esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Qualitative results indicate that the PCA-SVR model not only improves prediction accuracy but also reduces computational complexity. The model's ability to capture the underlying patterns in the data, despite the presence of noise, demonstrates its robustness. Furthermore, the engineering example showcases the practical application of the model in real-world scenarios, highlighting its potential for guiding shield-tunneling operations.  
While the PCA-SVR model performs well, there are still areas for improvement. For instance, the model's sensitivity to certain driving parameters could be enhanced by incorporating more comprehensive datasets. Additionally, exploring advanced machine learning techniques, such as deep learning, could further improve prediction accuracy and computational efficiency. Future research should also focus on validating the model across different shield-tunneling projects to ensure its generalizability and reliability.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Accuracy**} \\ \cline{2-5}
**Defense** & \multicolumn{2}{c}{**No Injections**} & \multicolumn{2}{c}{**With Injections**} \\ \cline{2-5}  & **Mean** & **Std Dev** & **Mean** & **Std Dev** \\ \hline
**CNN Classifier drop/drop** & 0.986131 & 0.009274 & 0.745306 & 0.010356 \\ \hline
**CNN Classifier no\_drop/no\_drop** & 0.985406 & 0.009316 & 0.709558 & 0.012134 \\ \hline
**LSTM drop** & 0.938945 & 0.079128 & 0.686017 & 0.027225 \\ \hline
**LSTM no\_drop** & 0.954807 & 0.057592 & 0.685402 & 0.025105 \\ \hline
**RL drop/drop** & **0.997037** & **0.004194** & **0.951322** & 0.014763 \\ \hline
**RL no\_drop/no\_drop** & 0.965619 & 0.008333 & 0.889717 & 0.013051 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of models trained and evaluated with and without injections on data collected from PASTA hardware environment, ablating for the inclusion of observation dropping.
## 3 The Adaptive Attitude Control Strategy of the MAV with Actuator Dynamics
### The Online Parameter Estimation Method for the MAV
#### Remarks  
i. As for the constraint given by inequality (10), on the one hand \(T_{a}\) should be chosen small enough such that inequality (10a) holds at any time, but on the other it is necessary beforehand to add a feedback signal like \(k_{q_{0}}q\) to the input signal \(\delta_{p_{min}}\) without any modification of the parameter regulation strategy if inequality (10b) cannot hold in some cases, where \(k_{q_{0}}\) is used to guarantee the stability of the plant.  
ii. The importance of the proper choice of \(G_{f}(s)\) has been mentioned above. Here we assume that:  
\[\begin{cases}G_{f}\left(s\right)=G_{f_{1}}(s)G_{f_{2}}\left(s\right)\\ G_{f_{1}}\left(s\right)=\dfrac{1}{T_{f1}s+1}\\ G_{f_{2}}\left(s\right)=\dfrac{1}{T_{f2}s+1}\end{cases} \tag{22}\]  
Thus, for any variable of interest \(\chi\), we have:  
\[\begin{cases}\dot{\chi}_{f}=\dfrac{1}{T_{f2}}\left(\chi_{f1}-\chi_{f}\right)\\ \ddot{\chi}_{f}=\dfrac{1}{T_{f1}T_{f2}}\left(\chi-\chi_{f1}-\chi_{f2}+\chi_{f} \right)\end{cases} \tag{23}\]  
where \(\chi_{f},\chi_{f1}\) and \(\chi_{f2}\) are acted as the outputs of \(G_{f},G_{f1}\) and \(G_{f2}\) in response to \(\chi\), respectively. Thus, we can conclude that the adaptive laws given by Equations (19) and (20) are computable.  
iii. \(\varepsilon_{f}\) can be chosen as \(\beta\left(T_{a}\ddot{e}_{f}+a_{\dot{q}}\dot{e}_{f}+a_{\dot{q}}e_{f}\right)^{ 2m+1}\) or \(\beta\,\)sgn\(\left(T_{a}\ddot{e}_{f}+a_{\dot{q}}\dot{e}_{f}+a_{\dot{q}}e_{f}\right)\), _etc._, where \(\beta\,\)>\(\,0\) and \(m\,\)=\(\,0,\pm 1,\cdots\). The deliberate choice of \(\varepsilon_{f}\) can speed up the convergence rates of identification parameters.
# Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters
###### Abstract  
Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step.  
sEMG; spatio-temporal image; image enhancement; multi-scale Hessian-based filter +
Footnote †: journal: Applied Sciences
# Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain
## 2 Data and Methods  
### Data  
In this study, remotely sensed GPP and ET datasets developed by Global Land Surface Satellite (GLASS) were used with a resolution of 0.05\({}^{\circ}\)[36; 37]. [PERSON], et al. [37] simulated the GPP dataset with high accuracy based on the revised light utilization efficiency model, and the coefficient of determination between the observed and estimated GPP values was 0.81 (R\({}^{2}\) = 0.81). [PERSON], et al. [36] combined five process-based algorithms using Bayesian model averaging to improve satellite-based global terrestrial LE estimates by comparing monthly LE observations at 240 sites with the algorithm's LE estimates. The algorithm has lower root mean square error (32.8 W/m\({}^{2}\)) and R-squared value (0.80). The verification of ground station data and estimated data shows the reliability of the GPP and ET remote sensing product data used in this paper. To analyze meteorological effects on WUE, the spatial resolution of temperature and precipitation data is 0.5' [38]. The reclassified land use data (MCD12C1) in the study area are shown in Figure 1. In this study, ecosystem WUE is analyzed and non-vegetation areas are excluded.
## 2 Perfluorocarbon (PFC) Filling
### Sonographic Imaging  
The use of PFC lung filling has been intensively investigated and applied in pneumology for decades, but only a few authors considered its further use for sonographic imaging. Initially, [PERSON] et al. [16] hypothesized the use of sonography for the guidance of interventions in a patent. Later work investigated the acoustic properties of PFC liquids (see Table 1) for such sonographic use in large animal sheep models [17]. Herein, a strong hyperechoic character of PFC filled lung, incapable of visualizing lung structure was reported. Only trans-pulmonary imaging of the beating heart, in proximity to the chest wall, was described.  
Later work investigated PFC filling in vivo by [PERSON] et al. [18] using porcine models and [PERSON] et al. [19] in infants. In these undertakings, visualization of central structures was not found and sonography appear as white lung syndrome with low penetrability in absence of A Lines (Figure 2). Table 2 summarizes the literature with corresponding sonographic finding of a PFC filled lung.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Liquid Used** & **Speed of Sound [m/s]** & **Impedance [MRay]** & **Reflection Coefficient** & \multicolumn{2}{c}{**Attenuation**} \\ \hline PFC - Perflubron [17] & 580 & 1.08 & 4.7\% (0.42 dB) & 0.22 & 1.8 \\ PFC - FC 77 [21] & 560 & 0.96 & 7.9\% (0.75 dB) & 0.35 & 1.6 \\ PFC-Decalin [17] & 620 & 1.25 & 1.0\% (0.087 dB) & 0.20 & 1.3 \\ Perfluorohexane [21] & 482 & 0.79 & 13.5\% (1.26 dB) & 0.35 & 1.6 \\ Saline (35 \({}^{\circ}\)C) [23] & 1520 & 1.53 & 0.03\% (0.026 dB) & 0.0014 & 2.0 \\ \hline Alveolar tissue [22] & 1640 & 1.72 & 0\% & 0.21 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of published values for speed of sound, impedance as well as attenuation (\(A\)\(f^{\text{th}}\)) for several PFC liquids, saline @35 \({}^{\circ}\)C as well as alveolar tissue. The reflection coefficient of intensities is based on the impedance mismatch to alveolar lung tissue.  
Figure 2: Sonographic image of the lung during static PFC administration in infants showing white lung between the rib shadows without the visualization of lung structure and absence of A-lines. (With permission from [PERSON] et al. [19]).
## 1 Introduction
Given that the image processing method is effective, simple, and does not require expensive equipment, it is a good solution to analyze the discharges under different experimental conditions. However, if the aim is to detect the conventional glow discharge (APGD), it can only be used with short exposure time (~10 ns, which requires an expensive camera). When higher exposure times are used, the filaments cannot be always distinguished. Nevertheless, the image processing can be still used to analyze the uniformity of the discharge: in this paper an image processing algorithm is set up to study the uniformity of the discharges rather than the discharge mode (homogeneous or filamentary). This analysis of the discharge uniformity is based on the image gray level histogram [13] and produces, as a result, an estimation of the \(\beta\) partial discharging ratio. Additional statistical analysis of the images also allows a quantified comparison of the uniformity of the discharges. Other diagnostics of DBD systems can be also achieved on the basis of measured electrical waveforms. Using these measurements, it is possible to identify the parameters of the well-known Manley diagram [14] and its associated DBD's equivalent circuit [15]. Figure 1: Typical discharge appearance with partial discharging and different \(\beta\) percentages (white frame showing the limits of the reactor). Originally based on the assumption that the discharge is uniform (with parameters of the equivalent circuit corresponding to a full use of the surface of the electrodes, i.e., \(\beta=1\)), this approach can be simply extended [8; 16] to consider partial discharging situation. In order to implement complementary approaches for diagnostic of the discharges obtained in a DBD reactor for NOx abatement studies, the two aforementioned approaches (image processing and equivalent circuit) are considered in this paper. The article is organized as follows: Section 2 presents first the reactor, the power supply and the experimental test-bench, for which measurements are used in this paper. Then, the image processing algorithm for diagnostic with estimation of the \(\beta\) percentage of the surface covered by the discharge is detailed. Finally, the equivalent circuit, considering partial discharging phenomenon and the identification of its parameters (chief the \(\beta\) ratio) using the electrical measured waveforms, is presented. In Section 3, the application of the two methods at different operating conditions obtained with the test-bench is proposed and the correspondence between the obtained \(\beta\) percentage values is discussed.
## 3 Simulation Results and Analysis  
Orbital collisions were divided into short-term natural evolution orbital collisions and long-term orbital collisions during the constellation lifetime to evaluate the influence of constellations in different states; the TLEs were obtained from Reference [20]. This study divided the space into multiple orbital layers to facilitate the calculation. Given that the megaconstellation satellites in LEO operate at near the designed orbit, only collisions in the orbital layer where the megaconstellation is located need to be calculated.
# Multirobot Task Planning Method Based on the Energy Penalty Strategy
## 3 Algorithm Model  
(1) Task quantity constraint of robot  
Suppose \(Q_{ij}\) represents the attribute value of the \(j\)th task quantity executed by the \(i\)th robot, when \(Q_{ij}\) = 1 means execution, and \(Q_{ij}\) = 0 means no execution. Each robot is required  
Figure 1: Task planning algorithm framework based on energy penalty.  
to complete at least one task, and each task must be performed. The formal description of the constraint is shown in Equation (2) below.  
\[s.t.\ \begin{cases}1\leq\sum\limits_{j=1}^{m}Q_{ij}\leq m-n+1,&\text{Task quantity constraint of the $i$th robot}\\ \sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}Q_{ij}=m,&\text{Task quantity constraint of all robots}\\ Q_{ij}*Q_{kj}\
eq 1,&\text{one task cannot be executed repeatedly}(i\
eq k)\end{cases} \tag{2}\]  
(2) Moving distance of robot to complete a task (Equation (3))  
\[Dist(R_{i})=\sum\limits_{j=i}^{m}Q_{ij}*l_{ij} \tag{3}\]  
where \(Dist(R_{i})\) is the total movement distance of the \(i\)th robot to complete all its tasks, and \(l_{ij}\) is the linear distance between the current position of robot \(i\) and the \(j\)th task point.  
(3) Energy consumption of robot (Equation (4))  
\[E(R_{i})=\sum\limits_{j=1}^{m}Q_{ij}*K_{j}*A*Dist(R_{i}) \tag{4}\]  
where \(E(R_{i})\) is the energy consumption of robot \(i\) to complete all its tasks according to the task sequence, and \(K_{j}\) is the task weight coefficient. In practical applications, the tasks of robots are usually different, which directly affect changes in energy consumption. Specifically, the tasks can also be the same. For general situations, we set \(K_{j}\) as Equation (5).  
\[K_{j}=1+(j-1)*0.1,j=1,2,\cdots,m \tag{5}\]  
where A represents the energy consumed per unit distance. In this paper, it is assumed that the robot is moving at a constant speed.  
(4) Objective function  
There is a need to ensure that the energy consumption of each robot is balanced and easy to implement. Thus, compared with the construction idea of penalty function and genetic algorithm in paper [34], this paper constructs penalty energy according to the mechanism wherein the energy consumption of each robot exceeds the average value, takes the minimum sum of the total energy consumed by the robot and the penalty energy as the optimization goal, and sets the adjustable energy consumption error threshold, which can achieve multi-objective balance on the basis of the optimal total energy consumption. The formal description of the objective function is shown in Equation (6).  
\[\min(F)=\sum\limits_{i=1}^{n}E(R_{i})+(PE)_{i} \tag{6}\]
###### Abstract  
As the leading platform of online education, MOOCs provide learners with rich course resources, but course designers are still faced with the challenge of how to accurately improve the quality of courses. Current research mainly focuses on learners' emotional feedback on different course attributes, neglecting non-emotional content as well as the costs required to improve these attributes. This limitation makes it difficult for course designers to fully grasp the real needs of learners and to accurately locate the key issues in the course. To overcome the above challenges, this study proposes an MOOC improvement method based on text mining and multi-attribute decision-making. Firstly, we utilize word vectors and clustering techniques to extract course attributes that learners focus on from their comments. Secondly, with the help of some deep learning methods based on BERT, we conduct a sentiment analysis on these comments to reveal learners' emotional tendencies and non-emotional content towards course attributes. Finally, we adopt the multi-attribute decision-making method TOPSIS to comprehensively consider the emotional score, attention, non-emotional content, and improvement costs of the attributes, providing course designers with a priority ranking for attribute improvement. We applied this method to two typical MOOC programming courses--C language and Java language. The experimental findings demonstrate that our approach effectively identifies course attributes from reviews, assesses learners' satisfaction, attention, and cost of improvement, and ultimately generates a prioritized list of course attributes for improvement. This study provides a new approach for improving the quality of online courses and contributes to the sustainable development of online course quality.  
MOOC; text mining; multi-attribute decision-making; course improvement; deep learning
## 5 Interpretation and Discussion  
### Maceral Group  
#### 5.1.1 Vitrinite  
The vitrinite mceral group is with a reflectance generally between the range of the darker liptinites and lighter inertinites. The vitrinite group consists of three subgroups and six macroals. These macroals are derived from different humic materials with varying pathways of transformation within the beats. The different types of mceral groups, as well as their origin, are presented in Table 3. Molecular components of plants do not produce macroals and maceral groups but constitute lithotypes, which later formed the coal. For instance, vitrinite, with or without small amounts of liptinite and inertinites, consists of vitrain, durain, and clarain. The durain is made up of three mceral groups in approximately equal proportions. The fusain dominates the inertinite group, whereas the clarain is a blend of vitrain and durain [23]. Colottellinite is the dominant vitrinite mceral with 38.5 to 57.7 inc. mm vol.% in the samples. The dominance of colottellinite gives a clear indication that the coal in the Vele colliery is derived from the parenchymatous and woody tissues of roots, stems, and leaves, composed of cellulose and lignin, and originating from herbaceous and arborescent plants [23]. By geochemical gelification (vitrinitization), the primary structures disappear. The precursor of colottellinite in low-rank coals is ulminite. At higher rank levels, colottellinite is also formed from telinite and its vitrinitic cell fillings. According to the vitrinite classification scheme [24], colottellinite is very common in shaly coals, and it is kerogen Type III. In the analyzed samples, colodetrinite ranges from 1.2 to 8.9 mmf vol.%. According to the vitrinite classification [24], it is derived from parenchymatous and woody tissues of roots, stems, and leaves, composed of cellulose and lignin. The original plant tissues are crushed by strong decomposition at the beginning of the peat stage. The small particles are cemented by humic colloids within the peat and successively homogenized by geochemical gelification (vitrinitization). Cellulose-derived substances may be the main source of collodetrinite rather than lignin-rich wood precursors of collodetrinite in low-rank coals such as attrinite and densinite [25]. The corpogellinite in the samples ranges from 1.0 to 19.4 maceral mmf vol.%. Corpogellinite belongs to the gelovitrinite subgroup within the vitrinite maceral group and is made up of homogeneous and discrete bodies, signifying cell infillings. Corpogelinite may be of a primary source corresponding to cell contents, derived from the tannin in part; it may also have been derived from secretions of the cell walls.
# Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites
## 4 Discussion
To summarize, our approach combined multiple data (surface reflectance, bottom reflectance, and bathymetry) derived solely from Dove satellite sensors to identify benthic compositions which are provided at a high spatial resolution and accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Benthic Classes** & **Users’** & **SD/Range** & **Depth** & **Surface** & **Surface** & **Bottom** & **Bottom** \\  & **Accuracy** & & **(m)** & **Blue** & **Green** & **Blue** & **Green** \\ \hline Coral Reef Crest & 0.58 & 0.303 & 1.09 & 0.0685 & 0.0748 & 0.0353 & 0.0395 \\ Coral Patch Deep & 0.64 & 0.292 & 7.4 & 0.0446 & 0.0412 & 0.027 & 0.0258 \\ Coral Back-reef/flat & 0.69 & 0.293 & 2.3 & 0.073 & 0.0773 & 0.0408 & 0.0446 \\ Coral Fore-reef & 0.77 & 0.295 & 6.2 & 0.0456 & 0.0424 & 0.0262 & 0.0259 \\ Gorgonian/Soft Coral & 0.49 & 0.288 & 8.3 & 0.0506 & 0.045 & 0.0345 & 0.0314 \\ Hardbottom/Algae & 0.65 & 0.328 & 10.4 & 0.0443 & 0.0371 & 0.03 & 0.026 \\ Seagrass Dense & 0.92 & 0.299 & 2.7 & 0.047 & 0.0485 & 0.0244 & 0.026 \\ Seagrass Sparse & 0.84 & 0.299 & 1.7 & 0.0853 & 0.0944 & 0.0474 & 0.0528 \\ Sand Shallow & 0.92 & 0.299 & 5.4 & 0.1156 & 0.1084 & 0.0832 & 0.0793 \\ Sand Deep/Macroalgae & 0.81 & 0.297 & 12.8 & 0.0561 & 0.0428 & 0.0535 & 0.0401 \\ Water & 0.88 & 0.298 & 19.2 & 0.01 & 0.0194 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy and mean physical values of different benthic compositions within the classified objects. SD/Range means standard deviation to range ratio calculated from Table 2.  
Figure 6: Mean surface reflectance and mean bottom reflectance of different benthic classes.
## 4 Stochastic Differential Power Control Game  
A promising direction in game theory is to design the method of transmit power control to improve the energy consumption of MTC users. This problem is modeled by utilizing the concept of a stochastic differential game with a proven Nash equilibrium existence. According to (9) the energy efficiency of MTCDs is directly proportional to the battery lifetime. Hence, the formulated problem is solved by manipulating the transmit power of MTCDs to optimize the energy consumption and eventually the operating time to prolong the network lifetime. The proposed SDG framework utilizes the concept of utility maximization for its players to come up with optimal control by considering system state and actions taken by players. The accuracy of the system state and utility model is of prime importance to the ability of an SDG to obtain an optimal control policy for its players.  
The transmit power control problem is a continuous time problem and an SDG design must be defined for all points in time from starting timing instant \(T_{i}\) to final time instant \(T_{f}\). Typically, a stochastic differential game \(\mathcal{G}\) consists of an independent set of players \(\mathcal{K}=\{1,\ldots,K\}\) (the MTC nodes) with associated action set \(\mathcal{P}_{k}\) and the corresponding state space \(\mathcal{S}_{k}\). A utility function \(\theta_{k}\) is a measure that analyses the performance of chosen actions and strategies by each player from set \(\mathcal{K}\). A control policy \(C_{k}\) is the set of power values to achieve an optimal power control objective.