esa-sceva/satcom-chunk-collection huggingface dataset:
# Tolerance Synthesis of Delta-like Parallel Robots Using a Nonlinear Optimisation Method
## 3 Results  
### Tolerance Synthesis of the Delta Parallel Robot  
In this section, both linear and angular geometric tolerances of the Delta parallel robot are calculated using the proposed tolerance synthesis method. The passive and active joints are gathered in the vector \(Q_{k}\) (Equation (11)), which are calculated for fifteen different postures of the robot (cf. illustration of Figure 5). These postures are located inside a regular workspace in the form of a cylinder [30].  
Figure 4: Choice of the tolerance of the dimensional parameters.  
The following optimisation problem is then used to calculate geometric tolerances:  
\[Min\ F(\Delta L_{i},\Delta\phi_{j}),i=1,\ldots,11;j=12,\ldots,47 \tag{13}\]  
Subject to:  
\[\left\{\begin{array}{l}-0.015\ \mathrm{mm}\geq|\Delta L_{i}|\geq 0.015\ \mathrm{mm}\\ -0.015^{\circ}\geq|\Delta\phi_{j}|\geq 0.015^{\circ}\end{array}\right.\]  
The parameters of the linear dimensions of the robot are: the fixed base and the moving platform are equiangular triangles with lengths \(O_{0}O_{1i}=Ra=194\ \mathrm{mm}\) and \(O_{\mathrm{Si}}P=Rb=30\ \mathrm{mm}\), respectively, and the nominal dimensions of the parallelepipeds are \(La_{i}=260\ \mathrm{mm}\), \(Lb_{i}=480\ \mathrm{mm}\), and \(d_{i}=20\ \mathrm{mm}\).  
The tolerances of the angular parameters are grouped in the vector \(\Delta\phi=\left\{\Delta q_{1i},\Delta q_{2i},\Delta q_{3i},\Delta q_{4i}, \Delta q_{5i},\Delta q_{6i},\Delta q_{7i},\Delta\theta_{i},\Delta\alpha_{1i}, \Delta\alpha_{3i},\Delta\alpha_{5i},\Delta\alpha_{6i}\right\}\), \(i=1,\ldots,3\). The solution of the formulated optimisation problem is calculated using MATLAB function fmincon, and the \"interior-point\" method is used.  
The tolerances of the linear parameters are grouped in the vector \(\Delta L=\left\{\Delta La_{i},\Delta Lb_{i},\Delta d_{i},\Delta Ra,\Delta Rb \right\},i=1,\ldots,3\).  
The solution of the optimisation problem is calculated using the Matlab function \"\(fmincon\)\" and the \"interior-point\" method is used. The maximum position error imposed is equal to 0.2 mm. The optimisation calculation was carried out for the points \(P1\), \(P3\), \(P6\), and \(P9\), shown in Figure 5. For each posture, six tests were performed, and the results of the calculations are summarised in Tables 1 and 2 (see Appendix A).  
By applying the tolerance range shown in Figure 4, the tolerance range of each dimensional parameter is computed, and they are shown in Table 3. It is worth noting that linear parameters are presented in mm and angular parameters are presented in degrees.  
Figure 5: Representation of the \(P_{i}\) points in the regular workspace of the Delta parallel robot.
# The Role of Physical Fitness on FRAN CrossFit(r) Workout Performance
## 4 Discussion
A recent systematic review revealed moderate-to-high correlations between 1-RM strength in the back squat and CrossFit(r) performance [37], results that are in line with the findings of the present study, since the correlation between the FRAN time-trial and 1-RM squat (\(r=-0.701\)) was the highest correlation found, compared to other 1-RM strength exercises. Consequently, 1-RM strength, especially in squat, seems to be a good predictor of the FRAN time-trial in CrossFit(r) athletes. However, coaches should focus not only on increasing strength in squat but in thrusters [8] and Olympic lifts as well. The findings of the current study support that 1-RM strength in the snatch and clean and jerk was correlated with the FRAN time-trial. Both Olympic lifts are considered to be highly technical multi-joint exercises which activate the entire neuromuscular system [24]. Therefore, practicing Olympic lifts may induce higher performance enhancement in CrossFit(r) workouts. This study has some limitations. The small sample size in each group might prevent generalization of the results. Male athletes participated in the study; thus, more studies are needed to investigate differences in the physical fitness of female athletes. There were no lactate samples, muscle biopsies, or electromyography signals recorder, which might have provided a better explanation into the difference between the FG and SG. Although body fat was significantly different between the FG and SG, more studies are required to better understand the physiological differences between faster and slower athletes, not only in FRAN but in other workouts as well. Moreover, several weaknesses may arise from the current cross-sectional study; thus, more research is required with cross-over or interventional studies.
# Edge Convolutional Networks for Style Change Detection in Arabic Multi-Authored Text
###### Abstract  
The style change detection (SCD) task asks to find the positions of authors' style changes within multi-authored texts. It has several application areas, such as forensics, cybercrime, and literary analysis. Since 2017, SCD solutions in English have been actively investigated. However, to the best of our knowledge, this task has not yet been investigated in Arabic text. Moreover, most existing SCD solutions represent boundaries surrounding segments by concatenating them. This shallow concatenation may lose style patterns within each segment and also increase input lengths while several embedding models restrict these lengths. This study seeks to bridge these gaps by introducing an Edge Convolutional Neural Network for the Arabic SCD task (ECNN-ASCD) solution. It represents boundaries as standalone learnable parameters across layers based on graph neural networks. ECNN-ASCD was trained on an Arabic dataset containing three classes of instances according to difficulty level: easy, medium, and hard. The results show that ECNN-ASCD achieved a high \(F_{1}\) score of 0.9945%, 0.9381%, and 0.9120% on easy, medium, and hard instances, respectively. The ablation experiments demonstrated the effectiveness of ECNN-ASCD components. As the first publicly available solution for Arabic SCD, ECNN-ASCD would open the door for more active research on solving this task and contribute to boosting research in Arabic NLP.  
Keywords:natural language processing; style change detection; multi-authored documents; graph neural networks; Arabic language; pretrained models +
Footnote †: journal: Applied sciences
## 2 Theoretical Background
### State Estimation  
In modern Energy Management Systems (EMS), the SE process is most important for situational awareness of power system operation and is used in many EMS applications, including the detection of bad data. The common approach to SE is using the classical Weighted Least Squares (WLS) method described in [15]. In this approach, the power grid is modeled as a set of non-linear equations based on the physics of the system:  
\[\mathbf{z}=h(\mathbf{x})+\mathbf{e} \tag{1}\]  
where \(\mathbf{z}\in\mathbb{R}^{1\times d}\) is the measurement vector, \(\mathbf{x}\in\mathbb{R}^{1\times N}\) is the vector of state variables, \(h:\mathbb{R}^{1\times N}\rightarrow\mathbb{R}^{1\times d}\) is a continuously non-linear differentiable function, and \(\mathbf{e}\in\mathbb{R}^{1\times d}\) is the measurement error vector. Each measurement error, \(e_{i}\), is assumed to have zero mean, standard deviation \(\sigma_{i}\) and Gaussian probability distribution. \(d\) is the number of measurements and \(N\) is the number of states.  
In the classical WLS approach, the best estimate of the state vector in (1) is found by minimizing the cost function \(J(\mathbf{x})\):  
\[J(\mathbf{x})=\left\|\mathbf{z}-h(\mathbf{x})\right\|_{\mathbb{R}^{-1}}^{2}= \left[\mathbf{z}-h(\mathbf{x})\right]^{T}\!R^{-1}[\mathbf{z}-h(\mathbf{x})] \tag{2}\]  
where \(R\) is the covariance matrix of the measurements. In [16], it is shown that the error can be decomposed into detectable and undetectable parts where the undetectable part is recovered through the Innovation Index. Hence, the composed measurement error \(CME\) is then used for Bad Data analysis, one of the main applications of SE [17], as  
\[J_{CME}(\mathbf{\hat{x}})=\sum_{i=1}^{d}\!\left[\frac{CME_{i}}{\sigma_{i}} \right]^{2}>\chi_{d,p}^{2} \tag{3}\]  
where \(\sigma_{i}\) the measurement's standard deviation, \(p\) is the probability (typically \(p=0.95\)) and \(d\) the degrees of freedom.
## 2 E-Sail Experiment Design for Solar Wind in Lunar Orbit
### Orbit Modification with E-Sail Thrust  
As shown in Figure 1, the spin plane is perpendicular to the solar direction in the orbit modification experiment, which maximises the E-sail thrust. We propose performing the experiment in three-day windows around these ideal scenarios, which occur twice per year. The estimated E-sail thrust in the ideal point is 1 mN and it scales down as the perpendicular geometry between the spin plane and the solar wind direction changes (not yet considered in our analyses).  
We estimate the resulting orbital displacement with a General Mission Analysis Tool (GMAT) simulation of an idealistic electric thruster, which has an incredibly high specific impulse of 100,000 s, and it does not decrement the spacecraft mass. The power consumption is not considered in GMAT (only the thrust), since it is modelled differently in an operational E-sail. We assume that such an electric thruster in GMAT emulates the thrust created by the E-sail. The use of GMAT provides us with high-fidelity orbital and celestial models, including a high-order lunar gravity field and eclipses.  
The GMAT thruster points in the solar direction and provides the thrust away from the Sun, as the E-sail does (see Figure 3 on the left). For preliminary analysis, we select an arbitrary lunar orbit of 763 km in altitude and a 15\({}^{\circ}\) inclination. For maximal orbital displacement, we execute a finite burn of 1 mN thrust when the ESTCube-LuNa spacecraft is moving towards the Sun, which, in turn, reduces the orbital altitude. The thrust is not provided while the spacecraft is in an eclipse. We simulate two spacecrafts--\"E-sail\" and \"NoSailClone\"--to compare how the thrust changes the initial orbit, which is the same for both spacecraft. By executing the thrust experiment for three days, the **orbital period** is changed by some 100 s between the \"E-sail\" and \"NoSailClone\" spacecrafts, as shown in Figure 3 on the right. The reduction in **orbital altitude** (the difference between semi-major axes) is in the rage of 14 km between the \"E-sail\" and \"NoSailClone\" spacecrafts at the end of the experiment. The change in the E-sail spacecraft's orbital period introduces a significant drift in the true anomaly and the orbital position when compared with the simulated spacecraft without the thrust.  
To characterise the orbital displacement, we propose a ranging experiment for precise orbit determination (see Section 5.4 for more details), which can be complemented with the Triangulated Celestial Navigation (TCN) optical solution, as described in Section 5.5.2, when ESTCube-LuNa will not be able to establish a link with the ranging ground station. In this case, precise orbit determination needs to be performed before and after the E-sail thrust experiment. A high-accuracy orbit propagator model and an orbit determination measurement uncertainty model are necessary for extrapolating the pre-experiment orbit and comparing it with the post-experiment orbit as a result of the E-sail thrust, both measured by radio frequency (RF) ranging.
## 4 Experimental Results
### Acoustic Feature Experiment  
The trained classifier successfully predicted if an audio sample originated from a particular touch input location with an average accuracy of 86.2% (Figure 12). The model was able to predict the touch input location with varying degrees of accuracy, achieving best performance near the corners of the touchscreen located near the on-board microphone. This fits with our understanding that using multiple microphones located at different positions allow for increased distinctiveness in acoustic signal attributes. Furthermore the relative position of a particular touch input location to the two microphones remains the same and thus maintains robustness of the acoustic features used for classification irrespective of changes in device position or orientation.  
The relative importance of the audio recognition features from both channels were computed over several experiments and the top common features are listed in Table 2. The classifier (newly created) from these subset of acoustic features resulted in a slight decrease in accuracy, was able to predict touch inputs with an accuracy of up to 82.2%. In the earlier experiment (Section 4.2), device position and orientation certainly matter to the success of the classifier but Section 4.3 show that acoustic features offers resistance to changes in device position and orientation with most of the distinguishing information found in chroma deviation and specific spectral features. The resulting confusion matrix corresponding to the classifier trained using a subset of acoustic features as shown in Figure 13 shows the decrease in accuracy and higher rate of misclassification in neighbouring touch input locations compared to Figure 12.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Percentage Contribution** \\ \hline Zero Crossing Rate & 2.2\% \\ Energy & 5.3\% \\ Entropy of Energy & 3.3\% \\ Spectral Centroid & 0.6\% \\ Spectral Spread & 0.2\% \\ Spectral Entropy & 2.6\% \\ Spectral Flex & 0.6\% \\ Spectral Roll-off & 7.0\% \\ MFCC & 28.3\% \\ Chroma Vector & 49.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contribution of acoustic features in Acoustic Feature Experiment.  
Figure 12: Confusion matrix for Acoustic Feature Experiment.  
Acoustic features were further analysed using t-SNE [25]. The resulting t-SNE map that reveals the inter-region heterogeneity and intra-region homogeneity is shown in Figure 14.  
In this figure, acoustic features extracted for a given touch input location are colored according to the location label. The t-SNE scatter-plot reveals clear dimensional separations based on acoustic characteristics. Figure 14 also highlights the chances of misclassification of touch input samples in each location with physically adjacent regions having a higher probability of misclassification. Note also a few stray points of red and blue near the cluster of black: this may reflect the possibility of misclassification attributed to chance similarity of the acoustic signals collected.
## 2 Materials and Methods
### Experimental Design and Validation of Models  
The optimization approach was carried out using The Design Expert (Version 11.0.0, Stat-Ease Inc, Minneapolis, MN, USA) according to a three level, three variable Box-Behnken design with 17 design points. Three independent variables consist of hydrolysis temperature (\({}^{\circ}\)C, \(X_{1}\)), time (min, \(X_{2}\)) and sulfuric acid concentration (M H\({}_{2}\)SO\({}_{4}\), \(X_{3}\)). A narrowed three levels of each variable were selected based on the results from the single factor experimentation, denoted as lower level (\(-\)1), upper level (\(+\)1) and including the center point (0). Levels were assigned accordingly for \(X_{1}\): 115 \({}^{\circ}\)C(\(-\)1), 125 \({}^{\circ}\)C(0), to 135 \({}^{\circ}\)C(\(+\)1); \(X_{2}\): 15 min(\(-\)1), 25 min(0), 35 min(\(+\)1); \(X_{3}\): 0.05 M(\(-\)1), 0.125 M(0), 0.2 M(\(+\)1). Responses (Y) are based on the recovered yield of oligosaccharide fraction in grams and hydroxymethyl furfural concentration in the reaction liquor (g/L). Experimental data were fitted to the following second order polynomial equation proposed for the analysis of each response (Y) shown by Equation (2):  
\[\mathrm{Y}=\beta 0+\sum_{i=1}^{3}\beta i\mathrm{Xi}+\sum_{i=1}^{3}\beta i \mathrm{Xi}^{2}+\sum_{i\
eq j=1}^{3}\beta i\mathrm{Xi}\mathrm{Xi}\mathrm{Y} \tag{2}\]  
In the equation, Y is the yield of oligosaccharide fraction and hydroxymethyl furfural concentration, predicted response; \(\beta_{0}\), \(\beta_{i}\), \(\beta_{ii}\), and \(\beta_{\bar{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{ \mathrm{ \mathrm{      }}}}}}}}}}}}}}\)\) are regression coefficients for intercept, linear, quadratic and interaction terms respectively; \(X_{i}\) and \(X_{j}\) are independent variables. The significance of the model was calculated in terms contributing to the regression sum of squares. The reduced model was then acquired through the exclusion of the non-significant coefficients from the initial model after the analysis the regression model coefficients (R\({}^{2}\)) and evaluating the model lack of fit using ANOVA (\(p<0.05\)) and the Fisher test value (F-value). Response surface plots were developed to explain the effects of independent variables (temperature, time and H\({}_{2}\)SO\({}_{4}\) concentration) on the response variables (ED-F1 fraction, HMF) [29].
# An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems
## 4 Experimental Study
### Parameter Setting  
Almost all of these algorithms are implemented by using C++ programming, and the implementation environment is Visual C++ 6.0 on a personal computer (Intel Core 3.8 GHz CPU and 8.0 GB RAM). In the implementation of MOEA/D-oDE, we carry out the trial DE vector operators and polynomial mutation on all of the test instances. Meanwhile, for the algorithms compared to MOEA/D [18; 24], the simulated binary crossover (SBX) [4] operator is employed for DTLZ problems, and the DE crossover operator is employed for F1 and F2. Polynomial mutation is used in MOEA/D for all of the test problems. Evolution operators used in NSGA-II are the same as in MOEA/D. The other parameters used in MOEA/D and NSGA-II are shown in Table 1, where \(n\) is the number of variables. Additionally, the parameters of MOEA/DD and MOEA/D-AWA are the same as the parameters used in MOEA/D, and the probability of choosing parents locally of MOEA/DD is set to 0.9.  
Moreover, in MOEA/D, both the number of objectives \(m\) and the parameter \(H\) control the number of set \(\{\lambda^{1},...,\lambda^{N}\}\), where the population size \(N\) is the amount of sub-problems. Each individual weight vector ought to take a value from \(\{0/H,1/H,...,H/H\}\). Therefore, the population size, which means the number of weight vectors, is \(N=C_{H+m-1}^{m-1}\), where \(m\) is the number of objectives. Additionally, for NSGA-II, the number of the non-dominated solutions would increase as the number of objectives increases [39]. For fair comparisons, we set the values of \(H\) as 17, 13 and 10 for all of the test instances with 4, 5 and 6 objectives, respectively, in MOEA/D. Hence, we can obtain that the values of the population size N are 1140, 2380 and 3003 for 4-objective, 5-objective and 6-objective continuous test problems respectively in all three algorithms. The number of the neighborhood size is set to \(T=20\) for all of the test instances in MOEA/D-oDE and MOEA/D. Each compared algorithm runs 20 times independently for each instance. When the number of function evaluations reaches the given upper limitation, three algorithms run out. The max number of the function evaluations is set to \(250\times N\) for four- and five- objective problems and \(500\times N\) for six-objective problems.
## 2 Image Sensor: System Description
### Hardware Design
It is able to produce raw or JPEG-compressed images [15] at a maximum resolution of 640 \(\times\) 480 pixels provided by the OmniVision OV7640 image sensor. The camera is connected with the sensor board through a UART-standard serial port that it can manage data rates up to 115 Kbps. The camera module requires only 60 mA in full operation and it also has available a power standby mode to significantly reduce power consumption at sensor node sleep periods. We use the USART0 interface to connect sensor board with the camera module trough the I/O corresponding pins. The USART0 was configured with an 8N1 character format and a bit rate of 115,200 bps. Finally, we would like to supply some details about the proposed battery unit. In addition to nominal voltage and capacity, the choice of the battery depends on the ambient conditions and the shelf life of the device. Since we consider non-rechargeable batteries, we chose a lithium thionyl chloride (Li-SOCl\({}_{2}\)) battery. This battery technology offers high energy density, operating voltage stable during most of the application lifetime, wide temperature range (-55 \({}^{\circ}\)C to 85 \({}^{\circ}\)C), performance independent from cell orientation and long shelf life and reliability (over 10 years). In particular the chosen reference was the ER14250 battery from EEMB or EVE, for example. The main characteristics are nominal voltage equal to 3.6 V and capacity equal to 1,200 mAh. The battery is a cylindrical cell of size \(14.5\times 25.2\) mm, according to the device size.
## 4 Analysis and Comparison of Results
### Comparison of Acceleration Response Spectra
Their comparison is shown in Figure 23. Figure 23a,b use black, red, and blue to indicate low, resonant, and high-frequency ranges, respectively, with circle, square, and triangle markers denoting X, Y, and Z directions, respectively. The solid lines represent experimental results, and  
Figure 20: Comparison of FRS (EQ1): (a) A2; (b) A3; (c) A4. the dashed lines signify numerical results. In Figure 23c, black indicates the experimental results and red indicates the numerical results, with circle, square, and triangle markers representing the X, Y, and Z directions, respectively. As in previous comparisons, the experimental results generally showed higher amplification than the numerical results across all three methods. Amplification ratios were higher at EQ3 than at EQ1 and EQ2. Notably, while R\({}_{\text{ZPA}}\) was lower than R\({}_{\text{Avg}}\) and R\({}_{\text{Peak}}\) in EQ1 and EQ2, it exceeded both in EQ3. The increase in amplification with cabinet height and seismic motion was generally linear. As illustrated in Figure 23c, the R\({}_{\text{ZPA}}\) measured at EQ3 exhibits somewhat more irregular and nonlinear behavior with respect to height compared to R\({}_{\text{Avg}}\) and R\({}_{\text{Peak}}\). In Figure 23a,b, R\({}_{\text{Avg}}\) and R\({}_{\text{Peak}}\) result in similar trends at EQ1 and EQ2; however, in the high-frequency range of EQ3, R\({}_{\text{Peak}}\) is relatively larger than R\({}_{\text{Avg}}\). As shown in Figure 22, in the resonant frequency range of 10-20 Hz, the acceleration response is uniformly amplified, resulting in relatively similar values for R\({}_{\text{Avg}}\) and R\({}_{\text{Peak}}\). In contrast, in the high-frequency range, acceleration amplification increases rapidly with frequency, causing R\({}_{\text{Peak}}\) to exceed R\({}_{\text{Avg}}\). Since the R\({}_{\text{ZPA}}\) corresponds to a high frequency of 45.25 Hz, it exhibited a pattern similar to that of R\({}_{\text{Peak}}\) in the high-frequency range. \[\text{R}_{\text{Avg}}(i)=\frac{\text{Average}_{a}(\text{floor})}{\text{ Average}_{a}(\text{base})},\qquad\text{R}_{\text{Peak}}(i)=\frac{\text{ Peak}_{a}(\text{floor})}{\text{Peak}_{a}(\text{base})},\qquad\text{R}_{\text{ZPA}}(i)= \frac{\text{ZPA}_{a}(\text{floor})}{\text{ZPA}_{a}(\text{base})} \tag{6}\]  
Figure 21: Comparison of FRS (EQ2): (a) A2; (b) A3; (c) A4. Figure 22: Comparison of FRS (EQ3): (**a**) A2; (**b**) A3; (**c**) A4.