esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology and Parametric Optimization-Based Design Processes for Lightweight Structures
## 4 Results
### Comparison of the Three Applied Design Workflows
When the mass reduction is of high importance, regardless of the optimization time, the simultaneous PO and TO workflow is the best choice. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Design Workflow** & **N** & **Mean** & **StDev** & **95\% CI** \\ \hline TO & 9 & 533.3 & 152.8 & (376.7–689.8) \\ \hline PO & 12 & 755.8 & 248.6 & (620.2–891.4) \\ \hline PO + TO & 9 & 783.2 & 261.7 & (626.7–939.8) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The essential statistics of the workflows. Figure 18: Presentation of the results clustered in the TO, PO, and PO + TO workflows: (**a**) Mass, (**b**) maximum stress, (**c**) optimization time, and (**d**) interval plot for the three case studies. Concerning the choice between AM and CMP, it seems that the TO mostly concerns the AM. However, a redesign procedure in the post-processing of the topologically optimized designs can allow their manufacturing from the CMP. In particular, processes (1), (5), and (8) constitute the processes without redesign, and thus, they mainly concern AM. On the other hand, redesign processes (2), (3), (6), (7), (9), and (10) can be time demanding but can decrease the maximum stress of the structures and make possible their manufacturing using conventional methods. Finally, a size/shape PO allows the designer to skip the TO level and create more traditional design solutions. In other words, for large complex structures, such as buildings [32] and airplanes [33], the first design workflow (TO) can be reasonable where the mass reduction rate is high. On the other hand, the second design workflow (PO) could be chosen either for parts of structures or components. The bicycle crank arm [34] and the ski binding [5] are two typical examples. Finally, the best choice for either small or customized components with small mass tolerances, such as human implants [35], could be the simultaneous PO and TO workflow. An illustration of these examples is depicted in Figure 19b.
# Study on Mechanism of Series-Flow of Pollutants between Consecutive Tunnels by Numerical Simulation
## 3 Results and Discussions
### Variation Characteristics of the Series-Flow Ratio  
The series-flow ratio \(\varphi_{s}\) is affected by tunnel operation parameters (i.e., \(u_{\text{up}}\) and \(u_{\text{down}}\)) and tunnel structure parameters (i.e., \(\Delta L\) and \(D\)).  
Figure 7 shows the relationship between \(\varphi_{s}\) and \(\Delta L\)/\(D\) for the tunnels of two lanes (\(D\) = 6.1 m) and three lanes (\(D\) = 8.6 m), where \(u_{\text{up}}\) = \(u_{\text{down}}\) = 1.5 m\(\cdot\)s\({}^{-1}\). It can be seen that \(\varphi_{s}\) decreases with increasing \(\Delta L\)/\(D\). Figure 8 gives, at \(u_{\text{up}}\) = \(u_{\text{down}}\) = 1.5 m\(\cdot\)s\({}^{-1}\), the longitudinal sections of CO concentration field of three-lane tunnels at different values of \(\Delta L\)/\(D\) (CO concentration is non-dimensionalized). It shows that for a larger value of \(\Delta L\)/\(D\), the jet is more fully developed with a larger boundary at the downstream tunnel entrance. Thereby, more pollutants are scattered into ambient atmosphere; namely, consistent with Figure 7, \(\varphi_{s}\) is smaller.  
Studies have indicated that while \(\varphi_{s}\) almost has no connection with \(u_{\text{up}}\) or \(u_{\text{down}}\) individually, it is clearly related to \(u_{\text{up}}\)/\(u_{\text{down}}\)[18]. Figure 9 shows how \(\varphi_{s}\) varies with \(u_{\text{up}}\)/\(u_{\text{down}}\) for the tunnels of three lanes (\(D\) = 8.6 m) at different values of \(\Delta L\)/\(D\). At a constant \(\Delta L\)/\(D\), \(\varphi_{s}\) assumes a slowing decrease with the increase of \(u_{\text{up}}\)/\(u_{\text{down}}\). If comparing the results of this paper and [PERSON]'s model [17], one can see that while \(\varphi_{s}\) has the similar change rules in the studies, the difference between them grows up with increasing \(\Delta L\)/\(D\) at a same \(u_{\text{up}}\)/\(u_{\text{down}}\). For example, at \(\Delta L\)/\(D\) = 23.4, the maximum deviation of interflow value between this paper and literature is 53.7%. [PERSON]'s series-flow model can predict the change rules of \(\varphi_{s}\) well for \(\Delta L\)/\(D\) \(<\) 11.6, but its applicability for \(\Delta L\)/\(D\) \(>\) 11.6 needs further verification.  
Figure 8: Distribution of CO concentration in the longitudinal section at different \(\Delta L\)/\(D\).  
Figure 7: Impacts of \(\Delta L\)/\(D\) on series-flow ratio \(\varphi_{s}\).
# Computer-Vision-Based Product Quality Inspection and Novel Counting System
## 3 Methods and Result
### Product Counting Algorithm Using Camera-Based Skeleton Tracking and Body Part Detection
#### 3.2.3 Unit Weight Calculation and Counting Method Using Image Differencing Technique
\[\mathrm{C_{product}}=\frac{\mathrm{\Delta W}}{\mathrm{W_{unit}}} \tag{12}\]
* Count Error Correction: In this system, only products weighing 1 kg or more are counted. If the weight change is less than 1 kg, it is excluded from the count. Specifically, weight changes of 0.5 kg or less are considered minor fluctuations and are disregarded in the count results. This prevents errors caused by small weight changes and ensures that only the actual weight changes of the products are accurately reflected in the count. The experimental results presented in Figure 10 and Table 2 were obtained in collaboration with an automobile parts manufacturer to verify product accuracy. The test was conducted by counting 10 products ranging in weight from 1 kg to 45 kg, achieving an accuracy of 99.268% in the product coefficient test. \[\mathrm{Overall\ Accuracy}(\%)=\frac{\mathrm{\Sigma\ Accuracy\ for\ Each\ Test}}{\mathrm{Number\ of\ Tests}} \tag{13}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multirow{2}{*}{**Product**} & **Product** & **Actual** & **Estimated** & **Accuracy (\%)** \\  & & **Weight** & **Quantity** & **Quantity** & **Quantity** & **Accuracy (\%)** \\ \hline
1 & A & 6.00 & 184 & 183 & 99.46 \\ \hline
2 & B & 43.58 & 24 & 25 & 95.83 \\ \hline
3 & C & 3.10 & 254 & 254 & 100.00 \\ \hline
4 & D & 3.93 & 92 & 91 & 98.91 \\ \hline
5 & E & 11.80 & 42 & 42 & 100.00 \\ \hline
6 & F & 4.27 & 66 & 67 & 98.48 \\ \hline
7 & G & 14.44 & 35 & 35 & 100.00 \\ \hline
8 & H & 4.23 & 111 & 111 & 100.00 \\ \hline
9 & I & 1.35 & 50 & 50 & 100.00 \\ \hline
10 & J & 2.30 & 30 & 30 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Product counting accuracy test results.
# Development and Validation of a System for Game Control Based on a Force Plate
## 6 Conclusions  
In this paper, a new system for game control is described which is based on measuring the player's CoP and the CoM acceleration along the z axis using a force plate. Controlling the games is accomplished by simulating pressing the UP, DOWN, RIGHT, LEFT, and SPACE keyboard keys or moving the mouse cursor and clicking the left mouse button, all based on the measurements of the player's CoP and CoM acceleration along the z axis. A mechatronic system was developed which consists of the following parts: a force plate used to measure the ground reaction force with load cells, an electronic board for measuring and PC communication via USB which processes the forces measured on the load cells, and a PC application used to gather the measured forces, calibrate the system, and control the games. A mathematical model of the fame control system was created which transferred the player's CoP from the force plate frame into the local frame for game control. The main part of the mathematical model for the game control system is the system calibration procedure, which is used to adapt the system to each individual player so as to ensure the best player experience in game control. The calibration procedure is conducted in three steps. In the first calibration step, the system calculates the player's mass and initial CoP position so that the game control will not depend on the way the player stands on the force plate. The next game control system calibration step enables finding the player's work plane so that all possible key combinations or all mouse cursor positions can be enabled. The third calibration step conducts measurements of intensity of the impulse half squats with the goal of defining a trigger threshold for the simulation of pressing the SPACE key or clicking the left mouse button. It has been experimentally proven that the game control system can be successfully adapted to the player, allowing for a very good player experience when it comes to controlling games. Along with controlling games, the developed mechatronic system can also be used to control a variety of other objects, such as mobile robots, or it can be used to analyze human walking, balance, and postural stability.  
Figure 17: Simulation of moving the mouse cursor.  
**Author Contributions:** Conceptualization, Z.V. and G.B.; methodology, Z.V. and I.S.; software, Z.V., G.B., K.H. and I.S.; validation, Z.V., G.B. and K.H.; formal analysis, Z.V., K.H. and I.S.; writing--original draft preparation, Z.V.; writing--review and editing, Z.V.; visualization, Z.V. and G.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from the one subject involved in the study.  
**Data Availability Statement:** The data used in the study are available from the authors and can be shared upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
## 3 Integrated Prediction Method Based on the LSTM-SVR Model
### Algorithm Evaluation Indicators  
There are many indicators for evaluating prediction performance, and these quantifiable indicators can better reflect the pros and cons of prediction results. In this paper, root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation of absolute percentage error (SDAPE), coefficient of determination (\(R^{2}\)), and refined Willmott index (RWI) are used as evaluation indicators for prediction effectiveness [41; 42]:  
(1) Root mean square error:  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{N}{(y_{i}-\widehat{y}_{i})}^{2}}{N}} \tag{13}\]  
Figure 4: \(R^{2}\) corresponding to weight coefficient.  
Mean absolute percentage error: \[MAPE=\frac{1}{N}\sum\limits_{i=1}^{N}\left|\frac{y_{i}-\widetilde{y}_{i}}{y_{i}} \right|\times 100\%\] (14) (3) Standard deviation of absolute percentage error: \[SDAPE=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(\left|\frac{\widetilde{y}_{i }-y_{i}}{y_{i}}\right|-MAPE\right)^{2}}\] (15) (4) Determination coefficient: \[R^{2}=\frac{\sum\limits_{i=1}^{N}\left(y_{i}-\overline{y}\right)^{2}-\sum \limits_{i=1}^{N}\left(y_{i}-\widetilde{y}_{i}\right)^{2}}{\sum\limits_{i=1}^ {N}\left(y_{i}-\overline{y}\right)^{2}}\] (16) (5) Refined Willmott index: \[RWI=1-\left[\frac{\sum\limits_{i=1}^{N}\left|\widetilde{y}_{i}-y_{i}\right|} {2\sum\limits_{i=1}^{N}\left|y_{i}-\overline{y}\right|}\right]\] (17) where \(N\) represents the predicted data length, and \(y_{i}\) and \(\hat{y}_{i}\) are the true value and the predicted value, respectively. The average absolute percentage error between \(y_{i}\) and \(\hat{y}_{i}\) is represented by the RMSE, which reflects the average deviation between the true and predicted values. The value of RMSE is generally greater than or equal to 0, and when RMSE = 0, the prediction accuracy reaches the highest. The closer the MAPE is to 0 and the closer \(y_{i}\) is to \(\hat{y}_{i}^{\wedge}\), the better is the prediction performance. The smaller the SDAPE value, the more robust is the prediction. The value of the \(R^{2}\) is between 0 and 1 and is equal to 1 when there is no error. The RWI is between 0 and 1; a value of 1 means that the estimate exactly matches the actual value, and 0 means that the estimated value does not match the actual value at all. The above three indicators are used as the evaluation basis for the prediction performance of the model.
# Considering the Consequences of Cybersickness in
## Appendix B
  
\begin{tabular}{c c c} \hline \hline
**Appendix B** & \\ \hline
**Section and Topic** & **Item** & **Checklist Item** & **Location Where Item** \\ \hline  & & TITLE & **Is Reported** \\ \hline Title & 1 & Identify the report as both systematic review and meta-analysis. & Page 1 \\ \hline  & & ABSTRACT & \\ \hline  & & Provide a structured summary including, as applicable: background; objectives; & \\ Structure summary & 2 & data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; conclusions and implications & Page 1 \\  & & of key findings. & \\ \hline  & & INTRODUCTION & \\ \hline Rationale & 3 & Describe the rationale for the review in the context of what is already known. & Page 2 \\ \hline  & & & \\ Objectives & 4 & Provide an explicit statement of questions being addressed with reference to & \\  & & participants, interventions, comparisons, outcomes, and study design (PICOS). & Page 2–4 \\ \hline  & & METHODS & \\ \hline  & & Present the systematic review registration number. And specify the inclusion & \\  & & and exclusion criteria for the review and how studies were grouped & Page 4 \\  & & for the syntheses. & \\ \hline  & & & \\ Information sources & 6 & Specify all databases, registers, websites, organizations, reference lists and other & \\  & & sources searched or consulted to identify studies. & Page 4 \\ \hline  & & & \\ Search strategy & 7 & Present the full search strategies for all databases, registers and websites, & \\  & & including any filters and limits used. & Page 4–5 \\ \hline  & & & \\ Selection process & 8 & Specify the methods used to decide whether a study met the inclusion criteria of & \\  & & the review, including how many reviewers screened each record and each report & Page 5 \\  & & retrieved, whether they worked independently, and if applicable, details of & \\  & & automation tools used in the process. & \\ \hline  & & \\ Data collection process & 9 & Specify the methods used to collect data from reports, including how many & \\  & & reviewers collected data from each report, whether they worked independently, & Page 5 \\  & & any processes for obtaining or confirming data from study investigators. & Page 5 \\ \hline  & & & \\ Data items & 10 & List and define all outcomes for which data were sought. Specify whether all & \\  & & results that were compatible with each outcome domain in each study were & Page 5 \\  & & sought (e.g., for all measures, time points, analyses). & \\ \hline  & & \\ Study risk of bias assessment & 11 & Specify the methods used to assess risk of bias in the included studies, including & \\  & & details of
## 5 Experiments and Results
### Baseline  
In this experiment, we use the MADDPG, MA2C and FMA2C as the baseline to compare with our algorithm. Among them, MADDPG is a classic algorithm in the multi-agent field. The algorithm can be applied in competitive or cooperative scenarios by setting different reward functions. The workers in our algorithm are improvements based on MADDPG. At present, variants of the MADDPG algorithm have been applied in many scenarios and achieved good results.  
MA2C is an extension of the A2C algorithm in a multi-agent TSC environment. In this algorithm, each agent can obtain the policy and action information of other agents to improve its local observability. At the same time, a spatial discount factor is designed according to the location information between the agents to weaken the signals of the farther agents, so that they can focus more on their surroundings. FMA2C is a combination of hierarchical reinforcement learning and MA2C algorithm. The algorithm designs a hierarchical MDP suitable for the TSC environment and proposes a specific form of the goal. The MA2C is implemented based on the source code released in the paper [19]. FMA2C implements a hierarchical architecture based on the MA2C. Similar to FMA2C, our proposed algorithm is a combination of hierarchical reinforcement learning and the MADDPG algorithm.
## 3 Results and Discussion
#### 3.1.1 Impact of the Different Extraction Factors on the Extraction Yield Percentage
Moreover, increasing the extraction temperature can also affect the physical properties of the solvent, in particular, its vapor pressure and viscosity [77], since there is a proportional relationship between the extraction temperature and the vapor pressure of the solvent. The increase in the extraction temperature leads to an increase in the solvent's vapor pressure, which improves the solvent's vapor diffusivity inside the bubble cavity and the collapse intensity of these bubbles [61]. The bubble's shear force increases, destroying the cell wall structure [31]. Furthermore, [PERSON] and [PERSON] [61] stated that there is an inversely proportional relationship between extraction temperature and solvent viscosity. Increasing the extraction temperature decreases the solvent's viscosity, accelerating its flow velocity and deep penetration into the sample tissue. The latter causes the complete solubilization of bioactive substances released by the destroyed cells [31; 32], thus maximizing the extraction yield [82]. On the other hand, a linear, inversely proportional correlation was found in Figure 2a,b between the extraction yield percentage and vegetal material concentration (solid-to-liquid ratio) of _C. monocantha_ Del. This result demonstrated that the extraction yield percentage increases when the solid-to-liquid ratio decreases to a ratio of 0.5:10 g/mL, which implies an increase in the amount of solvent in the extraction reaction medium. This result aligns with the findings of [PERSON] et al. [7], who reported that the extraction yield increases with increasing temperature up to 51 \({}^{\circ}\)C during the extraction of phenolic compounds from _Lagenaria sicteraria_ fruit for 30 min with a solid-to-liquid ratio of 0.5:10 g/mL. The same observation was reported by [PERSON] et al. [50], who found an increase in extraction yield of the kleeb Bua Daeng mixture with increasing solvent volume up to nine times that of the plant material.
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
## 2 Architecture
### Inertial-Aided Scene Matching Navigation (ISMN)
#### 2.1.2 Inertial-Aided Georeference  
SMN has a mismatching problem, because the attitude changes during UAV flight will cause differences between the base map and the input images. However, due to the immunity of inertial information to external influences, the inertial information can be utilized to project the real-time image to the reference image coordinates. The matching method aided by inertial information is shown in Figure 4. The attitude of the camera can be estimated from the mechanization results of the IMU. The transformation from the body frame to the navigation frame is \(C_{b}^{n}\)[17]:  
\[C_{b}^{n}=\begin{bmatrix}\cos\gamma\cos\psi&-\cos\theta\sin\psi+\sin\theta\sin \gamma\cos\psi&\sin\theta\sin\psi+\cos\theta\sin\gamma\cos\psi\\ \cos\gamma\sin\psi&\cos\theta\cos\psi+\cos\theta\sin\gamma\sin\psi&-\sin\theta \cos\psi+\cos\theta\sin\gamma\sin\psi\\ -\sin\gamma&\sin\theta\cos\gamma&\cos\theta\cos\gamma\end{bmatrix} \tag{11}\]  
where \(\psi\), \(\theta\) and \(\gamma\) denote yaw, pitch and roll, respectively.  
The conversion relationship between the camera coordinate system and the IMU coordinate system (body coordinate system) can be expressed as  
\[C_{c}^{b}=\begin{bmatrix}\cos\varphi_{y}\cos\varphi_{z}&-\cos\varphi_{x}\sin \varphi_{z}+\sin\varphi_{x}\sin\varphi_{y}\cos\varphi_{z}&\sin\varphi_{x}\sin \psi+\cos\varphi_{x}\sin\varphi_{y}\cos\varphi_{z}\\ \cos\varphi_{y}\sin\varphi_{z}&\cos\varphi_{x}\cos\varphi_{z}+\cos\varphi_{x} \sin\varphi_{y}\sin\varphi_{z}&-\sin\varphi_{x}\cos\psi+\cos\varphi_{x}\sin \varphi_{y}\sin\varphi_{z}\\ -\sin\varphi_{y}&\sin\varphi_{x}\cos\varphi_{y}&\cos\varphi_{x}\cos\varphi_{y} \end{bmatrix} \tag{12}\]  
where \(\varphi_{x}\), \(\varphi_{y}\), \(\varphi_{z}\) refer to the installation angle between the camera and IMU.  
The camera's optical axis taken from the reference image is almost perpendicular to the ground, \(Z_{c}\) is considered to be perpendicular to the ground downwards, \(X_{c}\) and \(Y_{n}\) are in the same direction and \(Y_{c}\) and \(X_{n}\) are in the same direction, Therefore, there are  
\[C_{n}^{z}\approx\begin{bmatrix}0&1&0\\ 1&0&0\\ 0&0&-1\end{bmatrix} \tag{13}\]  
The conversion relationship between the reference maps and real-time maps can be obtained from the above:  
\[C_{c}^{c}=C_{n}^{c}C_{b}^{n}C_{c}^{b} \tag{14}\]  
Figure 4: Inertial-Aided Georeference.  
According to the above conversion relationship and camera parameters, the squint image can be converted to an orthophoto:  
\[\tilde{U}_{i}=sKC_{c}^{z}K^{-1}U_{i} \tag{15}\]  
where \(s=\frac{1}{z_{c}}\), and \(z_{c}\) is the z-direction coordinate component of \(U\) in the camera frame.\(K\) denotes the internal parameters of the camera, which can be obtained by calibration.
# Effect of C-S-H Nucleating Agent on Cement Hydration
## 4 Conclusions  
In this study, the influence of CNA on cement hydration mechanism, early strength enhancement effect, 28 d hydration degree and 28 d hydration product fractal dimension was mainly investigated via hydration kinetic calculation, resistivity test, BSE image analysis and mechanical properties. Triethanolamine (TEA) and the blank group are the control groups. The following results were obtained:  
1. The incorporation of CNA does not change the hydration mechanism of cement, which increases the hydration degree in the nucleation and crystal growth stage (NG) by 0.5, and the interactions at phase boundaries diffusion stage(I) by 0.02, which makes the cement obtain a higher degree of hydration in the initial stage of hydration;
2. CNA shortens the beginning time of the induction period by 49.3 min and the end time of the cement hydration acceleration period by 105.1 min than the Blank sample, which can accelerate the setting and hardening of the cement;
3. CNA significantly improves the hydration degree of cement mixtures cured for 28 days, which is better than triethanolamine (TEA). CNA increases the fractal dimension of hydration products, while TEA decreases the fractal dimension;
4. CNA used in this work may provide a crystal nucleus for the generation of C-S-H gel, which reduces the potential energy of C-S-H gel generation and promotes the generation of C-S-H gel. The C-S-H content of cement mixtures with CNA is higher than that with TEA or the blank sample;
5. CNA significantly improves the early strength of cement mortars; the 1-day and 3-day strength of cement mortars with CNA are more than the 3-day and 7-day strength of the blank sample, which shows that it has an excellent early strength effect.  
Conceptualization, Y.J.; data curation, W.Z. and T.P.; formal analysis, W.Z., Y.J. and X.J.; funding acquisition, Y.J.; methodology, X.J. and T.P.; project administration, Y.J.; writing--original draft, W.Z.; writing--review and editing, Y.J., X.J. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China [grant numbers 51738003, 11772120] and the National Natural Science Foundation of China [grant numbers 52008191, 11772120] and the National Natural Science Foundation of China [grant numbers 51978318]; and the Primary Research and Development Plan of Jiangsu Province [grant number BE2016187].  
Data Availability Statement: Data sharing is not applicable.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manu-script, or in the decision to publish the results.