esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
At present, advanced scrap aluminum sorting technologies mainly include: Eddy Current Separation (ECS) technology [9; 10; 11; 12; 13]: by generating a changing magnetic field, which acts on the input scrap; due to the different conductivity and density of different types of aluminum scrap, the aluminum scrap is thrown out at different distances, and sorting can be completed by placing the collection device in advance, but this technology struggles to deal with small amounts of aluminum alloys; X-ray Fluorescence (XRF) analysis: Through the fluorescent radiation generated by emitted X-rays and detected with a solid-state photocathode detector, the concentration of elements in aluminum scrap can be obtained to classify the different alloy components, but it is not currently universally applicable due to the strict X-ray control needed; Laser-Induced Breakdown Spectroscopy (LIBS): With the rapid development of laser-induced breakdown spectroscopy in recent years [14; 15; 16], the most important present-day research on the classification of aluminum scrap uses laser-induced breakdown spectroscopy, sometimes combined with deep learning, machine learning, and neural networks, which can realize the rapid detection and classification of aluminum scrap but requires a clean and flat aluminum surface and has a high cost. All the above classification techniques have certain defects. The deep learning-based method proposed in this paper effectively avoids these problems. It has a lower operating cost, a faster detection speed, and can realize the classification of large batches, which has more universal applications. This paper conducted classification experiments on three grades (1060, 5052, 6061) of aluminum blocks: 1060 is pure aluminum, 5052 is alloy aluminum with high magnesium content, and 6061 is alloy aluminum with high magnesium and silicon contents. From the elemental composition, it can be seen that 1060 is very different from 5052 and 6061, while 5052 is not very different from 6061. As the difference between 5052 and 6061 is little, if we can realize the classification of 5052 and 6061, this study has strong practicality. This paper compared different grades of aluminum blocks in terms of light source, surface features, and color space using machine vision algorithms, created different datasets for the above-mentioned different grades of aluminum blocks, trained the different datasets using deep learning algorithms, selected the dataset with the highest correct model training rate as the best dataset for this paper, and used it as the subsequent training dataset. The ResNet18 network model was used to train the dataset, and the accuracy and stability of the model were improved by hyperparameter optimization. The experimental results show that the combination of machine vision and deep learning could effectively classify different grades of aluminum blocks.
## 5 Conclusions and Future Works  
Analysis of existing competency design and evaluation methods revealed that these processes are not formalized and, in most cases, rely on designers and tutors' competencies. Most of the e-learning systems require a knowledge database to adapt and personalize the learning process to the individual student. The design and format of the knowledge database could be standardized and adapted for both personal as well as automated learning. Therefore, we proposed a hierarchy-based competency structure, where competency tree is designed and can be used for different purposes.  
One of the competency tree application areas is e-evaluation. Current e-evaluation systems are not oriented to harmonized competency achievement evaluation--each course and tasks has its competencies, and there is no clear connection between them. By using competency tree and proposed evaluation method, all courses, test, and tasks will be mapped to one hierarchical structure; therefore, its analysis provides a clear dependency and the sequence between different courses and even tasks within different courses.  
The proposed competency tree design and its application for e-evaluation were tested with two groups of students. The results revealed that the mark distribution and values in the proposed e-evaluation method are more similar to the tutors' evaluation compared to the traditional e-evaluation system tasks. As well the test with multiple tasks, which are mapped to competency tree, can reveal students' competencies rather than summarized score of all competencies and allows individual selection of competency achieving path.  
The proposed method for competency tree design is based on context modeling of subject content and ensures the structure of competency tree. Meanwhile, the proposed quantitative score calculation based on competency tree methodology allows a smooth transition between traditional score marking and achieved competency marking.  
The next steps for the competency tree integration into existing study systems are the implementation of competency tree design tools, competency tree integration into existing e-learning systems, and visualization of student's achievement history.  
All authors contributed to designing and performing measurements, data analysis, scientific discussions, and writing the article.  
This research received no external funding.  
The authors declare no conflict of interest.
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## 5 Conclusions  
The current paper analysed the performance obtained by a GNC scheme employing GNSS-like signals from a Lunar navigation constellation to assist a landing mission. The current work is presented as a continuation of previous manuscripts analysing the topic of Lunar navigation constellations [14; 16; 20], where the specific case under study considers a landing mission to the Lunar South Pole, a target of many programmed and identified space missions.  
In particular, the lunar constellation optimised in [14] was exploited as a baseline service for the navigation task of a South Pole landing mission. The presented GNC chain comprises an offline optimised guidance to be used as a set-point for a reference tracking PID controller. Two different controller tunings have been considered, one targeting high landing precision (case A) and the other low propellant expenditure (case B). The nominal results provided in both cases demonstrate very good performance, with final navigation errors below 2 m and control errors around 56 m and 380 m for case A and case B, respectively. Naturally, given the much larger control errors with respect to the navigation ones, in both cases further improvements in the final landing precision can be obtained with alternative and more refined control laws. A Monte Carlo analysis was performed to assess the robustness of the proposed solutions and verify their success rate, considering a 500 m accuracy threshold in the horizontal plane at 1 km altitude. The high-performing case A provided a relatively low success rate of 51%, against the 86% of case B, which also resulted in the lowest fuel mass expenditure. For this reason, it is overall advisable to exploit the lower-performing tuning parameters.  
Future works should look in the direction of improving the guidance and control strategy, with the goal of combining the good pinpoint landing performance of case A with the low propellant mass expenditure and the high robustness of case B.  
Conceptualization, G.Z.; methodology, G.Z. and M.C.; software, G.Z. and M.C.; validation, G.Z. and M.C.; formal analysis, G.Z. and M.C.; investigation, G.Z. and M.C.; resources, G.Z. and M.C.; data curation, G.Z.; writing--original draft preparation, G.Z.; writing--review and editing, G.Z. and M.C.; visualization, G.Z.; supervision, M.L.; project administration, M.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability StatementNo new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflict of interest.  
Figure 15: Histogram representation of the required propellant mass for case A (dark violet) and case B (purple).
# Analysis and Design of Rateless Two-way Relay Networks Based on a Multiply-and-Forward Scheme
## 6 Conclusions  
In this paper, we investigated a fountain-coded MF-TWRN in the Rayleigh flat fading channel. Specifically, SLT codes are applied at both source nodes. The SERs of uncoded TWRN with different forwarding schemes were first derived and the theoretical analysis proved that our proposed MF-TWRN outperforms the traditional AF-TWRN. To analyze the BER performance of the proposed network, we first offered the equivalent channel model from the perspective of a point-to-point communication system. Using this equivalent channel model, we then studied the lower bound on BER in the MF-TWRN. The corresponding degree distribution was optimized for the Rayleigh flat fading channel. Through simulations, it is shown that our degree distribution has a significant BER performance increase for the proposed MF-TWRN compared to other degree distributions in the literature. In the future work, we will consider a more general application scenario, for example, asymmetric channels.  
Figure 8: Throughputs of the SLT-coded TWRN system with different degree distributions.  
Figure 7: FER performance of SLT-coded MF-TWRN using different degree distributions with \(k=4000\).  
**Author Contributions:** All the authors contributed extensively to the work presented in this paper. [PERSON] and [PERSON] conceived the original idea, and [PERSON] implemented all the experiments for the study; [PERSON] contributed to the article's organization and provided suggestions that improved the quality of the paper; [PERSON] revised the manuscript and guided the overall research; [PERSON] provided the computing resources and co-guided the research. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Funding of Jiangsu Innovation Program for Graduate Education, the Fundamental Research Funds for the Central Universities (Grant No. KYLX15_0287), National Natural Science Foundation of China (61601222), Natural Science Foundation of Jiangsu Province (BK20160789), China Postdoctoral Science Foundation (2018M632303).and National Key Research and Development Project (Grant No.2017 YFC0822404).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Reducing Q-Value Estimation Bias via Mutual Estimation and Softmax Operation in MADRL
## 2 Research Background  
### Deep Reinforcement Learning  
Reinforcement learning can be modeled as an agent is faced with a sequential decision making problem. At time \(t\), the agent observes state \(s_{t}\) from the environment, and adopts action \(a_{t}\) based on its policy. Due to the interaction of the agent, the environment transitions from state \(s_{t}\) to the next state \(s_{t}+1\) and returns a reward \(r\) to the agent. If the environment transitions to a termination state, we call it the end of an episode. The goal of reinforcement learning is to enable agents to learn an optimal policy to obtain the maximum cumulative reward (called return) from the entire episode.  
Reinforcement learning algorithms can be classified into policy function-based [5] and value function-based [6]. A common value function is the action value function, also known as the Q function, which inputs the current state of the environment and the action taken by the agent, and outputs an estimate of the expected return for that state-action pair. When the agent learns the optimal Q function, the optimal policy can be to select the action that can maximize the output value of the optimal Q-function in any state. In deep reinforcement learning, we use deep neural networks to fit the policy function, value function, and Q function, hence also known as policy networks, value networks, and Q networks.
# Improved DRUS 4H-SiC MESFET with High Power Added Efficiency
## 3 Results and Discussion  
The Effect of the Doping Concentration (\(N_{d}\)) and Thickness(\(H_{1}\)) of the Upper Layer Region on the Device Parameters  
The effect of the thickness and doping concentration of the upper layer region on DC and AC parameters are shown in Figure 3. It can be seen from the Figure 3a that as the doping concentration increases, the absolute value of the threshold voltage increases significantly. When \(H_{1}\) is less than 0.05 \(\upmu\)m, the absolute value of Threshold voltage (\(V_{1}\)) is gradually decreasing with the increase of \(H_{1}\). Because there is a longitudinal concentration gradient in the channel. The concentration gradient produces a longitudinal electric field that weakens the pinch-off voltage, which results in a decrease in the absolute value of the threshold voltage. When \(H_{1}\) is greater than 0.05 \(\upmu\)m, the absolute value of \(V_{1}\) is increasing with the increase of \(H_{1}\). Because as the doping thickness increases, the number of electrons in the channel increases, causing the threshold voltage to drift negatively. At \(H_{1}\) = 0.05 \(\upmu\)m, the absolute value of the threshold voltage has a minimum value. In Figure 3b. As the doping concentration increases, Gate-source capacitance (\(C_{\text{gs}}\)) also increases. At the same time, \(C_{\text{gs}}\) shows an upward trend with the increase of \(H_{1}\). In Figure 3c,d, as the doping concentration increases, the saturation current (\(I_{\text{dsat}}\)) increases significantly, and the breakdown voltage decreases. When \(H_{1}\) rises from 0 to 0.05 \(\upmu\)m, the saturation current increases rapidly. When \(H_{1}\) increases from 0.05 \(\upmu\)m, the rising trend of saturation current becomes slow.  
The Effect of the Doping Concentration (\(N_{d}\)) and Thickness of the Upper Layer Region (\(H_{1}\)) on PAE  
The influences of the doping concentration and thickness of upper layer region on PAE is shown in Figure 4. It can be seen from the figure that as the doping concentration of the upper layer region increases, the PAE significantly decreases. When the thickness of the upper layer region is less than 0.05 \(\upmu\)m, the PAE increases with the increase of \(H_{1}\). When \(H_{1}\) is greater than 0.05 \(\upmu\)m, the PAE decreases with the increase of \(H_{1}\). When the thickness of the upper layer region is 0.05 \(\upmu\)m, the PAE reaches a maximum value.
# Time-Dependence of the Mechanical Behavior of Loess after Dry-Wet Cycles
###### Abstract  
The structure, time-dependent mechanical deformation, and strength characteristics of loess, which is loose and porous with well-developed vertical joints, are greatly affected by the dry-wet cycles, which are attributed to periodic artificial irrigation, rainfall, and water evaporation. To better understand the creep characteristics of loess under the effect of dry-wet cycles, Q\({}_{2}\) loess samples obtained from the South Jingyang County, China, were subjected to different dry-wet cycles (0, 5, 10, 15, 20) and sheared in triaxial creep tests. The experimental results revealed that: firstly, the maximum value of the deviatoric stress corresponding to creep failure gradually decreases with an increase in the dry-wet cycles. Secondly, the long-term strength of the loess after dry-wet cycles were obtained through the Isochronous Curve Method. It is found that the long-term strength and the number of dry-wet cycles showed an exponential decreasing relationship. In addition, the creep damage mechanism of loess due to dry-wet cycles is proposed. This study may provide the basis for understanding the mechanical behavior of the loess under the effect of dry-wet cycles, as well as guidelines for the prevention and prediction of loess landslide stability.  
loess; dry-wet cycles; triaxial creep test; creep behavior; strain-time; long-term strength Article
## 2 Design of Analytical Models
### Ground Motion and Scaling  
Seismic hazard was defined based on the maximum considered earthquake (MCE) defined in Korean Design Standard (KDS 41 17 00: 2019) [12]. Effective ground acceleration S, that can be read from peak ground acceleration map, was assumed to be 0.22 g, corresponding to seismic zone I, and the site classification was assumed to be S\({}_{4}\). As a result, the site belongs to the seismic design category D, for which the building height of ordinary RC shear walls is limited below 60 m. The response spectrum for the MCE is plotted in Figure 3.  
Three ground motion suites were selected for the design of three analytical models, respectively. The individual suite is comprised of seven pairs of horizontal components. Ground motion records used in nonlinear dynamic analysis were selected from the NGA-West2 database of PEER [13]. The shear wave velocity corresponding to site class S4 is defined as more than 180 m/s, but the range of shear wave velocity was increased by a small amount to secure a sufficient number of ground motion suites. A method proposed by [PERSON] et al. [14] was used for selecting ground motion records fitting the target MCE spectrum in good agreement. The details of ground motion suites adopted for the design of three analytical models are summarized in Table 2.  
Figure 2: Axial force ratio by wall length. In accordance with KDS 41 17 00, the SRSS spectrum of two horizontal components for each ground motion pair shall be scaled not to fall below 90% of the target spectrum defined as 1.3 times the MCE spectrum. The range of the period for scaling individual response spectrum is defined as 0.2 to 1.5 times the first mode period, T1, in KDS 41 17 00, but the lower bound is modified to be the period where the modal mass participation ratio is over 90% in order to consider the effect of the higher mode sufficiently. Figure 3 shows the target spectrum and 90% thereof and individual scaled spectra for the Model B analysis model. In this study, a two-dimensional analysis was performed. Only the single horizontal component of ground motion was applied to the in-plane horizontal direction in a single nonlinear dynamic analysis. Each component, comprising a ground motion pair, was applied to the analysis alternately to take into account uncertainty in the azimuth of horizontal ground motion in accordance with KDS 41 17 00. Therefore, a total of 14 nonlinear dynamic analyses were conducted in the in-plane direction for the design of each analytical model.
###### Abstract  
With an extension in service years, bridges inevitably suffer from performance deterioration. Columns are the main components of bridge structures, which support the superstructure. The damage of pier columns is often more harmful to bridges than that of other components. To accurately evaluate the time-varying characteristics of corroded columns, this paper proposes a new model for the bearing capacity evaluation of deteriorated reinforced concrete (RC) eccentric compression columns based on the Hermite interpolation and Fourier function. Firstly, the axial compression point, the pure bending point and the balanced failure point were selected as the basic points, and the deteriorated strength of these basic points was calculated by considering factors such as concrete cracking, reduction of reinforcement area, buckling of the steel bar, bond slip and strength reduction of confined concrete. After that, the interpolation points were generated by a piecewise cubic Hermite interpolating polynomial, and the explicit expression of the interpolation points fitting function was realized by the trigonometric Fourier series model. Finally, comparison studies based on measured data from forty-five corroded RC eccentric compression columns were conducted to investigate the accuracy and efficiency of the proposed method. The results show that: (1) the prediction results for bearing capacity of corroded RC columns are in good agreement with the measured data, with the average ratio of predicted results to test results at 1.06 and the standard deviation at 0.14; (2) the proposed model unifies the three stress states of axial compression, eccentric compression and pure bending, and is consistent with the continuum mechanics characteristics; (3) the decrements of axial load carrying capacity for 10% and 50% of the corrosion rate are 31.4% and 45.2%, while in flexure they are 25.4% and 77.4%, respectively; and (4) the test data of small-scale specimens may overestimate the negative effect of corrosion on the bearing capacity of actual structures. The findings in this paper could lay a solid starting point for structural life prediction technologies based on nondestructive testing.  
b 2019 9 24 2019 10.3390/app9010024
## 4 Discussion  
### Passes  
Two of the variables with the greatest impact on our model are related to the distance of the passes. The results show that top teams prefer short passes, whereas other teams prefer long passes. These findings agree with existing research. Specifically, [PERSON] et al. [22] found that a lower rate of long passes is associated with more points and a better ranking position, whereas [PERSON] et al. [43] found that teams with the highest number of long pass attempts lose ball possession more frequently and that only less than 1% of long passes resulted in shots on goal. Additionally, [PERSON] et al. [44] found that teams that make more short passes score more goals, while teams that make more long passes score fewer goals, a finding that was also confirmed in the study by [PERSON] et al. [45], who found that teams increased the likelihood of a possession ending in a goal if they increased short passes and decreased long passes. It is worth noting that much earlier, [PERSON] et al. [46] had found that the greatest contribution to distinguishing between teams that finish at the top versus teams that finish at the bottom of the English Premier League was made by the total successful short passes completed by defenders. This preference for short passes is linked to better ball retention, reduced risk of turnovers, and the ability to build attacks gradually, leading to more high-quality scoring opportunities.  
Figure 3: This figure depicts (**a**) the SHAP feature importance and (**b**) the distribution of SHAP values for the LR best performing ML classifier across the testing instances.  
A way in which a player can transfer the ball to a teammate is through balls. Our research shows that top teams perform through balls more frequently, which agrees with the existing literature. Specifically, research has shown that there was a correlation between through balls and goals succeeded by a team [47]. As a result, through balls can increase a team's probability of success [21]. The frequent use of through balls by top teams highlights their tactical approach focused on breaking defensive lines and creating goal-scoring opportunities in the final third, which is a critical aspect of modern attacking strategies [48].