esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Design of the Spaceborne Connecting Structure for a Lightweight Space Camera
## 3 Optimization Design of the Spaceborne Connecting Structure
### Parametric Optimization Design
#### 3.2.1 Sensitivity Analysis  
In order to analyze the effect of size parameters on the camera performance, a sensitivity analysis was conducted by using a design of experiment method. Taking the geometric parameters \(L_{1}\)-\(L_{14}\) as test factors, mechanical performances are obtained by finite element analysis, including mass, fundamental frequency, and mirror surface nodes displacement. The relative displacement between mirrors is calculated by using the equations proposed in Section 2.2.  
Commonly used sampling methods for design of experiment include the orthogonal arrays method, central composite method, Latin hypercube sampling method, etc. [18; 19; 20]. The Latin hypercube random sampling method was used to extract 100 size parameter combinations. Based on these data, a multi-variable one-order linear regression model was established. The coefficients of the multi-variables linear regression equation reflect the contribution of each test factor to the mechanical performance, which also represents the sensitivity of the size parameters to the performance response. Figure 7 shows the normalized percentage of the contributions, which can easily identify the key parameters.
# Earthquake Damage Index and Fragility Analysis of Steel Damper for Seismic Isolation Bridge
## 2 Steel Damper Experiment
### Test Results  
The failed steel dampers are shown in Figure 4. The cylindrical #1 specimen failed after 54 cycles under the designed displacement, while the cylindrical #2 specimen failed after 41 cycles under a higher loading rate. The failure locations and modes were the same. The E-shaped #1 specimen failed after 40 cycles under the designed displacement, while the E-shaped #2 specimen failed after 33 cycles under a higher loading rate, with both failures occurring in the middle of the cross beam. Figure 5 shows the force-displacement curves for the four specimens with full hysteresis curves, which can withstand multiple cycles of reciprocating loads under the designed displacement, meeting the earthquake requirements. The experimental results indicate that the faster the loading rate, the more likely the specimen is to experience fatigue failure.
## 2 Dense Feature Extraction and Matching Theory and Mismatch Cause Analysis
### Structural Full-Field Dynamic Deflection Measurement Method Based on DFEM-DSMR  
After SIFT feature extraction and FLANN matching, the DSMR can effectively remove mismatched feature pairs in dense feature matching, the displacements can be calculated from the dense feature points, and the method is called as dense feature extraction and matching with dual-step mismatch removal (DFEM-DSMR). The DFEM-DSMR method can measure the structural full-field dynamic deflection and can plot dynamic deflection-time curves for any location of the structure as well as the deflection curves of the entire structural spatial field at any time. The process of high-precision extraction of structural full-field dynamic deflection based on DFEM-DSMR is illustrated in Figure 5.
## 4 Field Application and Results
### Pre-Stack Inversion
On the other hand, the reduction in Vp is due to decreasing the bulk modulus of reservoir rocks, compensating for lessing rock density. The low response of Vp/Vs in the horizon slice indicates the presence of hydrocarbons, as the bulk modulus of hydrocarbons reduces the Vp and rises the Vs through porous rocks. Additionally, there is a reduction in density as the water in rock is replaced by hydrocarbons. Figure 20: (**a**) Averaged density data horizon slice within an interval between Top H and H60 horizons; (**b**) Averaged Vp/Vs ratio data horizon slice within an interval between Top H and H60 horizons.
###### Abstract  
Detecting molecular drivers is crucial in the management of non-small-cell lung cancer (NSCLC). This study aimed to evaluate the use of pretreatment chest computed tomography (CT) radiomics features for predicting epidermal growth factor receptor (_EGFR_) mutation status in NSCLC. **Materials and Methods:** CT images were used to develop a radiomics-based model for predicting _EGFR_ mutation status. Two different groups were formed from the dataset, namely groups for training (n = 380) and testing (n = 86). Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm on a radiomics platform. Machine learning methods were then applied to construct the radiomics models. Receiver operating characteristic curve analysis was conducted to assess the performance of the radiomics signature across different datasets and methods. **Results:** The frequency of _EGFR_ mutation was 13.5% (58/430). A total of 1409 quantitative imaging features were extracted from CT images using the Radcloud platform. Among the six radiomics-based classifiers (k-Nearest Neighbor, Support Vector Machine (SVM), eXtreme Gradient Boosting, Random Forest, Logistic Regression, and Decision Tree), SVM demonstrated the highest area under the curve values in both the testing and training groups, reaching 0.87 and 0.98, respectively. Our model, which incorporated both clinical and radiomics data, successfully predicted _EGFR_ mutation status with an accuracy rate of 86.9%. **Conclusion:** Our findings highlight the potential of radiomics features as a non-invasive predictive imaging biomarker for _EGFR_ mutation status, which could enhance personalized treatment in NSCLC. Radiomics emerges as a valuable tool for identifying driver mutations, although further studies are necessary to validate its clinical utility in NSCLC.  
lung cancer; _EGFR_ mutation; biomarkers; computed tomography; radiomics
## 2 Advances in Multiple Access Techniques for 6G Mobile Wireless Networks
### Non-Orthogonal Multiple Access  
Compared to SDMA, a more aggressive interference management strategy is NOMA, which decodes and removes the interference through SIC operation. NOMA can increase the number of serving users and improve spectral efficiency compared to traditional OMA technologies. NOMA can be implemented using code and power domains within the same time-frequency resources [18]. In code-domain NOMA, user-specific spreading sequences are used to share the total resources, while in power-domain NOMA, the difference in channel gains between users is utilized for multiplexing through power allocation. The main idea behind the NOMA implementation lies in using superposition coding (SC) at the transmitter to transmit data and SIC at the receiver. However, in practice, it is challenging to achieve optimal SIC order in MIMO systems. This can severely degrade the performance of NOMA, and in some cases, its performance may even be worse than that of SDMA.
## 5 Performance Analysis
### Probability of Error from Relay-to-Destination Node  
The signal received at the destination, given by Equation (8), is normalized to the channel coefficient, resulting in:  
\[\frac{\mathbf{y}_{D}}{h_{RD}}=\hat{\mathbf{v}}_{R}+\frac{\mathbf{z}_{D}}{h_{ RD}} \tag{28}\]  
The following theorem gives the probability of error at the destination node.  
_Theorem_ 3: The probability of error in decoding the linear combination of signals sent from the relay to the destination for \(\mathcal{L}=\mathbb{Z}^{n}\) is given by:  
\[P_{2}(d,n)=1-\left(\frac{2}{\pi}\left(\arctan\left(\frac{1}{2d}\right)\right) \right)^{n}\]  
where \(d=\frac{\sigma_{2}}{\sigma_{h_{RD}}}\), such that \(h_{RD}\)\(\sim\)\(\mathcal{N}(0,\sigma_{h_{RD}}^{2})\), and the variance of added noise is \(\sigma_{2}^{2}\).  
_Proof:_ Using Equation (28), the probability of error at the destination is given by:  
\[P_{2}(d,n)=Pr\left(\left\|\frac{\mathbf{z}_{D}}{h_{RD}}\right\|\
otin\mathcal{ V}(\Lambda^{\prime})\right)\]  
where \(\Lambda^{\prime}\) is defined in Equation (23). The distribution of \(\mathbf{z}_{D}/h_{RD}\) is the quotient of two normal distributions with zero mean. Such a distribution is given by the [PERSON]'s distribution [18] with zero mean. Let us define ratio \(d\) as \(d=\frac{\sigma_{2}}{\sigma_{h_{RD}}}\). The probability density function of [PERSON]'s distribution takes the form of:  
\[\phi(x)=\frac{d}{\pi(x^{2}+d^{2})}\]  
An error occurs in the decoding if the noise falls outside the Voronoi region. Therefore, we integrate the distribution of effective noise from (\(-\)1/2,1/2) to obtain the probability of error-free transmission and, consequently, subtract it from one to find the probability of error. Hence, the resulting expression is given by:  
\[P_{2}(d,n)=1-\left(\int_{-1/2}^{1/2}\phi(x)dx\right)^{n}\]  
The above integration can be written as:  
\[P_{2}(d,n)=1-\left(\int_{-1/2}^{1/2}\frac{d}{\pi(x^{2}+d^{2})}dx\right)^{n}\]  
Using \(\int\frac{1}{x^{2}+a^{2}}dx=\frac{1}{a}\arctan\frac{x}{a}+C\), we get:  
\[P_{2}(d,n)=1-\left(\frac{d}{\pi}\left(\frac{1}{d}\arctan\left(\frac{1}{2d} \right)-\frac{1}{d}\arctan\left(-\frac{1}{2d}\right)\right)\right)^{n}\]  
Since \(\arctan(-x)=-\arctan(x)\), therefore:  
\[P_{2}(d,n)=1-\left(\frac{2}{\pi}\left(\arctan\left(\frac{1}{2d}\right)\right) \right)^{n} \tag{29}\]  
where \(d=\frac{\sigma_{2}}{\sigma_{h_{RD}}}\).  
For the special case of \(n=1\), the above reduces to:  
\[P_{2}(d,1)=1-\frac{2}{\pi}\left(\arctan\left(\frac{1}{2d}\right)\right)\]  
We now give the overall probability of error.
## 4 Results  
### Validation of the High Spatial-Temporal Water Body Dataset in China (HSWDC)  
[PERSON] et al. [11] published their paper and the GSWE, which included global surface water dynamics from 1984 to 2015 in 2016, and later updated the GSWE to 2018. Based on GSWE monthly water products in 2018, we divide China into two layers of water and land, and then use the stratified random sampling method to generate a set of points on each month of GSWE products. This set of sample points, marked with the corresponding month, includes water sample points and land sample points. We firstly generate a total of 14,523 sample points from the 12-month GSWE monthly water products. Because the GSWE data itself has some misclassification errors, which mainly come from mountain shadows, saline-alkali land, ridges, and roofs (Figure 4a), we visually check those samples one by one, combining the high-resolution images from Google Earth, and remove the sample points that are clearly not water bodies. In the end, there are 14,070 samples left, including 6554 water samples and 7516 land samples (Figure 4b). In order to avoid estimation errors caused by temporal inconsistence, the verification of the HSWDC is implemented monthly using the verification samples having the same date. In this way, we can obtain the number of correctly classified and incorrectly classified water/land samples per month. The confusion matrix is finally calculated based on the sum of the above corresponding samples across the year 2018. The overall accuracy of the HSWDC is 0.93, the kappa coefficient is 0.86, the omission error is 0.14, and the commission error is 0.01.
## 1 Introduction
To understand the dynamic mechanisms of earthquakes more completely, our objectives were to conduct a comprehensive analysis of the tectonics and seismogenic environment of these two earthquakes in the Linfen Basin using geophysical data such as seismic reflection profiles and velocity structures and develop a reasonable three-dimensional (3D) seismotectonic model. By reinterpreting deep seismic reflection profiles [22; 23] and combining them with a regional 3D velocity model and seismological observation data, we aimed to reveal the deep structures and seismogenic environments of the earthquake areas and develop a 3D geometric model of the main active faults in the Linfen Basin. We explored the influence of the 1303 Hongtong earthquake on the 1695 Linfen earthquake based on Coulomb stress theory and provided the first 3D seismic structure model of the study area. This model provides an important reference for simulating regional earthquake ruptures and strong ground shaking, predicting earthquake hazards, and other related analyses [39; 40; 41; 42; 43; 44]. Figure 1: Topographic map of China (**a**). Location and seismic structure map of the Shanxi Graben (**b**). White and blue boxes indicate the locations of Figures 1b and 2, respectively. Red circles represent earthquakes, black boxes represent cities, and black lines represent exposed surface faults. The green and blue lines represent the isoseismals for the 1303 and 1695 earthquakes, respectively, with the data sourced from the Atlas of Shanxi earthquake isoseisma, compiled by the Shanxi Province Institute of Earthquake Engineering Investigation [45]. (AB: Amurian Block, OB: Ordos Block, SCB: South China Block, NCB: North China Block, SCS: South China Sea, TP: Tibetan Plateau, DB: Datong Basin, XB: Xinding Basin, TB: Taiyuan Basin, LB: Linfen Basin, YB: Yuncheng Basin).
## 2 Materials and Methods  
### Materials  
Poly-\(\gamma\)-glutamic acid (M\({}_{\text{w}}\) = 200,000-500,000 g/mol) was purchased from Wako Chemicals Europe GmbH (Neuss, Germany). Poly(ethylene glycol) PEG1000 (M\({}_{\text{w}}\) = 1000 g/mol) and glycerol ethoxylate PEG1000-3-arm (M\({}_{\rm n}\) = 1000 g/mol) were purchased from Sigma-Aldrich Chemie GmbH (Steinheim, Germany). Poly(ethylene glycol) PEG400 (M\({}_{\rm w}\) = 400 g/mol), poly(ethylene glycol) PEG200 (M\({}_{\rm w}\) = 200 g/mol), 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC), 4-dimethylaminopyridine (DMAP), and phthalate buffer solution (pH = 3.0) were purchased from Acros Organics (Geel, Belgium). DMSO, acetone, phosphate buffered saline (PBS, pH = 7.4) were purchased from Avantor Performance Materials Poland S.A. (Gliwice, Poland). Dialysis membrane Spectra/Por (MWCO 6000-8000) was purchased from Carl Roth (Karlsruhe, Germany). Chloramphenicol powder was purchased from Fisher Scientific UK (Loughborough, UK). Chloramphenicol discs were purchased from Thermo Scientific (Hampshire, UK). Tryptic soy agar (TSA) was purchased from Lab M Ltd. (Heywood, UK). Dulbecco's Modified Eagle Medium (DMEM) was purchased from Gibco (UK); foetal bovine serum (FBS), L-glutamine, and antibiotic antimycotic (10,000 units/mL penicillin, 10,000 ug/mL streptomycin, and 25 ug/mL amphotericin B) were purchased from Gibco (UK). Thiazolyl blue tetrazolium bromide (MTT) was purchased from Sigma-Aldrich (UK). NaCl (5.8 g/L) and glycine (7.6 g/L), used for preparing [PERSON]'s glycine buffer, were purchased from Sigma-Aldrich (UK). MSTO-211H (human mesothelioma, CRL-2081) and PANC 1 (human pancreatic ductal adenocarcinoma, CRL-1469) were purchased from ATCC (UK). _Staphylococcus aureus_ NCIMB 6571 and _Escherichia coli_ NCIMB 9481 were obtained from the University of Wolverhampton culture collection. A mixture of _Lactobacillus_ strains (_L. acidophilus, L. casei, and L. rhamnus_) was obtained from Holland and Barrett, Ltd. (Nuneaton, UK).
## 2 System Model  
We consider a downlink cellular network consisting of a single base station (BS), a single MUST-enhanced UE, and \(N\) MUST-based UEs. We assume that the BS is equipped with a single transmit antenna but each user is equipped with \(N_{\mathrm{r}}\) antennas. Hence, the wireless channel from the BS to each user is modeled as a single-input multiple-output (SIMO) channel. We consider that the orthogonal frequency division multiple access (OFDMA) and the system bandwidth are assumed to be equally divided into \(N\) sub-carriers for simplicity.  
At the BS, a composite modulation symbol is generated by superposing two signals of an MUST-based UE and an MUST-enhanced UE. We adopt the MUST category 2 when generating the composite symbol, and thus the power ratio between two component signals can be adaptively adjusted and Gray-mapping is used for bit-labeling. An adaptive power allocation coefficient, \(\alpha\) (\(0<\alpha<0.5\)), is introduced to determine powers of the two signals. Hence, the \(\alpha\) value needs to be carefully adjusted, depending on the channel conditions of users. Each superposed signal is transmitted to both the MUST-based UE and the MUST-enhanced UE over a certain sub-band. Figure 1 shows an example of a composite constellation of the MUST technique with quadrature phase shift keying (QPSK) modulation for both users. The blue dots represent QPSK constellation points of the MUST-based UE whose power is equal to \(1-\alpha\) and the red dots around each blue dot represent the composite constellation points, consisting of QPSK constellations of both MUST-based and MUST-enhanced UEs, whose power is equal to 1. As shown in Figure 1, \(\alpha\) indicates the power portion that is allocated to the MUST-enhanced UE. As noted before, the signal of the MUST-enhanced UE is transmitted over multiple frequency bands (sub-carriers), while the signal of the MUST-based UE is sent over a single frequency band (sub-carrier) with the diversity-controlled MUST technique [13].  
At the MUST-based UE, the received signal is decoded by treating the signal of the MUST-enhanced UE as noise. On the other hand, at the MUST-enhanced UE, the received signal is obtained after the symbol-level SIC operation for each frequency band. Then, the obtained signals over multiple frequency bands (sub-carriers) are combined to obtain the frequency diversity gain.
## 3 Proposed Approach
### Feature Extraction
#### 3.2.5 Modified Run Length Matrix (MRL)  
RL statistics extract higher-order statistical features in terms of specified directions [91]. The RL method captures the coarseness of a texture in the specified directions. For a given image, a run-length matrix \(P\) involves \(p\)(\(i\),\(j\)) elements to define the number of runs of length \(j\) with pixels of grey level \(i\)[92]. Calculating the run-length while encoding for each typical orientation produces four run-length matrices [19]. The typical orientations are 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\).
## 4 Results and Analysis
### Effect of Water Availability  
Water availability is an important driver of tree productivity. Within the MODIS NPP calculations [50], this is an integral part because daily climate data are required for predicting both GPP and NPP. In a previous study by [PERSON] _et al._[2], similar discrepancies in comparing the \"space-based\" MODIS NPP with \"ground-based\" terrestrial NPP were reported. [PERSON] _et al._[2] concluded that water availability may be a limiting factor and proposed an \"available soil water index\" for improving and/or \"correcting\" MODIS NPP computations compared to terrestrial data. To test this hypothesis and analyze the effect of the water availability on our data, a water balance deficit index is calculated for each inventory plot as an estimate for water limitation according to the following procedure:  
\[WBD=ET0/P \tag{17}\]  
\[ET0=p\times(0.46\times T+8)\times 365 \tag{18}\]  
\(WBD\) is the water balance deficit index which is the ratio of _ET0_ potential evapotranspiration and \(P\) precipitation with _ET0_ [mm] of the period 2000-2010 estimated with the method of Blaney-Criddle [51] and \(P\) the mean annual precipitation 2000-2010 [mm]. \(p\) is a function of latitude (_p_ = 0.2729 for a latitude of 47.5\({}^{\circ}\) N) and \(T\) the mean annual temperature between 2000 and 2010 [\({}^{\circ}\)C]. Values of \(WBD\) less than 1 indicate a lack in available water, while values higher 1 exhibit water availability greater than evapotranspiration. All data are grouped into five \(WBD\)-classes: (i) \(<\)0.5; (ii) 0.5-1.0; (iii) 1.0-1.5; (iv) 1.5 - 2.0 and (v) \(<\)2.0.  
Figure 8 shows a consisted overestimation of terrestrial NPP by MODIS NPP throughout the different \(WBD\)-classes when using the original MODIS NPP. The right image in Figure 8 shows that addressing stand density effects reduces this overestimation substantially.
# Experimental Study on a Granular Material-Filled Lining in a High Ground-Stress Soft-Rock Tunnel
## 2 Principle of a Lining with Compressible Granular Filling Material
### Technical Characteristics of the Compressible Granular Filling Lining Support
#### 2.2.1 Comparative Analysis of the Plastic Zone of the Granular Filling Lining  
The plastic zone radius \(R\) comprises two parts: the radius \(\Delta r\) of plastic zone development before support and after excavation, and the radius \(r\) of plastic zone development after support. As shown in Equations (1) and (2):  
\[R=\Delta r+r \tag{1}\]  
\[r=r_{0}\bigg{[}(1-sin\varphi)\frac{c\ cot\varphi+\sigma_{z}}{c\ cot\varphi+P_{ \alpha}}\bigg{]}^{\frac{1-sin\varphi}{2 sin\varphi}} \tag{2}\]  
where \(r_{0}\) is the tunnel's excavation radius, \(\varphi\) is the angle of internal friction, \(c\) is cohesion, \(\sigma_{z}\) is initial ground stress, and \(P_{\alpha}\) is the horizontal support reaction force. For the same tunnel, only \(\Delta r\) and \(P_{\alpha}\) are different.  
The compressible granular filling lining is used as a secondary lining immediately after tunnel excavation. The surrounding rock's free time is \(T_{3}\ll T_{2}\approx T_{1}\); therefore, \(\Delta r_{1}\approx\Delta r_{2}>>\Delta r_{3}\approx 0\). The granular material filling lining can provide a uniform, controllable surface support force in time. It can reduce stress concentration, avoid plastic zone overdevelopment, and make \(P_{\alpha_{2}}<P_{\alpha_{3}}<P_{\alpha_{1}}\); thus, \(r_{1}<r_{3}<r_{2}\), and the radius of the plastic zone is between that of traditional support and yielding support.
# A Simple Water Retention Model Based on Grain Size Distribution
## 4 Materials and Methods
### Validation of the Proposed WRC Prediction Model  
The prediction accuracy of the proposed model was assessed by comparing predicted WRCs against the WRCs measured as part of this study and WRCs from a number of materials in the literature [13, 58, 59, 60, 61, 62, 63]. The relevant properties and measured GSDs of each material from the literature are shown in Appendix A. The accuracy of the proposed model was compared against several existing WRC models in the literature, to put its performance into context. The existing models used are the: Mohammadi-Vanclooster model (MV) [16], Arya-Paris model (AP) [11], Chang-Cheng-Qiao model (CCQ) [12], non-similar-media-concept model (NSMC) [20], modified Kovacs model (MK) [70], and intergranular mixing particle size distribution model (IMP) [71]. Each model from the literature is briefly characterized in Table 2, which details the model inputs, key equations to calculate volumetric water content or suction, and the model outputs. The reader is referred to Appendix B for a complete description of every input parameter used for each material, for the proposed model, and the WRC models from the literature. Table 2 shows that most of the models presented require input parameters that are either assumed or adjusted such that the model performs adequately for some WRC data. In other words, most models require some form of calibration for optimum results. In this study, one requirement for the proposed model is that calibration to WRC data must be avoided. Thus, if any of the comparison WRC models were to be used in place of the proposed model, they must also satisfy the requirement to avoid calibration. Therefore, no input parameters for the comparison models were specifically adjusted to optimize fitting of the WRC data; instead, the recommend values for these parameters were adopted from the model's source. For the proposed model, and for each model used from the literature, it is assumed the soil or material maintains a constant volume (constant void ratio or porosity) despite changes in suction. This is important to consider, as many of the WRCs being predicted in this study are from soils which undergo volume change. Consequently, assuming a constant volume reduces the accuracy of the predicted WRCs. Nevertheless, models with this assumption are still used on real soils, simply due to the difficulty in predicting a WRC in a more scientifically rigorous manner incorporating volume change.