esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Illustrative Examples
### Example II: A Cohesive-Frictional Slope with a Weak Seam
#### 3.2.1 Effect of ACF on Slope Reliability Analysis  
This part examines the effect of the ACFs on the reliability of the slope. Similar to example I, nine cases with different ACFs used for different soils were designed. Case 1, where both the clay and the weak seam use the same ACF of SEF, was considered as the baseline case here. Again, for simplicity, the nine cases were classified into two groups: Case 1-5 belong to the first group where SEF is used for the clay and the weak seam uses the other four ACFs. Case 1, 6-9 belong to another group, where SEF is used for the weak seam and the clay uses the other four ACFs. For all cases, the same SOFs are used for different soil layers, with \(\delta_{h}=20\)\(m\) and \(\delta_{v}=4\)\(m\). The effect of ACFs on the slope reliability can therefore be easily investigated by changing the ACFs for different soil layers according to the cases in Table 5. Note that the Latin hypercube sampling (LHS) was used here as the level of failure probability is relatively low. The slope reliability results are schematically shown in Figure 11, where the \(P_{f}\) values for different cases are signified by the histograms, and the COVs of the \(P_{f}\) are plotted by a curved line. In general, different ACFs show different influences on the slope reliability. For the first investigating group (i.e., case 1-5), the difference between the five cases is not significant, showing that the ACF of the weak seam has little influence on the slope reliability analysis. By contrast, the difference between  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Type** & **Parameter** & **Mean** & **COV** & **Distribution** \\ \hline Clay & c\({}_{1}\) (kPa) & 28.5 & 0.3 & Lognormal \\  & \(\varphi_{1}\) (\({}^{\circ}\)) & 20.0 & 0.3 & Lognormal \\  & \(\gamma_{1}\) (kN/m\({}^{3}\)) & 18.84 & NA & NA \\ Weak seam & c\({}_{2}\) (\(kPa\)) & 0.0 & NA & Lognormal \\  & \(\varphi_{2}\) (\({}^{\circ}\)) & 10.0 & 0.2 & Lognormal \\  & \(\gamma_{2}\) (kN/m\({}^{3}\)) & 18.84 & - & NA \\ \hline \hline \end{tabular} Note: “NA” means not applicable.  
\end{table}
Table 4: Statistics of soil parameters for example II.  
Figure 10: Slope geometry and deterministic slope stability analysis results for example II.  
the five cases in the other investigating group (i.e., case 1, 6-9) is comparably significant. Overall, the change of the ACF in the clay has little influence on the slope reliability results, but the reliability results can be significantly different when opposite ACFs are used (e.g., case 2 vs. 6). In addition, the difference of the results obtained using SEF and CEF (i.e., case 2 vs. 6) is the largest among all cases, which shows good consistency with example I.
## 4 Simulation Results  
In this section, we conduct numerical simulations to evaluate the performance of the proposed B-DNN-based detector for ESM systems (ESM-DNN). We mainly compare the DNN-based signal detection method with traditional methods to evaluate detection performance in terms of BER and time complexity. Note that in all simulations, we assume CSI is perfectly known. It is also worth noting that we train multiple DNNs to accommodate the different scenarios outlined in Table 1. Regarding BER, our criterion is that for the four cases, the number of simulations at a given SNR is consistent, and the BER is calculated as their average value. This approach helps to avoid inaccuracies in BER estimation due to excessive randomness in occurrence frequency.  
Figure 3 compares the BER performance of the proposed ESM-DNN-based detection method with other traditional detection methods, namely ML, ZF and MMSE, in a \(2\times 2\) ESM communication system with QPSK modulation. As shown in Figure 3, it is clear that the ESM-ML detection approach excels in terms of BER performance. The proposed ESM-DNN detection method closely matches the BER performance of the ESM-ML method and outperforms the ESM-ZF and ESM-MMSE detection techniques.  
In Figure 4, it is shown that the proposed ESM-DNN method also performs better under the \(2\times 4\) ESM system configuration with QPSK modulation. Figure 4 illustrates that the ESM-ML detection method consistently achieves the optimal BER performance. Within the SNR range of 0 to 4 dB, the BER of ESM-DNN performance is as same as that of ESM-MMSE and approaches that of ESM-ML. However, as the SNR increases, the ESM-DNN method demonstrates a distinct advantage, with its BER performance starting to significantly exceed those of ESM-MMSE and ESM-ZF while remaining close to ML.  
Figure 5 illustrates that ESM-DNN under the \(2\times 2\) setting shows a significant advancement in runtime performance. When compared with the ML detection method, ESM-DNN reduces computational time and enhances operational efficiency. At the same time, it achieves near-optimal BER performance, thus striking a commendable balance between efficiency and accuracy. In Figure 6, when the number of antennas at the receiver increases to four, the computational time increases, but the trend of overall performance remains consistent.  
The computational complexity analysis findings are summarized in Table 3. It becomes evident that the computational complexity associated with a DNN depends on the network architecture rather than the number of antennas used for transmission or reception. It is worth noting that to achieve an optimal balance between BER and computational complexity, the ESM-DNN stands out as the most suitable option.  
Figure 4: The BER comparison for various detectors under \(N_{t}=2\) and \(N_{r}=4\).  
Figure 3: The BER comparison for various detectors under \(N_{t}=2\) and \(N_{r}=2\).
## 3 Materials and Methods
### Characterization of DETA-MPs-GO  
Transmission electron microscopy (TEM, Hitachi H-7650, Kyoto, Japan) was used to test the morphology and dimensions of the DETA-MPs-GO composites, while Fourier-transformed infrared spectroscopy (FTIR) was recorded to measure the structure and composites of DETA-MPs on a FTIR spectrometer (NEXUS-470, Thermo Nicolet, Massachusetts, MA, USA). Furthermore, X-ray diffraction (XRD, Bruker D8 Advance) patterns were also carried out to determine the composites of the as-prepared materials.
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 3 Proposed Network
### Network Structure  
As can be seen from Figure 1, our GFSR consists of two branches, including the trunk branch and the gradient one. On the trunk branch, \(M\) Residual of Residual Inception Blocks (RRIB) and \(N\) Residual of Residual Receptive Field Blocks (RRRFB) form a deep feature extraction module to acquire the deep features of the input image, and a feature fusion block (FFB) is designed to integrate the gradient features produced on the gradient branch to the trunk branch to generate the reconstructed images. On the gradient branch, \(P\) RRIBs are used to extract features from the LR gradient map and receive the intermediate features at different levels produced on the trunk branch. Finally, the extracted gradient features from all RRIBs are used as an essential structural prior to guide the super-resolution process.  
On the trunk branch, the shallow feature \(F_{0}\) of the input image \(I_{LR}\) can be obtained by two cascaded \(3\times 3\) convolutional (Conv) layers,  
\[F_{0}=f_{ext}(I_{LR}), \tag{1}\]  
where \(f_{ext}(\cdot)\) stands for the shallow feature extraction function.  
Then, \(F_{0}\) is sent to the deep feature extraction module consisting of \(M\) RRIBs and \(N\) RRRFBs to further acquire the corresponding deep feature. Let \(f_{RRIB}(\cdot)\) and \(f_{RRRFB}(\cdot)\) respectively denote the functions of RRIB and RRRFB, and let \(F_{trunk}\) denote the features obtained by the upsampling module, then we can get,  
\[F_{M}=f_{RRIB}^{M}\Big{(}f_{RRIB}^{M-1}\big{(}\dots f_{RRIB}^{1}(F_{0})\big{)} \Big{)}, \tag{2}\]  
\[F_{N}=f_{RRRFB}^{N}\Big{(}f_{RRRFB}^{N-1}\big{(}\dots f_{RRRFB}^{1}(F_{M}) \big{)}\Big{)}, \tag{3}\]  
\[F_{trunk}=U(F_{0}\oplus F_{N}), \tag{4}\]  
where \(F_{M}\) represents the output features obtained by \(M\) RRIBs, \(F_{N}\) represents the output features obtained by \(N\) RRRFBs, \(\oplus\) stands for the element-addition operation, and \(U(\cdot)\) represents the upsampling operation.  
On the gradient branch, the gradient map \(I_{Grad}\) of the input image is first calculated through the gradient function \(G(\cdot)\), which is then passed through a series of modules (i.e., two Convs, \(P\) RRIBs, and an upsampling module) to obtain the corresponding gradient features \(F_{grad}\),  
\[I_{Grad}=G(I_{LR}), \tag{5}\]  
\[F_{G}=f_{ext}(I_{Grad}), \tag{6}\]  
\[F_{P}=f_{RRIB}^{P}\Big{(}f_{RRIB}^{P-1}\Big{(}\dots f_{RRIB}^{1}(F_{G})\Big{)} \Big{)}, \tag{7}\]  
\[F_{grad}=U(F_{G}\oplus F_{P}), \tag{8}\]  
where \(F_{G}\) denotes the shallow feature of the gradient map \(I_{grad}\) obtained by two cascaded \(3\times 3\) Convs, and \(F_{P}\) denotes the deep features obtained by \(P\) RRIBs.  
Finally, the gradient features \(F_{grad}\) produced on the gradient branch are fused to the trunk branch to generate the reconstructed image \(I_{SR}\),  
\[I_{SR}=t\Big{(}F_{trunk},F_{grad}\Big{)}, \tag{9}\]  
where \(t(\cdot)\) denotes the feature fusion block.
# NIRS-EMG for Clinical Applications: A Systematic Review
## 3 Near Infrared Spectroscopy  
Near Infrared Spectroscopy (NIRS) is an optical technique that exploits the fact that light in the spectral range between 600-1000 nm (the \"therapeutic window\") can penetrate biological tissues, up to a few centimeters, in a complete non-invasive way [52]. When light propagates into a turbid medium, such as into a biological tissue, photons undergo many absorption and scattering events, which affects the signal exiting from the medium. This absorption is related to the presence of chromophores, which absorb the radiation at specific wavelengths. By measuring the absorption coefficient (\(\mu_{\text{a}}\)) of a biological tissue at different wavelengths, it is possible to retrieve the concentrations of the main absorbing chromophores. The main absorbers which NIRS aims to investigate are HHb and O\({}_{2}\)HB,which absorb light both in the visible and NIR regions and play a central role in representing muscle oxidative metabolism functions [53]. Scattering events are related to changes in direction of the photons inside the medium, and are mainly caused by refractive index mismatches due to the presence of different morphological structures. From the knowledge of the reduced scattering coefficient (\(\mu_{\text{s}}\)'), it is possible to calculate the Differential Pathlength Factor (DPF), a factor that relates the geometrical distance between source and detector and the photon path-length in the biological tissue. In order to accomplish a NIRS measurement, we should be able to discriminate the contribution of both \(\mu_{\text{a}}\) and \(\mu_{\text{s}}\)' with an acquisition, such that it is possible to calculate the absolute values of O\({}_{2}\)Hb and HHb.  
Not all the NIRS techniques exploited in research studies or present in the market allow for the discrimination of the two coefficients. For this reason, in the next sub-section (Section 3.1) we will briefly present the three main modalities of the NIRS technique. In Section 3.2, the muscle parameters which can be obtained from NIRS measurements are, then, presented.
## 4 Experiment Results and Discussion  
### Vehicle Model Validation  
As described in Section 3.1, the longitudinal dynamics of the vehicle are treated as a first-order inertial system for simplicity. The inertial delay \(\tau\) is identified by the step-response approach. In the driving test on a flat road, the step acceleration and braking commands are given to the lower-level controller, and the actual acceleration response is measured. The model parameters for acceleration and braking are estimated using a least-squares method as follows: \(\tau_{acc}=0.15\), \(\tau_{brake}=0.52\). The validation results for the identified vehicle model, as shown in Figure 4, indicate that the simulation output of the model corresponds well with the measured actual acceleration curve. Thus, the vehicle model adequately describes the longitudinal vehicle dynamics. Notably, the shorter inertial delay for acceleration is highly contributed to by the fast response of the pure electric powertrain of the test vehicle.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 2 Critical Research Agenda
### Inverters as VRE Interfaces with Grids
#### 2.2.3 Frequency and Voltage Stabilities  
Power systems have been analyzed using rather simple representations of cycles-to-seconds timeframe dynamics. Specifically, quasi-steady-state analyses have been widely used to assess both short-term dynamics and longer-term electro-mechanical dynamics, rather than real-time analyses. However, the greater the VRE proportion, the greater the supply-side variability and uncertainty, increasing the number of real-time challenges to the paramount role played by the system operator in terms of grid frequency and voltage regulation.  
In most power grids, VRE already affects the frequency and voltage stability, which can, at least to some extent, be addressed using existing technologies and mitigated through conventional measures [45]. For example, low inertia and poor frequency stability have been addressed by reducing the minimum generated outputs and installing more synchronous condensers. Note that synchronous generators can remain connected in the form of synchronous condensers [4]. Similarly, all static synchronous compensators (STATCOMs), static VAR compensators (SVCs), synchronous condensers, and fixed and switchable shunt capacitors can support voltage stability. Moreover, rapid frequency and voltage controls for VRE sources are under vigorous development, particularly, for variable-speed wind turbines. Such mitigation measures will continue to evolve as VRE proportions increase. However, the upcoming high proportions of VRE call for more detailed models, better tools for stability assessment, and new ways to control frequency and voltage. This is principally because the stability and control principles for a VRE-dominated grid remain poorly understood. For example, it is not yet clear whether grid frequency is the best, right indicator of the supply-and-demand balance [38]. The proliferation of VRE inverters promises a seamless transition from existing AC-based transmission systems to DC-based systems of constant frequency (i.e., zero). DC applications are slowly but steadily growing in the form of high-voltage DC (HVDC) transmission systems and medium- and lower-voltage DC (MVDC and LVDC) distribution systems [46]. Fundamental decisions should then include, for example, the selection of suitable indicating parameters in the AC/DC hybrid grid, the role of frequency relative to the rotational speed of a synchronous machine, and the sensitivity of frequency-dependent loading.
## 2 Geological Setting  
The main criterion for the site selection of this study was to sample sedimentary formations with real potential to be used as CO\({}_{2}\) reservoirs (formations with suitable depth, permeability and with a demonstrated seal formation). The Basque-Cantabrian basin (northern Spain) (Figure 1) was selected according to the results of the research conducted by the Geological Survey of Spain (IGME) in CO\({}_{2}\) geological storage [29] and of previous studies concerning hydrocarbon exploration in the same areas [30]. The selection was further refined to the Homtomin structure area [31; 32], which has been widely studied and proved to have potentiality for geological storage due to the presence of early structural traps.  
The study focuses on the carbonate deposits of the Jurassic (Lias and Dogger) marine sequence of the western part of the Basque-Cantabrian basin [33; 34; 35; 36]; more precisely, on the evaporitic-stromatolithic facies belts of the shallow carbonate ramp, which have suitable porosity and permeability for CO\({}_{2}\) storage [37; 38]. In this area, the following sequence of facies belts has been described: (i) sabkha facies characterized by dolomites, marls and interbedded anhydrites, forming collapse breccias (\"carniolas\") on the surface due to the dissolution of the evaporitic components (Puerto de Palombera Fm., Hettnanigian); (ii) peritidal facies formed by stromatolites at the base and oolitic grainstones at the top (Villanueva de Puerta Fm, Lower Sinemurian); (iii) open ramp facies characterized by cycles of mudstone to grainstone sequences (Sopena Fm., Lower Sinemurian); and finally, (iv) fluvial to shallow marine mixed carbonate and siliciclastic deposits (Rio Polla Fm., late Lower Sinemurian) [35]. The materials of the shallow carbonate ramp are overlaid unconformably by alternations of marls, marls, marls and shales of a deeper (100-400 m) hemipelagic ramp [36]. The studied rocks were sampled in the south-western part of the Basque-Cantabrian basin and consisted of vuggy carbonates (\"Carniolas\") of the Puerto de la Palombera formation.
# Cooperative Guidance Law for the Mother-Cabin of the Anti-UAV Cluster Mother-Son Missile
## 3 Multi-Missile Cooperative Guidance Law Design  
### Guidance Law Design in the LOS Direction  
#### 3.1.1 Multi-Agent Consistency Theory  
In the time-coordinated interception of missiles, the communication network is used for inter-missile communication, and information such as the missile-target distance and time-to-go of other missiles is obtained to adjust its own speed. The communication topology between missiles can be described by an undirected graph, \(G(v,\varepsilon,A)\), where \(v=\{v_{1},v_{2},\ldots,v_{n}\}\) represents a group of communication nodes, the edge \(\varepsilon=v\times v\) represents the connection between nodes, and the adjacency matrix \(A=[\alpha_{ij}]\) represents the communication capability between missiles. If missile \(i\) and missile \(j\) can communicate with each other, then \(\alpha_{ij}=1\), otherwise \(\alpha_{ij}=0\), let \(\alpha_{ii}=0\). If any two nodes in \(G\) can find a path to connect them, it is said to be connected. That is, any missile can directly or indirectly cooperate with all other missiles.  
**Lemma 1** ([15]).: _Finite-time consensus: for a multi-agent system composed of \(n\) agents, the state equation of each agent is as follows:_  
\[\dot{x}_{i}=u_{i} \tag{4}\]  
_where \(x_{i}\) is the state of agent \(i\) and \(u_{i}\) is the consistency protocol, which is the function of state variables. If under any initial conditions and for any agent \(j\) there exists a finite time \(t^{*}\) such that when \(t\geq t^{*}x_{j}(t)=x^{*}\) holds, where \(x^{*}\) is a real number, then \(u_{i}\) is called a finite-time consensus protocol._  
**Lemma 2** ([37]).: _Considering the system composed of \(n\) agents with state \(\dot{x}=u_{i}\), when \(G(v,\varepsilon,A)\) is undirected and connected, the following consistency protocol \(u_{i}\) can ensure the uniform convergence of multi-agent states in finite time:_  
\[u_{i}=\operatorname{sgn}\left(\sum_{j=1}^{n}\alpha_{ij}(x_{i}-x_{j})\right) \left|\sum_{j}^{n}\alpha_{ij}(x_{i}-x_{j})\right|^{\alpha_{ij}} \tag{5}\]  
_where \(\alpha_{ij}\) is the element in adjacency matrix \(A\), \(\alpha_{i}\) is a constant, and \(0<\alpha_{i}<1\)._
## 3 Feasibility Test for Greening of Retaining Wall Face
### Test Materials  
The loess utilised in this experimental study was sourced from Chang'an County in Xi'an City. This loess belongs to the typical fourth-quarter Late Pleistocene loess category, characterised by its yellow-brown colour, uniform soil texture, sporadic calcareous streaks, the presence of plant roots, and a firm soil structure. To maintain consistency in the greening specimens for each group during the testing, the soil samples were meticulously collected on-site in strict adherence to the sampling location, stratum, and depth criteria. All soil samples were procured from a depth of 1 m to 2 m below the ground surface. After extraction, the soil samples underwent a sieving process to remove large impurities. They were then promptly sealed, placed in plastic bags, and appropriately labelled. This process is illustrated in Figure 5.  
For the preparation of the cement-modified loess slurry, air-dried soil samples were utilised. These disturbed soil samples were crushed into fine particles and then naturally air-dried. Following air-drying, the fine loess particles were passed through a 2 mm sieve. The moisture content of the naturally air-dried soil samples was determined to be 2.01%. The cement employed in the experiment originated from the same batch of 32.5R silicate cement produced by Shaanxi Yaoxian Cement Factory, with a production date not exceeding three months.  
Three types of grass seeds were chosen for this experiment, all of which are well-suited for planting in northern regions and possess a proven track record of successful germination and survival rates. These three varieties of grass seeds are depicted in Figure 6.  
Figure 5: Collected soil samples.
## 6 Conclusions  
Based on the salient characteristics of the defects in the surface defect image of steel sheet, we formulate the defect segmentation as a problem of saliency detection. We design a double low-rank and sparse decomposition model to obtain high-quality defect foreground image directly, which provides a robust way to segment the surface defect. We experimentally compare our proposed method with some state-of-the-art methods on surface defect images. The experimental results prove  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method Index** & **AUC** & \(F_{\zeta}\) & **MAE** & **Time (s)** \\ \hline RPCA [28] & 0.7636 & 0.3633 & 0.1860 & 0.1982 \\ IS [13] & 0.7140 & 0.2814 & 0.2485 & **0.0032** \\ ULR [23] & 0.7843 & 0.4780 & 0.2976 & 4.5504 \\ RBD [31] & 0.7125 & 0.4607 & 0.2090 & 0.0331 \\ SBD [32] & 0.6907 & 0.5038 & 0.2390 & 0.6619 \\ DSR [33] & 0.7786 & 0.6264 & 0.1626 & 1.1797 \\ RS [16] & 0.7469 & 0.6454 & 0.1758 & 0.1281 \\ SMF [24] & 0.7497 & 0.5655 & 0.2085 & 0.4615 \\
**Ours** & **0.8350** & **0.6060** & **0.1584** & 0.1713 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparisons in terms of area under ROC (AUC), \(F_{\zeta}\), mean square error (MAE), and Time.  
Figure 6: Quantitative comparisons: (**a**) precision-recall (P-R) curve; (**b**) receiver operating characteristic (ROC) curve; (**c**) F-measure curve.  
that the proposed method performs efficiently and competitively for the surface defect segmentation task and has a strong adaptive ability for the complex and varying surface defects of steel sheet. Our proposed method is an unsupervised framework, which skips the training process and therefore enjoys more flexibility. In the future, we will focus on combining our proposed method with convolutional auto-encoder and expanding the method to other industrial products' defect detection.  
[PERSON] designed the DLRSD model and performed the evaluation experiments. [PERSON] collaborated closely and contributed valuable comments and ideas. [PERSON] arranged the datasets, as well as reviewed the article. [PERSON] and [PERSON] developed the automatic optical inspection procedure. All authors contributed to writing the article.  
This research was funded by Natural Science Foundation of China, under grant number 61775172 and 51805386, Natural Science Foundation of Hubei Province, under grant number 2017 CFC830.  
The authors would like to thank [PERSON] and [PERSON] for providing the surface defect images. MATLAB procedure was revised and optimized from [23; 24; 33].  
The authors declare no conflict of interest.