esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Process Parameter Optimization Using Response Surface Methodology
The study revealed favorable conditions: a DES with 40% water content, a 60 min extraction time at 90 \({}^{\circ}\)C, and a liquid-to-solid ratio of 30:1, resulting in a CGA yield of 37.28 \(\pm\) 0.64 mg/g, which is 1.9 times greater than that obtained using ethanol. Both studies, including this one, demonstrate that incorporating 40% water reaches the target viscosity, and, additionally, for elevated phenolic values, a high liquid-to-solid ratio is essential. Furthermore, the authors considered that the extraction mechanism of CGA using betaine-based DESs indicated that systems with higher CGA extraction yields showed greater interaction energy and an increased number of hydrogen bonds. A three-level, three-factor Box-Behnken design was also applied by [PERSON] et al. [36], whereas the liquid/solid ratio, temperature, and time were optimized for the extraction of five phenolic acids from _L. japonica._ The authors confirmed that DESs are highly effective for extracting phenolic acids due to their ability to enhance solubility. A DES composed of 1,3-butanediol and choline chloride in a 6:1 molar ratio demonstrated superior extraction efficiency. The ideal conditions included 10% water content in the DES, a liquid-to-solid ratio of 9 mL/g, an extraction temperature of 60 \({}^{\circ}\)C, and a processing time of 20 min. Under these  
Figure 10: Overlaid contour plots illustrating the interaction between (**a**) time and temperature, (**b**) liquid/solid ratio and temperature, and (**c**) liquid/solid ratio and time on the optimization of total phenolic content (TPC) and antioxidant activity in the extracts from _L. japonica_ flowers extracted using 60% NADES4 (choline chloride–urea). parameters, the yields for chlorogenic, caffe, 3,4-dicaffeoylquinic, 3,5-dicaffeoylquinic, and 4,5-dicaffeoylquinic acids were 26.07, 0.15, 0.93, 23.67, and 8.85 mg/g, respectively. The potential for reusing and recycling NADESs in industrial applications is essential for sustainable growth. Research indicates that NADESs can be regenerated using methods such as anti-solvent precipitation, membrane filtration, and liquid-liquid extraction. Of these, anti-solvent addition and density-based separation boast high recovery efficiency but are energy-intensive, whereas membrane filtration and extraction methods provide varied scalability at different costs [37]. Many NADES precursors, including choline chloride, citric acid, and glycerol, are GRAS-certified, satisfying food and pharmaceutical standards. The NADESs derived from these precursors show low-to-moderate cytotoxicity and minimal phytotoxicity, and several are noted for their biodegradability [38].
## 2 Materials and Methods
### Selected Species
#### 2.1.4 Eucalyptus globulus  
_Eucalyptus_ (Figure 4), a genus within the Myrtaceae family, encompasses an extensive array of over 900 species and subspecies of trees and shrubs [29]. Notable for its remarkable resilience to environmental stressors such as fire, drought, and soil acidity, coupled with its rapid growth rate, this genus holds significant ecological and economic importance. Of particular interest are subspecies like _globulus_, _maidenii_, _bisocdata_, and _pseudo globulus_, originating primarily from Tasmania and Australia. _Eucalyptus globulus_ gained worldwide prominence, especially in Mediterranean, subtropical, and tropical regions, during the period spanning 1800 to 1850 [30]. It is noteworthy that the first introduction of _Eucalyptus_ to Europe dates to 1774 at the Kew Botanical Gardens [22]. Early records in Galicia, documented by [PERSON]'s catalog of exotic flora [31], initially classified _Eucalyptus_ as an invasive species upon its introduction for cultivation in 1955. However, recent reclassification in Spain has redefined its status as a non-native species rather than invasive [18].  
An extensive study conducted by the Confederation of Spanish Forestry Organizations (COSE) in 2019, titled \"Eucalyptus in Galicia: Environmental and Socioeconomic Aspects\", revealed that _Eucalyptus_ accounts for 20% of Galicia's forest population and represents 60% of the timber designated for forestry harvesting. Notably, _Eucalyptus_ bark, rich in chemical constituents, is frequently discarded when wood is processed for construction, paper production, or charcoal manufacturing. These discarded bark residues are often incinerated for energy generation [32]. However, research indicates their potential in various applications, including soil improvement [33], solvent utilization for aqueous contaminant removal [34], bioethanol production, antioxidant compounds [18], and the development of environmentally friendly adhesives utilizing tannins extracted from the bark [35].  
Figure 4: _Eucalyptus globulus_ species. Photographed in the Umia riverbank area in spring 2023.  
In the wood and paper industry, _Eucalyptus_ leaves are typically considered byproducts and are commonly repurposed for soil enrichment or energy generation through combustion. Nevertheless, scientific investigations have unveiled the valuable bioactive properties inherent in this biomass, making it a promising candidate for biorefinery processes [36].
# On the Energy Performance of Iridium Satellite IoT Technology
## 5 Evaluation
### Device Current Consumption  
Figure 7 illustrates the average current consumption of the device as a function of \(T\), for different \(p\) values, based on the model in Section 4. As expected, the average current consumption decreased with \(T\) as the influence of sleep intervals increased, and the average current consumption tends asymptotically to sleep state current consumption.  
The average current consumption increased with \(p\), since the number of application-layer retries also increases. However, impact of \(p\) on the average current consumption varies depending on the value of \(T\). For \(p\) = 0.1, and relatively low values of \(T\) (e.g., \(T\) = 10 min), the average current consumption increases by a factor of \(\sim\)20%. As \(T\) increases (e.g., for \(T\) = 100 min), such a factor reaches \(\sim\)31%, since intervals where the device slowly return to sleep become more significant (with greater duration and lower current consumption), and retries have a relatively greater impact than for smaller values of \(T\). As \(T\) increases further, the active intervals related with message transmission become less relevant compared with very long sleep intervals, rendering the active intervals asymptotically negligible.  
We also analyzed the impact of the payload size on the average current consumption. Note that payload transmission time contributes to both \(I_{OK}\) and \(I_{KO}\), therefore, it also contributes to \(E[T_{Act}]\). As payload size increases, \(E[T_{Act}]\) also increases, which in turn increases the average current consumption. While such an impact decreases with \(T\), we found that payload size actually had a low influence on the average current consumption for the whole range of considered \(T\) values (of 6.5% for \(T\) = 10 min, and below 1% for the rest of the considered \(T\) values). For the sake of illustration clarity, Figure 6 only shows the results for a 1-byte message payload size, since curves for the rest of the considered payload sizes (\(L\leq\) 300 bytes) show negligible differences.  
Figure 8 also depicts the average current consumption of the device as a function of T. In this case, the figure compares the results provided by the model with results obtained by measuring the average current consumption over intervals of a duration one order of magnitude greater than each corresponding value of T (except for T = 10,000 min, where the duration was equal to T). In the measurements, all transmissions were successful, therefore we show the model results for \(p\) = 0. As shown in Figure 8, the model generally predicts current consumption with high accuracy, especially for T \(\geq\) 100 min. For T = 10 min, the difference between the model and the measured results is greater (equal to 10%), since the transmission part, which is prone to variability depending on communication conditions, becomes more relevant.  
Figure 7: Average current consumption as a function of \(T\).
## 3 System Model
### Sum Rate
#### 3.2.1 Subchannel Allocation Matrix  
We denote \(A_{(M+N)\times(L+U)}\) as the subchannel assignment matrix, i.e.,  
\[A_{(M+N)\times(L+U)}=\begin{pmatrix}X_{M\times(L+U)}\\ Y_{N\times(L+U)}\end{pmatrix}, \tag{1}\]  
where \(X_{M\times(L+U)}=[x_{m}^{l}]\) represents the subchannel allocation matrix for the CUs in the licensed spectrum, and \(Y_{N\times(L+U)}=[y_{n}^{l}]\) is subchannel allocation matrix for the D2D users in the licensed spectrum. The values of \(x_{m}^{l}\) and \(y_{n}^{l}\) can be respectively defined as  
\[x_{m}^{l}=\begin{cases}1,&\text{the subchannel $l$ is assigned to CU $C_{m}$,}\\ 0,&\text{otherwise,}\end{cases} \tag{2}\]  
and  
\[y_{n}^{l}=\begin{cases}1,&\text{the subchannel $l$ is assigned to D2D user $D_{n}$,}\\ 0,&\text{otherwise.}\end{cases} \tag{3}\]  
In addition, we also denote \(s_{m}^{u}\in\{0,1\}\) and \(s_{n}^{u}\in\{0,1\}\) as the subchannel assignment for CUs and D2D users in the unlicensed spectrum, respectively. Specifically, \(s_{m}^{u}=1\) represents that CU \(C_{m}\) shares unlicensed spectrum \(u\); otherwise, \(s_{m}^{u}=0\). In addition, it is the same for D2D users.
## 2 Experimental Details
### Digital Detector Array Quality Assessment Using Radioactive Isotopes (Se-75 and Ir-192)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Radioactive isotopes: Se-75 (72 Ci)**} \\ \multicolumn{2}{c}{**IQI: ASTM 1A, 1B EN 10 FE (ISO 19232-1) [9]**} \\ \multicolumn{2}{c}{**Related standards:**} \\ \multicolumn{2}{c}{**(1) ASME BPVC Section V: Nondestructive Examination**} \\ \multicolumn{2}{c}{**(2) ISO 17636-2: Nondestructive testing of welds—Radiographic testing—Part 2: X- and gamma-ray techniques with digital detectors [10]**} \\ \hline \multicolumn{2}{c}{**No. Size/Type**} & \multicolumn{1}{c}{**Thickness**} & \multicolumn{1}{c}{**Material**} & \multicolumn{1}{c}{**Requirement**} & \multicolumn{1}{c}{**Acquired IQI**} \\ \multicolumn{2}{c}{**(mm)**} & \multicolumn{1}{c}{**(mm)**} & \multicolumn{1}{c}{**(mm)**} & \multicolumn{1}{c}{**(mm)**} \\ \hline \hline
2 & 2\({}^{\prime\prime}\) Pipe & 5.5 & Stainless steel & 0.20 (DWDI) \\ \hline
3 & 4\({}^{\prime\prime}\) Pipe & 13 & Carbon steel & 0.33 \\
4 & 10\({}^{\prime\prime}\) Pipe & 4 & Stainless steel & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{l}{\(\bullet\)**Radioactive isotopes: Ir-192 (46 Ci)**} \\ \multicolumn{5}{l}{\(\bullet\)**IQI: ASTM 1A, 1B EN 10 FE (ISO 19232-1) [9]**} \\ \multicolumn{5}{l}{\(\bullet\)**Related standards:**} \\ \multicolumn{5}{l}{(1) ASME BPVC Section V: Nondestructive Examination**} \\ \multicolumn{5}{l}{(2) ISO 17636-2: Nondestructive testing of welds—Radiographic testing—Part 2: X- and gamma-ray techniques with digital detectors [10]**} \\ \multicolumn{5}{l}{**No. Size/Type**} & \multicolumn{1}{c}{**Thickness**} \\ \multicolumn{5}{l}{**No. Size/Type**} & \multicolumn{1}{c}{**Thickness**} \\ \multicolumn{5}{l}{**Mickness**}  
The experimental results revealed that the image quality obtained with DDA was highly satisfactory when using Se-75. However, with Ir-192, the IQI requirements were met only at a thickness of 20 mm, making it unsuitable for other thicknesses. This issue arises because the higher radiation dose from Ir-192 causes the DDA pixels to pass through without producing a dense image. The image particles from Ir-192 are also tougher than those from Se-75, affecting the DRT image quality. Regarding these gamma ray characteristics, the International Organization for Standardization (ISO) provides an exception that slightly lowers the quality standard for certain thicknesses when using gamma rays and DDA, which aligns with our experimental results. Therefore, we used Se-75 to obtain stable image quality.
## 4 Experiments and Discussion  
### Experimental Platform and Datasets Description  
The resampling strategy and variable circular correlation time proposed in this paper are realized by MATLAB software, configured with Intel dual-core 3.5 GHz CPU of \(i7-5930K\) and 16.0 G RAM. The GNSS receiver is equipped with NT1065 \"Nomada\" and bladeRF as the RF front end. They can simultaneously receive various GNSS satellite signals, including GPS (L1, L2, L3, L5), GLONASS (E1, E5a, E5b, E6), BeiDou (B1, B2, B3), Galileo, IRNSS, QZSS. For comparison, experiments and performance analysis are conducted on 4 sets real GNSS signals collected at the same observation station. Different configurations are adopted, such as set-up status of the resampling strategy (on/off) and circular correlation time (10-20 ms). Table 1 presents the data type, intermediate frequency, and conventional sampling frequency of experimental datasets.  
Besides, basic parameters of the experimental GNSS signals are provided here: the bandwidth of main lobe is 2.046 MHz, the basis carrier frequency is 1227.6 MHz, the code frequency is 511.5 KHz, the range of frequency search band is \(\pm 5\) KHz, and the bandwidth of frequency bins is 100 Hz. Acquisition threshold is set as correlation ratio \(R_{c}=1.5\).
# Design and Testing of a LUT Airfoil for Straight-Baded Vertical Axis Wind Turbines
## 4 Results and Discussion
### Static LUT Airfoil Performance at Low Reynolds Number
#### 4.1.2 Lift Coefficient of the LUT Airfoil  
As shown in Figure 9, we investigated the relationship between the lift coefficient and the angles of attack at two Reynolds numbers, \(R_{e}\) = 3 \(\times\)\(10^{5}\) and \(R_{e}\) = 5 \(\times\)\(10^{5}\), obtained from Equation (17). In this Figure, the lines in black and red of the lift coefficient correspond to the results of the experiment and the numerical simulation (design results) for the LUT airfoil, respectively. In the case of \(R_{e}\) = 3 \(\times\)\(10^{5}\), the lift coefficient of the LUT airfoil linearly increases in the extending direction of the angles of attack from \(\alpha\) = \(-9^{\circ}\) to \(\alpha\) = 12\({}^{\circ}\) (this region is called the linear region). At an angle of attack of \(\alpha\) = 14\({}^{\circ}\), the lift coefficient reaches its maximum value at C\({}_{\text{L-max}}\) = 1.47, and then the curve of the lift coefficient  
Figure 8: Pressure distribution for the LUT airfoil: (**a**) the case of \(R_{e}\) = 3 \(\times\)\(10^{5}\); and (**b**) the case of \(R_{e}\) = 5 \(\times\)\(10^{5}\). The data were measured when the surface of the LUT airfoil was clean. As shown in this figure, the lines in black, red, blue, and pink show the pressure coefficients corresponding to the angles \(\alpha\) = \(-4^{\circ}\), \(\alpha\) = 0\({}^{\circ}\), \(\alpha\) = 8\({}^{\circ}\), and \(\alpha\) = 21\({}^{\circ}\), respectively.  
displays a gentle downward trend until an angle of attack of \(\alpha=21^{\circ}\), a phenomenon called a stall. This indicates that the LUT airfoil has good stall performance.  
In the case of \(R_{e}=5\)\(\times\)\(10^{5}\), the trend for the lift coefficient curve is the same as that of \(R_{e}=3\)\(\times\)\(10^{5}\) at the angles of attack from \(\alpha=-9^{\circ}\) to \(\alpha=12^{\circ}\). However, the maximum lift coefficient \(C_{\text{L-max}}\) is 1.5 at an angle of attack of \(\alpha=14^{\circ}\). Comparing the results of the experiment to the results of the numerical simulation (design results), in the linearly increasing areas, the values of the lift coefficient are almost the same for the two values of \(R_{e}\). However, after the stall angle of attack, the values of the lift coefficient for \(R_{e}=5\)\(\times\)\(10^{5}\) have a slight difference and are larger than the value of the lift coefficient for \(R_{e}=3\)\(\times\)\(10^{5}\). While the numerical simulation results are larger than those of the experiment, these differences are very small. Therefore, the results of the numerical simulation (design results) are in agreement with the results of the experiment reported in this paper.
# Augmented Reality in Precision Farming: Concepts and Applications
## 4 Findings
### What Technologies Is AR Coupled with to Provide a Service for Farmers?  
The seamless interaction, and flexibility, between real world and digital content in a real-time deployment makes AR an asset for precision farming. In both application domains (crop and livestock) discussed in Section 2.1, AR provides the farmer with an intuitive metric for the access of real-time data (e.g., per animal, or per tractor) to focalise efforts in potentially problematic subjects. A similar approach for interaction evaluation is discussed in [37]. Each case study focuses on a different deployment of AR. While the work by [16] advocates for the use of wearables for monitoring milking production (of stationary cows). [PERSON] et al. [13] focus on a smart phone mobile solution, highlighting the flexibility to implement AR solutions targeted to specific scenarios within livestock farming. Results from both applications demonstrate that technology is no longer a barrier for implementing AR applications in livestock, yet the limiting factors may include the development challenge. However, technologically, response times and computer vision  
Figure 6: Comparison Analysis of Technologies Coupled with AR in Farming. Dark grey refers to crop-based articles, whereas light grey is for livestock.  
integration (for example differentiating between single animations) are currently able to cater for real-time deployment, flexibility and scalability.  
AR solutions have demonstrated a decrease in the time farmers had to spend locating an individual and associating external information (i.e., productivity metrics, health etc.). Despite the differences in specific requirements for crop and livestock management, there is overlap in terms of coupled technologies in the encountered applications.  
A common trait between livestock and crop management is the use of AR as means of displaying information. However, it is the combination of AR with at least one but often several other technologies that provides the added value for precision farming application. Furthermore, the examples discussed can be classified into two broad categories. Firstly, those that use markers, either paper or object based to trigger augmentation (marker-based apps) of features in the real world and, secondly, those that use location instead (location-based apps), displayed in Table 1.  
Both types have advantages and disadvantages in their implementation in the field. Namely, adopting a QR code process limits the farmer to specific locations to use the AR, however tracking and the physical to digital integration process is improved. The non-QR code approach provides greater flexibility for the deployment of the AR, yet physical to digital integration may be more problematic.
# Levitating Control System of Maglev Ruler Based on Active Disturbance Rejection Controller
## 2 Analysis Model of Levitating Force
### Analysis Model of Buoyancy Force
The calculation equation for total magnetic reluctance is as follows:  
\[F_{R}=-\frac{1}{2}\frac{\phi^{2}}{\mu_{0}\cdot A} \tag{2}\]2  
Substituting the magnetic potential of the levitating coil and \(R_{\rm 5C}\) into Equation (5), we can obtain the magnetic flux of loop 5 \(\phi_{\rm 5}\):  
\[F_{R}=-\frac{1}{2}\frac{\phi^{2}}{\mu_{0}\cdot A} \tag{2}\]3  
where \(N\) is the number of windings of the upper left levitating coil, which is 47 [1]. Where \(i_{\rm 1}\) is the current of the upper left levitating coil. Substituting Equations (29) and (31) into Equation (3), the magnetic flux in the air gap on the upper left side of the MC can be calculated as follows:  
\[\begin{array}{l}\phi_{l,up\_air}=\phi_{\rm 1}+\phi_{\rm 5}=\frac{1}{3.81 \times 10^{3}-3.35\times 10^{3}\times y-1.89\times 10^{5}\times z_{l}-6.23  
According to the above calculation method, the magnetic fluxes \(\phi\)3 and \(\phi\)7 of loop 3 and loop 7 can be obtained. Then, according to Equation (4), \(\phi_{l,down\_air}\) can be calculated. Finally, combining Equations (1) and (2), the equation of the left levitating force of the MC can be obtained:  
\[ \begin{array}{l}F_{left}=-\frac{(\phi^{2}_{r,air,y-\phi^{2}_{r,air,down}})}{2 \eta_{0}\cdot A}=-9.95\times 10^{8}\\ \left[(\frac{3.81\times 10^{3}-3.35\times 10^{3}\times y-1.89\times 10^{3}\times z _{l}-6.23\times 10^{4}\times z_{r}}{2\,26\times 10^{4}+2.78\times 10^{4}\times y -3.07\times 10^{6}\times z_{l}-1.02\times 10^{6}\times z_{r}})^{2}\right.\\ \left.-(\frac{3.81\times 10^{3}-3.35\times 10^{3}\times y+1.89\times 10^{5}\times z _{l}+6.23\times 10^{4}\times z_{r}}{2\,26\times 10^{4}+2.78\times 10^{4}\times y +3.07\times 10^{6}\times z_{l}+1.02\times 10^{6}\times z_{r}})^{2}\right]\\ \end{array}  \tag{33}\]  
Similarly, the equation of the levitating force of the right side of the MC can be obtained:  
\[ \begin{array}{l}F_{right}=-\frac{(\phi^{2}_{r,air,y-\phi^{2}_{r,air,down}}) }{2\eta_{0}\cdot A}=-9.95\times 10^{8}\\ \left[(\frac{3.81\times 10^{9}-3.35\times 10^{5}\times y-6.23\times 10^{4}\times z _{l}-1.89\times 10^{5}\times z_{r}}{2\,26\times 10^{4}+2.78\times 10^{4}\times y -1.02\times 10^{6}\times z_{l}-3.07\times 10^{6}\times z_{r}})^{2}\right.\\ \left.-(\frac{3.81\times 10^{3}-3.35\times 10^{3}\times 10^{3}\times 10^{3}\times z _{l}+6.23\times 10^{4}\times z_{l}+1.89\times 10^{5}\times z_{r}}{2\,26\times 10^{4} +2.78\times 10^{4}\times y +1.02\times 10^{6}\times z_{l}+3.07\times 10^{6}\times z_{r}})^{2}\right]\\ \end{array}  \tag{34}\]  
As can be seen from Equations (33) and (34), the levitating forces on both sides of the MC are related to the MC plane position \(y\) and the upward offset of the MC. Next, this paper will discuss these characteristics.
# A LoRa-Based Linear Sensor Network for Location Data in Underground Mining
## 3 System Implementation and Trials
### Field Trials  
We have tested the system with two relays, and two tags. We varied the rate at which each tag generates messages from one every ten seconds to one every two seconds. A thousand messages were generated by each tag in each experiment. The time a relay waits before transmitting is exponentially distributed with mean delay of 100 ms. A Spreading Factor of 7 and bandwidth of 500 kHz were used in the LoRa physical layer giving a bit-rate of 21,875 bps. The results are shown in Figure 1. For this level of traffic the delivery success rate is very high.