a trifluoromethyl group on the quinoline ring of thiazole orange led to a redshift of thiazole orange [27]. Therefore, the two electron-withdrawing groups resulted in the largest Stokes shift with the longest excitation and emission wavelengths.
## 4 Proposed System Design Overview
### Preprocessing and Feature Selection  
As a result of the enormous amount of erroneous and redundant data in the collected IIoT datasets, removing such data from the datasets improves the accuracy of the IDS and reduces the burden on the IDS learning process. First, the FT-CID model applies Euclidean distance measurement to the raw dataset to remove erroneous and redundant data. The FT-CID model then employs IGWO on the dataset \((\mathrm{D}_{\mathrm{HII}\mathrm{GT}})\) to select the optimal feature  
Figure 3: Process flow of the proposed FT-CID model.  
subsets for the IIoT network [35]. IGWO is a type of swarm intelligence algorithm in which optimisation is based on the three best solutions for food searching and hunting behaviours. According to the IGWO algorithm, the proposed FT-CID model obtains the best solutions for three wolves from the preprocessed data based on hunting behaviour using Equation (1).  
\[(t+1)=\frac{(x_{\alpha}-A_{1}\,*\,|C_{1}\,*\,X_{\alpha}-X|)(x_{\beta}-A_{2}\,*\,| C_{2}\,*\,X_{\beta}-X|)(x_{\delta}-A_{3}\,*\,|C_{3}\,*\,X_{\delta}-X|)}{3} \tag{1}\]  
where \(A_{1},A_{2},\ A_{3},C_{1},C_{2},\ \text{and}\ C_{3}\) are all random vectors, and \(\alpha,\ \beta,\ \text{and}\ \delta\) are the best, second-best, and third-best positions of the three wolves, respectively. In every round, \(\delta\) represents the worst positions of neglected features. Finally, the FT-CID model selects the optimal feature. The selected subset is based on the values of search optimisers and delta. The selected feature subset is provided as the input to the CNN model of IIoT1. Furthermore, it repeats the IGWO-based feature selection process on the entire heterogeneous IIoT network to enforce a network sharing process of the deep learning model on the local dataset, i.e., AMI.
## 4 Discussion
### Uncertainties Analysis  
In this study, we only used the BEPS model to conduct the analysis. Although many studies have demonstrated the satisfactory performance of the BEPS model in simulating the global carbon sink, the model still results in some uncertainties due to some parameters and imperfect model structure. Besides, we only consider the climate attributions to NEP IAV. However, other environmental factors such as atmospheric CO\({}_{2}\) concentration and nitrogen deposition also influence the IAV of NEP globally. The rising CO\({}_{2}\) concentration can promote plant photosynthesis by accelerating the carboxylation rate over recent decades [47; 48]. The enhanced nitrogen deposition significantly increased plant foliar N concentration, which can stimulate photosynthesis rate and promote the uptake of atmospheric CO\({}_{2}\)[49]. Moreover, some disturbances can also influence the regional contributions to the global NEP IAV. For example, agricultural activities (e.g., fertilization and irrigation) could significantly impact carbon pool and carbon uptake by fertilization and irrigation [50]. Land-use change can also alter the ecosystem's carbon sink [51]. However, the influence of the land-use change on carbon uptake is minor [28]. In addition, some disturbances, such as fires and insect outbreaks, can rapidly cause severe damage to regional ecosystems and plant productivity [52], which may also cause some uncertainties.
## 5 Results and Discussions
### Results of Compositional Data Analysis
#### 5.3.2 Results of the BL Method

Table 7: _Cont._The leveling parameters determined through the boundary were extended to the entire region, with the data from the southern region used as the baseline to level the data from the northern region. The statistics of the leveled data parameters for each region are shown in Table 8.  
Similarly, the data leveled using the BL method was subjected to IDW interpolation, and the results are shown in Figure 13.  
From Figure 13, it can be seen that none of the element anomaly maps exhibit a shift effect. Taking the Sb element as an example, its geochemical distribution pattern is very similar to that of As, which is also a low-temperature element. Both exhibit a significant large-scale anomaly in the northern region, with relatively fewer anomalies in the southern region. On the other hand, elements such as W, Sn, Mo, and Bi, which are associated with high-temperature hydrothermal fluids, are mainly concentrated in the southern part of the study area, closely related to the positions of intrusive or extrusive rocks. Moreover, almost all the elements' extension orientations are close to NE, consistent with the main fault orientations in the study area. From the distribution of mineral points, the high-concentration anomalies of Cu in the geochemical map, after being leveled by the BL method, align well with the known mineral points. Compared to the CR method, there is almost no shift, indicating that the data leveling effect is good. For Sn, the improvement is most noticeable; in the raw data, no shift was observed for Sn, but after applying the CR method, a clear boundary appeared between the northern and southern regions. Clearly, this method is not suitable for this type of data. After processing with the BL method, the Sn element geochemical anomaly map performs well, with no shift and a large-scale anomaly in the southern region. This aligns with the geological background of widespread extrusive rocks in the southern region of the study area, confirming the good leveling effect of the BL method. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Element** & **Region** & **Min** & **25\%** & **50\%** & **75\%** & **Max** & **StdDev** & **Mean** & **Kurt** & **Skew** \\ \hline \multirow{2}{*}{Ag} & North & \(-5.21\) & \(-4.01\) & \(-3.86\) & \(-3.68\) & \(-0.24\) & \(0.35\) & \(-3.83\) & \(12.86\) & \(1.79\) \\  & South & \(-6.28\) & \(-4.07\) & \(-3.86\) & \(-3.65\) & \(0.27\) & \(0.49\) & \(-3.86\) & \Figure 13: _Cont._  
Figure 13: _Cont._  
Figure 13: Geochemical maps of each element after BL (1000 m) (**a**–**I**) corresponding to the 12 elements.
## 3 Results and Discussion
### UV-Vis Absorption Spectrum Analysis
#### 3.3.1 Light Absorption Spectra of Base Fluid ([Emim][OSO\({}_{4}\)] + DG)  
The UV-Vis absorption, scattering, reflection, and fluorescence are observed when an object is subjected to a light beam in a UV-Vis Spectrometer. In the absorption process, the electromagnetic radiation interacts with the objects or solution in the cuvette, where a small part of the radiation gets converted to another form of energy, namely heat. The tested base fluid's optical absorption spectra on the first and seventh day are presented in Figure 7a,b. The amount of light absorbed is indicated in the vertical axis, which can also be called absorbance. More absorption on a particular wavelength results in a higher absorbance value.  
The tested base fluid spectrum showed the maximum absorbance value at 200 to 300 nm wavelength (UV region), which was in good agreement with previous literature [15]. Depending on the absorption of photons, UV-Vis peaks could be smaller or larger. The absorption of large numbers of photons leads to larger peaks. The peak observed at 243 nm in the UV-Vis spectrum of the base fluid (Figure 7a) attributed to the conjugation of \(\pi\) electron transition (\(\pi\)-\(\pi^{\star}\) transition) from the lowest unoccupied molecular orbital (LUMO) to the highest occupied molecular orbital (HOMO). The conjugated aromatic domains contributed to the peak that was seen in the UV region of 270-300 nm.  
Figure 6: Variation of pH for different concentrations of Mxene nanoparticles.  
Due to the increase in the concentration of \(\pi\) electrons and changes in atoms' arrangement, the intensity of absorption in the spectral region (Visible, IR) decreased dramatically. To evaluate the absorption stability, the sample was tested after 7 days (Figure 7b), where the absorption peak was found at 256 nm with higher absorbance, presumably due to the slow reaction of ionic liquids with diethylene glycol [19]. After 300 nm, there were not any peaks observed that could be contributed by the destruction of electronic conjugation during the etching process of MXene.  
Figure 7: UV-Vis absorbance spectra of the base fluid on the (**a**) first day and (**b**) the seventh day.
# The Two-Parameter Bifurcation and Evolution of Hunting Motion for a Bogie System
## 2 Dynamic Model  
A schematic diagram of a railway bogie lateral dynamic model is shown in Figure 1. The bogie system consists of one frame and two wheelsets, which are assumed to be rigid bodies. The primary suspension connects the wheelsets and frame, and the secondary suspension connects the frame and car body. The suspension system is simplified to linear springs and dampers, where the primary suspensions are \(K_{1x}\), \(K_{1y}\), \(K_{1z}\), \(C_{1x}\), \(C_{1y}\), \(C_{1z}\); the secondary suspensions are \(K_{2x}\), \(K_{2y}\), \(K_{2z}\), \(C_{2x}\), \(C_{2y}\), \(C_{2z}\). The subscripts \"1 and 2\" denote primary and secondary suspensions, respectively. For convenience, leading and trailing wheelsets are denoted as wheelset-1 and wheelset-2, respectively. The bogie lateral dynamic model has 7 degrees of freedom (DOFs), including lateral displacement \(y_{zw}\) and yaw angle \(\psi_{zw}\) (\(i=1,2\)) of two wheelsets, lateral displacement \(y_{t}\), roll angle \(\psi_{t}\), and yaw angle \(\phi_{t}\) of the frame. The meanings and values of the relevant parameters are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(M_{w}\) & Mass of the wheelset & 1400 kg \\ \(I_{wy}\) & Spin moment of inertia of wheelset & 140 kg\(\cdot\)m\({}^{2}\) \\ \(I_{wz}\) & Yaw moment of inertia of wheelset & 915 kg\(\cdot\)m\({}^{2}\) \\ \(M_{t}\) & Mass of bogie frame & 3000 kg \\ \(I_{tx}\) & Roll moment of inertia of bogie frame & 2084 kg\(\cdot\)m\({}^{2}\) \\ \(I_{1z}\) & Yaw moment of inertia of bogie frame & 2496 kg\(\cdot\)m\({}^{2}\) \\ \(K_{1x}\) & Primary longitudinal stiffness & 10 MN/m \\ \(K_{1y}\) & Primary lateral stiffness & 5 MN/m \\ \(K_{1z}\) & Primary vertical stiffness & 5.5 MN/m \\ \(C_{1x}\) & Primary longitudinal damper & 0 N\(\cdot\)s\(\cdot\)m\({}^{-1}\) \\ \(C_{1y}\) & Primary lateral damper & 0 N\(\cdot\)s\(\cdot\)m\({}^{-1}\) \\ \(C_{1z}\) & Primary vertical damper & \(1.6\times 10^{4}\) N\(\cdot\)s\(\cdot\)m\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters.
# Routing Schemes in FANETs: A Survey
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AROD & Adaptive routing in dynamic ad hoc networks \\ BS & Base station \\ CH & Cluster head \\ CM & Cluster member \\ DOLSR & Directional optimized link state routing \\ FANET & Flying ad hoc network \\ GGF & Greedy geographic forwarding \\ GPS & Global positioning system \\ LOS & linear dichroism \\ MAC & Medium access control \\ MANET & Mobile ad hoc network \\ NLOS & Non line of sight \\ OLSR & Optimized link state routing \\ QoS & Quality of service \\ RLSRP & Reinforcement learning based self-learning routing protocol \\ RREP & Route reply \\ RREQ & Route request \\ SRCM & Semi-random circular movement \\ UAV & Unmanned aerial vehicle \\ UVAR & UAV-assisted VANET routing protocol \\ V2R & Vehicle to road side unit \\ V2V & Vehicle to vehicle \\ VANET & Vehicular ad hoc network \\ \end{tabular}
# A Compact Wideband Active Two-Dipole HF Phased Array
## 3 Design Considerations
### Preliminary Testing in a Reflection-Free Environment  
Preliminary tests before installing the array at the final site have been carried out in real conditions in a reflection-free environment as illustrated in Figure 10. The main focus of the test was to verify the performance difference between an HF quarter-wavelength two-dipole array with 90\({}^{\circ}\) difference feed with a high-impedance LNA (presented in Figure 7) versus the identical array with a traditional 50 \(\Omega\) input-impedance LNA [17]. The array has not been tested utilizing passive dipoles because of their narrow band response in terms of VSWR i.e., it would be required fine tune to 50 \(\Omega\) of each driven element in every frequency under test. A mini-circuit amplifier MAR-8+ has been chosen instead in order to ensure a robust 50 \(\Omega\) impedance across the spectrum at which the active dipoles are operated. The height of the masts used was 0.33 \(\lambda\), i.e., not an ideal height at 25 MHz. The length of the dipoles used was 5 m. In order to ensure a broadband 90\({}^{\circ}\) phase shift, each dipole was connected at the input port of the Minicircuits broadband splitter/combiner PSCQ-2-51W+ [18]. Using a relay switch presented in Figure 11, the 90\({}^{\circ}\) and 0\({}^{\circ}\) input ports of the hybrid were reversed.  
Figure 10: The active phased array under test.  
Figure 9: The isolation performance of the LNA simulated at 25 MHz. Top waveforms are at 0.2 V per division; bottom waveforms are at 0.5 V per division.  
The chart in Figure 12 presents the response of the F/B ratio with a 50 \(\Omega\) input-impedance MAR-8+ LNA in each dipole compared to the high-impedance LNA of Figure 7. To implement the test properly, a 30 dBm HF transmitter was installed in the far field, i.e., 1.5 Km away from the reception point that was connected to a half-wave-dipole antenna. For practical reasons, the tested array's dipoles were installed on a separate mast.  
From the preliminary measurements, it is obvious that the array F/B ratio is enhanced when the high-input-impedance LNAs (Figure 7) are used in the two dipoles. Specifically, the array provides a maximum F/B of 26 dB and an average of 19 dB across 18-30 MHz. In contrast, using the 50 \(\Omega\) Mar-8+ LNA, the array provides a maximum F/B of 17 dB and an average of 12 dB within the same spectrum, as illustrated in Figure 12.
## 3 Fabrication Process  
The semi-additive patterning (SAP) process creates fine copper patterns necessary for achieving the desired structures. Unlike conventional etching methods, SAP enhances control over the deposited copper profile, which is crucial for 5G applications [31; 32]. It reduces side-wall etching and undercutting, resulting in a rectangular cross-sectional profile of the metal patterns. The process begins when silane is used to treat a bare 100 \(\upmu\)m glass panel to improve adhesion to polymer dielectrics and prevent delamination. At this stage, the glass is brittle, so handlers must use proper procedures. A vacuum laminator laminates a 15 \(\upmu\)m Zaristo layer on both sides of the glass panel, followed by a curing process. Subsequently, we carry out electroless deposition of a thin copper seed layer, followed by patterning the M2 and M3 metal layers using photolithography. We perform electrolytic plating to achieve the required copper thickness, strip the photoresistor and differentially etch the seed layer using a chemical etcher. Finally, Atotech's Novabond process enhances the adhesion strength of Zaristo to the deposited copper by creating microscopic anchor sites [33].  
The remaining fabrication process is similar to the patterning of M2 and M3 with the addition of two steps: (1) the two-step lamination of Zaristo to achieve a 71 \(\upmu\)m thickness, and (2) the ablation of blind vials before seed layer deposition. A step-by-step illustration of the fabrication process is shown in Figure 6. The measured copper thickness is 7.8 \(\pm\) 0.5 \(\upmu\)m in the fabricated panel. Several coupons of various structures are shown in Figures 7-10. Due to the constraints of measurement setup for multi-port networks, we fabricated several coupons of identical structure. The following section will explore this topic in detail.  
Figure 5: Simulation results of a coupled–line coupler covering 24.25–40 GHz along with its layout.  
Figure 6: Step–by–step illustration of SAP process.  
Figure 8: Fabricated diplexer variants: (**a**) hairpin for bansd n257 and n260 (S\({}_{21}\) measurement only), (**b**) hairpin for bands n257 and n260 (S\({}_{31}\) measurement only), (**c**) interdigital for bands n258 and n260 (S\({}_{31}\) measurement only) and (**d**) edge–coupled for combined bands n257 and n258, and n260 (S\({}_{21}\) measurement only).  
Figure 7: Fabricated filter coupons: (**a**) hairpin for band n257, (**b**) interdigital for band n258 and (c) edge–coupled for band n260.  
Figure 9: Fabricated coupler variants: (**a**) for S\({}_{21}\) measurement only; (**b**) for S\({}_{31}\) measurement only.