esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Response Law and Parameter Influence Analysis of Karst Tunnel Dynamic Excavation
## 7 Conclusions  
This paper compares three working conditions around the tunnel: without cavities, with upper cavities and with lower cavities. The vertical and horizontal displacements of the tunnel are studied under three working conditions. The maximum principal stress and minimum principal stress are studied. Further, three influencing factors of caves and tunnels are selected in this paper: the span of the cave, the height of the cave, and the burial depth of the tunnel, and the degree of influence of the three factors on the stability of the surrounding rock is studied. The conclusions drawn from this study can be summarized as follows.  
* Compared with no karst cave, both the upper and lower karst cave will make the vertical displacement of the tunnel vault and arch bottom around the cavity smaller and the horizontal displacement of the arch waist smaller.
* Compared with the cavity-free strata, there is no significant effect of the upper cavity on the maximum and minimum principal stresses in the tunnel at the safe critical distance state; under the influence of the lower cavity, the maximum and minimum stresses in the tunnel will peak.
* The three factors of tunnel burial depth, cavern span and cavern height were tested orthogonally to obtain the critical safety distances of the tunnel and cavern. The obtained data were subjected to extreme difference analysis, and the importance of the factors for the upper cavern safety distance was ranked from largest to smallest: tunnel burial depth! cavern span? cavern height, and the importance of the factors for the lower cavern safety distance was ranked from largest to smallest: cavern height? tunnel burial depth? cavern span.  
Conceptualization, G.H. and Y.W. ([PERSON]); methodology, P.X.; software, P.X.; validation, X.L. and Y.W. ([PERSON]); formal analysis, P.X.; investigation, G.H.; resources, G.H.; data curation, Y.W. ([PERSON]); writing--original draft preparation, P.X.; writing--review and editing, H.B. and P.G.; visualization, P.X.; supervision, G.H. and P.G.; project administration, G.H.; funding acquisition, G.H. 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, grant number 42077249 and 42107082, the Natural Science Foundation of Anhui Province (CN), grant number 2108085 QD166, Jinan Rail Transit Group Co. Ltd. Funded Project, grant number QT-ZF-2020-241, the Fundamental Research Funds for the Central Universities, grant number JZ2023 HGTA0193 and JZ2023 HGQA0094, the Opening Project of State Key Laboratory of Explosion Science and Technology (Beijing Institute of Technology), grant number KFJJ23-04M and KFJJ23-05M.  
Not applicable.  
**Data Availability Statement:** Some or all data from the study are available from the corresponding author by request.
## Abbreviations  
The following abbreviations are used in this manuscript:  
22 RV1 human prostate carcinoma epithelial cell line  
ABTS 2,2'-azino-bis (3-ethylbenzothazoline-6-sulfonic acid)  
_ABCA1_  ATP-binding cassette transporter A1  
_ABCG1_  ATP-binding cassette sub-family G member 1  
ACh acetylcholine  
AChE acetyl-cholinesteraseerase  
ALT  alanine aminotransferaseases  
ApoE  atherosclerosis-prone apolipoprotein E  
AST aspartate aminotransferase  
BJ  human skin fibroblast cell line  
CAT  catalase  
CCD-986-SK  human skin fibroblast cell line  
CCR2b  C-C chemokine receptor 2b  
CETP  cholesterol ester transfer protein  
CO\({}_{2}\)  carbon dioxide  
COX  cyclooxygenase  
CYP26B1  cytochrome P450 26B1  
Detroit 551  human skin fibroblast cell line  
D-GalN  D-galactosamine  
DDD-1  human colorectal adenocarcinoma cell line  
DPPH 2,2-Dipheny-1-picrylhydrazyl  
FXR  farnesoid X receptor  
GM-CSF  granulocyte macrophage colony-stimulating factor  
H1792  human lung adenocarcinoma cell line  
HaCaT  human skin keratinocyte cell line  
HCT-116  human colon cancer cell line  
HeLa  human cervical cancer cell line  
HepG2  human liver cancer cell line  
HFF  human skin fibroblast cell line  
(P)HK (primary) human keratinocytes  
HL-60  human leukemia cell line  
HPLC  high-performance liquid chromatography  
HS578T  human triple-negative breast cancer cell line  
HUVECs  human umbilical vein endothelial cells  
IFN-Y  interferon-Y  
IGF-1  insulin-like growth factor 1  
IL-1/6/8  interleukin-1/6/8  
iNOS  inducible NO synthase  
IP-10  interferon gamma-produced protein of 10 kDa  
LC-MS  liquid chromatography-mass spectrometry  
LED  light-emitting diode  
LLC-PK1  pig kidney epithelial cell line  
LNCaP  human prostate cancer cell line  
LOX  Lipoxygenase  
LPS  Lipopolysaccharide  
LTB  Leukotrienes  
MCF-7  human breast cancer cell line  
MCP-1  monocyte chemoattractant protein 1  
MDA-MB-231/468  human triple-negative breast cancer cell line  
MI  myocardial infarction  
MIC  minimum inhibitory concentration
## 4 Conclusions  
The present research was conducted to study drought effects on water resources using remote sensing data. The data that were employed in this regard included the meshed precipitation data of TRMM with spatial resolution of \(0.25\times 0.25\) degrees, precipitation data of synoptic stations, and fountains' discharge data. The SPI was used in monitoring of drought conditions, and normalized fountain discharges were applied in comparison between precipitation and discharge data. The most important results taken in this investigation are as follows:  
* The precision assessment of TRMM precipitation data based on various statistical tools verified that this dataset enjoys a suitable accuracy.
* Monthly scale study of drought showed that in most months of the study period, the frequency and duration of dry periods were more than those of wet periods.
* In regards to seasonal scale, it was revealed that exclusive of winter, the frequency and duration of the droughts was longer than those of wet years. It should be noted that the duration of these periods were different.
* A remarkable direct relationship between drought and fountain discharges was observed in the study area. Depending on geological properties, the effects of droughts was immediate or with delay.  
**Author Contributions:** [PERSON], [PERSON], N.A.-A. and M.K.M., editing, data interpretation, and contributed reagents, materials, analysis data; [PERSON], writing--review and editing the paper and research method; [PERSON], editing the paper and consulting research method; [PERSON]-A. and [PERSON], provided critical comments in planning this paper and edited the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 7 Discussions and Findings
### Design for Manufacture and Assembly (DfMA)  
The manufacturer in case study pointed out that \"Lack of experience and expertise (C1) is a major problem for all participants. Designers cannot propose a good drawing if they have insufficient knowledge and experience of modular design and manufacture. An inappropriate design may result in extra expense and time in the stage of manufacture.\" Consequently, it is worth considering that proposing a relevant solution or a practical application aiming at the design stage is necessary. In recent years, DfMA provided substantial insights into the modular construction, dealing with manufacture and assembly in the early design stage for improved quality of modules and decreased construction period [84]. Derived from manufacturing, DfMA, a combination of DfM (Design for Manufacture) and DfA (Design for Assembly), embodies an innovative design method and philosophy and has been embraced by the construction industry [84, 85, 86, 87]. Given the consideration of the nature of industrialisation and modularisation, off-site construction, especially high-rise modular buildings embedded with completed prefabricated modules, is an ideal implementation scheme of DfMA. Empirical projects have identified its contribution to reduced construction period as well as wastes [26, 87, 88]. Nevertheless, DfMA still remains at a theoretical phase rather than a concrete tool or a standard operating procedure (SOP) that can be utilised simply for general modular buildings. Consistent with the findings of the case study, in spite of most designers understanding the significance of manufacture, transportation, and assembly in modular construction, only experienced senior designers could comprehensively take account of the entire construction period from the early design stage [89]. Developing an applicable guideline of DfMA can be an effective solution to current constraints in design stage, involving C1, \"lack of experience and expertise\" and C10, \"lack of building codes and standards\", which is necessary for the development of modular buildings.
# Evaluating the Cost Efficiency of Systems Engineering in Oil and Gas Projects
## 4 Discussion  
The biggest contribution this work made is that it developed a method that would predict the most probable economic effect from applying systems engineering. Systems engineering has already established itself as one of the most effective tools in creating successful systems in aerospace, military, and other industries, which are characterized by high complexity and a large number of system components. The authors tried to find common features between the listed industries and the oil and gas industry and, as a result, established a lot of similar characteristics.  
The oil and gas system comprises a large number of subsystems, from the hydrocarbon production subsystem to the external transport subsystem; it is described by a high uncertainty and a large volume of capital investments, which make the oil and gas system complex. An increase in the share of hard-to-recover reserves leads to a constant search for new technologies in field development and new approaches to organizing production. The cost of a failure at the stage of putting a field into operation can be very high, which will entail a complete non-return on investment. Therefore, it is important to develop a project implementation program and schedule at the initial stage of conceptual design to assess possible risks and opportunities, take into account stakeholders' requirements, and translate them into requirements and architecture.  
However, any innovation requires an investment justification before it is implemented. Due to this fact, the authors developed a calculation tool that would take into account the specifics of the oil and gas industry and the specifics of calculating the costs of systems engineering.  
The limitation of this method is the correlation of cost of the projects used for trade-off analysis--that is, projects with and without using systems engineering. Collecting statistics on projects' success to obtain the likelihood of outcomes should be performed depending on projects' categories, which are small, medium, and large. Thus, the use of SE in a large project will have a higher effect value than in a small project, since the additional costs will be justified. Additionally, projects in the same domain should be compared. However, since today there are no cases of using systems engineering in the oil industry, the authors compared oil projects with investment projects in the transport and defense industry.
# Capacity-Operation Collaborative Optimization for Wind-Solar-Hydrogen Multi-Energy Supply System
## 4 Case Study
### Best Compromise Solution Selection  
Upon completing the optimization computations, several Pareto front solutions emerged, each reflecting distinct system performance characteristics. To pinpoint the most practical compromise solution, additional constraints were necessary. The most effective approach typically involves employing decision-making techniques to extract the optimal solution. In  
Figure 8: The convergence process of the optimization algorithm.  
this context, a novel evaluation method was crafted, leveraging a comprehensive evaluation approach that combines the AHP-entropy assignment method and TOPSIS. The Pareto optimal solution, derived through this method, was selected as the final best solution after evaluating each index. The evaluation results under different weights are shown in Table 7.  
The optimal compromise results in an LCOE of 0.3096 USD/kW and CO\({}_{2}\) emissions of 630 t/y. The \(N_{\text{loops}}\), CSP capacity, TES capacity, PV capacity, \(N_{WT}\), PEME capacity, HST capacity, and PEMFC capacity for this treatment are 456, 55 MW, 20 h, 133 MW, 51, 10 MW, 7.144 m\({}^{3}\), and 5 MW, respectively. The economic analysis demonstrates that a 1% reduction in site CO\({}_{2}\) emissions corresponds to a 1.7% increase in the system's LCOE.
## 4 Our Proposed Algorithm
### Compressive Data Gathering  
WSNs are utilized for gathering physical signal from the real world in practical applications. Without using CS theory, which is the simplest method, a data-gathering scheme with the help of the tree topology is shown in Figure 6(a). In order to dramatically decrease communication costs and prolong the network lifetime, the authors of [12] consider that the sink node receives only \(M\) packets instead of \(N\) packets of original data from the whole network. In the end, at the sink, CS theory is used to reconstruct the original data. For the CDG algorithm, each node in the WSN multiplies its readings \(x_{j}\) using the corresponding \(j\) column vector of basis matrix \(\Phi\). Next, the sensor node adds them to its own readings after receiving all same-size vectors from descendent nodes and transmitting the final results to its parent node with \(M\) packets. Let us illustrate the product of CDG in Figure 8, where \(\Phi\) is \(M\times N\) matrix, and each column corresponds to one weight sum. In the plain CS [11], all nodes in WSNs transmit \(M\) packets and each has equal transmission costs; therefore, each CS measurement cost remains relatively high. An example of the plain CS mechanism is given in Figure 6(b). It is obvious that for these approaches (non-CS and plain CS), the former transmits fewer packets compared with plain CS from the point of view of child nodes. In [14], [PERSON] et al. provides the hybrid CS method, where non-CS is chosen when the number of packets is less than or equal to \(M\); alternatively, plain CS is used. Figure 6(c) illustrates the idea of hybrid CS. In Figure 6(c), thin circles indicate a forward node using non-CS, while thick circles denote the gathering node using plain CS.
## 2 Materials and Methods
### Sample Processing and Measurements  
Measurements of three randomly selected quadrats (0.5 m \(\times\) 0.5 m) were averaged in each plot. Quadrats were established in the middle of each plot at least 2 m from the edge. All plants were recorded, measured and clipped by species and divided into the following three categories: alfalfa, grass and weed. Sampling and measurements were carried out following a consistent manner three times on 24 June, 28 July and 24 August. Plant height was measured on 10 plant individuals per species within each quadrat. Coverage in percent of ground cover was the mean estimation of three people by a visual method. Individual numbers of each species in the quadrat were counted while clipping. The branch number was counted on five plant individuals per quadrat. Density determination was equal to the total number of individuals divided by the quadrat area. Photosynthetically active radiation (PAR) was observed by a plant canopy analyzer (TOP-1000) with five scans at the canopy height of alfalfa in each quadrat from 1 p.m. to 2 p.m. without cloud interference.  
After measuring, plants were hand-harvested at 2 cm above the ground level and collected by species. All unseeded species were classified as weeds. Samples were dried at 80 \({}^{\circ}\)C with air ventilation until reaching a constant weight to estimate the aboveground dry matter biomass. We used \(S\) (\(S\)=\(\mu\)/\(\sigma\)) to measure the productivity stability of the plant community during each period [30]. In the formula, \(\mu\) and \(\sigma\) represent the average and standard deviation of the dry matter biomass (DM) in each community, respectively. If there was no variation at all (\(\sigma\) = 0), \(S\) would be maximal. When the variation is larger than the mean, \(S\) is smaller. Therefore, a larger value of \(S\) indicates higher productivity stability.
## 3 Methodology
### Hybrid Algorithms  
The literature contains numerous studies conducted to improve ANN using optimization algorithms such as PSO, genetic algorithm (GA), ICA, and ABC (see [38; 61; 62; 63; 64; 65]). The back-propagation (BP) does not act strongly in exploring the accurate global minimum; as a result, the ANN model might obtain unwanted results [82; 83; 84; 85; 86; 87]. However, there is a higher probability for ANN to be trapped in local minima. In order to solve ANN problems and get a higher level of performance prediction, the weights and biases of ANNs should be optimized by implementing optimization algorithms such as ICA, ABC, and IWO. In particular, the hybrid ICA-ANN, ABC-ANN, and IWO-ANN models do not get caught in the local minima due to the robustness of the optimization algorithms. However, the power level of each hybrid algorithm depends on the power level of its optimization techniques i.e., ICA, ABC, and IWO. Therefore, the structure of optimization technique as well as their most effective parameters play an important role on results of hybrid ANN-based models. As shown in Figure 6, the most effective parameters on each optimization algorithm are presented. The present paper develops three hybrid models for the purpose of predicting TS: IWO-ANN, ICA-ANN, and ABC-ANN. In these hybrid models, IWO, ICA, and ABC are responsible for exploring global minimum; after that, ANN chooses it in a way to obtain the best results. Actually, the basic optimization algorithms were used to form new  
Figure 5: Invasive weed optimization (IWO) flowchart.  
combinations, and they were connected to ANN. Then, different parametric studies were conducted to obtain the best combination for each hybrid model. It is important to mention that the source of optimization codes was taken from their original studies. Figure 6 shows three stages of analysis to predict the BTS of the rock samples. The first stage of the analysis is related to ANN structure selection. In the second stage, the best structures of optimization algorithms should be selected based on the most important mentioned parameters on them. Then, in the last stage, the hybrid ANN-based models were evaluated using the most important performance indices.
## 5 Results and Discussion
### Effect of Rayleigh Number and Hartmann Number on Nanofluid Flow Behavior  
The simulations were carried out under the same data presented in Section 5.1.  
Figures 5 and 6 show the effect of Rayleigh number and the effect of Hartmann number on the flow streamlines and the surface velocity magnitude, respectively. The Rayleigh number varies, from left to right (columns), as \(Ra=10^{3}\); \(10^{4}\); \(10^{5}\), while the Hartmann number varies, from up to down (lines), as \(Ha=0\); \(10\); \(30\); \(50\).  
Figure 4: Effect of Hartmann number and Rayleigh number on the surface temperature distribution.  
Results show that the flow streamlines present globally an elliptical core vortex oriented towards the diagonal direction of the cavity. The minimum velocity magnitude of the flow was reached in the central zone and near the walls of the nanofluid filled cavity, while its maximum value was reached all around the central zone. This maximum value was increased as the Rayleigh number increased from \(Ra=10^{3}\) to \(Ra=10^{5}\).  
Results show, also, that, as the Hartman number increased, the zone of maximum velocity magnitude will be pushed away from the magnetic source, towards the down left side of the nanofluid filled cavity, while the maximum value of the velocity magnitude was decreased. It was to note, also, that the increase in Rayleigh number and Hartman number favors the establishment of a second vortex representing a zone of minimum velocity magnitude.  
Figure 5: Effect of Hartmann number and Rayleigh number on the flow streamlines.
# Prediction of the Tensile Load of Drilled CFRP by Artificial Neural Network
## 3 Results  
### Thrust Force Analyses  
The drilling force is a reaction force the against the displacement of the drill bit. Also, it is known that thrust force, which depends on drilling parameters, is a major factor affecting delamination [24; 25; 26; 27; 28; 29; 30; 31; 32]. Minimum thrust force is desired at the drilling operation. The variation of drilling parameters on the thrust force drilling of CFRP is shown in Figure 7.  
Figure 6: Artificial neural network (ANN) architecture for predict model.  
Figure 7: Variation of drilling parameters on thrust force: (**a**) 0.05 mm/rev; (**b**) 0.10 mm/rev; (**c**) 0.15 mm/rev.  
As can be observed from Figure 7 thrust force is affected by drilling parameters. The thrust force is most affected by the drill bit type. The lowest thrust force is obtained from WC drill bit. The coefficient of thermal conductivity of WC is between 63 and 120 W/mK [33; 34; 35; 36] and the coefficient of thermal conductivity of HSS is between 20 and 24 W/mK [37]. Similarly, WC has high wear resistance and high hardness. For this reason, the less wear occurs in the WC drill bit and the heat generated from the friction is moving away from the drilling area rapidly. In this case, drilling operation with the carried-out WC drill bit results in lower drilling forces. The thrust force is decreased with the increase of spindle speed and increased with the increase of feed rate. At low spindle and high feed rates, the drill bit does not have enough time to cut the fibers and the fibers prevents the drill bit from progressing. Therefore, thrust force increases.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 4 Optimization Algorithms Used for IRS-Assisted UAV Communications
### Traditional Optimization Algorithms  
It is important to optimize suitable parameters for UAVs and IRSs to improve the system performance. Previous studies showed that crucial factors for UAVs are placement, trajectory, and moving speed, and those for IRSs are the number of elements and phase shift. The detailed description of the various parameters and traditional optimization algorithms for IRS-assisted UAV communications systems can be found in [94].
## Materials and Methods
### Remote Sensing Data and Preprocessing
#### 2.2.2 Remote Sensing Data Preprocessing
However, due to the unstable data quality of HJ-1 CCD, the cross-radiometric calibration and atmospheric correction were accomplished simultaneous using the method proposed by [PERSON] et al. [57; 59]. The atmospheric correction for the HJ-1 CCD image was conducted using a space-based aerosol optical depth (AOD) retrieval method [59]. The differences between the AOD retrieved from the HJ-1 CCD data and that from the Aerosol Robotic Network (AERONET) measurements ranged from -0.14 to 0.31. Approximately 50% of the derived AOD values correlated with AERONET AOD values with low discrepancy (less than 0.15), and the RMSE values for Xianghe and Beijing were 0.18 and 0.21, respectively. The ZY-3 MUX image was corrected by its own coordinate file (*.rpc) that was acquired from NASG [53]. The geometric corrections of the HJ-1 CCD and GF-1 WFV images were conducted in ENVI software using the geometric correction image of ZY-3 MUX as the reference, and a second-order polynomial transformation with bilinear interpolation was used in the resampling. There were approximately 20 control points that were manually selected from the images, and the geometric registration error was less than one pixel of the images. The remote sensing data for the selected study area were projected to the World Geodetic System of 84 (WGS-84).