esa-sceva/satcom-chunk-collection huggingface dataset:
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 1 Introduction  
GNSS receivers have been successfully adopted as key elements of the navigation systems for many space borne missions. Typically, they are used in LEO where the availability of the GNSS signals is very good and high accuracy positioning can be achieved. Using a GNSS receiver as the core navigation system enables greater autonomy of a spacecraft and reduces the burden and costs of network operations [1]. For this reason, the autonomous navigation capabilities of GNSS are extremely appealing, not only for LEO but for higher altitude Earth orbits as well. It is even possible for spacecraft orbiting at altitudes beyond the GNSS constellations to receive weak signals from the opposite side of the Earth. These receivers are picking up the spillover of the main lobe of the GNSS transmit antenna pattern around the Earth mask and also signals from the weaker side lobes of the antenna pattern. Research presented in [2; 3; 4; 5; 6; 7; 8] shows feasibility studies of GNSS as navigation system for space missions in High Earth Orbits (HEO) and beyond up to Moon altitude. Other research work, such as [9; 10; 11; 12] and [13] has proposed GNSS receivers designed for such missions and the demonstration of a receiver positioning in HEO is detailed in [14]. In [15; 16; 17] experimental demonstrations are described that show the GNSS signals availability even at higher orbits than GEO. Research continues to investigate the potential of using GNSS for lunar missions, in particular for Earth-Moon transfer orbits.  
In this paper, we describe the GPS L1 C/A hardware receiver, named \"_WeakHEO_\" receiver, which we have designed and developed as a proof of concept for lunar missions at the ESPLAB EPFL. Firstly, the hardware architecture of the receiver is presented followed by a description of the defined mission and expected GNSS signal environment in which the receiver has to operate. The high-sensitivity acquisition and tracking algorithms of the receiver are then described before concluding with navigation test results from the receiver.
## 4 Discussion
In summary, the findings of this study proved that PS-derived variables and environmental variables integrated with SDM are effective in identifying the distribution of small-scale Acacia _decurrens_ plantation forests. This can be attributed to the high spatial resolution of the PS image, suggesting potential applications of PS imagery in small-scale forestry.
## 2 Materials and Methods
### Proposed DC/DC Converter
#### 2.2.2 Design of the Fuzzy Controller  
The average current generated by PVG follows solar radiation, \(G\). When the radiation is increased (decreased), the operating point of the inductor in the \(B\)-\(H\) curve moves to (from) the saturation zone. The inductor value in this case must follow any changes in the generated current.  
Figure 8: (**a**) Exploded drawing of the coupled inductor; (**b**) picture of assembled inductor.  
Figure 7: (**a**) Coupled inductor, (**b**) added flux, (**c**) subtracted flux.  
From this observation, the idea to integrate a fuzzy control came up [14; 22]. From the deviation between the current \(I_{2}\) and its reference, \(I_{2n\!f}\), coming from the measured \(I_{pv}\), we can decide whether to increment, decrement or keep the same duty cycle. One of the advantages of this strategy is that only one measurement is required, namely the inductor current \(i_{2}\).  
The control diagram is presented in Figure 9. The fuzzy algorithm has been designed using two linguistic input variables (error and change in error). Five membership functions have been chosen for each variable. The output of the algorithm gives the required duty cycle of the converter. The membership functions of the input and output variables are highlighted in Figure 10a,b, respectively. The used fuzzy rules are summarized in Table 2.  
Figure 10: (**a**) Membership functions of the input variable; (**b**) membership functions of the output variable.  
Figure 9: Diagram of the fuzzy controller.  
Figure 11 shows the result of the FC: reference current (\(I_{2 ref}\)) in red color and measurement current (\(I_{py}\)) in blue. We note that the FC achieved good tracking performances.
# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 1 Introduction
### Benefits of Smart Farming  
Smart farming has many benefits, including [5] (i) improving crop quantity and quality by optimizing resources and precise crop management, so farmers can achieve higher yields and better-quality crops; (ii) cost reduction in terms of water usage, fertilizers, and pesticides; (iii) improving productivity by using robotics and drones to efficiently reduce the time spent on tedious tasks, allowing farmers to work on other tasks; (iv) enhanced decision making that is augmented by machine learning algorithms to provide farmers with valuable insights to make better decisions about their crops and livestock; and (v) improving sustainability by reducing the environmental impact of farming on aspects such as water pollution and greenhouse gas emissions.  
Figure 1: Technologies used in smart farming.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 4 Simulation Results
### Optimum Number of Cluster Evaluation
#### 4.3.3 Reliability for Seven Packet Duplication  
In Figure 10, 7-packet duplication achieves the best reliability of 99.9886% when 3-clusters are used at an arrival rate of 5000 pkt/s. Having more duplication, there is a need for fewer subcarrier channels, which eventually reduces the number of clusters--using 120 subcarrier channels with 3-clusters and arrival conditions starting from 100 pkt/s to 2500 pkt/s the 7-packet duplication providing almost 100% reliability. At 5000 pkt/s, the reliability percentage is 99.9886%. If we take 4-clusters, at 5000 pkt/s, the reliability is 99.9814%. A 6-cluster placement offers a better reliability percentage than 5-clusters placement. The 5-clusters provide the reliability of 99.9608%. For 6-clusters, the percentage  
Figure 9: Determine reliability for different clusters using 5-packet duplication. (**a**) Mathematical, (**b**) Simulation.  
is 99.9700%. However, 2-clusters have only 99.8276% reliability. So for 3- and 5-packet duplication, we receive the best outcomes from 4-clusters. For 7-packet duplication, 3-clusters is the most suitable.
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 4 Simulation Results
### Communication Quality Performance Evaluation  
The average network throughput is calculated as follows:  
\[A_{th}=\frac{\sum_{r=1}^{N_{r}}\sum_{i=1}^{N_{i}(r)}O(i,r)\times\left[T_{i}(r)-T_{ d}(r)\right]}{\sum_{r=1}^{N_{r}}\sum_{i=1}^{N_{i}(r)}T_{i}(r)} \tag{10}\]  
where \(A_{th}\) is the average throughput, \(T_{i}(r)\) represents the network dwell time when executing the \(r\)-th time, and \(T_{d}(r)\) represents the network handover delay time when executing the \(r\)-th time.
# Experimental Verification of the CFD Model of the Squeeze Film Lifting Effect
## 3 Results
### Experimental Results  
The results of the levitation heights are presented in Figure 16. The measurements were carried out at 1.31 kHz with differing excitation amplitudes: 6.2; 17.1; 38.3; 57.1 \(\upmu\)m. The parameters of the samples are shown in Table 1. The experimental results show similar behavior as predicted by calculations. The levitation height rises with the rising amplitude of motion for the oscillator. The increase of the mass of the lifted sample with the constant area of the load proves to decrease the obtained levitation height.  
Figure 14: Radial pressure profiles for cases (**a**) m = 5 g; f = 5 kHz; A = 0.5 \(\upmu\)m; (**b**) m = 5 g; f = 25 kHz; A = 0.5 \(\upmu\)m.  
Figure 15: Fluid radial velocity contours for cases m = 5 g; f = 5 kHz; A = 0.5 \(\upmu\)m and m = 5 g; f = 25 kHz; A = 0.5 \(\upmu\)m (air thickness increased proportionally \(\times\)50 to improve the readability of the drawing).  
With differing masses and diameters of the lifted object, there is an opposite influence of mass increasing with the diameter and causing the decrease in the levitation height and, on the other hand, the influence of increasing area of the sample, which undoubtedly causes the increase of levitation height. To show these effects, at the same time, the levitation heights are shown as a function of mean pressure in the gas film, being the quotient of the weight of the samples in [N] and the area in [m\({}^{2}\)]. This result is shown in Figure 17, and it again shows the dependence of the heights on the amplitude.
## 2 Existing ASs and the Related Market Design
### Categories of ASs  
Maintaining a commitment to established standards and regulations is imperative in managing the quality of electrical energy from its production to end-user consumption. As mentioned before, in traditional systems characterized by unidirectional power flow, ASs are provided by production facilities under the supervision of TSOs, aiming to regulate voltage and frequency parameters, ensuring they remain within predefined limits. A general classification of these ASs is depicted in Figure 1[34].
## 3 Method
### Policy Training  
At time step \(t\), the robot agent takes an action \(a_{t}\) according to current state \(I_{s,t}\) and policy \(\pi_{\Theta}\), receives reward \(r_{t}\), and moves to the next state \(I_{s,t+1}\). Repeating the above procedure, an episode trajectory is obtained \(\tau:\left\{I_{s,0},a_{0},r_{0},\cdots,I_{s,t},a_{t},r_{t},\cdots,I_{s,T}\right\}\). The reward function \(r_{t}\) is set to be  
\[r_{t}=\left\{\begin{array}{rl}-10,&\text{encountering obstacle},\\ 0,&d\leq\delta,\\ -d,&\text{otherwise},\end{array}\right. \tag{6}\]  
where \(d=\|x-x^{*}\|\) is the Euclidean distance between the robot (or gripper) position \(x\) and the target object center \(x^{*}\), \(\delta\) is the threshold to determine whether the agent reaches the target position  
Figure 3: Architecture of the designed neural network policy. Two stride steps and a filter size of 3 \(\times\) 3 are used for all three convolution networks. The ReLU nonlinear activation function is used throughout, with no pooling and no dropout techniques applied. The input is three RGB images from a simulated environment downsized to (240 \(\times\) 240) with 9 channels, and the output is action.  
Figure 2: Illustration of generating a task-relevant simulated environment. The target object is simplified to its geometry center of the corresponding regions in the semantic image. The robot (or gripper) is equivalent to a solid ball, the center and diameter of which are calculated from its semantic pixel region contour. The obstacles are completely preserved, and other irrelevant objects that cannot be objects are ignored.  
(\(\delta=1\) pixel). When encountering obstacles, the agent receives a reward of \(-10\). We adopt the DRL method of proximal policy optimization (PPO) [31] to maximize a surrogate objective \(L^{clip}(\theta)\):  
\[L^{clip}(\theta)=E[min(\frac{\pi_{\Theta}(a_{t}|I_{s,t})}{\pi_{\Theta old}(a_{ t}|I_{s,t})}\widehat{A}_{t},k(\varepsilon,\widehat{A}_{t})], \tag{7}\]  
where  
\[k(\varepsilon,\widehat{A}_{t})=\left\{\begin{array}{c}(1+\varepsilon) \widehat{A}_{t},\widehat{A}_{t}\geq 0\\ (1-\varepsilon)\widehat{A}_{t},\widehat{A}_{t}<0\end{array}\right.. \tag{8}\]  
\(\varepsilon=0.2\), \(t\) specifies the time index in \([0,T]\), \(\pi_{\Theta old}\) denotes the old policy before the update, and \(\widehat{A}_{t}\) is the estimated advantage function \(A_{t}\),  
\[\begin{split}\widehat{A}_{t}=r_{t}+\gamma r_{t+1}+\cdots+\gamma^ {T-t+1}r_{T-1}+\\ \gamma^{T-t}V_{\varphi}(I_{s,T})-V_{\varphi}(I_{s,t}),\end{split} \tag{9}\]  
where \(V_{\varphi}(I_{s,t})\) is the estimated value function for state \(I_{s,t}\), and the parameter \(\varphi\) is updated by regression on mean-squared error,  
\[\varphi=\arg\min E(\sum_{\tau}\sum_{t=0}^{T}\big{(}V_{\varphi}(I_{s,t})-R(t) \big{)}^{2}). \tag{10}\]  
The policy parameter \(\theta\) is updated by  
\[\theta=\theta+\alpha\
abla_{\Theta}L^{clip}(\theta), \tag{11}\]  
where \(\alpha\) is the learning rate.
# Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach
## 2 Literature Review  
### Spacing Policy  
To completely take advantages of platoon, several spacing policies are proposed which can be roughly divided into three categories.  
The desired spacing in CD policy between vehicle nodes is a preset constant which won't be affected by driving state as shown in Equation (1) and the \(d_{cd}\) denotes the preset space in platoon [9].  
\[J_{ct}=d_{cd} \tag{1}\]  
CTH policy is suggested as a safe practice for human drivers and is largely used in adapted cruise control [10]. The desired spacing determined by CTH is a function of velocity \(v_{i}\) and a preset time headway \(t_{\tau}\), as shown in Equation (2).  
\[J_{cth}=d_{cth}+t_{\tau}v_{i} \tag{2}\]where \(d_{cth}\) denotes the minimum safe space between the vehicles in standstill in CTH policy.  
NLD policy [5; 11] consider the host vehicle as a non-linear function. Human range policy is extracted from the test database and is found to be in the form of a quadratic curve, as shown in Equation (3). Thus, the NLD is transformed into an optimal problem.  
\[J_{nld}=d_{nld}+Tv_{i}+Gv_{i}^{2} \tag{3}\]  
where \(d_{nld}\) denotes the minimum safe space between the vehicles in standstill in NLD policy, \(T\) and \(G\) are constant coefficients.  
Through the mathematic analysis of different spacing policy, the CTH policy can guarantee the string stability without the acceleration information from the reference vehicle. However, the traffic flow stability and road capability are weakened in comparison with the CD policy because that CTH policy can't maintain the traffic flow stability. NLD policy has potential to improve traffic stability and capability through the optimization of the control parameters. According to the current researches, we may find that the space policy will strongly affect the following performance:  
1. The choices of the desired spacing will influence the traffic capability and traffic stability [12].
2. The aerodynamic drafting effect will result in the significant reduction of the fuel economy [13; 14; 15]. It requires that the spacing in platoon should be small enough.
## 3 Weighted Wavelet Fusion Approach
### Contrast Enhancement Based on CIELAB Color Space  
After color correction, the image still exhibits low contrast, a limitation rooted in the physics of underwater imaging. Consequently, additional contrast-enhancement techniques  
Figure 2: Distribution of RGB channel CDF curves for the original and MSRCR color-corrected images: (**a**) Original image; (**b**) CDF curves of original image; (**c**) MSRCR color correction image; (**d**) CDF curves of color correction image.  
are required. [PERSON] et al.'s imaging model expresses the total irradiance received by the camera as follows [27]:  
\[E_{T}(x,y)=E_{d}(x,y)+E_{f}(x,y)+E_{b}(x,y) \tag{3}\]  
where \(E_{d}(x,y)\) is the directly transmitted signal, \(E_{f}(x,y)\) is the forward-scattering component, and \(E_{b}(x,y)\) is the back-scattering component. Forward scattering occurs when light reflected from the scene is deflected by suspended particles yet continues roughly toward the sensor. The resulting blur softens edges and depresses local contrast. Backward scattering arises when illumination strikes particles and is redirected toward the camera. This veiling light elevates background brightness, lowers global contrast, and obscures the true color and shape of distant objects.  
Global histogram equalization (HE) is the simplest means of enhancing the contrast of underwater photographs; yet, by stretching the dominant gray-level range, it often overemphasizes specific regions, accentuates sensor noise, and introduces chromatic artifacts. Adaptive histogram equalization (AHE) improves on HE by computing histograms in sliding windows, but it still amplifies noise when local variance is low. CLAHE addresses this drawback by (i) partitioning the image into small, non-overlapping tiles and (ii) clipping each local histogram at a predefined threshold before redistribution. In our implementation, the clip limit was set to 2.0, which provided a good balance between local contrast enhancement and noise suppression in underwater scenes. The clip limit prevents excessive amplification and makes CLAHE the de facto standard for localized contrast enhancement in underwater imaging [28].  
To decouple luminance manipulation from chromatic information, the RGB frame is first mapped to the device-independent CIELAB color space defined by the International Commission on Illumination in 1976 [12]. In this perceptually motivated space, \(L\) encodes lightness, whereas the opponent axes \(a\) (green \(\leftrightarrow\) red) and \(b\) (blue \(\leftrightarrow\) yellow) describe chromaticity. Because the channels are decorrelated, applying CLAHE solely to the \(L\) component enhances the image contrast without altering the color balance established during the preceding correction stage.
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 5 Discussion
### Literature Review  
As illustrated in Figure 2a and Table 3, the PRISMA analysis identified 24 published documents (from journals and conferences) between 2007 and 2024. Although publications were sparse in the early years, a significant increase after 2022 indicates growing interest in this field.  
The geographical distribution of these publications, shown in Figure 2b, highlights Spain as the leading contributor (nine publications), followed by China (four publications).  
Regarding the methods implemented, Figure 2c shows that the 24 documents employed 28 methods, with some studies utilizing multiple approaches. When analysing the number of publications per method (Figure 2d), Genetic Algorithms (GAs) emerged as the most frequently used technique, appearing in ten publications, followed by Gradient-Based Algorithms, as detailed in Table 4.  
After the PRISMA systemic review, a compilation of existing heuristic methods available was performed on the literature and then summarized in Table 5, where it presents a list of 26 heuristic methods identified in the literature. However, only eight methodswere explicitly mentioned as being applied to thermal model correlation or reduction. This means that 65% of the methods listed remain unexplored, highlighting a significant knowledge gap. This study aims to bridge this gap by evaluating the performance of these global optimization methods in thermal model correlation and reduction.
# EBIC Imaging of Conductive Paths Formed in Graphene Oxide as a Result of Resistive Switching
## 3 Results and Discussion
This provides evidence of the direct link between the bright spots revealed by EBIC and the conductive paths formed during resistive switching. The typical normalized EBIC profiles of the same bright spot measured at \(E_{b}\) of 10, 15, and 20 keV are shown in Figure 4.0 It is clearly seen that the profile width essentially exceeds the e-beam size (which is about a few tens of nm) and increases with the beam energy. The full width at half maximum (FWHM) calculated based on the data in Figure 7 is equal to 1.2, 1.6, and 2.2 \(\mu\)m for 10, 15, and 20 keV, respectively. At \(E_{b}\) larger than 20 keV, the FWHM values do not increase further, but even show a tendency to decrease (Figure 8). As mentioned above, the number of carriers contributing to the induced current at a bright spot is 320 and 200 per one primary electron for 10 and 20 keV, respectively, while the total number of generated carriers at 20 keV is two times higher than that for 10 kV. This implies that the collection efficiency at 20 keV is about three times lower than that at 10 keV, and thus is in agreement with the theoretical predictions for the rectifying nanocontact [46; 47], showing that the collection probability strongly decreases with the distance from the contact. This allows to explain the observed FWHM dependence on \(E_{b}\). The width of the generation volume at z \(<R\) is roughly proportional to \((z^{3}/R)^{1/2}\), where \(z\) is the depth and \(R\sim E_{b}\)[1; 75] is the electron range [48]. Thus, while dimensions of the generation region are roughly proportional to \(R\) and increase with \(E_{b}\), the width of a part of the generated volume adjusting the contact decreases  
EBIC signal, increase with the increasing \(E_{b}\). Competition of these factors explains the non-monotonic dependence presented in Figure 4.1 Thus, the observed \(E_{b}\) dependence of the bright spot width supports an assumption of the formation of a nanosize rectifying barrier at the conductive path/Si interface. It should be noted that, for the rectifying contact, the depletion region width, which plays the role of the collector for the minority carriers (holes), can vary from a few hundreds to several thousands of nanometers depending on the barrier height and the dopant concentration [49]. It should be emphasized that the real size of the conductive channels can essentially be smaller.
# Numerical and Experimental Analysis of the Oil Flow in a Planetary Gearbox
## 2 Materials and Methods
### CFD Modeling of the Oil Flow
#### 2.4.4 Boundary Conditions and Numerical Settings  
The main kinematic characteristic of a planetary gearbox and is that the planets perform a roto-translation around the center axis. This means that the velocity of the planets is zero at the pitch point with the ring gear, and it increases linearly in the radial direction. The velocity contour plot that describes the kinematics is shown in Figure 5.  
A dedicated boundary condition that applies the roto-translation was implemented and assigned to the three planets. The planet carrier is assigned a boundary condition that applies a pure rotation around its center axis. The housing and the ring gears are fixed in space and do not undergo any deformation. With it being a closed system, the pressure and the volumetric fraction fields have a Neumann boundary condition, i.e., must be calculated. Wall adhesion models can be included by specifying the contact angle parameter (angle formed by a liquid at the three-phase boundary where liquid, gas, and solid intersect). Factors such as porosities, surface roughness, and impurities can affect this parameter influencing the wettability of the wall surfaces. In the current work, the contact angle was not considered, and the Neumann boundary condition (_zeroGradient_) was applied to the alpha field on the walls.  
The PIMPLE (merged PISO-SIMPLE) algorithm was used since it exploits the possibility to use relatively high time steps with relaxation (SIMPLE) and to maintain the temporal information (PISO). The time step was set to 0.1\({}^{\circ}\) rotation of the planet carrier. In Table 5, the numerical settings used in the simulation are summarized.  
Figure 4: Flowchart of the implemented mesh-handling strategy.  
Figure 5: Velocity contour plot of the system in the central partition.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 2 Background Theory
### Wave Formation
#### 2.1.1. Pipe End  
The received signals from the pipe end are shown in Figure 1a-c. As can be seen in (a), a pure T\((0,1)\) wave is generated, since both the excitation sequence and the feature are perfectly axisymmetric. However, in the cases of (b) and (c), where the flexurals are created by imperfect excitation and from wave mode conversion, respectively, the received response is the superposition of T\((0,1)\) and flexural waves. The scattering from the pipe end would result in all of the energy of the wave to be reflected. Thus, a strong proportion of the received signal energy corresponds to the T\((0,1)\) wave, and small variations are caused by flexural waves.