esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Challenges and Open Research Problems
### Limited Energy Storage Capacity  
One of the key challenges of using UAVs to support communication networks operations is that they have limited lifetime due to the low capacity of their batteries, which limits how long they can be deployed [341]. In fact, most UAVs have an endurance of just a few hours [342]. To reduce the weight of UAVs and the attendant energy drain problem, it is often necessary to use smaller batteries so as to avoid expending too much energy on flying as the energy required to fly the UAV varies with the payload size [343]. However, small batteries have low storage capacities, further complicating the energy situation of the UAV. In addition, there exist uncertainties in estimating the remaining battery charge in UAVs [344], leading to conservative estimates of left-over flight time to avoid _dead stick conditions_, whereby the UAV runs out of battery power in-flight, which could have disastrous consequences. Supercapacitors, on the other hand, are not ideal for UAVs due to their low energy density [49]. Improvements in battery technology are required to enhance the storage capacity of UAVs.  
One of the solutions that have been proposed to address the battery capacity limitations of UAVs include wireless charging [49], whereby the UAV is recharged during operation via RF energy harvesting [343], laser power beaming [345; 346], use of electrical power lines [347] by directly perching on current-carrying cables or harvesting electromagnetic energy generated by the cables. Other alternatives for recharging the UAV when its battery becomes depleted have been explored in [46], including UAV swapping (replacing those with depleted batteries with others that are fully charged) [341] and battery swapping. Alternatively, the UAV could be tethered to a mains power supply [348] or powered with fuel cells [43].
## Materials and Methods
### Methods  
The surface morphology of vermiculite was determined using a scanning electron microscope (Vegall-Tescan Company, Brno-Kohoutovice, Czech Republic). The study was supplemented with micro area analysis using the EDS detector. The sorbent surface and pore sizes were determined by a low temperature sorption study performed by Micromeritics ASAP2010 (Norcross, GA, USA). Raw and modified vermiculite samples were subjected to FTIR analysis. Infrared spectra were obtained using an FTIR spectroscope (Nicolet 380, Thermo Scientific Brand, USA), where the spectra were recorded from 3900 to 400 cm\({}^{-1}\). The structure of the raw and activated materials was studied by analysis of XRD patterns. A Philips X'Pert camera (Malvern, UK) with monochromator PW 1752/00 CuK\(\alpha\) was used in the study. In order to characterize the adsorption material, densimetric analyses were made using the pycnometric method. Bulk density and porosity were also established.  
Actual density of a substance is the ratio of the material mass to its volume (the core of the material without pores):  
\[d_{a}=\frac{m_{m}}{V_{a}} \tag{1}\]Apparent density (pycnometric density) is the ratio of the sample weight to the total volume of the sample (the core of the material and open pores):  
\[d_{p}=\frac{m_{m}}{V_{p}} \tag{2}\]  
Bulk density is the ratio of weight to volume of a material without compaction:  
\[d_{b}=\frac{m}{V} \tag{3}\]  
Relative density is the ratio of bulk density to actual density:  
\[d_{r}=\frac{d_{b}}{d_{a}}\times 100\% \tag{4}\]  
Total porosity describes the number of pores per unit volume. It is calculated from the following equation:  
\[P_{t}=\left(1-\frac{d_{b}}{d_{a}}\right)\times 100\% \tag{5}\]  
Open porosity of the analyzed material is determined from the equation:  
\[P_{o}=\left(1-\frac{d_{b}}{d_{p}}\right)\times 100\% \tag{6}\]
# Sparse Low-Rank Based Signal Analysis Method for Bearing Fault Feature Extraction
## 3 The Proposed Fault Information-Based Sparse Low-Rank Algorithm
### Algorithm Derivation  
In this section, variable splitting and the ADMM are used to solve the proposed optimization problem. After variable splitting, objective function (2) can be rewritten as:  
\[\{X^{*},Z^{*}\}=\arg\min_{X\in R^{max}}\left\{F(X):=\|Y-X\|_{F}^{2}+ \lambda_{0}\sum\limits_{i=1}^{k}\sigma_{i}(Z)+\lambda_{1}\sum\limits_{m_{1}=1 }^{M_{1}}\sum\limits_{m_{2}=1}^{M_{2}}\phi(X_{m_{1},m_{2}};a)\right\} \tag{4}\] \[s.t.X=Z.\]  
Then, the constrained optimization problem (4) can be converted to the following simple iterative procedures by using the ADMM algorithm, where the scalar augmented Lagrangian parameter is represented by \(\mu\). When \(\mu>1\), the convergence of the ADMM can be guaranteed [38].  
\[X\leftarrow\arg\min_{X}\frac{1}{2}\|Y-X\|_{F}^{2}+\frac{\mu}{2}\|X-(Z+D)\|_{F }^{2}+\lambda_{1}\sum\limits_{m_{1}=1}^{M_{1}}\sum\limits_{m_{2}=1}^{M_{2}} \phi_{\varepsilon}(X_{m_{1},m_{2}};a) \tag{5}\]  
\[Z\leftarrow\arg\min_{Z}\frac{\mu}{2}\|X-(Z+D)\|_{F}^{2}+\lambda_{0}\sum \limits_{i=1}^{k}\sigma_{i}(Z) \tag{6}\]  
\[D\gets D-(X-Z) \tag{7}\]  
For the sub-problem (5), if constant terms are ignored it can be rewritten as:  
\[X\leftarrow\arg\min_{X}\left\{\frac{1}{2}\|\frac{1}{1+\mu}(Y+\mu(Z+D))-X\|_{F }^{2}+\frac{\lambda_{1}}{1+\mu}\sum\limits_{m_{1}=1}^{M_{1}}\sum\limits_{m_{2} =1}^{M_{2}}\phi_{\varepsilon}(X_{m_{1},m_{2}};a)\right\} \tag{8}\]  
The above equation is strictly convex when the convex condition analyzed above is satisfied, and it can be solved by executing the proximal operator associated with \(\varphi\), i.e.,  
\[X\leftarrow\mathit{prox}_{\phi}(Y;\lambda_{1}/(1+\mu),a) \tag{9}\]  
The sub-problem (6) as a standard nuclear norm minimization (NNM) problem, and its global optimal solution can be obtained by using the \"singular value thresholding\" (SVT):  
\[Z\leftarrow\mathit{U}\bullet soft(\Sigma,\lambda_{0})\bullet V^{T} \tag{10}\]  
where, \(Y=U\Sigma V^{T}\) is the SVD of \(Y\); and \(\mathit{soft}(\_,\_) is the soft-thresholding function [39].  
\[soft(x,T)=sign(x)\bullet\max(|x|-T,0) \tag{11}\]  
By iteratively solving (9), (10) and (7), the proposed objective function (2) can be finally solved.
# Experimental Investigation on Self-Excited Thermoacoustic Instability in a Rijke Tube
###### Abstract  
The experimental investigations into the thermoacoustic instability in a Rijke tube are presented. In order to capture the dynamics of the temperature, a single-ended tunable diode laser absorption spectroscopy (TDLAS) technique was developed, with a measurement rate of 5 kHz. The temperature was found to fluctuate periodically at a dominant frequency of 230 Hz, corresponding to the fundamental frequency of the Rijke tube used in the experiment. The flame chemiluminescence was detected by a high-speed camera to demonstrate flame response to thermoacoustic instability. It was evident that the flame front stretched regularly and had jagged edges. To quantitate the fluctuations of chemiluminescence intensity, the relative area was defined. According to the result, the intensity also oscillated at 230 Hz. Furthermore, the same feature was found in regard to pressure at the exit of the Rijke tube. Compared with temperature and chemiluminescent intensity, the pressure oscillations presented the most approximate standard waveform, as they suffered the least disruptions. The results indicated that the dominant frequencies of temperature, chemiluminescent intensity and pressure were consistent, in accordance with the fundamental frequency of the Rijke tube in the experiment. In addition, etalon effects on the TDLAS signals were mitigated efficiently by a lowpass filter.  
thermoacoustic instability; single-ended TDLAS; Rijke tube; chemiluminescence
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 6 Impact of Quantum Money on the Banking System
### Challenges and Future Considerations  
While quantum money could provide a more stable and transparent financial system, its implementation would face challenges, including:  
* Technological infrastructure: The need for quantum-secured financial networks to store and transfer quantum money.
* Resistance from commercial banks: Given their reliance on debt-based money creation, commercial banks may oppose such a transformation.
* Economic growth considerations: Some argue that restricting money creation could slow down economic expansion by limiting credit availability.  
In a future where large-scale quantum networks enable distributed quantum computing, quantum money transactions could be facilitated by entanglement-based transfers between financial institutions. For instance, quantum entanglement could be distributed between two quantum computers located in banks via satellite links, ensuring secure, tamper-proof quantum transactions. Such a system could redefine monetary policy and financial stability on a global scale.  
The replacement of traditional digital deposits with quantum money could fundamentally alter the structure of the banking system. By enforcing physical constraints on money supply expansion, quantum money could restore central banks' control over the economy and prevent financial excesses caused by unchecked credit creation. Future research should focus on the feasibility of large-scale quantum money deployment and its implications for global finance.
# Suppression of Harmonic Current in Magnetic Bearing-Rotor System with Redundant Structure
## 4 Suppression of Harmonic Current in AMB with Redundant Structures
### Simulation Verification and Analysis
It is obvious that the proposed repetitive controllers had a good suppression effect on harmonic current in the redundant magnetic suspension bearing-rotor system. In order to verify that current harmonic suppression could improve the suspended performance of the rotor, the displacements of the rotor in \(x\)- and \(y\)-directions are displayed in Figures 17 and 18, respectively. Figure 11:2 Transient response of current in each coil with the proposed control strategy. For a rotor suspended by a magnetic bearing with redundant structures, Figures 17 and 18 display the displacement of the rotor before and after repetitive control implementation in red and blue, respectively. When repetitive controllers were not applied to the system, the value of \(x\) fluctuated in the range \(\pm 1.6\)\(\times\)\(10^{-5}\) m, and \(y\) fluctuated in the range \(\pm 1.5\)\(\times\)\(10^{-5}\) m. However, for the system which adopted repetitive controllers, the value of \(x\) fluctuated in the range \(\pm 0.5\)\(\times\)\(10^{-5}\) m, and \(y\) fluctuated in the range \(\pm 0.8\)\(\times\)\(10^{-5}\) m. It is obvious that the corresponding amplitudes of \(x\) and \(y\) decreased by about 68.7% and 46.6% when using the proposed control strategy in this paper. However, due to the existence of the unbalanced force of the rotor, the vibration of the rotor could not be completely eliminated, and there was still a vibration of displacement as shown by the blue lines in Figures 17 and 18. Furthermore, a time of about 1 s was needed for the rotor to maintain a stable state after the repetitive controllers were applied to the system. Figure 11:3 Trajectory of the rotor in \(x\)-direction with repetitive control (blue curve) and without repetitive control (red curve). Figure 11:4 Trajectory of the rotor in the \(y\)-direction with repetitive control (blue curve) and without repetitive control (red curve). The simulation results show that the repetitive controllers designed in this paper can be applied to a redundant structure magnetic bearing-rotor system, and the harmonic current of each coil can be effectively suppressed. This allows not only reducing the system's power consumption but also improving the robustness of the magnetically levitated rotor.
# Monitoring Forest Phenology in a Changing World
## 2 Monitoring Phenology
### A Brief Description of Phenological Monitoring Methods  
The monitoring methods we discuss fit into three broad categories: those that use direct visual assessment, those that use spectral characteristics captured by camera, and those that use a proxy such as biomass or gas flux. Visual assessment is the simplest approach, and consists of walking transects within the forest and visually observing tree canopies for the presence of phenological events. In some areas of the tropics where canopies can be extremely high [93], this is sometimes done with binoculars. Spectral based assessments can be broadly split into three types that relate to the distance of the sensor from the ground, of which the first is remote sensing by satellites. This form of monitoring is best achieved using surface reflectance products that account for the effect of the atmosphere on the measured radiance by the satellite, including the bi-directional reflectance effects (i.e., the fact that the angle of illumination and observation varies and affects measured radiances). These effects can be modelled with the Bi-Directional Reflectance Distribution Function (BRDF) and used to create nadir BRDF-adjusted reflectance (NBAR) [21]. The NBAR bands are then used to calculate a Vegetation Index (VI), mostly in the form of Enhanced VI (EVI) or Normalised Difference VI (NDVI) [30], which has been shown to correlate with Leaf Area Index (LAI), leaf biomass and percent vegetation cover [21; 94]. Besides broadband spectral satellite sensors, multiple other sensors have been applied for phenology assessments, such as active and passive microwave sensors, or sensors capable of assessing fluorescence [95; 96; 97; 98; 99]. Second, sensors on board planes or UAVs can be used instead of satellites to capture plant spectral characteristics [100]. Finally, cameras can be placed on observation towers above forest canopies [42; 101] or on the ground facing up [102]. Slightly different methods are used to segment or isolate the focal plants or regions of interest (ROI) for analysis, and the spectral resolution of the camera being used will determine the exact analysis being conducted on these last two approaches to collecting spectral data. Many methods extract Red-Green-Blue (RGB) pixel channel data in the form of digital numbers (DNs) for individual plants [103; 104]. Further non-linear transformations are then made on these DNs to reduce the effects of scene illumination, the result of which are individual RGB chromatic coordinates (Rcc, Gcc, Bcc) [105]. Some studies also calculate an Excess Green value, to emphasis the influence of phenological change on the green band [24]. These chromatic coordinates and the excess green value are used to track changes in phenology
# Low Power FPGA Implementation of a Smart Building Free Space Optical Communication System
## 4 Results
### Software Results  
The simulation of the receiver circuit was tested for different optical input signal voltages to validate the recognition of bit levels based on the power constellation division done at the modulation stage. For both the laser driver and photo-detection circuit, the TIP31C transistor was the most compatible chip with the desired specifications due to its high switching capability and power utility, making it frequently used in power linear applications and various amplifying stages. Figure 15 shows the output voltages at each comparator terminal represented in the left collector voltages of each transistor for a fed 3.7 V optical signal. The threshold voltage for logic high detection was designed in the proposed circuit to be 3 V, while any less value will be received as logic low. Hence, the four transistors will be on, and their output voltages are interpreted to be logic high.  
Another example is shown in Figure 16, where the input optical signal voltage is 2 V. The output at Q6 and Q8 is high, while Q4 and Q2 had their collector voltages lowered, resulting in a logic low recognition. This can be summed up as four logic combinations, depending on the bit constellation received by the photo-detector.  
Figure 14: UART random text Communication between FPGA and PC.  
Figure 15: Receiver Circuit Simulation for 3.7 V input.
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 3 Simulation Analysis of Factors Affecting Detection Effect
### A Scheme for Selecting Laser Thermography Parameters  
Considering the influence of laser power, laser scanning speed and thermal image acquisition frequency on the temperature gradient at the crack, we give the selection window for the following thermographic process parameters:  
1. For materials with low thermal conductivity, such as nickel-based alloys and iron-based alloys with similar thermodynamic properties, considering the detection time and quality, in the detection process of cracks with a width within 40-80 \(\upmu\)m, the laser scanning speed is set to be 10-20 mm/s. Considering the thermal influence range under this scanning speed, the scanning line spacing can be selected as 1-3 mm. Additionally, considering the diffusion of heat, the laser power should be controlled at 3000 mW.
2. For components with cracks less than 40 \(\upmu\)m in width, the scanning speed and scanning line spacing should be reduced, and a higher frequency of thermal image acquisition should be used to achieve more accurate detection. If the detection area is large, the laser scanning speed should be appropriately increased.
## 4 Cavitation Effects
### Effect of the Backpressure  
Not only the inception and development of cavitation but also the backpressure conditions are important for inferring the cavitation control in a hydrodynamic torque converter. Figure 17 shows the vapor volume distribution in the stator of the hydrodynamic torque converter at different backpressures. The backpressure had a significant effect on both the cavitation intensity and stability. At a backpressure of 0.2 MPa, the cavitation reached its maximum intensity because the difference between the charge pressure and backpressure is greatest. The cavitation intensity decreased as the backpressure increased further. Only attached cavitation was predicted in the stator domain when the backpressure reached 0.8 MPa, at which point, the cavitation intensity in the stator reached its minimum.  
Figure 16: Normal velocity distributions at selected monitoring locations along the suction side of the stator under non- cavitation and cavitation conditions (\(SR=0\)).  
This indicates that the oil operating conditions are essential for controlling cavitation in turbomachinery.  
Table 4 presents the vapor volume and cavitation number at different backpressures. The pressure difference between the charge pressure and backpressure decreased with increasing backpressure, and the pressure in the entire working chamber increased. The vapor volume gradually decreased with an increasing cavitation number. This indicates that a reasonable increase in the backpressure can significantly suppress the cavitation risk inside the hydrodynamic torque converter.
## 5 Discussion  
Traffic management plays a crucial role in tracking the growth of cities such as Bouskoura, and our system has demonstrated its reliability in the experimental phase. The data collected are a valuable tool that helps the municipality make informed decisions regarding changes to the infrastructure, as well as to better understand the impact of opening of a new shopping center in the city. Specifically, our system has helped the municipality of Bouskoura to calculate, by class, the number of vehicles that pass through the roundabout. It can also help to determine the percentage of vehicles from each lane, which has been instrumental in deciding what lanes to extend and how to manage traffic flow to support the economic growth of the city. Overall, our data-collection system is an invaluable tool for the Bouskoura municipality in making informed decisions about traffic management and infrastructure adaptation to better meet the needs of its growing population.
## 4 Results and Discussions
### Comparison between the RVE Model and the Full-Size Model
#### 4.1.1 Temperature Field  
Figure 7 shows the temperature fields of the RVE model and the full-size model. The temperature distribution of the RVE's surface is shown in Figure 6(a). In the figure, regions A and C are the matrix and region B is the fiber bundles. It can be seen that the temperature distributions of the three regions had significant differences. In the regions between the fiber bundles and the matrix (A and B; B and C), the directions of the isotherms had considerable variations. The reason for this was that in the process of heat transfer from the high-temperature surface to the low-temperature surface (via the red arrows shown in Figure 6(a)), there were different regions with different thermal conductivities, which led to heat conduction along multiple walls. In the fiber bundles, the horizontal yarns and the diagonal yarns had different directions, which led to different ATCs, so the transmission paths of the heat in the fiber bundles also varied.  
Figure 6(b) shows the temperature distribution on the surface of the full-size model. The temperature field was also heterogeneous. There were significant thermal gradients in the region between the fiber bundles and the matrix, which was similar to the RVE model. The reason for this was also that the transmission path of the heat changed due to the difference of the fibers' and the matrix's thermal conductivities.  
Figure 6(c),d shows the distributions of the temperature fluctuation \(\delta_{T}\) in the middle sections of the \(z\)-axes of the two models. The results show that \(\delta_{T}\) for these sections was obvious in both the fiber bundles and the matrix. Additionally, in the middle section of the full-size model, \(\delta_{T}\) of the part with  
Figure 7: The temperature fields.  
the same size of the RVE model was found. The distributions of \(\delta_{T}\) in those two models had significant differences, and \(\delta_{T}\) in the RVE model had more dramatic changes compared to \(\delta_{T}\) in the full-size model. For the RVE model, the maximum \(\delta_{T}\) in the middle section was 9.96%, while for the full-size model, the maximum \(\delta_{T}\) in the middle section was 6.39%.
# Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation
## 3 Proposed Framework
### Comparison of Learning Models  
In this section, experiments are conducted by modifying the structure of various layers and parameters to compare and identify better methods relative to the one proposed in this paper. For comparison, models such as linear regression, learning representation, and LSTM are used. Learning representation involves models with 1-layer and our method, respectively.  
Linear regression and learning representation (1-layer) were designed to check if the input data structure could be represented in a lighter structure than that proposed by the model, while LSTM was selected as part of the experimental group to explore its ability to ensure temporal continuity in simulations.  
Figure 4 shows a model using LSTM to infer level-set values, referencing the method of improving prediction accuracy through Warm-up in CFDNet [31]. LSTM, used for learning sequence data, requires the use of some inferred values as input due to its time-dependent characteristics. As the calculated level-set values increase, the resource consumption used for tracking the fluid surface grows; thus, the sequence data used in LSTM were limited to three frames to ensure meaningful experimental results. Following are the equations for RMSE (Root Mean Square Error) and MSE (Mean Square Error) used for evaluation (see Equations (10) and (11)).  
\[RMSE=\frac{1}{N}\sum_{j}^{N}\sqrt{\left(\left|y_{t}\right|-\left|y_{p}\right| \right)^{2}} \tag{10}\]  
\[MSE=\frac{1}{N}\sum_{j}^{N}\left(y_{t}-y_{p}\right)^{2} \tag{11}\]  
where \(N\) represents the total number of particles, \(y_{t}\) is the ground truth, and \(y_{p}\) is the prediction value.