esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of a Coupled Eulerian-Lagrangian Approach to the Shape and Force of Scientific Balloons
## 1 Introduction
By setting the balloon membrane as ALE elements, large deformations of the membrane can be simulated, while SPH particles are injected into the membrane to simulate the helium gas entering the zero-pressure balloon. Using this method to simulate the balloon during launching provides a force figure of the impact on the membrane. Although this approach can simulate the stress and shape changes of the membrane in small scientific balloons, it is challenging to apply to large scientific balloons. As the volume of the balloon increases, the number of mesh elements and SPH particles also increases, causing the computational effort to grow geometrically. Therefore, more suitable computational methods need to be found. The Coupled Eulerian-Lagrangian (CEL) method is a fluid-structure interaction analysis approach available in Abaqus/Explicit. The CEL method uses a Eulerian mesh to divide the fluid region and a Lagrangian mesh for the solid components, coupling the two through contact algorithms. The advantage of CEL lies in its compatibility with large deformations. Additionally, it can simulate the internal and external flow fields of the balloon and calculate the interaction between the changing membrane shape and the flow fields. This paper uses the CEL method to compute the static ascent and launching process of scientific balloons so as to verify that this approach can handle the fluid-structure interaction of the internal and external flow fields during balloon ascent. Subsequently, the CEL method is applied to simulate the dynamic ascent and launching process, as well as the sail shape condition during launching. Results such as the tension, velocity, stress, and displacement curves are obtained, providing a valuable reference for the optimization of the design of actual launching tasks. This simulation method demonstrates high adaptability, not only accurately predicting the launching and launch processes of scientific balloons but also extending its application to the mechanical behavior analysis of other large-scale flexible structures. It offers an efficient and universal solution for related fields.
## 4 Experimental Studies at Laboratory Scale
### Overview
Laboratory experiments are designed to recreate real UHS conditions and test different combinations of rock types, gas mixtures, and p-T conditions [73]. According to [PERSON] et al. [32], multi-scale parameters are crucial in underground hydrogen storage. These parameters can be classified into three main categories: solid properties (such as permeability, porosity, and stress) (Section 3.2.1), fluid properties (including density, viscosity, solubility, and diffusivity) (Section 3.2.2), and solid-fluid interactions (such as wettability, interfacial tension, capillarity, mobility, saturation, adsorption-desorption dynamics, chemical reactions, biological/bacterial responses, and geomechanical properties) (Section 3.2.3) (Figure 7). Laboratory experiments aim to characterize these parameters and their evolution during the rock/gas/fluid integration process. Unlike other forms of hydrogen storage (liquid or cryo-compressed), the pressure and temperature parameters of compressed gaseous hydrogen storage make it possible to use geological formations. Typically, in both large-scale projects and laboratory research, storage facilities operate within a range of storage pressure between 5 and 20 MPa and temperatures between 30 and 120 \({}^{\circ}\)C (e.g., [110]), resulting in a relatively low volumetric hydrogen storage density of approximately 10 g/L [134]. Samples can be disaggregated or processed into specific geometric shapes. In the case of lithified sediments, disaggregation reveals fresh mineral surfaces, promoting geochemical reactions that are representative of natural reservoir systems and observable on laboratory time scales. These geometric shapes can also be used to study surface properties or conduct mechanical tests before and after exposure to gas. Occasionally, aqueous solutions with different salinities are used, and deionized water is used as a control for the chemical reactions between gas, water, and rock. Experiments are conducted with different rock-water ratios to evaluate the effect of mineral phase concentration on hydrogen-related geochemical reactions. Gas mixtures of different compositions are used. Control experiments are conducted with inert nitrogen to ensure that any observed geochemical reactions are induced by the presence of hydrogen.
## 2 Materials and Methods
### Surface-Air Temperature Difference (Ts\(-\)Ta)  
Surface temperature has long been used to understand vegetation water status [36]. As plants transpire, water loss reduces leaf temperature due to evaporative cooling. The surface temperature of vegetation often becomes much lower than that of the surrounding air in situations of optimum water supply. Conversely, in water-stressed situations, vegetation transpires less and vegetation surface temperature increases, usually rising above the surrounding air temperature [37]. The difference between the surface and air temperatures (Ts\(-\)Ta) is therefore considered a logical variable to assess vegetation water status.  
The air temperature data, close to the time of the satellite overpass, were sourced from the Bureau of Meteorology (www.bom.gov.au, accessed on 8 December 2023) and the SILO website (www.longpaddock.qld.gov.au/silo/, accessed on 8 December 2023) for multiple weather stations across and surrounding the study area. The point data of air temperature (Ta) were rasterized, using inverse distance-weighted (IDW) method to match the Ts from satellite.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 5 Algorithm Design and Implementation
### Initial Solution  
The initial solution is constructed as shown in Figure 5 using integer encoding to generate the initial vehicle routing and drone paths. The drone path is encoded using a multi-layer chromosome, where the first layer of the chromosome represents the drone takeoff point, second layer represents the drone service points, and the third layer represents the drone landing point. The implementation steps are as follows:  
Figure 4: Flowchart of HGAVNS-LM.  
Step 1: First, insert the distribution center 0 at the first position of the vehicle routing. Then, check whether placing the next customer point in the vehicle routing satisfies the vehicle-restricted area requirements. If it satisfies the conditions, proceed to Step 2. If not, move the customer point to the last position of the unvisited customer points and continue this process until all customer points have been visited, generating the vehicle path. Then, proceed to Step 3.  
Step 2: Check whether the vehicle's capacity and battery status requirements are met. If they are, insert the customer point into the vehicle routing. If not, insert the distribution center 0 into the vehicle routing and reassign the vehicle for delivery.  
Step 3: Identify the customer points that satisfy the drone's weight constraint and construct a set of customer points that can be served by the drone. From the existing vehicle route, find all possible drone takeoff and landing points, and construct a set of feasible drone paths. Then, proceed to Step 4.  
Step 4: Traverse the set of drone paths and identify those that meet the weight, battery status, and no-fly zone requirements. If multiple drone paths satisfy the conditions, select the optimal drone path. For those customer points that cannot be served by any drone, return them to their original position in the vehicle routing.
# A Compact Triple Band Antenna Based on Multiple Split-Ring Resonators for Wireless Applications
###### Abstract  
In this paper, a compact multi-split-ring resonator-based antenna is presented for wireless applications. The proposed antenna integrates multiple resonators to achieve multiband operation, where each resonator corresponds to a specific frequency band. A theoretical analysis is conducted to model the equivalent circuit of the proposed antenna, followed by an analytical study to calculate the resonant frequency of each resonator. By integrating these resonators, the proposed antenna achieves a compact size of 23 \(\times\) 24 \(\times\) 1.6 mm\({}^{3}\) (0.19 \(\times\) 0.2 \(\times\) 0.01\(\lambda^{3}\)), resulting in a size reduction of 81.6% compared to a conventional patch antenna, while maintaining gain, improving bandwidth, and providing excellent impedance matching. The proposed antenna covers the 2.4-2.8 GHz (14.55%), 3.25-3.75 GHz (14.28%) and 4.5-7.84 GHz (54.13%) frequency bands, providing acceptable gains of 1.5 dBi, 2 dBi and 3.2 dBi, respectively. The antenna was designed with CST, its performance was verified with HFSS simulations and it was validated with an equivalent circuit in ADS. Finally, the antenna was fabricated to confirm the accuracy and reliability of the simulation results, and it was found that the measurements agreed well with the simulations. This multiband functionality, combined with a compact form factor and simple feed line, makes the antenna cost-effective, easy to manufacture and suitable for various wireless communication applications, including 5G sub-6 GHz mid-band (2.5/3.5/5/5 GHz), RFID (2.45/5.8 GHz), WiMAX (2.4/3.5/5.8 GHz), Wi-Fi 5/6/6E (2.4/5/6 GHz) and WLAN (5.2/5.8 GHz).  
compact antenna; equivalent circuit; multiband antenna; metamaterial; splitting resonator; wireless communication +
Footnote †: journal: Electronics Letters
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Cohesive Subgraph Search
#### 4.3.5 Community Search  
Community search involves identifying dense subgroups in a given graph and aims to find the best local community for a given set of seed vertices. For dynamic environments, Refs. [88; 146] investigate evolving community structures, while Ref. [89] introduces Local Weighted-Edge-based Pattern Summary to characterize homogeneous regions in weighted graph streams which aims to tackle the problem of discovering dynamic communities in weighted graph streams. And then, Ref. [84] presents an algorithm for dynamic seed set expansion, which incrementally updates the community as the underlying graph changes.  
The study in [85] proposes Agglomerate, an innovative algorithm for dynamic graphs that adapts the parallel CNM approach [147] from [10]. This method utilizes a greedy static strategy to maximize modularity while maintaining a merge history for backtracking capabilities. The most relevant comparative work is [148], which similarly develops two dynamic algorithms for updating CNM-based clusterings.  
Advances in community detection include three distinct approaches: Ref. [87] utilizes personalized centrality measures for local community identification and seed set expansion, while Ref. [86] develops a fine-grained incremental algorithm tracking community evolution through modularity optimization. Complementing these, [PERSON]. [149] introduces an adaptive multi-centrality aggregation method specifically targeting influential nodes and overlapping community structures in dynamic networks.  
Recently, The work in [83] introduces a parallel dynamic frontier algorithm that efficiently identifies an approximate set of impacted vertices during batch edge updates (insertions/deletions) with minimal computational overhead.
## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Tiling
#### 3.2.4 Viewport Adaptive Streaming  
In [12], The adaption algorithm initially chooses the video's Quality Emphasized Region (QER) based on the viewport center and the Quality Emphasis Center (QEC) of the available QERs. Each QER-based video is composed of a pre-processed collection of tile representations that are then encoded at various quality levels. This allows for faster server maintenance (fewer files, resulting in a smaller media presentation description (MPD) file), a simpler selection procedure for the client (through a distance computation), and no need to reconstruct the video prior to viewport extraction. However, improved adaption algorithms are required to predict head movement, as well as a new video encoding approach to do quality-differentiated encoding for high-resolution videos.
## 1 Introduction
Specifically, nickle-based super alloys such as IN718 are used for critical applications like gas turbine blades, aircraft engines, etc., where safety is the highest priority, and addressing false positives or negatives are critical for NDE. Developing standards for inspection such as ASTM E3166-20e1 [26], is dependent on the ability to consistently measure the baseline response and use it to differentiate between defects. Among all the NDE methods, ultrasonic testing is one of more favorable techniques due its volumetric assessment, and its sensitivity to features spread within the bulk-micro length scale. Three parameters are typically used in ultrasonic testing: ultrasonic velocity, attenuation and backscatter. The ultrasonic velocity of a sample is dependent on the elastic properties of the constituents including phases and other inclusions [27; 28; 29]. The attenuation in a material and velocity are related through the dispersion characteristics described by the Kramers-Kronig relationship [30]. The phase velocity is a good indicator of the presence of any microporosity and other scatters as demonstrated earlier by several researchers [31; 32]. Similar to the attenuation coefficient, the acoustic energy that is scattered from grains and inclusions and picked up by the transducer is characterized by the backscatter coefficient. The backscatter coefficient has been used to obtain a quantitative and qualitative measure of grain size [33; 34], influence of multiple phases and the morphology of grains including elongated grains [35]. Most of the existing studies in literature on ultrasonic NDE have focused on characterizing samples made using powder bed fusion (PBF) and only a handful of articles have focused on laser DED [36; 37; 38; 39]. Ultrasonic wave propagation in complex metastable structures (like DED processed structures) is relatively unknown and poorly understood. The objective of the present study is to use UT to characterize the as-fabricated state of IN718 fabricated via laser DED process. This article reports on the use of three parameters to characterize DED processed IN718 structures: (a) ultrasonic velocity, (b) an attenuation coefficient, and (c) a backscatter coefficient. We further compare the baseline ultrasonic properties of the DED samples to the ultrasonic properties of conventional IN718 sample reported in literature. Conjectures were developed based on the observations on the velocity, attenuation and backscatter coefficient based on known theories. To the authors' knowledge, this is the first comprehensive work that reports on all the ultrasonic properties of DED processed IN718. Therefore, the focus of the article is only on the nondestructive characterization and developing conjectures.
# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
## 4 Experimental Results
### Experiment of Point Target Simulation Data
At the same time, in order to compare the calculation efficiency of various methods,  
Figure 4: Imaging results of point target simulation data via comparison methods: (**a**) matched filtering method; (**b**) 1D-SR method; (**c**) MB-SL0 method; (**d**) CNN method. Figure 5: Imaging result of point target simulation data via MIMO-LI-Net.
## 2 Materials and Methods
### Calculation of Brightness Temperature in Biological Tissues  
The brightness temperature is determined by the volume integral [2; 12; 59; 72; 90; 94]  
\[T_{B}=\int\limits_{V_{b}}\Omega(x,y,z;\upsilon)T(x,y,z)dV, \tag{12}\]  
where \(V_{b}\) is the volume in which the microwave radiation is formed and then intercepted by the antenna (see Figure 8). The quantity  
\[\Omega=\frac{P_{d}(x,y,z;\upsilon)}{\int_{V_{b}}P_{d}dV} \tag{13}\]  
under the normalization condition  
\[\int_{V_{b}}\Omega dV=1\]  
determines the weight function in terms of the electromagnetic energy density  
\[P_{d}=\frac{1}{2}\sigma(x,y,z;\upsilon)\cdot|\vec{E}(x,y,z;\upsilon)|^{2}, \tag{14}\]  
where \(\sigma\) is the electrical conductivity. It is important to emphasize that the electromagnetic characteristics of biological tissues depend on the frequency of microwave radiation. A detailed description of the numerical implementation of the mathematical model is discussed in [12; 95].  
Figure 9 shows internal temperature distributions for models with different sets of parameters \(\left\langle\vec{\mathcal{G}},\vec{\mathcal{F}}\right\rangle\) for a fixed size and shape of the breast. A sufficiently strong spatial temperature variability is well distinguished due to small-scale heterogeneity of biological tissue. Characteristic scales of temperature inhomogeneity may be smaller than the size of the working area of the antenna of device RTM-01-RES used for medical measurements [32; 48]. This is the physical basis for switching to measurement schemes with a larger number of points compared to the classical scheme, which is based on only 22 points (see next subsection). These brightness temperature calculations do not include any contribution from the sternum in order to emphasize the influence of the internal structure of the breast.  
Figure 9: Brightness temperature distributions based on the results of simulations using a modified measurement scheme for four models (1—**a**,**e**; 2—**b**,**f**; 3—**c**,**g**; 4—**d**,**h**): top row—without tumor, bottom row—with tumor. Tumor parameters: \(Q^{(can)}\) = 35,400 W\(\cdot\)m\({}^{-3}\), \(R\) = 1 cm.  
Separately, we note that the development of mathematical and computer models of the temperature field dynamics in various organs/tissues has acquired particular importance in the last decade. This is due to the development of new generations of radiothermometers [56], among other things.
###### Abstract
Furthermore, CNNs are flexible, and pre-trained models can be used for new image recognition tasks [10]. The convolution layer is a crucial component of the CNN architecture responsible for feature extraction through a combination of linear and nonlinear operations. The layer requires an RGB image input with three dimensions (height, width, and depth), a filter, and a feature map. The feature detector or filter moves across the image and conducts mathematical operations to check for the presence of specific features. The filter is typically a 3 \(\times\) 3 matrix that is shifted by a stride across the image to extract distinct feature maps representing different characteristics of the input tensor. The feature detector's weights are shared as it moves across the image, allowing for fixed local feature patterns and spatial hierarchies of feature patterns and reducing the number of parameters to learn. The convolution layer's hyperparameters, including the number of filters, stride, and zero-padding, need to be adjusted before training to affect the output's volume size. A rectified linear unit (ReLU) transformation is applied to the feature map to introduce nonlinearity to the model. If one convolution layer follows another, the CNN structure becomes hierarchical [11]. The pooling layer is an essential component of CNNs responsible for reducing the input's dimensionality by decreasing its spatial size. Unlike the convolutional layer, the pooling operation does not carry any weights but applies an aggregation function to the values within the receptive field. The two most commonly used types of pooling are max pooling, which outputs the maximum value from a selected set of pixels, and average pooling, which returns the average value of the given pixel portion as an output. Max pooling is more commonly used nowadays and can be considered a denoising operation applied to the input. These layers limit the risk of overfitting and improve classification efficiency. The final crucial element of a CNN is the FC layer. This layer effectively processes and learns from the output of the previous layer, which typically represents complex, nonlinear combinations of high-level features. In an FC layer, each output node is directly connected to the previous layer's node, making it particularly effective for classification tasks. To produce a probability distribution as the final output, FC layers often use a softmax activation function. It should be mentioned that there are a wide range of pre-trained CNN architectures available for download, each specialized for different classification tasks. When applied appropriately to the input data, these architectures have demonstrated versatility and efficiency [9; 12; 13; 14].
## 3 Results
### Robot Platform Application  
When the robot walks on different terrains, different gaits have different effects on the robot's stability, performance, and energy consumption. The experiment showed that the gait can be changed based on the output of the terrain classifier. In the experiment, the hexapod robot walked for 30 s across three terrain types: asphalt, soil, and grass. The sampling period of the Kinect is 1 s. The gait pattern of the hexapod robot was set according to the output of the terrain classifier. The pseudocode of the gait transition algorithm is depicted in Table 4.  
The value of \(G\) has a great influence on the smoothness and efficiency of motion on different terrains. The terrain classification results, including gait value, leg current from robot legs SRL1, and attitude angle, are shown in Figure 13. From 0-5 s, the terrain is supposed to be asphalt. Thus, the robot moves in tripod gait. From 5-6 s, the robot is in transition gait and ready to stride across mixed terrain consisting of asphalt and soil. The value of the terrain curve is nominal, showing that the terrain is complex, e.g., 1.3 means the terrain is changing from type 1 to type 3. From 6-21 s, the robot moves forward with its current gait. Then, from 22-23 s, it changes gait to get ready for another terrain. Finally, from 23-30 s, the terrain is grass and the robot continues to move in a wave gait. Figure 12: Recognition results: (**a**) terrain with obstacles; (**b**) mixed terrain. The experimental results show that the robot can walk stably on a single-terrain type and transform its gait successfully according to different terrains. At the initial moment, captured images from the Kinect are classified by system. The confidence rating of the classified asphalt is greater than 30%, so the terrain image is judged by the system to be asphalt pavement in a single terrain. Similarly, at 14 s and 22 s, the system outputs a single terrain label for soil and grass. At 5 s and 22 s, the terrain image corresponds to the uncertain category and the highest confidence is less than 30%. Therefore, the terrain is supposed to be mixed, and image infilling method is used until the classification result meets recognition reliability requirements. The system outputs the labels of two terrains and causes the robot to make the corresponding gait transitions. The robot is switched off. Note: \(G\) is the walking gait; typically, 0.5 for tripod gait, 0.75 for quadruped gait, and 0.83 for wave gait. \(S_{D}\) is the confidence score; \(j\) refers to terrain type: 1 for asphalt, 2 for tile, 3 for soil, 4 for gravel, 5 for sand, 6 for grass; \(i\) is the serial number of images; \(B\) refers to the result of obstacle detection: \(B=1\) means there is an obstacle, \(B=0\) means no obstacle. \(T_{i}\) is the output label of the terrain classifier. Image infilling processing I represents the ILI module, and image infilling processing II represents the SPI module.