esa-sceva/satcom-chunk-collection huggingface dataset:
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 4 Simulation and Results
### Formation Moving Using Virtual Linkage
#### 4.1.1 Simulation Setup  
In order to complete this task and illustrate the effectiveness of the proposed approach, a virtual linkage which consists of two virtual links is designed (See Figure 11). It is worth mentioning that a virtual linkage with only one virtual link degenerates into a virtual structure with no reconfiguration ability. Therefore, a virtual linkage consisting of two virtual links can be considered as the smallest unit to illustrate the standard validity of the proposed approach. In this simulation, the configuration of the virtual linkage is predefined:  
\[\text{virtual linkage}\,=\,\left\{\begin{array}{l}\text{virtual link1}\,=\,\{\text{agent1, agent2, agent3, agent4, agent5}\}\\ \text{virtual link2}\,=\,\{\text{agent5, agent6, agent7, agent8, agent9}\}\end{array}\right. \tag{11}\]  
By controlling the virtual joint at 180\({}^{\circ}\), the nine agents are able to maintain a line formation. Since the nine agents are required to align in a line formation with uniform distance 0.1 m, each virtual link then holds a length of 0.4 m in Figure 11.  
Recall that the position of the \(i\)th point in the \(j\)th virtual link coordinate frame is represented as \({}^{vj}p_{i}\), and the designed virtual linkage has the following points.  
\[\begin{array}{l}{}^{v1}p_{1}=[0,0],{}^{v1}p_{2}=[0.1,0],{}^{v1}p_{3}=[0.2,0 ],{}^{v1}p_{4}=[0.3,0],{}^{v1}p_{5}=[0.4,0]\\ {}^{v2}p_{5}=[0,0],{}^{v2}p_{6}=[0.1,0],{}^{v2}p_{7}=[0.2,0],{}^{v2}p_{8}=[0.3, 0],{}^{v2}p_{9}=[0.4,0]\end{array} \tag{12}\]  
The nine agents are randomly initialized at the region \(\{(x_{i},y_{i})|x_{i}\in[0,1],y_{i}\in[0,1]\}\).
# Continuous-Wave THz Imaging for Biomedical Samples
## 4 CW THz Tomography Imaging  
The idea of CW THz tomography imaging comes from the X-ray computed tomography (CT). CW THz computed tomography (THz-CT) is another interesting approach for three-dimensional (3D) THz imaging. The penetrability in THz radiation is poorer than X-ray, which provides a better contrast for biological soft tissue imaging. The internal structure of samples can be reflected more accurately, giving comprehensive three-dimensional information of biological tissues. THz-CT is a non-destructive detection method [71]. It detects the one-dimensional Fourier transform of the incident beam or scattering field at different projection angles and then calculates the one-dimensional Fourier transform of the projection of each angle. Finally, these data are combined to construct the two-dimensional Fourier transform of the cross-sectional image of the object. The commonly used data processing method is the filter back projection (FBP) algorithm.  
In 2014, [PERSON] et al. described a THz-CT imaging system based on an IJJ-THz emitter at 440 GHz and a bolometer detector as shown in Figure 12[72]. The emitted beam was converted to the parallel beam by the first off-axis parabolic mirror, PM\({}_{1}\); then, it is focused on the object plane by the second off-axis parabolic mirror, PM\({}_{2}\). The sample was a dried pea containing three seeds. The object was placed on the turntable and rotated 360\({}^{\circ}\) with a rotating speed of 10 mm/s. The scanning time was 10 min. The typical heart of a pea was seen to be made up of three ventricles, each of them with a thin-wall compose of triangular endothelium, and all of them pressed together. Three round seeds in the different ventricles were also visible at the bottom of the pea.
# Inter-Beam Co-Channel Downlink and Uplink Interference for 5G New Radio in mm-Wave Bands +
## 5 Simulation Results  
For the assumptions described in Section 4, we carried out simulation studies using the MATLAB environment. The results for the DL and UL scenarios are discussed in Section 5.1 and Section 5.2, respectively. Section 5.3 contains the comparison of inter-beam interference evaluation obtained based on the MPM and 3 GPP statistical model [14]. In this case, we chose the DL scenario to present exemplary results.
# Experimental Study on the Permeability of Microbial-Solidified Calcareous Sand Based on MICP
## 3 Results and Discussion
### Scanning Electron Microscopy (SEM) Analyses  
Due to space constraints, SEM tests were not conducted on all of the samples in this study, and only representative samples were selected for testing. Therefore, the cored samples, obtained from samples A1 (\(C_{c}\) = 1.5, \(C_{u}\) = 2) and B1 (\(C_{c}\) = 1.5, \(C_{u}\) = 8) after drying, were subjected to SEM tests to study the mechanism of the MICP treatment in different grain gradations of sand. Figure 9 show the images of the MICP-treated sands (A1 and B1) at magnifications of 200\(\times\) and 4000\(\times\), respectively.  
From the microscopic scanning results, the surface of the sand grains of the two samples became rough because the sand particle surface was coated with generated CaCO\({}_{3}\) crystals, and the particle pore spaces were partially filled with crystals. The CaCO\({}_{3}\) crystals were mainly in the form of rhombic crystals, which is consistent with other research results [52]. Meanwhile, the additional observation was the formation of CaCO\({}_{3}\) crystal clusters (Figure 9c) on the particle surface or in the interparticles [52]. The clusters or CaCO\({}_{3}\) crystals provide a bonding effect in sand grains that can fill pore places to reduce permeability or porosity. However, the cementation product coating on the particle surface is less helpful in reducing permeability and belongs to the ineffective CaCO\({}_{3}\) crystals [51]. The cementation products bonding with adjacent particles and filling pore spaces are effective CaCO\({}_{3}\) crystals, which can reduce the seepage space. A schematic diagram was drawn to better understand the ineffective and effective CaCO\({}_{3}\) crystals, as shown in Figure 10. This phenomenon can partially explain the smaller reduction in the permeability coefficient at MT = 5-7 (Figure 7).  
It is worth noting that despite the different particle gradations of the samples, the microstructure shows similar filling and coating effects on the surface of sand particles or interparticles after the MICP treatments. This phenomenon indicates that even if the initial pore space is larger (B1), after MICP treatment, the crystal filling allows more improvement in permeability and porosity, thus exhibiting a microstructure similar to that of the sample with smaller initial pore space (A1). Thus, with sufficient MICP treatment cycles, larger \(C_{u}\) provides more pore space for the filling of CaCO\({}_{3}\) crystals, thus leading to the significant reduction in pore space and lower permeability. However, larger \(C_{u}\) may also result in a smaller size of induced CaCO\({}_{3}\) crystals, which tends to result in poor solidification effectiveness [53].  
Figure 10: Schematic diagram of ineffective and effective CaCO\({}_{3}\) crystals in cemented samples.  
Figure 9: SEM images of MICP-treated sand (A1 and B1).
## 1 Introduction
Among the mechanical characteristics that influence clinical performance, flexural properties such as flexural strength, flexural modulus, and stress relaxation are particularly important, as they relate to an aligner's ability to deliver controlled forces without excessive deformation, maintain structural integrity, and sustain orthodontic force over time, all of which contribute to predictable tooth movement. These properties are commonly evaluated using the three-point bending test, a standardized method outlined in ISO 20795-2:2013, which applies to orthodontic base polymers, including thermoplastic materials [16]. This test assesses how aligner materials respond to flexural stress, using flat specimens that may be either untreated (\"as-received\", \"raw\") or thermoformed. Despite its widespread use, simplicity, and reproducibility, which make it useful for comparing material performance, there is considerable variability in how testing is conducted across studies. Differences in sample preparation, specimen dimensions, and thermoforming protocols contribute to inconsistent reporting of flexural strength and modulus across materials. Furthermore, clinically relevant conditions such as aging, moisture exposure, or intraoral temperature are not always considered, making it difficult to translate in vitro findings into meaningful clinical insights. A clear understanding of these flexural properties is essential, as insufficient flexural strength or suboptimal stiffness can compromise force delivery and potentially reduce the effectiveness of tooth movement during clear aligner therapy. The purpose of this scoping review is to identify in vitro studies investigating the flexural properties of thermoplastic clear aligner materials, summarize the methodologies used to evaluate these properties, and explore factors that may influence their clinical performance.
## 3 Performance Evaluation of the Proposed SAR Processor
### Timing Performance Analysis  
After evaluating the performance of the proposed SAR processor in term of precision, the processor was evaluated in terms of timing performance. In the SAR raw data, every single range consisted of 4096 samples, and the SAR sensor moved along the flight path to capture 2048 azimuth lines, meaning the produced SAR image has dimensions of 4096 \(\times\) 2048. Therefore, in the range compression, a 4096-FFT coprocessor was used in order to transform one line of range into the frequency domain and perform the matched filtering, followed by 4096 IFFT.  
Figure 16: The difference in azimuth-compressed data, SAR image (Range Profile) by MATLAB and the proposed SAR processor.  
Figure 17: Difference in azimuth-compressed data, SAR image (azimuth Profile) by MATLAB and the proposed SAR processor.  
The time taken for a single 4096 FFT /IFFT was 168.59 \(\upmu\)s. The time taken for complex multiplication in matched filtering for one range line of 4096 samples is 12.04 \(\upmu\)s (one clock cycle, 2.94 ns \(\times\) 4096 samples). The whole operation was repeated 2048 times in order to complete the range compression of the SAR raw data. Thus, the time taken to complete 4096 points FFT and IFFT for 2048 azimuth samples was 345.27 ms for each operation, while the complex multiplication took 24.66 ms. The processing time spent for such SAR raw data samples sizes are tabulated under the range-compression column in Table 2. Meanwhile, the azimuth compression was repeated in the same manner, except a 2048-FFT coprocessor was used, as there was a total of 2048 azimuth lines. The time taken for a single 2048 FFT /IFFT was 78.27 \(\upmu\)s. Hence, the time taken to complete 2048-point FFT and IFFT for 4096 range samples was 320.6 ms for each operation, while the complex multiplication was the same, at 24.66 ms. The processing time spent for the azimuth compression is tabulated under the azimuth column of Table 2. The total summation of the time is 474.31 ms, which represents the total time required by the proposed SAR processor in order to process an SAR image.
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 3 Optimal Control for Expected Total Rewards
### Grid Energy Consumption and Throughput Trade-Off  
Figure 7 shows the grid energy consumption and throughput when \(\omega_{t}\) takes different values. When \(\omega_{t}\) is very small, the energy consumption and throughput of all schemes are similar. In this case, the system has a high demand for grid energy consumption, which will impose strict limits on energy consumption. When \(\omega_{t}\) increases, the throughput plays an increasingly important role in the reward. Although our schemes consume a little more energy than the JPLA and PCA, it greatly improves the throughput. When the value of \(\omega_{t}\) is large, all schemes pursue maximum throughput regardless of energy consumption costs. Therefore, all throughput gains are very close. However, the BIBAoSE consumes the least energy and is closest to the JPLA-F and BIBAoSE-F. In addition, once the throughput constraints are given, we can find the value of \(\omega_{t}\) and get the minimum grid energy consumption.  
The energy consumption and throughput are shown in Figure 8. As can be seen from the graph, the BIBAoSE consumes less grid energy than the JPLA when achieves the same throughput. And the BIBAoSE can transmit more bits than the JPLA with the same grid energy supply. In short, the  
Figure 5: The total rewards versus time slots, \(\lambda=2\) and \(\omega_{t}=1\).  
Figure 6: The total rewards versus energy harvesting intensity, N = 30 and \(\omega_{t}=1\).  
BIBAoSE has a better trade-off between energy consumption and throughput, which is closer to the ideal situation such as the JPLA-F and BIBAoSE-F.
## 4 Experiment and Results
### Results
#### 4.2.4 Result of the Evaluation  
In this study, the point cloud generated by the 3D reconstruction was converted to 3 DTiles format and imported into a seismic information visualization system, which was developed based on Cesium. In Figure 6, the model is marked with a Web page as a carrier to visually view the results of the assessment of the disaster situation. Address at www.peteralbus.com:8085 (accessed on 29 August 2022).  
Based on the results of the above comparison, in this experiment, the reconstruction results of the CasMVSNet network were selected to evaluate the damage grade of 153 houses in the Jinniu Village area according to the EMS-98 standard. Furthermore, the proportion of the number of damaged houses at each grade to the total number of houses was calculated and compared with the evaluation results of other methods in this area. In total, two comparison methods were involved. The first was the result obtained by [PERSON] et al. In this research, visual interpretation was conducted on orthophotos of UAV images of the old street near the Yunlong Bridge in Yangbi County [37]. Another method was visual interpretation using spliced orthophotos. The comparison results are shown in Table 4. As can be seen in the table, our results are very close to those of the other two methods. Compared to the results of the field survey, the relative error was 1.3%, 1.0%, 0.6%, 1%, and 0.6% for G0, G1, G2, G3, and G4, respectively, which indicates the effectiveness of the proposed method.  
Figure 5: Visualization results of various 3D reconstruction methods. (**a**) \(D^{2}\)HC-MVSNet; (**b**) AA-RMVSNet; (**c**) CasMVSNet.  
Figure 6: Results of the damaged assessment in Jinniu Village, Yangbi.
## 2 Geological Setting
### Geology of Fe-Ti-V Oxide Deposits  
The Fe-Ti-V deposits were discovered in the NE part of Poland within the crystalline basement mafic rocks through an intense drilling exploration program launched in the 1960s-1980s. Two large Fe-Ti-V deposits, the Krzemianka and Udryn deposits, were documented within the eastern part of the Mesoproterozoic SAM of the AMCG affinity  
Figure 1: Schematic geological map (without Palaeozoic–Cenozoic sedimentary rock cover) of the Suwalki Anorthosite Massif (modified after [9]) with the location of the Krzemianka and Udryn Fe-Ti-V deposits in NE Poland.  
(Figure 1). The AMCG suite is unconformable, covered by Mesozoic-Cenozoic sedimentary rocks of thicknesses varying from 500 m on the east to 1000 m on the north-west [9,12]. Anorthosite and norite intrusions compose the core part of the massif and are surrounded by diorites and gabbronorites [9,12]. The SAM is divided by major faults of variable direction into three tectonic blocks [22]. The western one, with the Krzemianka and Udryn Fe-Ti-V deposits, is uplifted.  
The ilmenite-magnetite ores are located in tectonic nodes and fill tectonic fractures at the gabbronorite-diorite-anorthosite contacts [21,22]. The deposits under consideration here are made of titanium-and vanadium-bearing magnetite, containing ilmenite and hematite-ilmenite, occurring in various proportions from 1:1 to 1:10 [23]. Hematite-ilmenite series can be found, disseminated in the host rocks. Ilmenite grains or aggregates contain two systems of hematite exsolutions [23,24,25,26,27]. Five types of Fe-Ti ores were distinguished: norite ore and poor pyroxene ores (15-25% Fe), pyroxene ores (22-25% Fe), plagioclase ores (25-35% Fe), spinel ores (35-48% Fe), and brecciated ores [28]. The richest parts of orebodies, called \"ferrolites\", are characterized by Fe-Ti oxide concentrations exceeding 40% of the rock volume. The ore-oxides represent two solid solution series: magnetite-ulvospinel and ilmenite-hematite, depending on the P-T and oxidation conditions in Fe-Ti-V ores and their host rocks [23,24,29].  
In total, the Krzemianka and Udryn deposits have resources estimated at about 1.34 billion tons of Fe-Ti-V ore, containing approximately 388.2 million tons of iron (Fe\({}_{2}\)O\({}_{3}\)), approx. 98 million tons of titanium (TiO\({}_{2}\)), and approximately 4.1 million tons of vanadium (V\({}_{2}\)O\({}_{5}\)), [30]. They are classified as sub-economic on account of their low metal contents, especially of vanadium (0.26-0.31% V\({}_{2}\)O\({}_{5}\) on average) and because of their occurrence at a depth exceeding 300 m [31]. However, the Fe-Ti-V mineralization contains lower contents of Fe-Cu-Ni-Co sulphides (1-3% by rock volume) and some amounts of REE carbonates and other trace elements [32,33,34,35,36]. The obtained Re-Os model age for pyrite, pyrrhotite and magnetite is 1536 \(\pm\) 67 million years [37,38]. The Krzemianka and Udryn deposits belong genetically to the iron deposits of the Allard Lake type [23]. In addition, sub-economic Fe-Ti-V mineralization has also been identified at the jezioro Okragle and Jeleniewo prospects [28].
# Mechanical Strength Characterization of Plastic Fiber Reinforced Cement Concrete Composites
## 3 Results
### Pre-Peak Strain Hardening Toughness  
It is well represented in stress-strain curve given in Figure 8 that the pre-peak strain hardened region of concrete specimens is a good indicator of continuous load carrying capacity with significant nonlinear region. Strain hardening of the concrete is a function indirect measure of micro-crack generation with the corresponding increase in the ultimate strength of the composite. However, compared to the reference concrete, the pre-peak and post peak characteristics of all plastic fiber substituted concrete mixes were appreciably improved. The overall toughness characteristics were primarily influenced due to the controlled failure offered by plastic fibers.  
Figure 8: Stress-strain plot of various fiber concrete mixes in compression.  
The sudden failure in brittle plain concrete (RCM-30) was altered suitably in plastic fiber substituted concrete mixes. A maximum pre-peak strain hardening toughness of 192.38 N-m was observed for the PFC-05H concrete mix and a maximum post-peak toughness of 357.75 N-m was observed in the case of the PFC-10H concrete mix. Nevertheless, a maximum overall toughness of 516.38 N-m was obtained in the case of PFC-10H. The test results justify the fact that effective fibers in tension zone confined concretes were able to control the originating cracks in the matrix and at large deformation (post-peak), bridge the crack opening. Fibers present in the matrix tended to delay the crack origination and subsequently control the crack multiplication in the matrix, leading to improved first crack stress. Additionally, the overall toughness increase of 321.5% was noticed in the case of the PFC-10H concrete mix. Thus, the parameters measured in this study signify the importance of plastic fiber action in the matrix.
## 2 The Proposed Methods
### KAMS Model Network  
The KAMS model presented in this paper utilizes a lightweight 1D-MobileNet convolutional network embedded with an SE block. The 1D-MobileNet network efficiently extracts features from one-dimensional vibration signals, while the SENet module enhances feature representation and classification performance by adaptively adjusting channel weights. This achieves the dual objectives of maintaining computational efficiency and a lightweight model design while significantly improving the accuracy and robustness of bearing fault diagnosis. The network structure is shown in Figure 4. The architecture consists of two depthwise separable convolution layers, one SE block module, two max pooling layers, and one adaptive average pooling layer. Each depthwise separable convolution layer first performs independent depthwise convolution (DW Conv) and then merges channel information through pointwise convolution (PW Conv). The SE Block attention module generates attention weights for each channel using adaptive average pooling and fully connected layers, which are then applied to the input data. The max pooling layers reduce the length of the feature vector, and the fully connected layer uses the softmax function to classify the samples based on the flattened feature vector. In this paper, the input vibration sample signal is of size \(1\times 1024\). The specific parameters of  
Figure 3: The structure of 1D depthwise separable convolution.  
the model are outlined in Table 1. After comparing different positions for embedding the SE block, it was found that placing it after the first pointwise convolution layer produces the best results. The selected convolution kernel size strikes an optimal balance between classification accuracy and the number of network parameters.
## 4 Numerical Results  
### Study of Natural Waves  
The numerical solution of (22) and the transcendental equation obtained from (20) after application of the boundary conditions are identical. This can be considered as numerical validation.  
The studied waveguide consists of two sub-structures, which support natural waves. One is a dielectric waveguide. The other is an infinite graphene plane. The set of natural waves splits into two families, which correspond to each sub-structure. We denote them as \(H_{1}\) and \(P_{1}\). Such splitting is also observed in other types of dielectric structures covered with graphene (see, for example, [34]). Figures 2 and 3 show propagation constants of the dominant (in the \(H\)-polarization case) natural waves \(H_{1}\) and \(P_{1}\) vs. the frequency for various values of the chemical potential and two values of the width of the dielectric slab, which forms the waveguide. Wave \(H_{1}\) is the natural wave of the dielectric waveguide, which experiences slight perturbation because of the presence of the graphene plane. Wave \(P_{1}\) is a plasmon natural wave. Its propagation constant significantly depends on the value of the chemical potential and is almost independent on the width of the dielectric slab. Field distribution of these two waves is represented in Figure 4. The waveguide walls are shown as vertical green lines. The electric and magnetic fields of plasmon wave \(P_{1}\) have visible maximum near the graphene plane; this is surface wave, which propagates along the graphene sheet. Magnetic field of \(H_{1}\) has maximum approximately at the center of the waveguide.  
Figure 2: Dependences of the propagation constant of the plasmon natural wave \(P_{1}\) on the frequency, \(\varepsilon=2.25\). (**a**) Real part; (**b**) Imaginary part.  
The strong dependence of the plasmon wave propagation constant on the chemical potential makes it preferable in our further analysis of the tunable properties of the waveguide loaded with graphene strips.