esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Tests and Performance Comparison
### Tests and Comparison of the nomRL-PFC and the \(m\)-randRL-PFC
This is the reason the return in Figure 8 also slightly decreases with higher values of \(m_{\mathrm{ext}}\). Nevertheless, the \(m\)-randRL-PFC is able to achieve a high return for all values considered for the external mass \(m_{\mathrm{ext}}\). Observing the results above, we can state that the \(m\)-randRL-PFC shows robustness against mass variations. This agent shows satisfying performances for the complete interval of \(m_{\mathrm{ext}}\in[0\) kg, \(300\) kg]. The performance of the nomRL-PFC, however, decreases drastically \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**External Mass** & \multicolumn{2}{c}{\(m_{\mathrm{ext}}\) = 0 kg} & \multicolumn{2}{c}{\(m_{\mathrm{ext}}\) = 300 kg} \\ \hline
**Agent** & **nomRL-PFC** & \(m\)**-randRL-PFC** & **nomRL-PFC** & \(m\)**-randRL-PFC** \\ \hline \(e_{y}^{\mathrm{P}}\) [m] (RMS) & 0.013 & 0.009 & - & 0.013 \\ \(e_{y_{z}}^{\mathrm{P}}\) [m] (RMS) & 0.106 & 0.149 & - & 0.594 \\ \(e_{y_{z}}^{\mathrm{P}}\) [rad] (RMS) & 0.020 & 0.032 & - & 0.033 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The root mean square (RMS) errors of the nomRL-PFC and the \(m\)-randRL-PFC after executing the agents on the path for \(m_{\mathrm{ext}}=0\) kg and \(m_{\mathrm{ext}}=300\) kg. The best metric for each \(m_{\mathrm{ext}}\) value is marked green. Figure 9: The pathways of the nomRL-PFC (blue line) and the m-randRL-PFC (orange line) for \(m_{\mathrm{ext}}=300\) kg. The reference path is depicted by the dashed black line, whereas the road boundaries are represented by the solid black lines. when the vehicle carries an external mass. This demonstrates that the nomRL-PFC agent is not robust against additional vehicle loads. Therefore, it fails to generalize to other parameter values that impose different dynamic behavior on the vehicle. Here, applying dynamics randomization to the mass during training solves this problem and enables the \(m\)-randRL-PFC to generalize successfully.
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
###### Abstract  
With the development and popularization of Intelligent Transportation Systems (ITS), Vehicle Ad-Hoc Networks (VANETs) have attracted extensive attention as a key technology. In order to achieve real-time monitoring, VANET technology enables vehicles to collect real-time traffic updates through information collection devices and transmit this information to Roadside Units (RSUs), which are processed and integrated by an information processing center. However, high vehicle density leads to a conflict between minimizing the interval for vehicles to send Basic Safety Messages (BSMs) to RSUs and the limited communication resources of VANETs. To address this issue, we propose a MAC protocol based on the 802.11 CSMA/CA mechanism with the Preemptive-Resume Priority scheme. The arbitration device provides preemptive service to data packets with higher priority levels, thereby reducing data transmission delay. Moreover, queuing theory is employed to calculate the total delay for vehicles to send BSMs to a drone receiver, minimizing the BSM transmission interval and achieving minimal delay to meet safety driving requirements. The effectiveness and superiority of this mechanism and algorithm are demonstrated through simulation experiments.  
VANETs; UAV; CSMA/CA; Preemptive-Resume Priority; queuing theory Article
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 5 Results and Analysis
### Scenario 2: Slight Fluctuations in Weather  
The system experienced some weather fluctuations in scenario 2, although all the fluctuations occurred during the night and none during the day period. Even with the fluctuation in weather condition, the system RMSE during the night was the lowest under this scenario, while during the day readings it was the highest. Even with the fluctuations in weather conditions, the system RMSE was 0.18 m, the lowest night-reading RMSE across all scenarios. On the contrary, the system RMSE during the day was 0.51 m, the highest during the day periods across the testing period.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
###### Abstract
However, this method is not suitable for a wide cross laser-pattern, and cannot achieve attitude positioning, therefore, it does not meet project requirements. In this paper, cross laser-pattern is used as cooperative measuring marker in shipboard environment with stray light, smoke, water mist and other interference, a method is proposed to process the acquired digital image with poor contrast, low signal-to-noise ratio (SNR) and uneven energy distribution, detect the center point and attitude of cross laser-pattern image, based on the combination of Gaussian fitting and least square fitting. The results show that the method is robust, and meets the requirements of real-time and precision of the project.
## 5 Numerical Experiments
### Results and Discussion
There have been some works in the literature that address this issue in particular, such as the Clustering-based Adaptive Multi-Objective _EA_ (_CA-MOEA_) by [PERSON] et al. [34], who suggested using a clustering-based method to overcome those problems with irregular _PF_ shapes. The use of clustering helps to diversify the tentative solutions. We also did an experimental comparison of this algorithm and other relatives/variants with the ours. _MOWOATS_ is compared with this method according to _IGD_ metric, computed as [34]:  
\[IGD=\frac{\sum_{i=1}^{|P|}d(P_{i},P^{*})}{|P|}, \tag{27}\]  
where \(P\) represents the true _PF_ solutions, \(P^{*}\) stands for the optimal solutions generated from the algorithm, and \(|P|\) is the number of the true _PF_. Specifically, Table 6 contains a comparison among _MOWOATS_, _CA-MOEA_, _MOEA/D_, _EMyO/C_, _RVEA*_, _NSGA-II_, and _NSGA-III_ according to _IGD_ metric computed according to Equation (27) [34]. The results confirm that _MOWOATS_ reaches the lowest mean values of the _IGD_ metric for all test functions, except for _DTLZ73_, for which it yields the second rank. Therefore, we can confirm that _MOWOATS_ is able to handle _MOPs_ even if they have solutions with quite irregular solution spaces. Finally, we compare _MOWOATS_ with hybrid variants of _SI_-based optimizers that use the _R2_ indicator to assess the quality of the solutions regarding the _PF_. We chose (_R2 HMOPSO_, _R2_ indicator combined with _MOPSO_), _R2 HMOPSO1_, _MOEA/D_, _NSGA-II_, _MOPSO_, and _R2 MOPSO_ and, as in previous cases, computed the _IGD_ (27) after solving the problems in the benchmarks _ZDT_ (two objectives) and _DTLZ_ (three-objective functions) [15]. The values appear in Table 7. Please note that although _R2 HMOPSO_, for instance, uses _R2_ indicator to improve the diversity of _MOPSO_, _MOWOATS_ still gets better results for all test functions. This can be explained by the introduction of the new diversification step and memory elements in guiding swarm members during the search process, confirming that both procedures contribute to the improvement of solutions. From previous discussion, we can see that _MOWOATS_ revealed its superiority over many enhanced _SI_ methods. This presents the correctness of our analysis for _PSO_ and its rivals regarding their need for mature memory elements. The new diversification technique employed in _MOWOATS_ allowed it to cover the whole solution space. It also enhanced its capability to solve _MOPs_. _MOWOATS_ results showed that it can find solutions very near the true _PF_ of test functions. Also, solutions generated from _MOWOATS_ are distributed evenly over the solution space. This presents the superiority of _MOWOATS_ and qualifies it to be used for real-life _MOPs_, such as bioinformatics, engineering, and manufacturing.
###### Abstract  
The use of pulse-compression in ultrasonic non-destructive testing has assured, in various applications, a significant improvement in the signal-to-noise ratio. In this work, the technique is combined with linear phased array to improve the sensitivity and resolution in the ultrasonic imaging of highly attenuating and scattering materials. A series of tests were conducted on a 60 mm thick carbon fiber reinforced polymer benchmark sample with known defects using a custom-made pulse-compression-based phased array system. Sector scan and total focusing method images of the sample were obtained with the developed system and were compared with those reconstructed by using a commercial pulse-echo phased array system. While an almost identical sensitivity was found in the near field, the pulse-compression-based system surpassed the standard one in the far-field producing a more accurate imaging of the deepest defects and of the backwall of the sample.  
ultrasonic testing; phased array; coded signals; chirp; pulse-compression; pulse-echo; total focusing method; composites +
Footnote †: journal: Applied Sciences  
0021202111508. [[https://doi.org/10.3390/app11041508](https://doi.org/10.3390/app11041508)]([https://doi.org/10.3390/app11041508](https://doi.org/10.3390/app11041508))
# Incorporating New Technologies in EEIO Models
## Appendix D Comparative Life Cycle Assessment-All impacts  
Table 14 shows all impacts that can be calculated using the USEEIO model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Units** & **All Technologies** & **Gas Fermentation** & **Guerbet Reaction** & **Fischer Tropsch** \\ \hline Acidification & g SO\({}_{2}\) eq & 3.57 & 5.29 & 2.13 & 3.29 \\ Potential & g & 1.58 & 1.80 & 1.37 & 1.58 \\ Commercial & g & 5.13 & 5.98 & 4.38 & 5.03 \\ Commercial & g & 4.24 & 3.45 & 5.01 & 4.28 \\ Construction and & g & 4.24 & 3.45 & 5.01 & 4.28 \\ Demolition Debris & MJ & 80.22 & 90.49 & 69.14 & 81.02 \\ \hline Eutrophication & g N eq & 0.622 & 1.21 & 0.251 & 0.416 \\ Potential & & & & \\ \hline Freshwater & & & & \\ \hline Ecotoxicity & CTUe & 0.217 & 0.244 & 0.188 & 0.219 \\ Potential & & & & \\ \hline Freshwater & kg & 17.70 & 21.25 & 13.13 & 18.69 \\ withdrawals & kg CO\({}_{2}\) eq & 0.215 & 0.229 & 0.21 & 0.207 \\ \hline Hazardous Air & & & & \\ Pollutants & g & 0.0946 & 0.101 & 0.0847 & 0.098 \\ \hline Human & & & & \\ Health—Cancer & 10 \(\times\) 10\({}^{-9}\) CTUh & 0.198 & 0.207 & 0.182 & 0.204 \\ \hline Human Health— & & & & \\ Noncancer & 10 \(\times\) 10\({}^{-9}\) CTUh & 5.17 & 5.18 & 4.95 & 5.38 \\ \hline Human Health— & & & & \\ Respiratory & g PM2.5 eq & 0.375 & 0.487 & 0.298 & 0.342 \\ Effects & & & & \\ \hline Human Health & & & & \\ Toxicity & 10 \(\times\) 10\({}^{-9}\) CTUh & 5.37 & 5.39 & 5.13 & 5.59 \\ \hline Jobs Supported & 10 \(\times\) 10\({}^{-6}\) jobs & 10.24 & 13.05 & 9.82 & 7.91 \\ \hline Land use & m\({}^{2}\)*yr & 13.06 & 13.85 & 11.59 & 13.73 \\ \hline Minerals and & kg & 0.231 & 0.183 & 0.266 & 0.244 \\ Metals Use & MJ & 2.86 & 3.04 & 2.80 & 2.74 \\ \hline Nonrenewable & & & & \\ Energy Use & MJ & 2.86 & 3.04 & 2.80 & 2.74 \\ \hline Ozone Depletion & mg CFC-11 eq & 0.146 & 0.11 & 0.234 & 0.096 \\ \hline Pesticides & mg & 4.09 & 4.76 & 3.56 & 3.95 \\ \hline Renewable Energy & & & & \\ Use & MJ & 77.37 & 87.45 & 66.34 & 78.28 \\ \hline Smog Formation & & & & \\ Potential & g O\({}_{3}\) eq & 50.73 & 55.29 & 34.21 & 62.34 \\ \hline Value Added & \$ & 1.12 & 1.13 & 1.12 & 1.13 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Biofuels impacts per GGE with 3.68% substitution.
# SDA-Vis: A Visualization System for Student Dropout Analysis Based on Counterfactual Exploration
## 7 Discussion and Limitations  
We developed SDA-Vis considering the suggestions of the domain experts. We guided the design based on the analytical objectives detailed in Section 3.1. However, we identified some limitations and possibilities for future work during our construction process.  
* **Automatic student performance prediction.** We used a counterfactual explanation to generate synthetic solutions for a dropout student. However, the domain experts were also interested in automatically determining student performance. This analysis can improve the quality of feedback given to students [65]. In future work, we plan to address the analysis of secondary school grades in our research. Moreover, our approach could also design proper vocational orientation for a particular student.
* **Multiple Data Sources and Scenarios.** Combining different types of information about students and their environments such as high school grades, parents' educational level, socioeconomic level, distance to the university, and university infrastructure would be helpful for analyzing the whole scenario. Given the increasing number of initiatives by the university authorities to provide that information, an immediate direction for future work will be to combine different data sources to enrich the SDA-Vis system. Moreover, although this system was applied to the studied university, our approach could be extended to other universities, considering different data types and scenarios. The user can choose the model, data source, and scenario to improve academic performance, student retention, and curriculum design. Our approach could be versatile enough to be applied to different contexts such as loan analysis, crime reduction, and analysis of the spread of disease.
* **Global approach.** SDA-Vis only used the counterfactual explanations to prevent student dropout. Although this satisfied the users' requirements, we have discussed constructing a global student scenario analysis system. This could be used, for instance, to apply counterfactuals to improve the design structure of lectures, recommend courses to students, improve a professor's performance, and calibrate the university's fee. We are interested in tackling educational problems by using counterfactuals or other mathematical and computational mechanisms in a unique integrated analytical system, in future work.
* **Longitudinal analysis.** Despite the experiments, case studies, and validation process we conducted with the university's real data set, we consider that a longitudinal study of current students could be interesting, to address the system's usefulness in reality. We are interested in applying SDA-Vis's suggested actions to current students and analyzing the changes over time, in future analyses.
## 4 Experimental Setup and Results
### Validation of the Position Control  
The motion control accuracy of the proposed device was evaluated as shown in Figure 8. The desired path was given as a rectangular path with the dimensions of 50 \(\times\) 50 mm on the \(X,Y\) axis and an additional linear path of 20 mm on the \(Z\) axis. The velocity of the desired point along the path was set to 10 mm/s. For a smooth and precise movement of the end-effector, a cubic velocity profile was used. The desired position of the end-effector on the path is shown in Figure 8a. The movement of the end-effector started and ended at the home position of {0, 0, 0}. Figure 8b shows the desired and measured positions and the position error in each axis. The maximum absolute value for the position error was measured as 3.7, 2.54, and 1.45 mm for the \(X,\ Y\), and \(Z\) axis, respectively. The average standard deviation and RMS of the measured errors are shown in Table 3.  
Figure 8: Comparison of the desired position and measured position in (a) 3D space and (b) 1D space.
## 6 Conclusions  
This paper proposes a kinematic calibration method using optimal measurement poses based on the observability index \(O_{1}\), which can be used for six-hardpoint positioning mechanisms to effectively identify and compensate the kinematic parameter errors of the mechanisms, thereby improving the accuracy of the primary mirror pose. The proposed method selects specific measurement poses based on the index \(O_{1}\) to reduce the negative impact of measurement noise, achieving higher calibration accuracy than the traditional method. Firstly, the error model of the six-hardpoint positioning mechanism is derived. Then, introduce the observability indices and the algorithm for selecting measurement poses based on observability indices. We improved the DETMAX algorithm and used it for measurement poses selection. The performance of the five observability indices was compared through simulations of calibration, and the best performance observability index \(O_{1}\) was obtained; 2000 simulation calibrations were conducted under different conditions, ensuring that the results were statistically significant. Finally, an overall evaluation of the proposed calibration method was conducted, and the results obtained by the proposed calibration method and the traditional calibration method were compared. The results show that the proposed calibration method can accurately identify kinematic parameter errors, and has higher accuracy compared with the traditional method. The mean position error decreased by 78.4%, and the mean attitude error decreased by 70.6%. The proposed method can be used to improve the kinematic accuracy of six-hardpoint positioning mechanisms. There are two main aspects of future work planning. This paper mainly studies the kinematics parameter error and measurement noise. We will study other factors that affect the accuracy of the six-hardpoint positioning mechanism in future work, such as thermal deformation and gravity deformation. In addition, we will study the observability index and kinematics calibration on more types of robots.  
Conceptualization, Z.Y.; methodology, Z.Y. and X.W.; software, Z.Y.; validation, Z.Y., X.W. and F.W.; data curation, Z.Y.; writing--original draft preparation, Z.Y.; writing--review and editing, Z.Y., X.W. and F.W.; supervision, F.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 12133009).  
**Institutional Review Board Statement:** Not applicable.  
Figure 10: Relationship between integrated error and number of measured poses.
# Robust Kalman Filter Aided GEO/IGSO/GPS Raw-PPP/INS Tight Integration
## 2 Methods  
In this section, the mathematical models of the GPS/BDS raw PPP, INS mechanization, and R-EKF enhanced PPP/INS tight integration are described. As shown in Figure 2, INS provides position, velocity, and attitude for users by processing the specific force and angular rate measurements from inertial measurement units (IMU) in the INS mechanization [10] after the correction for sensors errors (e.g., IMU sensor biases and scale factors) and motion errors (e.g., rotational, sculling, and coning motion) [25]. Then, the EKF prediction is applied to provide the predicted covariance matrix according to the state functions and the initial covariance matrix. Afterwards, time synchronization between GPS/BDS observations and INS solutions is checked. If there is no visible GPS/BDS observation, it goes to the next IMU epoch. Otherwise, the EKF innovation is calculated by obtaining the difference between GPS/BDS observations (e.g., code, phase, and Doppler) and the INS predicted values; the INS predicted values are computed by using the INS updated position/velocity and the satellite position/velocityafter considering the GPS/BDS related errors (e.g., antenna phase center offset/variation (PCO/PCV), ionosphere delay, troposphere delay, and earth rotation [33]). To distinguish the real impacts of BDS GEO and IGSO on PPP/INS tight integration, the R-EKF is utilized. Then, the corrections for position, velocity, attitude, and IMU biases and scale factors are estimated and fed back to compensate for IMU sensor errors in the next IMU epoch.
# Scale Reduction Techniques for Computing Maximum Induced Bicliques
## 3 Exact Algorithm Based on the Proposed Scale Reduction
### Scale Reduction Techniques
#### 3.2.1 Scale Reduction for the MB Problem
\(P^{\mathcal{L}^{*}}(E,k+1)\subseteq P^{\mathcal{L}^{*}}(E,k)\subseteq P^{ \mathcal{L}}(E,k)\)_._
2. \(E^{*}\subseteq P^{\mathcal{L}}(E,k)\) _for all_ \(k\)_._  
In particular, this lemma implies that an edge from a subgraph induced by a maximum biclique will never be labeled as a conflict edge during the scale reduction process. Hence, it is tempting to remove all the conflict edges and restrict the search for a maximum biclique to the residual graph. Unfortunately, this cannot always be done since removing a conflict edge may increase the size of a maximum biclique. Consider, for example, a star graph on \(n\geq 4\) nodes with an extra edge \(\{u,v\}\) between two leaf nodes. Clearly, dropping either \(u\) or \(v\) gives a maximum biclique on \(n-1\) vertices. However, for \(\mathcal{L}=n-1\) the scale reduction process will label \(\{u,v\}\) as a conflict edge, resulting in \(G[P^{\mathcal{L}}(E,1)]\) given by a star on \(n\) vertices, which is an \(n\)-vertex biclique. On a positive side, it is safe to remove nodes adjacent to conflict edges only, i.e., the nodes that would become isolated if all the conflict edges were removed. This provides basis for the proposed scale reduction technique. The algorithm, outlined in Algorithm 2, proceeds by computing \(P^{\mathcal{L}}(E,k)\) until no new edges are labeled as conflict edges, at which point we compute \(\hat{V}=V(G[P^{\mathcal{L}}(E,k)])\), which is the set of vertices in the subgraph induced by non-conflict edges (this essentially removes the vertices that become isolated if all conflict edges are removed). The subgraph induced by \(\hat{V}\) is output as the residual graph. ```
1:Input: \(G=(V,E)\), \(\mathcal{L}:\) a feasible lower bound
2:\(k\gets 1\);
3:\(P^{\mathcal{L}}(E,0)=E\);
4:repeat
5:\(k\gets k+1\);
6:until\(P^{\mathcal{L}}(E,k)=P^{\mathcal{L}}(E,k-1)\)
7:\(\hat{V}\gets V(G[P^{\mathcal{L}}(E,k)])\); \(\rhd\) set of vertices of \(G[P^{\mathcal{L}}(E,k)]\)
8:return\(G[\hat{V}]\);
```  
**Algorithm 2** Scale reduction algorithm. Even though not all conflict edges can be removed as a result of the scale reduction procedure, the fact that they cannot be included in a subgraph induced by an optimal biclique can be used to develop optimality cuts and thus strengthen the IP formulation of the MB problem for the residual graph. Indeed, let \(\{i,j\}\) be a conflict edge that was included in the residual graph. Since \(\{i,j\}\) cannot be included in the optimal solution, at most one of the two vertices \(i\) and \(j\) can belong to a maximum biclique, and since each vertex can only be in one part of a bipartite clique, it implies that  
\[x_{i1}+x_{i2}+x_{j1}+x_{j2}\leq 1 \tag{13}\]  
for any optimal solution of (1)-(6). The inequality (13) is added to the IP for the residual graph for each remaining conflict edge \(\{i,j\}\).
## 3 Results and Discussion
### Volatile Analysis
#### 3.3.1 Variable Importance in Projection (VIP)  
For assessment of relative variable importance in each PLSR model, the information of each variable (representing a signal measurement at a specific time point) was assessed by its VIP [23]. In this study, VIP scores for each variable in the partial least-squares regression (PLSR) models arising from analysis of fatty acids, amino acids, and volatile composition were examined in relation to sensory attributes. VIP scores for each explanatory variable were examined to determine their relative importance towards sensory perception. This allows a quick identification of explanatory variables that contribute the most to sensory perception.