esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data  
A series of natural geographical and socio-economic factors were used to quantify the suitability of different land types, including elevation, slope, soil type, nighttime light value, population spatial distribution data, Gross Domestic Product (GDP) spatial distribution data, point density of point of interest (POI), and several variables based on proximity (Table 1). The land cover data are the global 30-meter fine land cover dynamic monitoring product produced by the team of [PERSON] and [PERSON] from 1985 to 2020 ([[https://data.casearth.cn/](https://data.casearth.cn/)]([https://data.casearth.cn/](https://data.casearth.cn/)), accessed on 27 December 2021) [32]. The update period of the land cover data is 5 years and includes 29 land cover types. According to the characteristics of land use/cover in Chongqing, the land use/cover map is divided into six categories: forest, cropland, grass, shrub, impervious surfaces, and water area. The GDP dataset is provided by the Resource and Environmental Science and Data Center ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), accessed on 8 November 2021) every 5 years. The variables based on proximity are Euclidean distances to roads, water area systems, and railways at all levels. The source data come from the national basic geographic dataset provided by the national geographic information resource directory service system. The ASTER GDEM V3 data ([[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)), accessed on 3 September 2021) set developed by METI and NASA and distributed to the public free of charge is used for elevation data. Slope data are calculated based on elevation data. POI data are provided by Gaode open platform ([[https://lbs.amap.com/](https://lbs.amap.com/)]([https://lbs.amap.com/](https://lbs.amap.com/)), accessed on 30 July 2020), including the location data of hospitals, railway stations, bus stations, parks and infrastructure, government agencies, highway toll stations, shopping malls, etc., and calculate various POI point density raster data in ArcMap10.7 software. Population data were extracted from the world pop website's annual global population dataset of countries with a resolution of 3 arc from 2000 to 2020, in units of the number of people per pixel. Nighttime light data are derived from the National Earth System Science Data Center ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)), accessed on 5 November 2021). This product has parameter properties
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 6 Open Issue and Future Direction
### UAVs  
Advanced wireless communications will face several challenges, particularly in the UAVs, since UEs serviced by UAVs are more likely to suffer interference from terrestrial BSs. Thus, the development of interference management strategies for UAVs is still an urgent issue. Therefore, we illustrate many crucial challenges in IM for future UAVs. First, the UAVs cannot handle the high-speed communication requirements due to new traffic demands without AI function. As a result, AI is critical to the development of B5G communication. The use of AI in UAVs can enable more adaptive interference coordination and optimize SE and EE in comparison to the current fixed RA strategy. Primarily by interacting with the environment, AI solves the issues of learning variations, classifying problems, forecasting future obstacles, and identifying possible solutions. Another challenge is designing a simple interference coordination strategy based on AI that can completely match the data. The derivation of this strategy, which contains several parameters, will be challenging to interpret and certain values may even be lost during practical application. In this case, the strategy based on AI will not have any positive impact on IM for UEs served by UAVs. Additionally, the adoption of complex interference coordination models to address issues necessarily increases the computational burden on UAVs and BSs. Despite the attractive efficiency of AI in the future network, the long training time and computational complexity are still urgently challenged [180; 181]. Second, the consumption of energy has become one of the critical challenges. Although RA can help to minimize the energy consumption of UAVs, several challenges remain. As a result, the developed methods to minimize interference and maximize EE for UAVs will be an important issue in future studies. Finally, although several efforts are being implemented to address the capacity needs of IM strategies in UAVs, the increasing demand for data will place additional strain on the front and backhaul links in the future.  
Existing UAVs have significant difficulties in minimizing interference in polynomial time as the number of UEs supplied by UAVs and terrestrial UEs increases. Therefore, a sub-optimum solution with less complexity should be suggested for future UAVs. For the backhaul connection, with the densification of heterogeneous nodes such as BSs, remote radio heads (RRHs), and SCs in terrestrial networks, a sophisticated self-organizing function should be used to enable the terrestrial nodes to operate intelligently and autonomously. Additionally, the restricted backhaul capacity influences the performance of IM strategies, since considerable amounts of data and control signals must exchange among BSs to coordinate their operations. In the future, an efficient strategy with less complexity should be implemented to improve the performance of UAVs with non-zero latency and restricted capacity [182].
## 3 Model Description
### The Fracture Weighted Formula  
During hydraulic fracturing, energy can well explain the fracture propagation, which includes the elastic modulus, normal stress, Poisson's ratio and so on [1]. Hence, the fracture weighted formula with energy will be innovatively deduced in this section. Energy can be divided into two categories: mechanical energy and interface energy. The mechanical energy is for keeping natural fractures open, and it is related to the fracture length. Interface energy is for creating new fractures, which is related to the fracture length [24]. The energies during fracture propagation are described by weighted value in the shortest path problem. Considering fracture length, fracture type and so on, the weighted values of fracture are different from each other. Mechanical energy should be considered in natural fractures. However, for new fractures, both mechanical energy and interface energy should be considered during computing the weight values of new fractures [24]. The derivation processes of the fracture weighted formulas with energy are described as follows:  
Assuming that fracture propagation length is \(c\) and the normal stress is \(P\). The expression is [24]:  
\[P=\sigma_{xx}\sin^{2}\beta-2\sigma_{xy}\sin\beta\cos\beta+\sigma_{yy}\cos^{2}\beta, \tag{2}\]  
where \(S[\sigma_{xx},\sigma_{xy},\sigma_{yy}]\) is in situ stress, and \(\sigma_{xx}\) and \(\sigma_{yy}\) are the \(x\) and \(y\) direction stress. \(\sigma_{xy}\) is shear stress and \(\beta\) is the dip angle, and the schematic describing the fracture and stresses is shown in Figure 6.  
Figure 5: The fracture intersection points of discrete fracture networks.  
Figure 6: Transformation of stress.  
Mechanical energy \(U_{M}\) is the sum of the bearing system potential energy \(U_{A}\) and elastic strain energy \(U_{E}\)[25]:  
\[dU_{M}=dU_{A}+dU_{E}=-P^{2}d\lambda(c)+\frac{1}{2}P^{2}d\lambda(c)=-\frac{1}{2}P ^{2}d\lambda(c), \tag{3}\]  
\[G=-dU_{M}/dc=\frac{1}{2}P^{2}d\lambda(c)/dc, \tag{4}\]  
\[\lambda(c)=\frac{2(1-\
u)}{G}\cdot c, \tag{5}\]  
where \(G\) is the energy per unit area or length [25], \(c\) is the length of fracture, \(\lambda(c)\) is elastic compliance, and \(\
u\) is Poisson's ratio. Generating Equations (4) and (5) into Equation (3), it is easy to obtain  
\[U_{M}=\frac{P^{2}c^{2}(1-\
u)}{G}. \tag{6}\]  
In another case, new fracture length is \(c\), then, interface energy \(U_{S}\) is:  
\[U_{S}=4\cdot c\cdot\gamma, \tag{7}\]  
where \(\gamma\) is the surface energy per unit area or length.  
During fracture propagation, the weighted value of natural fracture is computed only by considering mechanical energy \(U_{M}\), which can be better understood by the energy of keeping the fractures open during gas extraction. The weighted value of new fractures is computed by combining mechanical energy \(U_{M}\) and interface energy \(U_{S}\).
# Sampling-Based Path Planning Algorithm for a Plug & Produce Environment
## 1 Introduction
In these algorithms, a new collision check function is responsible for adjusting the sampling value when an obstacle exists between the \(q_{near}\) and \(q_{mid}\) sampling points. For evaluation and comparison, the research presented includes sampling-based algorithms in different ways to test in a Plug & Produce environment, i.e., considering non-optimal and optimal path planning with active and passive algorithm classification. In this research it was purposed to:  
* Investigate suitable online path planning algorithms for a multi-agent-based manufacturing system;
* Validate if the developed novel algorithm (adaptive RRT*) has the same characteristics as existing sampling-based algorithms;
* Prove that it is the best-suited algorithm for online path planning, even though more constraints are present in the environment. The move-along time of the path generated and energy consumed by each path are measured and validated for optimal path planning in real time. There is importance in employing this research as an automated online path planner in the manufacturing industries, particularly in cases requiring extensive customization, as follows:  
* Cutting tool change applications in the production industries [15];
* Optimized online path planner for the kitting application in assembly lines [16];
* Used as an online path planner in wood manufacturing industries to construct unique walls [17];
* Use of all the above-mentioned path planners [15; 16; 17] as a path agent in the MAS to generate online paths, depending on the required application in the manufacturing industries [11].
## 4 Conclusions  
This paper investigates a microwave photonic channelization scheme based on signal polarization multiplexing, which modulates the broadband RF signals to be received onto orthogonal polarizations and then demodulates them separately. By utilizing two sets of dual-line optical combs with different FSRs, the scheme can simultaneously receive 16 sub-channels. Its greatest advantage lies in further improving the channelization efficiency of the OFC without increasing the number of comb teeth. The experiment shows that a broadband RF signal covering 2-18 GHz is completely received by 16 sub-channels with a bandwidth of 1 GHz. The in-band interference suppression ratio is 28-30 dB for all sub-channels and the SFDR is about 96 dB\(\cdot\)Hz\({}^{2/3}\). In practical applications, in order to achieve automatic polarization adjustment, negative feedback PC can be used, such as commercial polarization trackers (POS-002-E), which can be used in our proposed solution.  
All authors contributed in varying degrees to this paper. B.C.: conceptualization; investigation; methodology; and writing--original draft. J.W.: writing--original draft and editing (equal). Y.L.: methodology (equal). J.T.: methodology (equal). C.L.: supervision. Q.D.: supervision (equal). All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Key Research and Development Program of Shaanxi under Grant 2021 NY-213 and 2021 GY-096, the Special Scientific Research Program of Shaanxi Provincial Department of Education under Grant 23 JK0711, and the Xianyang Normal University Research Program under Grants 2023 YB62, XSGG202102 and XYSFXY2024023.  
The data provided in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
# Flexural Behavior of a 30-Meter Full-Scale Simply Supported Prestressed Concrete Box Girder
## 4 A Nonlinear Numerical Model for the Tested Box Girder
### Method of Analysis  
The load application was simulated by changing of the curvature, that is, increasing curvature for loading and reducing curvature for unloading. A flow chart of the analysis procedure is shown in Figure 9, and the detailed steps are as follows:  
Figure 8: Load–deflection curve.  
1. The beam is divided into \(n\)-many elements with the same length along the length direction, and each element is divided into finite strips in the height direction.
2. The moment-curvature-axial force \((M-\Phi-N)\) relationship is solved, by which the initial strain of each section under prestress is obtained.
3. The loading and unloading process of the prestressed concrete beam is simulated, and the bending moment-curvature \((M-\Phi)\) curve of the tested girder is obtained by the method of adding or reducing curvature, respectively.
4. Based on the moment-curvature \((M-\Phi)\) curve, by means of changing the curvature, \(M_{i}\), \(\Phi_{i}\), and \(P_{i}\) are obtained. \(M_{i}\), \(\Phi_{i}\), and \(P_{i}\) are the moment, curvature, and applied load at the \(i\)th load step, respectively.
5. Then, the bending moment of each element under applied load \(P_{i}\) can be calculated. Therefore, the curvature for each section of the element can be obtained from the \(M-\Phi\) curve.
6. \(\theta_{i}\) and \(\delta_{i}\) can be obtained, where \(\theta_{i}\) and \(\delta_{i}\) are the rotation angle and deflection of each section under the \(i\)th applied load \(P_{i}\), respectively.
7. Repeat Steps (4)-(6) to complete the whole analysis process for the flexural behavior of the tested girder.  
Figure 9: A flow chart of the analysis procedure of the suggested model.
## 5 Conclusions  
A planar AESA capable of producing a pencil beam with invariant sidelobe level over a wide angular range and at any frequency in C band has been designed with fully analytical techniques. An efficient MATLAB implementation of the Tseng-Cheng technique is presented that looks particularly attractive for realizing a software-defined-radar (SDR) interface for real-time control of the beamforming network. The radiating performance of a case study is analyzed in detail, showing the effects of a reduction in the filling factor. A smaller \(4\times 6\) demonstrator has been fabricated and measured to test the main radiating performance of the proposed antenna architecture. The flexibility of the polarization control, the low level of cross-polarization, and the good isolation between ports over a 10% fractional bandwidth make this prototype attractive for future SDR applications.  
Conceptualization, W.F. and A.G.; methodology, W.F. and A.G.; software, W.F.; validation, N.R., L.I. and W.F.; writing--original draft preparation, W.F. and N.R.; writing--review and editing, W.F., N.R., L.I. and A.G.; supervision, T.M., A.M.F. and A.G.; funding acquisition, A.M.F. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Leonardo S.p.A. under the contract COLB-CTR-2017-009-A.  
Not applicable.  
Figure 14: Gain obtained from the \(\Gamma_{act}\) measurements for the three main frequencies (\(\tilde{J}_{\text{min}}\), \(\tilde{J}_{0}\), \(\tilde{J}_{\text{max}}\)) on the planes (**a**) \(\phi=0^{\circ}\) and (**b**) \(\phi=90^{\circ}\) for H polarization. In (**c**,**d**), as in (**a**,**b**) but for V polarization. (**e**,**f**) The gain in dB obtained from \(\Gamma_{act}\) measurements for \(\theta=0^{\circ}\) versus the expected gain for H and V polarization, respectively.  
Data Availability Statement: The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to [EMAIL_ADDRESS].  
This work has been developed under the contract COLB-CTR-2017-009-A between Leonardo S.p.A. and Sapienza University of Rome.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Distributed Task Allocation for Multiple UAVs Based on Swarm Benefit Optimization
## 5 Discussion
### Further Analysis  
The experimental results presented above demonstrate that the proposed BV method outperforms the traditional CBBA and PI methods in terms of generating greater swarm benefits. This superiority is attributed to the fundamental concept of individual benefit variation introduced in this paper, which directly optimizes the overall swarm benefit. Besides, the calculation of individual benefits considers various factors such as task distance, initial value, and value decay, and is updated in real-time to reflect the current swarm's benefit status accurately.  
Furthermore, the proposed method exhibits good scalability due to its polynomial computational complexity. Unlike precise algorithms that seek the optimal solution to the task allocation problem, this method aims to quickly derive a good feasible solution, thereby avoiding the computational explosion issue caused by the exponential growth of computational complexity with increasing task numbers. In reality, finding the optimal solution to the UAV swarm task allocation problem is an NP-hard problem, currently unsolvable. In practical applications, the number of UAVs does not impact the computation time of individual UAVs, as each UAV only computes its own task allocation results. Although the number of tasks does have some influence, with more tasks available, UAVs face a larger number of potential benefits to evaluate, leading to increased computational load. However, this computational burden remains low, as the task combination optimization that could cause computational explosion has been mitigated through the fast heuristic method proposed in this paper. UAVs only need to select the task that brings the maximum incremental benefit based on the already chosen tasks. During actual deployment, there are no hardware constraints, and the current computational platforms are more than adequate.  
It is worth noting that, although this study takes logistics distribution as an example, the proposed method is equally applicable to other fields, including search and rescue, environmental monitoring, and so on. Different task characteristics, environmental factors, or UAV capabilities do not impair the superiority of this method, as it considers real-time, authentic, and comprehensive task conditions, thereby enhancing the swarm benefits of task allocation.
## 3 Numerical Simulation
### Law of Material Composition
#### 3.2.1 Air  
Regarding air material, this study used the No.9 MAT_Null material model of ANSYS/LS-DYNA. The state equation was also used in conjunction with the material model due to the great volumetric change of air material in a blast reaction. Set out below is the state equation:  
\[P=C_{0}+C_{1}\mu+C_{2}\mu^{2}+C_{3}\mu^{3}+(C_{4}+C_{5}\mu+C_{6}\mu^{2})E_{0} \tag{3}\]  
\[E_{0}=\rho_{initial}C_{v}T \tag{4}\]  
where \(P\) denotes air pressure, \(E_{0}\) denotes initial internal energy per unit reference specific volume, \(\mu=\frac{\rho_{current}}{\rho_{initial}}-1\), \(C_{i}\) (\(i=0\sim 6\)) denotes the control coefficient, \(\rho_{initial}\) denotes the initial density of air, and \(\rho_{current}\) denotes the current density of air.  
The above state equation is used for describing the air as the ideal gas, where control coefficients \(C_{1}\), \(C_{2}\), \(C_{3}\), and \(C_{6}\) must be set at 0, and \(C_{4}=C_{5}\) equals \(\gamma-1\), air \(\gamma=1.4\); \(\gamma\) denotes the ratio of specific  
Figure 11: Diagram of mesh features of Lagrangian Description, Eulerian Description, and arbitrary Lagrangian–Eulerian (ALE) Description [14].  
Figure 12: Schematic diagram of ratio of Eulerian meshes to Lagrangian meshes [15].  
heats; \(C_{v}\) denotes the specific heat at a constant volume of air (\(0.7165\frac{kl}{kg-K}\)); and \(T\) denotes the initial temperature of air. Equation (5) can be further simplified as follows:  
\[P=(\gamma-1)\frac{\rho_{current}}{\rho_{initial}}E_{0}+C_{0}. \tag{5}\]  
When the scope of the initial temperature of air is assumed from 273.15 to 3000 \(k\), the initial internal energy of air \(E_{0}\) and \(C_{0}\) parameters are set, as shown in Table 2. In this study, the actual temperature of air material was 288.15 \(K\) (15 \({}^{\circ}\)C).  
The input parameters required for air material include the Mass Density (Ro), Pressure Cutoff (Pc), Dynamic Viscosity Coefficient (Mu), Relative Volume (Terod, Cerod), Young's Modulus (Ym), and Poisson's Ratio (Pr) of the material. The setting values of the parameters are shown in Table 3. The Dynamic Viscosity Coefficient is obtained through the Sutherland equation, as shown in Equation (6)  
\[\text{Mu}=\frac{0.68\times 10^{-2}}{T+122}\left(\frac{T}{273}\right)^{\frac{3}{2 }}\left(\text{Pa}\cdot\text{s}\right) \tag{6}\]
## 3 Structure Design and Optimization
### Lattice Design
The cell size was chosen according to the total wall thickness. For instance, the first type was 5 \(\times\) 5 mm\({}^{2}\) size in a plant. In order to allow a self-supporting surface of arms, the height of the cell was set to 8 mm: this way the lowest overhanging angle was 48.53\({}^{\circ}\) which is greater than the 42\({}^{\circ}\) critical angle for AlSi10 Mg. The geometry of this cell is shown in Figure 5a. The positioning and repetition within the wall are shown in Figure 5b. Other cell sizes were selected considering bigger total wall thicknesses and beam dimensions. Later, the dynamic simulation will highlight that complying with the specification is the most challenging aspect of this work and many modifications will be required. In Table 4 the employed lattice types are reported. The total wall thickness is the sum  
Figure 5: Investigated TPMS structures: Schwarz Gyroid (**a**), Primitive (**b**), Diamond (**c**), Nevious (**d**), Schoen I-WP (**e**), Fischer-Koch S Surface (**f**). Chosen lattice structure (**g**) and positioning within the wall (**h**). of the wall skin and the \(x\) dimension of the cell. It can be noticed that the lattice type B and D differ in the cell-to-wall joining: the latter showed better outcomes by means of structural rigidity. Preliminary tests were carried out on these lattice types proving the fabricability of the small components and the module corner shown in Figure 6.