esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Hybrid-electric propulsion is currently under extensive investigation regarding its application to transport aircraft [1, 2, 3, 4, 5, 6, 7]. The interest in this type of propulsive architecture is mainly found in its theoretical potential to reduce fuel consumption [8, 9, 10, 11, 12, 13] and consequently the related greenhouse emissions. One of the main limitations of integrating electric propulsion lies in the low gravimetric energy density (BED) of batteries [14, 15, 16], which, even in future development forecasts, realistically allows us to believe that such propulsion systems can only be effectively applied to small aircrafts, up to the regional category [17, 18]. An effective solution to mitigate the weight limits introduced by the low energy density of batteries has been identified in increasing the energy efficiency of novel aircraft configurations. In particular, this aspect involves combining electric propulsion with other technological developments, at the overall aircraft level, in order to decrease the energy demand to complete a typical mission scenario [19]. In this context, different evolutionary technological solutions have been proposed, such as the incremental development of structures and materials. However, solutions capable of providing even more significant performance boosts seem to be the disruptive ones, which involve the study and integration of radically novel lifting architectures [20, 21]. Amongst these, the box-wing lifting configuration [22, 23, 24, 25], characterized by a closed lifting system composed of a pair of horizontal wings joined to the tip by vertical wings [26, 27, 28], has been proven to be able to introduce aerodynamic, structural, and weight benefits capable of improving the aircraft energy efficiency. In this regard, a detailed performance analysis of a hybrid-electric box-wing regional aircraft has been recently proposed by the authors in [17], where the study confirms that the combination of the two innovations, i.e., airframe and propulsive, is capable of producing significant reductions in fuel consumption throughout the entire operational envelope of the aircraft.  
In the light of these promising results, a design and performance comparison between regional hybrid-electric aircraft with traditional tube-and-wing (TW) and box-wing (BW) configurations was proposed in [29]. In particular, the results presented in [29] show that the aerodynamic and structural benefits of the BW concept allow significant advantages compared to the corresponding TW configurations, enabling considerable reductions in fuel consumption. This paper aims to start from the performance results presented in that recent work [29] and broaden the study, presenting a more general impact analysis, as described in the following subsections.
# Study on Microseismic Monitoring of Landslide Induced by Blasting Caving
###### Abstract
The results indicated that underground mining altered the stress environment of the slope, inducing differential settlement in the rock mass and ultimately leading to instability and failure. [PERSON] et al. [21] simulated potential slip surfaces and displacements in a mine landslide, identifying three distinct stages: surface collapse crack initiation, crack propagation, and slope failure, with detailed mechanistic analyses for each phase. This study addresses the Shizhuyuan polymetallic mine's mining-induced landslide through microseismic monitoring. By analyzing microseismic event distributions, slip surface orientations, and rock mass failure mechanisms, this work proposes a novel methodology for investigating slope instabilities triggered by underground mining operations.
# Shaft Wall Damage to High-Depth Inclined Ore Passes under Impact Wear Behavior
## 2 Shaft Wall Damage Model for High-Depth Inclined Ore Passes
### 3 Engineering Case
#### 3.1.1 Engineering Survey  
The ore body of a deep mine in Yunnan, China mainly occurs in the strata of the Baizuo Formation with rock dip angles of 55-65\({}^{\circ}\), which is like a type of net-vein ore body. The main mining method is the mechanized panel overhand (underhand)-supported mining method, with a production capacity of 700,000-750,000 t/a, and the ores produced are mainly primary lead-zinc sulfide ores. At present, the mining depth and the middle production level height of the mine are 1614 m and 60 m, respectively. After years of mining, paste supporting has been completed in the middle production levels at +1764 m and above; the four middle production levels at +1584 m, +1404 m, +1344 m, and +1274 m are being stopped; and the middle production levels at +1104 m and below are being developed.  
It can be seen from Figure 3 that, in the mine, ore passes are drilled using raise-boring machines, and more than 20 ore passes have been completed. All of these ore passes have a shaft diameter of about 2 m, an ore-pass dip angle of 50-80\({}^{\circ}\), an ore-pass depth of 60-360 m, and an inclined angle of the chute of 45-70\({}^{\circ}\).
# Optical Channel Selection Avoiding DIPP in DSB-RFoF Fronthaul Interface
## 3 Calculation Results
### Relative Delay and CP-OFDM Modulation
The results of the relative delay of the sidebands are shown in Figure 16:0 The calculations were made for the fronthaul optical path with the maximum allowable length of 20 km. The optimal channel bandwidth was taken into account, i.e., 100 MHz, which is available in all bands from the FR1 and FR2 ranges, intended to be used in the 5G NR interface. The calculation results show that the relative delay is several orders less than the allowable relative multi-path delay over the radio link. The allowed relative delay value in the CP-OFDM modulated signal is governed by the duration of the cyclic prefix. In this case \(T_{CP_{\min}}\gg\tau_{\max}\), so it can be concluded that the OFDM symbol used in the RFoF link is safe (Table 8). Figure 18 shows the calculation results for the optical carrier wavelength of 1550 nm and the radio carrier frequency of 84 GHz (this frequency is currently outside the range of FR2 but will likely be used in B5G/6G solutions). However, by testing the link parameters in the radio domain and by extended setting of the channel state information (CSI), this quantity should be taken into account.
###### Abstract  
The Internet of connected vehicles (IoCV) has made people more comfortable and safer while driving vehicles. This technology has made it possible to reduce road casualties; however, increased traffic and uncertainties in environments seem to be limitations to improving the safety of environments. In this paper, driver behavior is analyzed to provide personalized assistance and to alert surrounding vehicles in case of emergencies. The processes involved in this research are as follows. (i) Initially, the vehicles in an environment are clustered to reduce the complexity in analyzing a large number of vehicles. Multi-criterion-based hierarchical correlation clustering (MCB-HCC) is performed to dynamically cluster vehicles. Vehicular motion is detected by edge-assisted road side units (E-RSUs) by using an attention-based residual neural network (AttResNet). (ii) Driver behavior is analyzed based on the physiological parameters of drivers, vehicle on-board parameters, and environmental parameters, and driver behavior is classified into different classes by implementing a refined asynchronous advantage actor critic (RA3C) algorithm for assistance generation. (iii) If the driver's current state is found to be an emergency state, an alert message is disseminated to the surrounding vehicles in that area and to the neighboring areas based on traffic flow by using jelly fish search optimization (JSO). If a neighboring area does not have a fog node, a virtual fog node is deployed by executing a constraint-based quantum entropy function to disseminate alert messages at ultra-low latency. (iv) Personalized assistance is provided to the driver based on behavior analysis to assist the driver by using a multi-attribute utility model, thereby preventing road accidents. The proposed driver behavior analysis and personalized assistance model are experimented on with the Network Simulator 3.26 tool, and performance was evaluated in terms of prediction error, number of alerts, number of risk maneuvers, accuracy, latency, energy consumption, false alarm rate, safety score, and alert-message dissemination efficiency.  
clustering; driver behavior analysis; alert message dissemination; personalized assistance; E-RSU; fog computing +
Footnote †: journal:
# Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control
## 1 Introduction
Nonlinear orbit control provides feedback guidance for the low-thrust direction and magnitude, only relying on the instantaneous state (position and velocity) of the spacecraft, without any need for a precomputed transfer path. This strategy can be regarded as a viable alternative to optimal control, especially in those mission scenarios where uncertainties on the initial state or technological failures can occur. Although optimal control outperforms nonlinear orbit control, the performance penalty associated with the use of feedback guidance in place of optimal control can be considerably reduced through proper tuning of the control gains Tundra and Molniya (2016). Nonlinear orbit control was already proven to be very effective for Earth orbit transfers and maintenance Tundra and Molniya (2018) and constellation deployment Tundra and Molniya (2018); Tundra and Molniya (2019). This research aims at extending the range of application of nonlinear orbit control to elliptic orbits with some prescribed, time-varying orbit elements. Numerical simulations are being presented, assuming either nominal or nonnominal flight conditions, i.e., stochastic failures of the propulsion system, errors in estimating orbit perturbations, and significant displacements of the initial conditions from the nominal values, due to injection errors at the planetary capture. In summary, the major objectives of this work are (i) the identification of a repeating-ground-track, quasi-synchronous, elliptic orbit suitable for Martian constellations, (ii) the related coverage analysis, with a special focus on high-latitude regions, (iii) the extension of nonlinear orbit control to the case of target elliptic orbits with some time-varying orbit elements, and (iv) the numerical testing of the nonlinear feedback strategy for satellite deployment, both in nominal and nonnominal flight conditions.
## 6 Numerical Examples
### Generalized Euler Flows  
We consider the motion of \(N\) particles of an incompressible fluid, e.g., water, in a bounded domain \(\Omega\) in discrete time steps \(t_{k}\coloneqq(k-1)/(K-1)\), \(k\in[K]\). We assume that we know the function \(\sigma\colon\Omega\to\Omega\), which connects \(N\) initial positions \(x_{i_{1}}\in\Omega\) of particles with their final positions \(x_{i_{K}}\in\Omega\). At each time step \(t_{k}\), we know an image of the particle distribution, which is described by the discrete marginal measure \(\mu^{k}\), \(k\in[K]\), with uniform weights \(\mu^{k}_{i_{k}}=1/N\). We want to find out how the single particles move, i.e., their trajectories. Due to the least-action principle, this problem can be formulated as the MOT problem (4) with the circle-structured cost  
\[C_{i}=\left\|x_{i_{K}}^{K}-\sigma\Big{(}x_{i_{1}}^{1}\Big{)}\right\|_{2}^{2}+ \sum_{k=1}^{K-1}\left\|x_{i_{k+1}}^{k+1}-x_{i_{k}}^{k}\right\|_{2}^{2},\quad i \in I,\]  
see [55; 56; 57]. Then, the pair marginal  
\[\mathbf{\Pi}_{1,k}\coloneqq\sum_{\ell\in[K]\setminus\{1,k\}}\sum_{i_{\ell} \in[n_{\ell}]}\mathbf{\Pi}_{i_{1},\ldots,i_{K}}\]  
of the optimal plan \(\mathbf{\Pi}\) provides the (discrete) probability that a particle that was initially at position \(x_{i_{1}}\in\Omega\) is in position \(x_{i_{k}}\in\Omega\) at time \(t_{k}\), \(k=2,\ldots,K-1\). The one-dimensional problem has been studied by several authors [25; 26; 39], where the particles are assumed to be on a grid. Here, we consider the case where the positions are uniformly distributed. We draw \(N=400\) uniformly distributed points on \(\Omega=[0,1]\). We use \(K=5\) marginal constraints and the entropy regularization parameter \(\eta=0.05\). Figures 8 and 9 display the probability matrix \(\mathbf{\Pi}_{1,k}\) describing the motion of the particles from initial time \(t_{1}=0\) to time \(t_{k}\), where we use \(r=50\) iterations for both the NFFT-Sinkhorn and the Sinkhorn algorithms, and two different connection functions \(\sigma\).  
Figure 7: Given measures: \(\{1,4,6,7\}\), entropy regularization parameter \(\eta=5\cdot 10^{-3}\), \(r=150\), \(\bar{w}=\frac{1}{4}(1,1,1,1)\). NFFT-Sinkhorn parameters: \(M=156\), \(p=3\). The test images are adapted with permission (MIT License) from [[https://github.com/PythonOT/POT](https://github.com/PythonOT/POT)]([https://github.com/PythonOT/POT](https://github.com/PythonOT/POT)), see [54]. 2016, [PERSON]. (**a**) Sinkhorn. (**b**) NFFT-Sinkhorn.
###### Abstract  
Determining the fracture toughness of rock materials is a challenging, costly, and time-consuming task, as fabricating a sharp crack in rock specimens will lead to failure of the specimen, and preparing specimens for determining the rock fracture toughness requires special equipment. In this paper, the relationship between mode I fracture toughness (KIC) with the rock index properties, mineralogy, and petrography of limestone is investigated using simple nonlinear and simple/multiple linear regression analyses to provide alternative methods for estimating the fracture toughness of limestones. The cracked chevron notched Brazilian disk (CCNBD) method was applied to 30 limestones with different petrographic and mineralogical characteristics under both dry and saturated conditions. Moreover, the index properties of the same rocks, including the density, porosity, electrical resistivity, P and S wave velocities, Schmidt rebound hardness, and point load index, were determined. According to the statistical analyses, a classification based on the petrography of the studied rocks was required for predicting the fracture toughness from index properties. By classifying the limestones based on petrography, reliable relationships with high correlations can be introduced for estimating the fracture toughness of different limestones using simple tests.  
mode I fracture toughness; limestone; petrography; rock index properties +
Footnote †: journal: Applied sciences
# Ultrasound Intima-Media Complex (IMC) Segmentation Using Deep Learning Models
## 4 Experimental Results
### Evaluation
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F-Measure** & **Dice** & **Jaccard** & **FPS** \\ \hline DeepCrack\_CNN & 0.631 & 0.675 & 0.652 & 0.652 & 0.484 & 17.074 \\ \hline DeepCrack\_CNN + Post-processing & 0.834 & 0.618 & 0.697 & 0.697 & 0.544 & 17.074 \\ \hline DeepCrack\_Self (\(q\) = 3) & 0.652 & 0.688 & 0.669 & 0.669 & 0.503 & 13.45 \\ \hline DeepCrack\_Self + Post-processing & 0.792 & 0.691 & 0.721 & 0.721 & 0.571 & 13.45 \\ \hline PiDiNet & 0.687 & 0.825 & 0.750 & 0.750 & 0.60 & 20.62 \\ \hline PiDiNet + Post-processing & 0.876 & 0.740 & 0.791 & 0.791 & **0.661** & 20.62 \\ \hline Transformer & 0.68 & 0.826 & 0.746 & 0.746 & 0.595 & 11.427 \\ \hline Transformer + Post-processing & **0.882** & **0.849** & **0.801** & **0.801** & 0.656 & 11.427 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the proposed and implemented models on the CUBS dataset. The **bold** and underline fonts respectively represent the **first** and **second** place. Figure 4: (**a**) The precision-recall curve for ultrasound IMC segmentation using the self-ONN with different \(q\) settings, (**b**) using the CNN and self-ONN with \(q\) = 3 and \(q\) = 5.  
boundaries of the segmented object to become slightly blurred and more diffuse, resulting in a larger area being assigned to the object than is actually present in the ground truth.
## 3 The Proposed System
### Pre-Processing Unit  
The pre-processing unit normalizes the images to have zero mean value and standard deviation equal to one. We use the z-score normalization [15] method. Let the pixels from an image are \(\{x_{i}\}\) and the data matrix \(X=[x_{0},x_{1},x_{2},\cdots x_{n}]\). The normalized image data \(x^{\prime}\) can be computed as:  
\[x^{\prime}=\frac{x_{i}-\overline{x}}{\sigma} \tag{1}\]  
where \(\overline{x}\) is the mean value of \(\{x_{i}\}\) and \(\sigma\) is the standard deviation of \(\{x_{i}\}\) of the entire dataset.
## 1 Introduction
The authors [31] also proposed a real-time superpixel segmentation method (DBSCAN) to apply density-based spatial clustering. Small initial superpixels are further merged with adjacent superpixels to ensure connected superpixels. [PERSON] and [PERSON] [32] proposed the linear spectral clustering (LSC) to generate superpixels based on simple k-means clustering. The algorithm maps a five-dimensional color space into a ten-dimensional space, resulting in superpixels with high boundary adherence. All the above clustering-based methods usually consist of two steps: (1) Regular initialization of seeds to generate superpixel, and (2) update seeds to the center of mass. The results of this algorithm depend on the initial position of seeds, and the seed regular initialization ignores the content sensitivity. To address this issue, we propose a novel superpixel segmentation algorithm, which adopts the image local entropy and the boundary adjustment strategy to generate superpixels. In summary, the contribution of this paper includes the following three respects:  
1. The seeds initialized by most cluster-based superpixel algorithms are spatially uniform. Our method introduces image entropy at seeds sampling, which aims at generating content-sensitive superpixels to improve the accuracy of superpixel segmentation. The image local entropy can well measure the content sensitivity of the image and can initialize the seeds at a lower computational cost. 2. We used boundary adjustment to optimize superpixels. Adaptive boundary adjustment can generate regular superpixels in sparse content areas and superpixels with good boundary adherence in dense content areas, and the boundary adjustment can quickly converge. 3. The proposed algorithm was evaluated on the Berkeley Segmentation Database (BSDS 500) [33]. Compared with other five methods, the algorithm achieved better segmentation accuracy while ensuring superpixel compactness. The rest of the paper is organized as follows. The Section 2 introduces our method and analyzes the parameters in detail. The experimental results, compared with the five representative methods, are shown in Section 3. Experimental results are discussed in Section 4. In Section 5, we show the foreground segmentation performance of the superpixel algorithm proposed in contour closure. Finally, a brief summary is made in Section 6.
## 2 Materials and Methods
### Image Segmentation
#### 2.3.3 Segmentation Quality Evaluation  
As discussed previously, optimal segmentation maximizes the accuracy of the classification results. Over-segmentation is acceptable but leads to plurality of solutions while under-segmentation is unfavorable and produces mixed classified objects that defy classification altogether. In this study, we used a supervised metric, namely the Euclidean Distance 2 (ED2), to estimate the quality of segmentation results. This metric optimizes two parameters in Euclidian space: potential-segmentation error (PSE) and the number-of-segmentation ratio (NSR). The PSE gauges the geometrical congruency between reference objects and image segments (image object candidates) while the NSR measures the arithmetic correspondence (_i.e._, one-to-one, one-to-many, or many-to-one) between the reference and image object candidates. The highest segmentation quality is reported when the calculated ED2 metric value is close to zero. We refer the reader to [46] for further detail on the mathematical formulation of PSE and NSR. In order to generate a reference data set for quality evaluation, we tasked an expert to manually digitize guano stains.
## 3 Experimental Results and Analysis
### Wilcoxon Rank Sum Test  
[PERSON] et al. [30] proposed that it is not sufficient to evaluate the performance of metaheuristic algorithms using only the average and standard deviation. Therefore, it is essential to perform statistical tests. The Wilcoxon rank sum test, a binary hypothesis, determines whether there is a significant difference between two samples. It is assumed that H\({}_{0}\): the overall difference between the two samples is not significant, and H\({}_{1}\): there is a significant difference between the two samples. If the significance level between two samples is less than 0.05, then the H\({}_{0}\) hypothesis can be rejected; otherwise, the H\({}_{1}\) hypothesis is rejected. The significance level between such algorithms is denoted by \(p\).  
In this section, the results of one run of each meta-heuristic algorithm are taken as 1 element in the corresponding sample, and there are 30 elements in each set of samples. Combining the sample data of ISSA and the sample data of either metaheuristic algorithm can help to find the \(p\) value between them. NaN implies that the two samples are identical and cannot be tested with the Wilcoxon rank sum test, which indicates that the performance of the two sets of algorithms is the same. The \(p\) values on each benchmark function are shown in Table 2. The \(+\) located in parentheses after \(p\) represents a significant difference between the two samples, and \(-\) represents no significant difference. \(p\) combined with the AVG corresponding to the two algorithms can effectively determine the superiority of the algorithms. If AVG of ISSA is smaller than AVG of the comparison algorithm and H\({}_{1}\) holds, it means that ISSA performs better than the comparison algorithm. If AVG of ISSA is larger than AVG of the comparison algorithm and H\({}_{1}\) holds, the performance of ISSA is weaker than that of the comparison algorithm. If AVG of ISSA is the same as AVG of the comparison algorithm and H\({}_{1}\) holds, then the two algorithms have the same performance. In the remaining cases, it can be regarded as uncertain in terms of statistical significance.  
Table 3 gives the statistical results of the performance advantages and disadvantages between ISSA and the remaining five algorithms on 23 benchmark functions, respectively. As shown in Table 3, the performance of ISSA is better than that of SSA on 13 benchmark test functions, while the performance of ISSA is the same as that of SSA on the remaining 10 benchmark functions. This indicates that the proposed algorithm in this paper has achieved some success in terms of the performance of the optimization search. Compared with the GTO proposed by [PERSON] et al. in 2021 Abdollahzadeh et al. (2021), ISSA outperforms each other on all six benchmark functions and has the same performance on 13 benchmark functions. This indicates that ISSA still has some advantages over the recently proposed meta-heuristic algorithm.