esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 2 Related Work
### Network Decision Method  
In the research on network resource allocation, there are two main aspects: traditional non-AI resource allocation algorithms and AI-assisted intelligent resource allocation algorithms. Because of its unique flexibility, the latter can often greatly improve the efficiency of the network.  
_In non-A1 Methods_, a distributed power allocation was performed in [34] to optimize the performance of cell-edge users by Lagrange method. In [35], the author introduced a distributed structure for the resource allocation problem by forming a convex optimization problem, maximizing the overall system utility function. In [36], a new resource allocation optimization and network management framework for wireless networks using neighborhood-based optimization has been proposed instead of fully centralized or fully decentralized methods to reduce interference and increase capacity.  
_In A1-based Methods_, researchers are aiming to offer a more dynamic and responsive approach to resource allocation. In [37], the author proposed a multi-agent deep reinforcement learning (DRL) approach with an action space reduction strategy to achieve the dynamic VNF orchestration, backup and mapping solution. In [38], the author proposed a resource allocation (RA) method using dueling double deep Q-network reinforcement learning (RL) with low-dimensional fingerprints and soft-update architecture (D3 QN-LS) based on a Manhattan grid layout urban virtual environment. In [39], an improved deep Q-network (DQN) algorithm was introduced to improve the efficiency of resource utilization. In [40], the author investigated the dynamic offloading of packets with finite block length (FBL) in an edge-cloud collaboration system consisting of multi-mobile IoT devices (MIDs) with energy harvesting (EH), multi-edge servers and one cloud server (CS) in a dynamic environment based on a multi-device hybrid decision-based DRL solution. In [41], based on the enhanced K-mean algorithm and multi-agent PPO (MAPPO) algorithm, a cooperative trajectory design method was proposed for the UAVs to minimize interaction overhead and optimize deployment efficiency. In [42], the author proposed a novel algorithm based on Soft Actor-Critic (SAC) to solve the system cost minimization problem considering vehicle users' satisfaction, RSUs' cost and vehicle workers' reward.  
In summary, although AI-based network resource allocation methods solve the problems of traditional non-AI methods in terms of flexibility, the current AI methods, especially the use of deep network cases, still have problems when talking about real-time processing. In addition, these methods have rarely taken space-ground integrated circumstances into consideration.
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory  
Stereo-DIC is fundamentally an extension of 2D DIC, which relies upon two or more cameras simultaneously capturing images of the specimen (where specimen refers to the body being tracked) from different perspectives in order to determine its in-plane and out-of-plane displacements. As such, there are aspects of Stereo-DIC which are identical to those of 2D DIC; namely calibration and correlation. Stereo-DIC relies upon epipolar geometry to determine in-plane and out-of-plane displacements in the real world from in-plane displacements determined within images. Section 2.5 discusses how calibration and correlation are employed by Stereo-DIC based on epipolar geometry and how triangulation methods are used to perform displacement transformations.  
Although Stereo-DIC can employ more than two cameras [50], ADIC3D is limited to the conventional two-camera implementation. Variables that need to be defined on a per camera basis have the subscript \(j\) to indicate that they are associated with the \(j^{\text{th}}\) camera (i.e., camera 1 and 2). Within this work, an image series is defined as the collection of images captured by a single camera and an image set refers to the two corresponding image series. Thus, an image pair refers to the two images, one from each image series, which were captured simultaneously.
# Self-Adaptive Alternating Direction Method of Multipliers for Image Denoising
## 1 Introduction
To mitigate the impact of penalty parameters on convergence rates, an adaptive approach based on a balance principle for selecting appropriate penalty parameters is introduced. This methodology, proven effective in projection techniques and Uzawa block relaxation algorithms [26; 27], is leveraged in this study to propose a self-adaptive ADMM strategy. This strategy dynamically selects variable penalty parameters to expedite algorithm convergence, markedly enhancing algorithmic efficacy. This paper aims to enhance denoising effectiveness by introducing a collaborative regularization model and proposing a self-adaptive alternating direction method of multipliers to achieve faster algorithm convergence. The key contributions are as follows:  
1. Formulate a collaborative regularization model that maintains structured sparsity within images and explores spatial correlations among pixels. 2. Propose a self-adaptive alternating direction method of multipliers to achieve faster algorithm convergence. The remaining part of this paper is organized as follows. In Section 2, the related works are discussed. In Section 3, low-rank matrix recovery and the alternating direction method of multipliers are introduced. In Section 4, the proposed collaborative regularization model and improved algorithm are proposed. In Section 5, two examples are provided. Finally, some concluding remarks are given in Section 6.
## 3 Test Results and Analysis
### Discussion for the Test Results  
In this paper, the macroscopic damage phenomenon and damage mechanism of the structure are obtained by using the plane charge explosion loading technique. Compared with the C30 concrete materials, the utilization of high-strength materials and the foam concrete energy-absorbing materials can effectively improve the resistance of underground structures.  
(1) In terms of the research test methods, reference [3; 4] uses a scaled test with a small charge, while this paper innovatively uses a field high-pressure plane charge explosion loading test technique with a total charge of 384 kg of TNT to carry out the explosion resistance performance test of prototype underground structures. In order to compare and study the anti-explosion resistance performance of different types of structure, three test sections are designed at the bottom of the explosion chamber to match the requirements of the plane wave loading. This designed one-shot test method controls the variables in the explosion loading test, and saves on the cost of the test.  
(2) With regards to the structural study aspects, the C30 reinforced concrete structure is taken as the reference test section, while the high-performance concrete structure and the composite structure are designed as comparative test sections to investigate the anti-explosion performances of the three different types of structure. For references [28; 29; 30; 31; 32], the similar simulation tests were adopted, and the attenuation rate of the foam concrete backfill layer to peak stress reached more than 65% due to the installation of bolts in the reinforcement layer of the surrounding rock, which was better than the mean peak stress attenuation rate of 55.34% in this paper.  
Due to the large scale of the test, high requirements of the test site, poor test repeatability, difficulty in testing, and other adverse factors, in order to study the anti-explosion performance of the underground structures under the plane charge explosion loading conditions more systematically, some relevant studies should be conducted on the antiknock performance of structures with different backfill materials and bolts in the later stage, so as to provide technical support for the anti-explosion designs of the underground prototype engineering structures under the plane wave explosion loading.
# Bulk and Rayleigh Waves Propagation in Three-Phase Soil with Flow-Independent Viscosity
## 5 Conclusions  
In this article, considering the influence of both the flow-independent and flow-dependent viscosities for soils on the elastic wave propagation, a modified viscoelastic dynamic model of the three-phase partially saturated soil is established to investigate the motion behaviors of bulk and Rayleigh waves. The characteristics equations of bulk and Rayleigh waves have been yielded analytical in explicit form. The dependences of the motion behaviors (speed and attenuation) for various waves on the physical parameters are graphed through employing several numerical examples. The main conclusions are summarized as follows: (i) Both the wave speed and attenuation coefficient of P\({}_{1}\), S, and Rayleigh waves increase obviously with the flow-independent viscosity at both the partially and completely saturated states; (ii) In the high permeability zone, both the flow-independent and flow-dependent viscosities have an apparent influence on the propagation for P\({}_{1}\), S, and Rayleigh waves; (iii) In the low and middle permeability zone, only the flow-independent viscosity affects the propagation behavior for P\({}_{1}\), S, and Rayleigh waves. All authors conceived and designed the study. Conceptualization, methodology, and writing--review and editing: Q.G., H.L. and G.D.; writing--original draft preparation and visualization: Q.G. and H.L. ; investigation and resources: [PERSON] and Q.G. ; supervision: G.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (NSFC Grant Nos. 52078128, 51878160). Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflict of interest. The nomenclature of symbols in this paper  
**Roman Symbols**  
\(A^{ph}\) scalar potential amplitudes of \(ph\) phase  
\(B^{ph}\) vector potential amplitude of \(ph\) phase  
\(c_{ij}^{e}\) isotropic elastic coefficient matrix of soil skeleton  
\(c_{ij}^{e}\) isotropic viscoelastic coefficient matrix of soil skeleton  
\(d\) fitting parameters of unsaturated soil  
Figure 13: Rayleigh wave propagation for different intrinsic permeabilities and relaxation times. (**a**) Wave speed. (**b**) Attenuation coefficient.
## 3 Results and Discussion
### Experiments in Flight Conditions  
The GLD method was verified in real-world experiments on the same UAV and for the same parameter configuration as in simulation tests. The method was tested with great attention paid to the efficiency of obtaining altitude controller gains and the tracking quality. From variety of conducted experiments, the author decided to present and discussed, a few, which are the most representative. Supplementary materials (video and listings) are available at: [[http://www.uav.put.poznan.pl](http://www.uav.put.poznan.pl)]([http://www.uav.put.poznan.pl](http://www.uav.put.poznan.pl)).
# Topology Comparison Study of Five-Phase Wound-Field Doubly Salient Fault Tolerant Generators
## 3 Comparative Study and Simulation of Five-Phase WFDSG
### The Comparative Analysis of Two WFDSGs  
Considering the influence of the windings on the core, two WFDSG with 30/24-pole and 20/16-pole were comparatively studied, the parameters are shown in Table 1. The field slot fill factor usually used is not more than 0.5, and it is slightly more difficult to wind.  
According to Table 1, the simulation models five-phase 30/24-pole and 20/16-pole WFDSG are established by using the magnetic field finite element software. The simulated no-load magnetic field distribution is shown in Figure 1.  
When the speed is 3000 r/min, the simulated no-load electromotive force is shown in Figure 2a,b. It can be seen from the waveforms that the five-phase WFDSG is different from the conventional generator. The electromotive force waveform has a flat top width of about 144\({}^{\circ}\) electrical angle, and the phase difference between two adjacent phases is 72\({}^{\circ}\). At any time, there are two phases with positive or negative voltages at the same time, which is a backup supply for each other.  
Figure 2c,d show the flux linkages of the two WFDSG with a field current of 3A. It can be seen from the waveform that the magnitudes of the flux linkages of the 30/24-pole and 20/16-pole five-phase doubly salient electromagnetic machine (DSEM) are equal, and they are symmetrical phases.  
Since the two WFDSGs have the same number of excitation amps and the total turn numbers of the armature windings are equal, the magnitude of the flux linkage and the magnitude of the EMF are similar at the same speed, which is consistent with Equation (11). However, due to the small distance between two poles, the leakage effect of the 30/24-pole WFDSG is larger than that of the 20/16-pole one, and the minimum flux linkage and the EMF amplitude are slightly smaller, too. When the inner and outer diameters of the stator and rotor, as well as the axial length and the maximum magnetic density, are constant, the EMFs of the WFDSGs with different pole numbers and the same phase coil number are almost equal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **30/24-pole** & **20/16-pole** \\ \hline Stator/rotor pole number & 30/24 & 20/16 \\ Number of turns per phase & 6 \(\times\) 6 & 9 \(\times\) 4 \\ Poles wound by an excitation coil & 3 & 2 \\ Number of turns/excitation winding & 60 \(\times\) 10 & 60 \(\times\) 10 \\ Stator yoke thickness (mm) & 9 & 7.5 \\ Field slot fill factor (\%) & 55.4 & 48.4 \\ Armature slot fill factor (\%) & 26.7 & 30.6 \\ Rotor pole arc coefficient & 0.5 & 0.45 \\ Outer diameter of the stator (mm) & 136 & \\ Inner diameter of the stator (mm) & 92 & \\ Air-gap (mm) & 0.3 & \\ Iron stack length (mm) & 40 & \\ Core iron & DW470-50 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of five-phase WFDSG.
## 4 Experiment
### Cross-Model Evaluation with Human Users Involved
#### 4.2.3 Results  
The results of the cross-model evaluation on success rate and average turns are shown in Table 2. In Group-S test, M-MADM-OM achieves the best performance. In Rule-S test, although M-MADM-OM does not achieve the best performance, it is only 0.2% lower than Rule-M and Peng 2018 [9]. In human users test, M-MADM-OM achieves the best performance. Above all, our proposed M-MADM-OM achieves the best performance in cross-model evaluation.  
For the simulators performance, comparing Group-S test with Rule-S test, dialogue managers trained with Rule-S show the bad performance while interacting with Group-S. This phenomenon shows that Group-S may generate some user behaviors that Rule-S are unable to simulate. Comparing Group-S test with human users test, the results of human users are better than Group-S, which means that Group-S generate some user behaviors that human users may not produce. Even so, to our surprise, the Group-S can improve the concurrent dialogue manager performance on human users test.  
Since our method applies a dynamic adjusted simulator without extensive involving of human laboring, the built model is more time efficient in a long run, even though it is slower in learning an optimal dialogue manager compared with the one-to-one methods with rule-based user simulator (including the work in Reference [9]). As empirical analysis, we observed that dialogue manager with dynamic adjusted simulator is four hours slower than deep dyna-Q method in Reference [9] as the same experimental settings, finally we obtained the optimized simulator with better generalization ability and without involving any more human efforts.
## 3 Mathematical Modeling  
### Nanofluid Problem Statement  
Some assumptions were taken into consideration when establishing the governing equations of the nanofluid MHD natural convection problem [14; 15; 16; 17]:  
* The nanofluid is incompressible;
* The nanofluid is Newtonian; It is important to explain here that the assumption of Newtonian nanofluid used for our investigation is absolutely valid. In fact, the used base fluid is the water which is a Newtonian fluid. The behavior of the nanofluid (especially in terms of viscosity) is almost similar to that of the base fluid since the volume fraction \(\phi\) of the nanoparticles is low (\(\phi=0.05\));
* The nanofluid density variation is governed by the Boussinesq model;
* The nanofluid flow is steady, laminar and two dimensional;
* The induced magnetic field is neglected;
* The induced displacement currents are neglected;
* The induced heating joule is neglected;
* The radiation and energy dissipation are neglected.  
The nanofluid MHD natural convection problem is governed by the following equations [18]:
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 6 Implementation, Results, and Analysis
### System Setup, Simulation Parameters, and Assumptions
#### 6.1.1 Mno  
In RANaaS, the MNOs adapt/control the band state, the bandwidth, and the antenna configuration on the three bands at each site, as seen in Table 4. We considered two different throughput degradation limits of 0 and 20% for each MNO. These limits specified the maximum permissible reduction in the network throughput due to the network optimization actions taken by an MNO's agent. The agents took these limits into account during training. Furthermore, the number of users served by each MNO simultaneously on the three bands was limited to 1/1/8, respectively. These numbers indicated that the users on band 1 and 2 were served sequentially while those on band 3 could be parallelized. In terms of user distribution, we assumed a uniform distribution of users across all sites for all the MNOs.
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 2 Methodology
### Metaheuristics  
As discussed in the previous section, the main focus of this paper is based on the application of the WSA algorithm to solve feature selection problems. To demonstrate the performance capability of WSA in feature selection problems, it is compared with seven other algorithms. All these algorithms considered in this paper are briefly introduced hereunder.
## 3 Results and Discussion
### Spectroscopy Analysis
#### 3.4.2 FTIR-ATR
Finally, the band centred at 3420 cm\({}^{-1}\) is assigned to the --OH groups (water, phenolic, alcoholic, and carboxylic), present in the cell wall of the algae [91]. Figure 7 presents the principal component analysis (PCA) plot showing the distribution of samples from different species and locations based on PC1 and PC2, which together explain 91 per cent of the total variance. To perform this spectral analysis, baseline corrections, the standard normal variate (SNV), and a derivative Savitzky-Golay transformation with 15 smoothing points were applied. The PCA score plot highlights a clear differentiation between species, with some level of similarity observed between the two groups of _S. muticum_ (SMB and SMSM), possibly due to their shred taxon. PC1, which accounts for 62 per cent of the total variance, is the primary axis distinguishing GMBU (located on the far right) from the other groups, primarily SMB and SMSM. Meanwhile, PC2, which explains 29 per cent of the total variance, separates UB (located in the upper region of the biplot) from the other species. Figure 7 (right side) depicts the PCA loadings with the Savitzky-Golay derivative for two principal components (PC-1 and PC-2). This plot shows variations in the spectral data, highlighting the differences and trends taken by the two principal components where the most representative regions for achieving the differentiation between species are between the 1600-600 cm\({}^{-1}\) wavenumber regions. These findings are crucial for the development of future calibrated models to predict algae nutrient composition.
## 4 Discussion
### Comparison of H-LUE with Other LUE Models  
In order to further verify the performance of H-LUE, we ran another widely used LUE model (i.e., EC-LUE model) for additional comparison. Detailed information regarding the EC-LUE model can be found in Appendix B. We ran the four models (i.e., H-LUE, EC-LUE, VPM, and VPRM) on 11 sites of the test dataset (one site of the test dataset was excluded due to the unavailability of the net radiation data that is required by EC-LUE). The performance comparisons are shown in Figure 8. The H-LUE showed the best performance (R\({}^{2}\) = 0.87 and RMSE = 1.72 gC m\({}^{-2}\) d\({}^{-1}\)) among the four models, which further confirmed the advantage of the H-LUE.  
In addition, EC-LUE achieved higher R\({}^{2}\) and lower RMSE values when compared to the other two LUE models (i.e., VPM and VPRM), which was consistent with a previous model evaluation [16]. [PERSON] et al. [16] assessed the performance of seven LUE models (CASA, cFix, cFlux, EC-LUE, MODIS-GPP, VPM, and VPRM) using flux measurements from 157 eddy covariance sites and found that EC-LUE showed better performance than VPM and VPRM. These differences were expected. Model structural differences were considered the most important reason of different GPP estimates among models [16]. EC-LUE uses the same equation as VPM and VPRM to estimate temperature stresses, but the water stress equations are different. In the EC-LUE model, water stress is estimated using the ratio of latent heat flux to net radiation because decreasing amounts of energy partitioned to evaporate water suggests a stronger moisture limitation [49; 50]. VPM and VPRM use a satellite-derived water index (LSWI) to estimate the seasonal dynamics of water stress [6]. A previous study indicated that water stress algorithms generate greater variation among models than temperature factors [16]. Another study revealed that although most models, including EC-LUE, VPM and VPRM, found strong positive correlations between the water availability and GPP estimates across large areas and various ecosystems, different LUE models showed different areas displaying this positive correlation [17]. Therefore, the difference in GPP estimates may be attributed to the model discrepancy in quantifying water stress. Moreover, FPAR of EC-LUE is calculated using NDVI, while that of VPM and VPRM is derived from EVI. Although NDVI and EVI are complementary vegetation indices [51], there was a significant difference in the long-term change between the two vegetation indices over the global scale [17].  
It remains difficult to characterize the effects of environmental stresses such as temperature and water availability on vegetation photosynthesis over large areas from physical modeling, and this limits the accuracy of conventional LUE models. The best performance of H-LUE among the four models indicated that applying ML to simulate the LUE can improve the accuracy of estimating the GPP.