esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Behavior and Libration Control of an Electrodynamic Tether System for Space Debris Capture
## 4 Dynamic Response Analysis of the EDT System
### Analysis of Libration Angle Stabilization in EDT Systems
However, an interesting observation is that the time required for the amplitude to decrease when current-based control is applied in Case 2 is longer than in Case 1. Figure 12c illustrates the dynamic response of the libration angle for Case 3, where the debris velocity is increased to 7900 m/s, while the mass remains unchanged compared to Case 1. In comparison to Cases 1 and 2, the amplitude of the libration angle response without current-based control is significantly larger in Case 3, indicating that debris velocity has a significant impact on the amplitude of the response. Furthermore, the time required for amplitude reduction with current-based control in Case 3 is longer than in Cases 1 and \begin{table}
\begin{tabular}{c c c} \hline  & **Mass** & **Velocity** \\ \hline Case 1 & 50 kg & 7800 m/s \\ \hline Case 2 & 100 kg & 7800 m/s \\ \hline Case 3 & 50 kg & 7900 m/s \\ \hline \end{tabular}
\end{table} 
Table 2: Mass and velocity of space debris for three simulation cases. 2. In conclusion, for all three cases, the libration angle of the EDT system can be reduced by applying current-based control.
## 3 Result
### Analysis of EDEM-Fluent Coupled Simulation Results for the Screening Process  
#### 3.2.1 Flow Field Analysis  
The sieving module utilizes gas flow to achieve the separation of raisins and foreign objects. Due to the varying movement patterns of airflow within the sieving zone, its impact on the materials also differs. Therefore, to enhance the working efficiency of the sieving module, it is essential to investigate the movement patterns of airflow within the sieving zone. When airflow enters the sieving zone, a pressure drop occurs, which is primarily attributed to the following factors: first, the drastic reduction in inlet area when airflow enters the sieving zone; second, energy loss occurs when airflow interacts with raisins, pedicels, branches, stones, plastics, and other materials; finally, pressure drop is also induced by the abrupt changes in airflow interface at the inlet and outlet. Consequently, the pressure within the sieving zone varies during the sieving process. The velocity contour plot and velocity vector plot of the Z-axis cross section in the sieving zone are shown in Figure 22a and Figure 22b, respectively.  
During the screening process, wind pressure was simulated, and total pressure distribution cloud maps of the Z-axis section in the screening zone (Figure 23a) and dynamic  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Rotational Speed (r/min)**} & \multicolumn{2}{c}{**Number of Raisin**} & \multicolumn{2}{c}{**Number of Raisin**} \\  & **Particles Free from** & **Particles with Adhered** \\  & **Adhered Foreign Objects** & **Foreign Objects** \\ \hline
200 & 338 & 523 \\
400 & 872 & 35 \\
600 & 856 & 4 \\
800 & 869 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quantity of raisin particles and raisin particles with adhered foreign objects in the collection bin at different rotational speeds.  
Figure 22: Z-plane velocity contour plot.  
pressure distribution cloud maps of the same section (Figure 23b) were captured at identical positions. Analysis of Figure 23a reveals that total pressure is highest at the air inlet, gradually decreasing toward the outlet before slightly increasing near the exit. This phenomenon occurs because airflow entering the screening zone interacts with raisins, foreign objects, and structural components (walls/baffles), causing energy exchange per the conservation of energy principle--airflow energy decreases while material energy increases, resulting in lower outlet total pressure compared with the inlet. The slight outlet pressure rise is attributed to the restricted outlet size. Examination of Figure 23b shows higher dynamic pressure at both inlet and outlet with a gradient decrease toward the center, reaching minimum values in the lower section of the device. This dynamic pressure pattern facilitates raisin-foreign object separation. Notably, pressure variations remain minimal and stable in the visual inspection and fine-removal zones beneath the screening module, indicating minimal operational interference from the screening process. This stability enhances the overall system reliability and removal performance.
## 3 Results and Discussion
#### 3.1.1 Sugars  
Sugars were the most dominant primary metabolite class identified in all apricot fruits and seed kernels comprising 15 peaks mostly represented by mono- and di-saccharides ranging from 189.98-268.32 mg/g in fruits and from 11.9-58.1 mg/g in seed kernels. D-Glucose (peak 24 and 28) was the main sugar detected in all samples, except for apricot sweet seed kernel AP-S\({}_{2}\)-S (muddy soil) and apricot mild bitter fruit AP-M\({}_{3}\)-F (sandy soil), with sucrose (peak 33) being the predominant sugar detected at 10.7 mg/g and 91.1 mg/g respectively. D-Glucose was detected in all apricot specimens ranging from 67.1-132.3 mg/g in fruits, and with larger variance in seed kernels ranging from 0.51-35.7 mg/g.  
By comparing each organ separately, it was observed that the lowest glucose level (67.1 mg/g) was detected in apricot mild bitter fruit AP-M\({}_{3}\)-F (sandy soil) in comparison to other fruits (AP-B\({}_{1}\)-F, AP-B\({}_{2}\)-F and AP-S\({}_{2}\)-F) grown in muddy soil. In contrast in seed kernels, apricot sweet seed kernel AP-S\({}_{2}\)-S showed the lowest glucose level (0.51 mg/g) compared to sucrose (10.78 MG/G). However, the highest glucose level (132.3 mg/g) was detected in apricot bitter fruit AP-B\({}_{1}\)-F cultivated in muddy soil.  
Next to glucose, fructose (peaks 20 and 21) and galactofuranose (peak 22) were detected in all specimens though at lower levels, found richer in fruits than seed kernels. Sugars detected at trace quantities included xylose and allofuranose in both fruits and seed kernels.  
Sucrose and fructose are the prominent contributors to fruits sweetness in case of apricot, being the most important sensory quality for consumers [27]. Apricot fruits from Egypt sugar results are in accordance with that reported by [PERSON] et al., [28] in which higher glucose level than sucrose has been reported in apricot fruits from other regions [28]. Extensive studies conducted in different apricot growing regions indicate that cultivar type determines the fruit sugar level rather than origin [27; 29]. Moreover, variability in sugar levels among different allocations can be attributed to differences in the soil, nutrients as well as climatic conditions, in addition to fruit development and ripening [30; 31; 32]. Other sugars such as sorbitol, mannose, maltose and raffinose reported in apricot fruits in varying levels were not found in our samples [33]. Regarding sugar alcohols, inositol (peak 30) was detected in apricot fruits (0.9-1.7 mg/g) and seed kernels (0.2-0.9 mg/g). Other sugar alcohols included myo-inositol (peak 29) and mannitol (peak 25) detected at low levels in fruits and seed kernels and suggestive that apricot does not present a good source of sugar alcohols compared to other _Prunus_ fruits such as _P. domestica_[34].
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 3 Improved Security AODV Routing Protocol
### Elliptic Curve Cryptosystem
#### 3.1.2 Scalar Multiplication of Elliptic Curves  
Assuming that m is an integer and \(G\) is a point on an elliptic curve, scalar multiplication can be expressed as follows:  
\[mG=m\times G=\underbrace{G+G+\cdots+G}_{m} \tag{6}\]
## 2 Modeling of Strain Actuation on Curved Beams
### Theoretical Models
Since the deformation is considered constant along the bonding line, this does mean that its field may be replicated through some equivalent forces that are deployed at the borders of the contact region, representing the action of the actuator on the structural surface. It is reasonable to think that such a result is not modified in the case of a curved element, as the adhesion is considered perfect. However, since the structure is curved, the border forces do act on the same plane; to maintain the equilibrium, further forces are applied normally to the surface. In turn, this would mean that the strain in the contact region cannot be considered constant anymore because of the crossed effects of the loads on the deformation, driven by the Poisson modules. This effect grows as the curvature grows, meaning it can be expected that at a certain point, the adopted model cannot be considered valid any longer. The modeling strategy that was adopted here consists of simple steps, combining numerical simulations and theoretical assumptions (in turn based on the previous results), with the following aims: 1. To simulate the presence of a piezoelectric actuator on a series of curved beams with different characteristics (in terms of the curvature radius, thickness, and so on); 2. Remove the piezoelectric actuator and impose the expected force field at the boundaries of the contact region; 3. Evaluate the displacement at certain characteristic points of the structure, such as the tip; 4. Verify the ratio among the displacements coming from the theoretical models (flat plates) and the numerical ones (with the piezo device and with its effect only). The first aspect is expected to confirm the theoretical predictions for small values of the curvature; the second and third aspects are expected to provide coincident results as long as the performed assumptions hold. Then, the results are expected to diverge, establishing the validity of the adopted considerations. Formally, it is assumed that the transmitted strain always has the following form:  
\[\text{Actuated Strain}\ \ \ \ \ \frac{\varepsilon_{t}}{\Lambda}=f \tag{4}\]  
As we talk in terms of the transmitted force, the expressions are a bit different. In the hypothesis of a linear strain function along the structural system, the expression for \(\varepsilon_{t}\) is sufficient to define the law for the deformation along the thickness as:  
\[\text{Actuated Strain}\ \ \ \ \varepsilon(y)=\frac{2\varepsilon_{t}}{t_{s}}y \tag{5}\]  
and the expression for the transmitted moment as:  
\[M=\frac{E_{s}t_{s}^{2}\varepsilon_{t}}{6} \tag{6}\]  
Here, the transmitted force may be written as:  
\[M=Ft_{s}\Rightarrow F=\frac{E_{s}t_{s}\varepsilon_{t}}{6}\ \textit{or}\ F=\frac{fE_{s}t_{s} \Lambda}{6} \tag{7}\]  
The maximum transmittable force is the blockage piezo force, i.e.,:  
\[F_{M}=E_{p}t_{p}\Lambda \tag{8}\]  
Therefore, the ratio between the transmitted force and the maximum transmitted force may be written as:  
\[\frac{F}{F_{M}}=\frac{f}{6}\frac{E_{s}t_{s}}{E_{p}t_{p}}=f\ \frac{\psi}{6} \tag{9}\]
## 3 Monitoring Methods and Principles
### Operating Principle and Data Acquisition of the Monitoring System  
The GNSS monitoring system used its positioning function to determine the location of monitoring points. Since the focused point of this study was relative displacement, and the requirement for absolute  
Figure 2: GNSS real-time monitoring system for the Luhu village slope.  
coordinate accuracy was low, the initial coordinate system was determined by the default Universal Transverse Mercator (UTM) projection system. GNSS receiver model was LPR2000 series, which has built-in UB380 board module to receive signals from BDS, GPS, GLONASS satellite, shown in Table 1; built-in low-power radio to support differential data transmission between stations; equipped with GPRS-4G module to support remote interface control and remote data transmission to realize remote monitoring ability of measuring points. The HC Monitor software was used for GNSS data processing, it has the advantages of using the nonlinear Kalman filtering of double-differential and three-differential algorithms, the improved ionospheric correction model, and supporting the multi-reference station solution and real-time independent baseline network adjustment. The horizontal accuracy was better than 3 mm and the elevation was 5 mm. Calculating coordinates at a speed of once per 5 s, and achieve millimeter level accuracy; the solution data was transmitted to the server at a speed of once per 5 s through GPRS-4G, the coordinates and the displacement, speed and acceleration calculations of the monitoring points can be read and downloaded at the port of the web page version of the ground disaster monitoring system, which also sent the relevant alarm information to the user by detecting whether the deformation data was greater than the warning value. Similarly, the rainfall sensor and the video sensor were used to obtain real-time rainfall information and a video of the slope, respectively. The data were transmitted in the same way as the GNSS monitoring module data. The end user could obtain deformation and rainfall information, as well as other data through the internet and via mobile phone. The LAN differential location data of the monitoring system was obtained for a 90-day period in this study, with an acquisition frequency of once a day.
## 3 Results and Discussion
### Emulsion Studies  
Figure 9 shows the emulsion breaking process, promoted by the amphiphilic materials. Figure 9(a) shows a sample of a real biodiesel effluent as a stable emulsion with a microscopic view (Figure 9(b)). When the magnetic amphiphilic composite was introduced (Figure 9(c)) the nanostructured particles locate at the interface biodiesel/water, around the dispersed oil drops (Figure 9(d)). In this stage (even before magnetic separation) it can already be noted an oil phase separation by the presence of a thin black layer at the top of the test tube. The black color of this layer is related to the presence of small amounts of the composited retained by the oil phase. After applying a magnet, the material is attracted and dragging the drops together, breaking the emulsion. It was observed a decrease of turbidity after magnetic separation process (Figure 9(e)).  
The efficiency of biodiesel wastewater emulsion separation was monitored by turbidimetric measurements. Table 2 shows the surface area and turbidity reduction of the emulsified biodiesel wastewater comparing different materials, _i.e._, Fe1%, Fe25%, chrysotile fibers before and after treatment up to 800 \({}^{\circ}\)C and high surface area activated carbon (AC) used as comparison.  
It was observed that the materials were efficient in the turbidity removal of the biodiesel effluent reaching _ca._ 97% of turbidity removal. This result suggests that the destabilization/breaking of emulsion promoted by amphiphilic material is more efficient than the adsorption process using activated carbon which removed only 77% of turbidity (Table 2). These results clearly show that magnetic amphiphilic materials are much more efficient in the process of emulsion breaking and the separation of the material.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sample** & **M\({}_{\text{S}}\)(emu/g)** & **M\({}_{\text{R}}\)(emu/g)** & **S\(=\)M\({}_{\text{R}}\)/M\({}_{\text{S}}\)** & **H\({}_{\text{C}}\)(Oe)** \\ \hline Fe1\% & 7.97 & 0.63 & 0.08 & 158 \\ \hline Fe25\% & 19.46 & 1.91 & 0.10 & 226 \\ \hline \end{tabular}
\end{table}
Table 1: Relevant data obtained by magnetization analysis of Fe1% and Fe25%  
Figure 9: **(a)** Real biodiesel wastewater emulsified. **(b)** Optical microscopy images of oil drops into real biodiesel wastewater. **(c)** Real biodiesel wastewater with amphiphilic material. **(d)** Oil drops covered by amphiphilic material. **(e)** Real biodiesel wastewater after magnetic separation process.
## 3 Results
### X-ray Rates for One- and Two-Photon Absorption  
Based on the rate equations for the 3-level model described in Section 2, we first performed calculations on the absolute X-ray rates for the OPA and TPA processes versus the incident X-ray flux. With these computations, we could validate the time-dependent approach by comparing the calculated X-ray rates with the previously reported experimental data. The calculated X-ray rates were obtained by integrating the differential decay rates \(\mathrm{dN_{2}}/\tau_{2}\) (OPA) and \(\mathrm{dN_{3}}/\tau_{2}\) (TPA) over the time lapse of the incident X-ray pulse. The results, presented in Figure 2, show a good agreement with the reported experimental results [13].  
In Figure 2, the measured X-ray emission rates corresponding to the OPA and TPA processes are plotted versus the fluence of the incident X-ray photons. The expected linear (\(f=ax\)) and quadratic dependencies (\(f=ax^{2}\)) of the measured rates for OPA and TPA processes, respectively, can be clearly seen. The calculated slope of the linear dependence of the OPA signal provides a one-photon absorption  
Figure 2: Comparison between calculated (solid line) and measured (squares) X-ray rates at different incidence X-ray flux for one photon absorption (OPA) (blue) and two-photon absorption (TPA) (red) processes. The experimental results were obtained at the Linac Coherent Light Source(LCLS) X-ray Free Electron Laser and are taken from Ref. [13].  
cross-section of 5.6 \(\times\) 10\({}^{-22}\) cm\({}^{2}\). Since the data are plotted on Log-Log scale, the TPA quadratic nature is distinguished from different curve slope as compared to OPA dependence. This value is in agreement with the reported experimental cross-section of 5.5 \(\times\) 10\({}^{-22}\) cm\({}^{2}\). The calculated coefficient of the quadratic function describing the TPA intensity is equal to 2.8 \(\times\) 10\({}^{-55}\) cm\({}^{4}\)s, hence a qualitative agreement with the fitted experimental value of 4.1 \(\times\) 10\({}^{-55}\) cm\({}^{4}\)s is given. The obtained results validate, the assumptions made in the theoretical three-level system model, including assumption of a Gaussian shape for the temporal profile of the incident X-ray pulse.
## 5 Experimental Results
Figure 8: Angular coordinates \(q_{1}\), \(q_{2}\) and \(q_{3}\). Figure 10: Torques at the passive joint: (**a**) without using the proposed controller; (**b**) using the proposed controller. Finally, in Figure 11 we show the complete movement of the system in Cartesian coordinates along the time (blue line) using the proposed controller. The sketch of the manipulator at \(t=0\) is included to have a better idea of the posture of the system during the movement. The desired trajectory that the end effector (beak) has to follow is also depicted (red line). In Figure 12 the different desired positions of Table 5 was enumerated in order to show the positions of the entire structures along the experiment. We can see that a good trajectory tracking with the end effector is achieved while the system maintains the equilibrium. The maximum error is around 8 mm during the transitions, however, when the system finished these transitions, the error is less than 2 mm. These errors are negligible in comparison with the size of the manipulator (length of the manipulator is around 400 mm). In summary, these experiments validate the proposed controller for this bio-inspired prototype with small-torque motors opening the possibility to perform trajectory tracking with the end effector of underactuated manipulators which has promising applications, such as performing contact inspection.
# A New Bit Repair Fast Reroute Mechanism for Smart Sensors IoT Network Infrastructure
## 4 Evaluation  
In this section, we provide the evaluation of the proposed B-REP mechanism. In its verification, we used simulations performed in the OMNeT ++ simulator. The results obtained from simulations performed in the OMNeT++ simulator are presented. In addition, a comparison with other existing FRR mechanisms has been performed.
# 2D Triangulation of Signals Source by Pole-Polar Geometric Models
## 2 The Proposed Geometric Models  
The proposed geometric models are applicable to \(m\geq 2\) receivers and one emitter. A system comprises \(m\) receivers to triangulate the emitter location \(E_{xy}\big{(}E_{x},E_{y}\big{)}\) by geometric relationship applying elements of pole-polar geometry.  
Our algorithmic descriptions (and the simulator) use as input data the coordinates of each receiver position \(C_{k}(x_{k},y_{k})\), \(k=1,2,\ldots,m\) and its respective signal range \(r_{k}\). In real world, these signal ranges values are computed from acquired signal data by each receiver using, for example, a time-based or strength-based model. Only after can be applied a method capable to triangulate the signal emitter device location.  
In this section we present the proposed geometric methods. To evaluate such methods, we developed an experiment, in an outdoor environment, capable of acquiring signal strength data (Section 3). In this environment we define a local coordinate system that allows controlling the geometry of the localization system and guarantees precision in the positioning of the emitters and the receiver. The analyses of the acquired data in this experiment, the results produced by the application  
Figure 2: (**a**) Illustration of Convex Hull algorithm applied to a set of polar-points. (**b**) An example of signal data and it respective signal ranges with its polar points. This case represents sets of signal data with satisfactory quality. The red dotted rectangle mark a region where a set of polar points enveloping the location of an emitter device.  
of the proposed methods and comparisons with results obtained by other three methods are in Sections 3.2 and 3.3.
# A Review of Oil Spill Remote Sensing
## 2 Detection and Mapping of Oil on a Water Surface
### Passive Means of Detection and Mapping  
The most common means for oil spill remote sensing is to use passive observation of the sea surface to detect and map oil spills. These include the techniques of using cameras in the visible and infrared spectra. Other wavelengths such as ultraviolet and near infrared are less frequently used.
## 4 Near-Surface Defect Detection and Identification
### Near-Surface Defect Detection Based on SST
On the other hand, SST concentrates the energy along the frequency direction, transforming some time-frequency points to zero. The results obtained indicate that the ratio of zero value increases from 0 to 45% of the original time-frequency result after SST processing, which removes nearly half of the values that are close to zero, ultimately making the time-frequency distribution more precise. Although SST can detect and locate near-surface defects, it cannot directly determine the type of defect. Therefore, this paper proposed a CNN model for identifying the defect types, which can achieve the identification of general and blind zone defects.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 4 Experimental Planning and Test Bed Design  
This section deals with the description of the hardware and software specifications needed in the test bed. We also overview the measurement scenarios where we conduct the experiments.
# Current Sensorless MPPT Control for PV Systems Based on Robust Observer
## 3 Problem Formulation  
A current sensorless PV system requires the capability to track the maximum power point at every instant with high precision even when model uncertainties arise. Such objective can be achieved using a robust estimation scheme which allows any current dependent MPPT technique to drive system towards MPP. To perform this estimation task in a fast and accurate form under model uncertainty conditions, a robust Luenberger observer based on AEM is proposed and INC algorithm is applied to generate the voltage references that move the system around the MPP.