esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix C MothPy Simulations  
We used MothPy [75] to run simulations to compare to the results of our Plume Navigation Model (Section 5). We initialized a single stationary female moth at position \((x,y)=(25,500)\) cm with 200 male moths uniformly distributed within the rectangle \(\{(x,y)\mid x\in[393,450]\text{ cm},y\in[490,499]\text{ cm}\}\). The male moth navigational strategy we chose to simulate is called \"Strategy A\" in [PERSON] et al. [33] and is the same as that implemented in [PERSON] et al. [28]: upon detecting the pheromone plume, male moths surge upwind, and upon losing the plume, they cast. The wind and plume models used are from the software package PomPy ([PERSON] [76]), which implements the models in [PERSON] et al. [46] and as described in Equations (21) and (22). Contrary to our Plume Navigation Model implementation, in MothPy [75], the pheromone puff release is modeled as a stochastic Poisson process and the wind velocity includes random white noise. Finally, MothPy [75] focuses on very short time scales, of the order of tens of seconds, whereas our Plume Navigation Model unfolds over several hours. We use the default parameters as given in version 0.0.1 of MothPy [75] but vary MothPy's male moth quality and female calling indicators through a parameter sweep of four variables as follows:  
* female pheromone puff release rate: \[f_{\text{r}}=\{50,66.66,83.33,100,116.66,133.33,150,166.66,183.33,200\}\text{ s}^{-1}\]
* female pheromone puff molecular amount: \[m_{\text{p}}=\{0.1,0.68,1.26,1.84,2.42,3.00,3.58,4.16,4.74,5.32,6.48,8.80,13.44, 22.72,41.28\}\text{ pg}\]
* male ground flight speed: \[v=\{50,140,230,320,410,500,590\}\text{ cm}/\text{s}\]* male pheromone detection threshold: \[C_{\text{tol}}=\{120,500,880,1260,1640,2020,2400\}\text{ pg/cm}^{2}\]  
Each of the 7350 simulations is run for 20 s, and a male moth is considered to have reached the female when located within a radius of \(r_{\text{cap}}=15\) cm from her. We show the percentage of successful mating in Figure 12 and the first arrival time of male moths in Figure 12.  
Although run over a different timescale than our Plume Navigation Model simulations, these mothPy simulations confirm the trends discussed in the main text. Specifically, increasing female effort, here represented by pheromone release rate \(f_{\text{r}}\) and titer amount \(m_{\text{p}}\), increases the mating likelihood of males traveling at speed \(v\) and characterized by pheromone detection threshold \(C_{\text{tol}}\) while at the same time decreasing the mean first arrival time. This is also observed for our long-term Plume Navigation Model simulations. Also note that the probability of successful mating for males with relatively low speed \(v\) is is more sensitive to changes in the female's calling effort (\(m_{\text{p}}\) and \(f_{\text{r}}\)) as can be seen from the broader color range in the bottom rows of panels in Figure 12 (e.g., for \(v=140,230,320\) cm/s) as opposed to the top rows (e.g., for \(v=410,500,590\) cm/s), another feature observed in our Plume Navigation Model simulations and discussed in the main text.
# Levitating Control System of Maglev Ruler Based on Active Disturbance Rejection Controller
## 2 Analysis Model of Levitating Force
### Magnetic Potential Calculation and Leakage Coefficient  
The MR uses four permanent magnets to provide bias magnetic fields for the six air gaps of the system. The magnetic field strength of the four permanent magnets was analyzed using finite element analysis, and their average values were:  
Average magnetic field intensity: \(H_{BM}\) = 265 \(\times\) 10\({}^{3}\) A/m.  
The average magnetic potential of permanent magnets: \(V_{m}=H_{BM}\cdot I_{BM}\) = 2650 A.  
The MR system chooses four permanent magnets of the same size, so the magnetic field strength of the four permanent magnets can also be considered to be the same.  
There are two types of magnetic levitating loops: the first is a basic levitating loop based on permanent magnets, which produces bias magnetic fields in the air gap; the second is a control levitating loop based on levitating coils, which produces control magnetic fluxes. During the operation of the MR, its MC is in a magnetic saturation state. Therefore, there must be significant magnetic losses and magnetic leakage in the magnetic loop, and two types of leakage coefficients need to be introduced to compensate for the air gap reluctance model.  
The two magnetic leakage coefficients are:  
\(\sigma_{BM}\) is the leakage factor of the basic levitating loop;  
\(\sigma_{CM}\) is the leakage factor of the control levitating loop.
## 3 Results
### Studies Implementing Vision Systems (VSS)
#### 3.2.1 Overall Statistics
Single-camera solutions are more easily manageable, transportable, and therefore feasible for SHM of large-scale structures. In addition, they do not require complex calibration procedures, as is the case with multiple cameras. Regarding the type of cameras, high-resolution and high-speed cameras are often used to ensure spatial and temporal image quality, especially for estimating displacements and vibrations and for taking accurate long-distance measurements. It is also interesting to note the increasing number of studies using smartphone cameras (10 studies). The analysis also revealed that cameras are commonly mounted on tripods to ensure stability. However, new emerging alternatives for mounting cameras, such as UAVs (6 studies), USVs (1 study), and robots (1 study), are becoming common in for reaching locations that would otherwise be difficult to access. Finally, two other fundamental aspects of VSS concern measurement accuracy and application in real-world or, at least, outdoor scenarios. In the first case, it is essential to  
Figure 3: Percentage distribution of VSS (Table 1) with respect to the type of infrastructure (**a**) and the type of structural health monitoring (**b**). compare the measured quantities with those provided by a gold-standard system through a validation procedure. In the second case, it is crucial to verify the behavior of the implemented systems in real situations, where weather and lighting conditions can become critical issues for the system itself, affecting its overall performance. The following sub-sections provide a detailed analysis of the studies shown in Table 1 regarding these two key points.
###### Abstract  
The extraction of Cu from mixed-metal acidic solutions by the thiourea-functionalized resin Puromet MTS9140 was studied. Despite being originally manufactured for precious metal recovery, a high selectivity towards Cu was observed over other first-row transition metals (\(>\)90% removal), highlighting a potential for this resin in base metal recovery circuits. Resin behaviour was characterised in batch-mode under a range of pH and sulphate concentrations and as a function of flow rate in a fixed-bed setup. In each instance, a high selectivity and capacity (max. 32.04 mg/g) towards Cu was observed and was unaffected by changes in solution chemistry. The mechanism of extraction was determined by XPS to be through reduction of Cu(II) to Cu(I) rather than chelation. Elution of Cu was achieved by the use of 0.5 M-1 M NaClO\({}_{3}\). Despite effective Cu elution (82%), degradation of resin functionality was observed, and further detailed through the application of IC analysis to identify degradation by-products. This work is the first detailed study of a thiourea-functionalized resin being used to selectively target Cu from a complex multi-metal solution.  
copper; ion exchange; thiourea; X-ray photoelectron spectroscopy (XPS); reductive extraction; resource recovery +
Footnote †: journal: _Eng_  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-00000-0000-0000-00000-00000-00000-00000-00000-000000-00000-00000-00000-000000-000000-000000-000000-0  
PCB wastes, it follows that a complex waste stream containing multiple metals would be produced. As such, the need for an effective method of selectively recovering copper away from other metals present in solutions is further emphasised.  
As a complexing reagent, thiourea (SC(NH\({}_{2}\))\({}_{2}\)) is most commonly used during the hydrometallurgical leaching of gold from ores as a less-toxic alternative to cyanide [13,14]. Further uses include the extraction of gold and silver from PCBs [15], and, more recently, it has been explored as part of a dual-lixiviant treatment process for waste activated carbon [16]. Given its high affinity for metals as a ligand, thiourea has been commercially incorporated into solid-phase extraction media, particularly ion exchange resins, though to date it has only been applied for precious metals extraction [17]. This paper describes the results of a previously unexpected interaction exhibited by the thiourea-functionalized resin Puromet MTS9140. Initially performed as part of a mixed-metal screening experiment to assess resin metal extraction behaviour, a high selectivity towards copper was observed and further investigated, the results of which are presented here in detail for the first time.
## 2 LFC Modeling in the Hybrid AC/DC System with Virtual Inertia  
We present the LFC system modeling in this section. The Kundur model in [33] is used to represent a general interconnected power system. Here, we highlight the difference between the test system of this article and the original Kundur model in [33]. First, to meet the changes in modern scenarios, the test system is equipped with AC/HVDC transmission lines and inertia emulation capabilities by ESS, based on the work in [27]. The block diagram of the system is shown in Figure 1, where two areas, four generation units (GENs), and two load demand centers are involved, and converters are installed for controlling the HVDC link and the added ESS. Second, the system model used in this paper is modified to be suitable for LFC or automatic generation control (AGC) analysis. We consider the LFC because, as mentioned in Section 1, the high-level control of LFC with a relatively slow dynamical response would rely more on communication networks such as the SCADA system to transmit measurements and control data, while such a system is known to be vulnerable to various attacks, as reported in [4; 5; 6]. From the perspective of system modeling, considering the timescale of LFC, it is generally a linearized model with certain levels of abstraction that simplify some elements of the initial detailed Kundur model. We show the details of the system model in the following.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 4 Analysis and Discussion
### Localization  
In this section, we demonstrate the performance of GPS and VO integrated localization under various scenarios of degraded and intermittent GPS reception. We refer to the optimized position track shown in Figure 6 (red line) as the ground truth position track. We acknowledge that this track has not actually been ground truthed with survey grade equipment; however, this track is deemed optimal, given the available data.
## 2 Dielectric Resonator Antenna
### Dual- or Multiple-Feed CP DRA
\begin{tabular}{c} **Impedance** \\ **Matching** \\ \end{tabular}  & **Advantages** & **Disadvantages** \\ \hline Metal feeding strip & Easy & Better & Easy &
\begin{tabular}{c} • Low cost \\ • High gain \\
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 3 AI and Ophthalmology: View of the Ensemble
### AI and Posterior Segment Diseases
#### 3.3.3 AI and Retinopathy of Prematurity  
Blindness caused by retinopathy of prematurity (ROP) is largely preventable through early detection and timely treatment. However, ROP monitoring is a practice that takes a long time and needs specially trained staff. It has been stated that interclinic partiality and geographical variability in the detection of ROP lead to poorer outcomes.  
Automated techniques using DL have recently been validated in the identification of ROP by means of retrospective data but have not yet been fully approved in the clinical environment [46]. To ameliorate the generalization of the DL algorithms used, some diversity in fundus imaging must be considered, including different camera models and lenses.
# A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception
## 6 Simulation Analysis
### Parameter Setting  
It is assumed that the enemy radar signal, our detection signal, the jamming signal, and the communication signal are time-frequency mixing. After the mixing signals are separated, DOA estimation, anti-main lobe jamming, target range and velocity estimation, and BER detection are carried out. The target is located at the 2000 th range bin, the radial velocity is 60 m/s, and the angle is 60\({}^{\circ}\). The radar transmits a linear frequency modulation (LFM) pulse signal. The bandwidth is 10 MHz, the time width is 10 us, and the pulse repetition period is 100 us. The jamming signal is sliced jamming of smart jamming, located within the main lobe, and the angle is 58\({}^{\circ}\). The communication signal is quadrature phase shift keying (QPSK) with angle 80\({}^{\circ}\), the carrier frequency is 5 MHz, and the code rate is 2 M/s. The enemy radar transmits a linear FM continuous wave signal. The starting frequency is 0 MHz, the cutoff frequency is 15 MHz, and the angle is 20\({}^{\circ}\). The sampling rate of the above signal is 100 MHz, and the observed time is 500 us. The SNR varies from 0 to 20 dB, and the JSR varies from 10 to 30 dB. The radio frequency has been changed to intermediate frequency. Array element spacing is half of the minimum wavelength of the signal and the number of array elements is 20.
## 4 Results and Discussion
### Analysis of the Subsoiling Resistance
#### 4.1.1 The Effect of Vibration Frequency on Subsoiling Resistance  
Figures 9-11 show the effect of the vibration frequency of forced vibration subsoilers on subsoiling resistance. Under the conditions of six subsoiler tips and three speeds, the vibration subsoiling method could reduce the subsoiling resistance. In the experiment, the higher the vibration frequency was, the smaller the subsoiling resistance was. At the speed of 3 km/h, compared with no vibration, the maximum resistance reduction rate of subsoiling resistance of six subsoiler tips was 14.9-19.6%. At the speed of 5 km/h, the maximum resistance reduction rate of six subsoiler tips was 14.5-21.2%. At the speed of 7 km/h, the maximum resistance reduction rate of the six subsoiler tips was 14.2-17.6%. These experiment results showed that high-frequency vibration could significantly reduce the subsoiling resistance. The superior vibration frequency was higher than the antilo vibration frequency, which might be that this study used PWM to regulate the speed of the vibration motor, and the vibration power was smaller when the frequency was lower. However, the best vibration frequency range needs to be further verified.
### Paper Focus and Outline  
In the context of the state-of-the-art overview, the major motivation for the research described in this paper was to conduct field experiments and analyse the performance of GNSS devices of different quality operating simultaneously. The goal was to gain knowledge to characterise the behaviour of each device during kinematic positioning and to gain information to improve trajectory determination by GNSS and INS in post-processing. The main objective of the research was to compare the positions in terms of the reference positions based on double difference processing and to determine the positioning quality, especially under difficult signal reception conditions. The focus of the research, meanwhile, was to identify and investigate anomalies in smartphone Xiaomi Miss and u-blox receivers.  
The main research question of this study was: 'Is there a significant difference in performance between low-cost, i.e., u-blox receivers and smartphones, and professional GNSS receivers in kinematic vehicle positioning?' More specifically, this study focuses on three different questions related to the following:  
* Determining the quality of georeferencing in purely kinematic GNSS mode;
* Distinguishing between quality of observations and kinematic results from several GNSS devices under consideration; and
* Evaluating the performance of GNSS devices compared to the mobile mapping system (MMS).  
The innovation of this study lies in the reference trajectory being generated using a professional MMS. As far as the authors are aware, there are no examples of kinematic comparisons of smartphone and u-blox positioning performance against positioning systems using the acquisition of an accurate MMS as a benchmark. The remainder of this document is organised as follows: Section 2 describes the experimental setup in the study area with the locations where the measurements were performed, including a description of the hardware used. The processing aspects that were followed in performing this analysis are described in Section 3. The experimental analyses are described in Section 4 and the final conclusions are drawn in Section 5.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### Homogenous Macro/Small Cell Deployment
#### 3.1.2 Interference  
The central cell is prone to the interference offered by the cells in the two interfering rings. In each cell, the UEs are uniformly distributed. Following the outage probability in the fluid model presented in Reference [35], 20 UEs can be scheduled by each eNodeB with a 5% outage probability. To quantify the system performance, the analysis was conducted by considering six positions of the UE within a cell. The position of the UE was such that UE with a lower index was near the eNodeB, while a large index indicated an increase in the distance between the UE and the serving eNodeB. Let us consider UE \(u_{i}\) (where i = 1-6 indicates the UE index) scheduled by the central cell eNodeB \(x\). As the frequency reuse factor of unity is assumed, the user \(u_{i}\) suffers external interference due only to one UE per PRB from each of the network's cells. From Equation (3), the received power at the eNodeB \(x\) from UE \(u_{i}\) can be written as:  
\[p_{\mathrm{rx},u_{i}}=p_{o}.\mathrm{pl}_{u_{i},x}^{\alpha-1} \tag{6}\]  
where \(P_{o}\) = 10 log (\(p_{o}\)) (\(P_{o}\) in dBm and \(p_{o}\) in mW) is the target received power and \(\mathrm{pl}_{u_{i},x}\) is the pathloss between the UE \(u_{i}\) and eNodeB \(x\). By this approach, the mathematical expression for the external interference offered by the interfering rings to the central cell eNodeB \(x\) is approximated from Reference [34] as:  
\[I=\int_{R}^{R_{\mathrm{nr}}}\int_{0}^{2\pi}\rho_{uv}p_{o}\mathrm{pl}_{u_{i},x} ^{\alpha}\mathrm{pl}_{u_{i},x}^{-1}rdrd\theta \tag{7}\]  
where \(\rho_{uv}\) designates the uniform distribution function of the UE scheduled by eNodeB \(x\). Employing a uniform UE distribution provides a better insight to model and analyze the network as the non-uniform distribution assumption complicates the analysis, especially when the locations of the UE and eNodeBs are dependent. The pathloss \(pl\) can be expressed as path gain \(pg\), where \(pg=1/pl\). The assumption here is that path loss only depends on the distance between the UE and the serving eNodeB. The expression for path gain is given as:  
\[\mathrm{Pg}_{u_{i},x}(r)=Ar^{-\eta} \tag{8}\]  
where \(\eta\) = 3.5 is the pathloss coefficient and A is a constant. Consider user \(t\) scheduled by eNodeB \(y\), located on the first interfering ring of cells around the central cell. The distance range of user \(t\) from eNodeB \(x\) is \(r\in(R;2R)\). As depicted in Figure 1, the user \(t\) is at a distance of \(2R\)-\(r\) from eNodeB \(y\). The interference offered by the area \(2\pi rdr\) around UE \(t\) is \(\rho_{uv}p_{o}\mathrm{A}^{-\alpha}(2R-r)^{\alpha\eta}Ar^{-\eta}2\pi rdr\). Similarly, if UE \(t\) is located in the distance range \(r\in(2R;3R)\) from eNodeB \(x\), it is at a distance \(r-2R\) from eNodeB \(y\). Hence, the interference offered now by the area \(2\pi rdr\) around UE \(t\) can be estimated to be
## 3 Classical Methods for Generating OAM Waves in RF Domain
### Diffraction Grating  
Diffraction grating is an optical structure composed of either parallel slits or parallel lines to operate in transmission or reflection mode, respectively. In OAM field, it is used as a hologram. When illuminated by a light beam, the structure diffracts the light into several beams in different directions and an interference pattern (hologram) is obtained by the interference between the incident and the diffracted beams. It is then possible to record the hologram between the two beams and then to use it to generate one beam from the other [40]. Such hologram has been applied in Ref. [29] to generate OAM vortex waves at 60 GHz (Figure 3) by illuminating the grating structure with a Gaussian beam transmitter.  
The main elements of holographic masks are observable in the fork-grating dislocation (Figure 3), where the plane input wave is transformed through this zone. The shape and behavior of fork-grating is controlled by the topological charge \(l\). Conversely to the spiral phase plate or flat drilled phase plate, and by observing the patterns in Figure 3, the hologram not only produces OAM vortex waves but also a Gaussian beam at boresight (\(0^{\circ}\)) due to the illuminating wave. The output signal carries OAM on the sides of the centered Gaussian beam.
## 3 Results and Discussion
### Evaluation of the Acute Toxicity of the PFOS  
Figure 3 presents the variations in the acute toxicity of PFOS over time.  
The TU value (95% confidence interval) of the PFOS sample at its initial concentration of 8 mg/L is recorded as 1.68 (with an EC\({}_{50}\) of 59.46%). After applying CP for 24 h, the TU value is 1.41 (with an EC\({}_{50}\) of 70.71%).  
There was no change in acute toxicity within the first hour of CP exposure. However, by hour three, the TU value had sharply increased to 2.99 (with an EC\({}_{50}\) of 33.48%). After that, acute toxicity gradually decreased, until the EC\({}_{50}\) reached 11.25%. This result suggests that PFOS's degradation intermediates may be more toxic than the original PFOS, although the toxicity continuously decreases with additional exposure to CP.  
In general, some AOPs may increase toxicity due to the chemicals added as part of the process [70]. However, CP does not use chemicals, meaning that any increases in toxicity  
\begin{table}
\begin{tabular}{c c c c} \hline
**Process** & **Target Pollutant** &
\begin{tabular}{c} **Rate Constant** \\ **(k, h\({}^{-1}\))** \\ \end{tabular} & **Reference** \\ \hline Fenton & Real textile effluent & 0.4 & [58] \\ \hline VUV/UV & Levofloxacin & 5.4 & [59] \\ \hline Ultrasound & Nafcillin & 3.3 & [60] \\ \hline Photocatalytic oxidation (TiO\({}_{2}\)) & 2\(\mathcal{A}\)-dichlorophenoxyayacetic acid & 0.04 & [61] \\ \hline Electron beam & PFOS & 1.2 & [62] \\ \hline Photochemical (UV/NTA) & PFOS & 0.3 & [63] \\ \hline Ultrasonic & PFOS & 1.3 & [64] \\ \hline Aeration-assisted CP & PFOS & 3.1 & This study \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of first-order kinetic constant of non-biodegradable contaminants and PFOS for various AOPs.  
must be caused by PFOS's degradation intermediates. These findings suggest that PFOS's intermediates acted as scavengers, resulting in the incomplete degradation of PFOS.