esa-sceva/satcom-chunk-collection huggingface dataset:
# XCT and DLW: Synergies of Two Techniques at Sub-Micrometer Resolution
## 2 Materials and Methods
### X-ray Computed Tomography (XCT) Setup  
A ZEISS Xradia 620 Versa XCT device with a nominal basic spatial resolution of about 500 nm, when using the 40\(\times\) optical magnification beamline [49], was employed. Scans of the DLW samples were acquired and reconstructed using the ZEISS Scout & Scan controller and reconstructor software (build version 16). A transmission tube (Nordson Dage Mark IV, Nordson DAGE, Aylesbury, UK) with a tungsten target and a nominal focal spot size of about 1.7 \(\mathrm{\SIUnitSymbolMicro m}\) (full width half maximum, specified by the manufacturer) was utilised. Images were captured by an Andor CCD camera (iKon-L Series, Andor Technology, Belfast, UK) with 1024 \(\times\) 1024 pixels at binning of two and a true physical pixel size of about 13.5 \(\mathrm{\SIUnitSymbolMicro m}\). Note that the achievable resolution of this setup is limited by the (unknown) scintillator  
Figure 3: Exemplary illustration of a slice through the tool paths and naming conventions for sample 2 shown in PrusaSlicer software, 8 outer walls in red/orange for the outermost wall, purple for the solid bottom layer and red the hatched infill every 4 \(\mathrm{\SIUnitSymbolMicro m}\). The shown sliced pyramid is scaled by a fourth.  
Figure 2: Partial Fractal octahedron structure (as used in sample 1) cut along the X and Y-plane for illustration purposes to highlight the complex inner structure; the shown object was manufactured via Fused Deposition Modelling.  
thickness deposited at the used objective [26]. While rotating a sample at 360\({}^{\circ}\) within the X-ray beam path, a set of radiographic projections was taken at distinct angular positions. The set of projections served as input data for a 3D reconstruction [50]. The imaging device parameters are summarised in Table 2. A low tube voltage was used to achieve the highest resolution possible. Samples were scanned out-of-substrate-plane. For image analysis, we used the software Dragonfly Pro 2020.2 (Object Research Systems) and VG-Studio Max 3.4.5 (Volume Graphics), and for X-ray attenuation and spectrum simulation--the software aRTist 2.10.0 (BAM Berlin) [51] was used. The X-ray energy spectrum was calculated using the following parameters: a tungsten-diamond transmission target with a 2 \(\upmu\)m thick tungsten layer on a 500 \(\upmu\)m thick diamond layer, maximum photon energy of 40 kV, vacuum window, 10 mm air filter. Parameters for the X-ray attenuation properties of used materials can be found in the description of Figure 4. Note that because of the applied X-ray setup, X-ray attenuation contrast is expected, accompanied by partial phase contrast (edge enhancement) caused by the flat X-ray cone beam angle in this particular ZEISS device [26].
# Sparse Multiuser Receiver Design in Large Scale Array System
## 5 Simulation Results
### Performance Simulation of Analog Matrix  
The traditional beam generated by the DFT matrix has excellent spatial selectivity signifying the high gain in the target direction but poor gain in other regions, which will lead to a sharp deterioration in the SNR of signals from users out of the target direction. Conversely, the method based on the proposed analog matrix shows excellent gain performance in all directions without horrible cases. The spatial responses comparing the DFT analog matrix and the proposed analog matrix are illustrated in Figure 2a. We simulate and compare the array gains obtained by methods based on the two matrices with the assumption of the hybrid array structure with six RF chains to service eight and ten users (see Figure 2a,b respectively). Obviously, for the DFT matrix, it can only form a fixed number of six beams pointing to six users separately, which results in seriously insufficient array gains for the remaining users. As shown in Figure 2a,b, users near \(0^{\circ}\) have a loss of nearly 25 dB in SNR, which causes serious deterioration of communication performance. For the proposed analog matrix, the simulation results demonstrate that the array allocates an independent beam for each user to ensure that the SNR of each user can meet the communication requirements. Despite the slight loss of 3 dB in array gains for target users, the proposed analog matrix is superior to the traditional method based on the DFT matrix with a performance improvement of 20-30 dB for the other users.  
Figure 3a,b, respectively, show the array gains when 6 RF chains are applied to serving 12 and 24 users. The red curve represents the array gain performance of the analog matrix proposed in this paper, and the blue curve represents the array gain performance of the DFT matrix. The incident angle of the user is uniformly distributed in the interval [\(-90^{\circ}\), \(+90^{\circ}\)]. Similar to the conclusion of Figure 2, when the number of RF chains is only half or a quarter of the number of users, the beam generated based on the DFT matrix cannot cover all users. For the users in the target direction of the beam generated by the DFT matrix, the performance loss is only about 5 dB when the proposed analog matrix is used instead. For the vast majority of users who are not pointed to based on the DFT matrix, the proposed  
Figure 2: Array gain of massive array based on the proposed GAMP and the DFT-based GAMP: (**a**) on the condition that \(K=8\), (**b**) on the condition that \(K=10\).  
analog matrix has a gain advantage of more than 20 dB. This shows that the analog matrix designing method proposed in this paper is advantageous for making fewer RF chains serve more users without sacrificing the SNR seriously.
# Design and Analysis of an O+E-Band Hybrid Optical Amplifier for CWDM Systems
## 1 Introduction
Finally, the transmission performance of the HOA was evaluated as a pre-amplifier in a CWDM system of eight OOK-modulated optical signals over 60 km of SMF and an aggregate data rate of 80 Gbps. We implemented the proposed HOA using the well-known commercial tool called OptiSystem [28]. The proposed HOA can be used to enable amplification in future optical access networks.
## 4 IDPSO Algorithm Analysis and Design
### Pareto Optimal Solution Set and _gbest Selection_  
In one update process of particle swarm optimization, there may be multiple particles in the population that are not dominated by other particles, and these particles constitute the Pareto solution set of this iteration. In this paper, the method in the literature [27] is used to construct the Pareto solution set, and its Pareto level 1 solution set is selected as the Pareto solution set in this algorithm.  
In each iteration of PDPSO, the Pareto non-inferior solution set is updated and maintained through an optimal set, \(\mathbf{S}\), so that the saved solutions are gradually closer to the real Pareto front and distributed as evenly as possible. In this algorithm, the optimal set, \(\mathbf{S}\), and _gbest_ are selected at the same time so as to ensure that individuals in the optimal set, \(\mathbf{S}\), have at least one particle _gbest_, so that the algorithm can search all Pareto fronts as far as possible and avoid local convergence. In the optimal set, \(\mathbf{S}\), each particle is a Pareto boundary-efficient solution, and particles do not dominate each other. Therefore, a _gbest_ selection strategy needs to be developed. The method is as follows: First, select particles with high fitness according to target \(f_{1}\). Secondly, if the number of particles chosen by \(f_{1}\) is greater than one, choose particles with high fitness according to target \(f_{2}\). Finally, if the number of particles chosen by \(f_{1}\) and \(f_{2}\) is greater than one, choose a particle with high fitness according to target \(f_{3}\).
## 3 Results and Discussion  
### Search for the Optimum Number of Gaussian Components  
The search for an optimal number of Gaussian functions was carried out among all available samples of data and was based on the analysis of the minimum achievable _NRMSE_ errors vs. number of Gaussians (Figure 2). In the methodology of the R peak decomposition run, an arbitrary _NRMSE_ threshold of 0.1% was applied as a stop criterion for the whole algorithm. In other words, it was decided that 0.1% of _NRMSE_ was a threshold of overfitting of a model. Each non-symmetric Gaussian (_NSG_) introduces four parameters, namely, center, peak, and two spread constants (Equation (2)). In Figure 2, a common relationship between prediction error (_NRMSE_) and number of _NSG_ is presented--the more the Gaussians, the more accurate the model is. The minimum achievable _NRMSE_ approximated 0.1% with three Gaussians and dropped below 0.1% for the four Gaussians; thus, three _NSG_ were chosen as the system with best accuracy, yet still preventing over-fitting. The optimized number of three _NSG_ accounting for three fractions of water was chosen for further analysis. An exemplary decomposition of the DSC curve into three components is shown in Figure 3.  
Figure 3: Representative three-component decomposition of the DSC curve, obtained from hydrated raw HPC—W\({}_{\text{c}}\) = 0.92 g/g; dark blue line—original DSC curve overlapped by a red line, that stands for the sum of Gaussians; yellow line—FW fraction of water; green line—NFW fraction of water; brown line—FBW fraction of water; light blue line—baseline.  
Figure 2: Optimal number of Gaussian functions, chosen with the 0.1% _NRSME_ criterion.
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 2 System Dynamics and Problem Statement
### Feasible Inputs for Quadcopters  
The actual thrust \(f_{t}\) that quadcopters can generate is limited to a certain range:  
\[0\leq f_{\min}\leq f_{t}\leq f_{\max} \tag{7}\]  
Among them, \(f_{\min}\) is non-negative and is usually limited by the lowest speed of the rotor motor during operation. The size of the angular velocity command is constrained within a three-dimensional sphere:  
\[\|\boldsymbol{\omega}\|\leq\omega_{\max} \tag{8}\]  
Among them, \(\|\cdot\|\) is the Euclidean norm, which can ensure invariance under rotation and is easy to calculate. This limitation is usually due to the saturation rate of flight control gyroscope components, or the working conditions of some loads installed on quadcopters.
## 2 Literature Review
### Max-Flow Problems in Network Optimization  
This section addresses max-flow problems in network optimization, focusing on maximizing flow through a network from source to sink under capacity constraints. We review key models and algorithms, such as the Ford-Fulkerson method, and their applications in optimizing network performance across various domains.  
The paper [28] develops a method for finding the maximum flow between source and target nodes in a network using the \"max-flow, min-cut\" theorem from graph theory. This theorem identifies a set of links that limits the flow of commodities from a given source to the target node. By applying this approach to transportation networks, the study effectively separates the network into source and target domains and directs commodity flow to its maximum capacity using the minimum number of edges. The results are applicable totransportation problems, particularly in minimizing the capacity of cuts and the cost of sensor placement for traffic data collection.  
The study [29] explores the maximum flow problem, a fundamental challenge in optimization theory that seeks to determine the highest feasible flow from a source to a sink in a network. It highlights various techniques developed for handling maximum flow, including the Ford-Fulkerson algorithm, [PERSON]'s algorithm, and methods such as the max-flow min-cut theorem, scaling algorithm, and push-relabel algorithm. This research presents a new approach for calculating maximum flow between source and target nodes and introduces an innovative algorithmic method for addressing transportation problems by minimizing costs. The proposed method is noted for requiring fewer iterations to reach optimal solutions compared to established meta-heuristic algorithms.  
The study [30] focuses on maximizing flow in wireless sensor networks (WSNs) by addressing bottleneck nodes with poor energy. It explores using mobile chargers (MCs) to improve sensor node energy in an energy-harvesting environment. By modeling the problem with linear programming (LP) and employing a heuristic approach to optimal MC deployment and scheduling, the study demonstrates that its method, bottleneck, effectively increases maximum flow. Simulation results confirm significant flow improvements through this approach.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 6 Simulation Results
### Performance Comparison
#### 6.3.5 Alive Nodes  
Figure 11 depicts the number of nodes alive in the network after a certain time period. At 20 s, the alive node number is different in each scheme, but remains almost within 470-500. However, the decrease is gradual in each scheme after a certain time period, which is obvious because of more and more data transmissions among the network nodes. WDFAD-DBR has a low number of alive nodes as it sends data to the neighbor node, which has further at least one more neighbor node. Thus, the immutable forwarder nomination leads to quick death of the node and creates a partition in the network; while RE-PBR has very strict criteria for neighbor selection based on the link quality, which also causes repeated selection and leads to void node creation. This void node creation is handled via A-DBR. CA-DBR, C-DBR and B-DBR all have almost the same alive node number.
###### Abstract  
The unique depositional characteristics of shale reservoirs lead to the extreme development of shale laminae, natural fractures, and other weak surfaces, which have an important influence on the hydraulic fracture extension behavior and the final fracture geometry. Therefore, because the deep shale laminar development and hydraulic fracture interaction theory and influence mechanism are not clear, this paper focuses on the analysis of the influence law of the ground stress difference, laminar surface cementation characteristics, laminar surface dip angle, and other factors on the hydraulic fracture extension pattern in shale reservoirs based on the cohesive unit method. Numerical calculation results show that (1) the hydraulic cracks are first expanded along the direction perpendicular to the minimum horizontal principal stress after starting to crack from the injection point and then turn to expand along the side of the laminar surface when encountering the laminar surface. (2) Three laminar dips of \(5^{\circ}\), \(30^{\circ}\), and \(60^{\circ}\) were compared and found. A certain ground stress difference is conducive to communicating more laminated joints and forming a more complex fracture network, while too large of a ground stress difference reduces the complexity of artificial fractures. (3) The smaller the inclination of the laminate surface is, the greater the positive stress it is subjected to, the more difficult it is for the laminate joints to undergo shear slip, and the more difficult it is to open. (4) Under the same ground stress combination state and laminar surface inclination, laminated cementation strengths of 2 and 0.5 MPa were compared, and it was found that the lower the cementation strength of the laminate is, the more likely the hydraulic fracture will be activated to shear slip damage. At the same time, the reliability of the simulation study on the effect of laminar surfaces on hydraulic fracture extension based on the cohesive unit method is verified by combining with the corresponding indoor hydraulic physical model test, which provides a reference for the accurate description of the interaction mechanism between laminar surfaces and hydraulic fractures.  
deep shale; laminar development; hydraulic fracturing; numerical simulation +
Footnote †: journal: Applied Sciences
# A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
## 3 Experiment
### Experimental Results
This underperformance suggests a significant domain gap between the model's training data (generic natural images) and the specialized noise characteristics of the dataset, which likely includes structured or non-Gaussian noise components not adequately addressed by the pre-trained weights. In contrast, a self-learning adaptation of the DnCNN framework, specifically tailored for synthetic aperture radar (SAR) imagery, exhibited remarkable denoising performance. This method leverages domain-specific self-supervised training on SAR data to adaptively learn noise statistics without requiring clean reference images. On the dataset, it achieved an SNR of 35.27 dB, a BSF of 7.62 and an SSIM of 0.71, surpassing both classical methods and the generic pre-trained DnCNN. The high BSF value indicates robust suppression of background noise while preserving structural details, a critical requirement for SAR applications where subtle features (e.g., terrain textures, man-made structures) must remain intact. The poor performance of the pre-trained DnCNN highlights the importance of domain adaptation in deep learning for denoising. Models trained on generic noise distributions fail to generalize to specialized imaging modalities like SAR. The success of the self-learning approach underscores the value of task-specific training strategies, particularly in scenarios where paired noisy/clean data are unavailable.
## 3 Data Analysis and Results
### The Parnitha Aftershock Sequence  
On 19 July 2019, at 11:13:15 GMT (Greenwich Mean Time), an earthquake of \(M_{w}=5.1\) struck Athens, the Capital of Greece. The mainshock's location parameters were obtained from the catalogue of [PERSON] et al. (2020) [36], summarized in Table 1. The event took place NW of the Thiassio basin. The aftershock distribution of the 436 events covers the region between longitudes \(23.47^{\circ}\) E-\(23.67^{\circ}\) E and latitudes \(38.05^{\circ}\) N-\(38.18^{\circ}\) N and characterizes aftershocks with magnitudes \(1.0\leq M_{w}\leq 4.2\). The catalogue of this earthquake with completeness magnitude \(M_{c}=1.0\), covers the period from the day of the main event up to 21 August 2020.  
In Figure 6a, \(q=1.71\) is obtained by fitting the \(Q\)-exponential function to the observed data up to a value close to \(T_{c}\). In the present aftershock sequence, there is a slight increase in the parameter \(q\) compared to the previous ones (see also Table 1). The deviation of the \(Q\)-logarithmic operation from the expected linearity is observed at a \(T_{c}\) value of \(\approx\)175,460 s (Figure 6b). In the early aftershock period, there are more interevent times with values lower than \(T_{c}\) indicating that the Tsallis entropy description dominates the aftershock evolution in the immediate to the main shock time. As time passes, some of the traits of the early aftershock sequence related to NESP are insignificant and BG statistical physics are recovered.  
Figure 5: (**a**) The interevent times CDF of the 2020 \(M_{w}7.0\) Samos Earthquake, for the aftershocks with \(M>M_{c}\). The scarlet solid stroke is the \(Q\)-exponential fitting with \(q=1.63\). The change of colors indicates the crossover between the NESP (blue circles) and BG statistics (green triangles). (**b**) The \(ln_{Q}\)(\(P\)-\(T\)) as a function of \(T\), where the red line presents the fitting with \(q=1.63\) and correlation coefficient 0.9942 up to \(T_{c}\). A \(T_{c}\) value close to 9761 s is suggested by the deviation from linearity. (**c**) Interevent time \(T\) evolution with time \(t\) since the main shock. The scarlet line illustrates the \(T_{c}\) value.
## 2 Design Principle and Experimental Scheme
### Experimental Scheme  
Cr, Co, Ni and Ta were used to prepare CoCrNiTa\({}_{\rm x}\) alloys, the purities for those elements are approximately 99.9%, then subjected to be melted by using vacuum arc melting casting. Then the alloys were melted at least five times to obtain homogeneity. The crystal structures were characterized by an X-ray diffractometer (XRD D8 Discover, Bruker, Karlsruhe, Germany), using Cu K\(\alpha\) radiation scanning from 30\({}^{\circ}\) to 100\({}^{\circ}\) at the speed of 3\({}^{\circ}\)/min.  
The samples were chemically etched by the aqua regia solution [HCl: HNO\({}_{3}\) = 3:1 (in vol%)]. The microstructures were revealed using the scanning electron microscopy (SEM, Gemini 500, ZEISS, Jena, Germany) equipped with the energy-dispersive spectrometer (EDS) and electron backscattered diffraction (EBSD). The average hardness of MEAs were measured by a Vickers micro-hardness tester (HMV-T2) under a load of 1000 g for 15 s and 5 different points were tested on each sample. Nanoindentation tests were performed to measure the mechanical properties of each phase employing nanoindenter with a Berkovich diamond at room temperature (Ti980 Hysitron, Billerica, MA, USA). Compression tests were carried out on \(\mathfrak{a}3\times 5\) samples at room temperature using a material testing machine (INSTRON 3382) with a strain rate of 1.6 \(\times\) 10\({}^{-3}\) s\({}^{-1}\)[16].