esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Result Analysis
### DLC Test  
The DLC test is often used to measure the function effect of ESP. The DLC test in this work only compares P-ESP with MFCDC-ESP to show the superiority of the latter, especially given the result of the sine wave input test emphasizing that a vehicle without control can easily lose stability. The basic parameters of the DLC test simulation analysis are shown in Table 9.  
The results of the simulation are shown in Figure 11.  
The simulation curve shows that the vehicle maintains good stability under the two control strategies because of the ESP. In comparison with those of P-ESP, the roll angle rate of MFCDC-ESP is smaller, and the comfort felt by passengers is better. Given the influence of the sky-hook control of MFCDC-ESP, the damping force of MFCDC-ESP is generally less than that of P-ESP when driving in a stable state. However, at 2.5 and 5.7 s, the damping force of MFCDC-ESP suddenly increases. Combined with the steering angle, these two positions are the critical moment for the change of the steering angle and are the most vulnerable periods for the vehicle to lose stability (this point of view can be also clearly reflected in the previous sine wave steer input test). Moreover, the vertical forces are redistributed. The rear axle with great braking potential undertakes heavy load distribution without lateral force; the same effect is desired for vehicle control. Therefore, the control strategy of MFCDC-ESP can change the distribution of vertical forces and quickly compensate for the occurrence of instability; it even exerts a certain predictive effect. In addition, CDC plays an important role in driving stability with auxiliary effects. The simulation data are reported in Table 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Results (Unit)** & **P-ESP** & **MFCDC-ESP** & **Percentage Point** \\ \hline \multirow{2}{*}{Yaw angle rate (rad/s)} & absolute maximum & 0.2266 & 0.2059 & \(-\)9.14\% \\  & standard deviation & 0.1199 & 0.1158 & \(-\)3.42\% \\ \multirow{2}{*}{Roll angle rate (rad/s)} & absolute maximum & 0.3896 & 0.357 & \(-\)8.37\% \\  & standard deviation & 0.09699 & 0.09082 & \(-\)6.36\% \\ \multirow{2}{*}{Vertical force L1 (N)} & absolute maximum & 3344 & 3368 & 0.72\% \\ \multirow{2}{*}{Vertical force R1 (N)} & absolute maximum & 3518 & 3508 & \(-\)0.28\% \\ \multirow{2}{*}{Vertical force L2 (N)} & absolute maximum & 2960 & 3018 & 1.96\% \\ \multirow{2}{*}{Vertical force R2 (N)} & absolute maximum & 3135 & 3172 & 1.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Main comparison results of DLC test (P-ESP, MFCDC-ESP).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Parameters** & **Value** & **Unit** \\ \hline \multirow{2}{*}{Longitude target speed} & 120 & km/h \\ Friction coefficient & 0.6 & - \\ Steer input angle peak value & 90 & deg \\ \hline \hline \end{tabular}
\end{table}
Table 9: Main parameters of DLC test.
# Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text
## 3 Proposed Sentiment Analysis Method
We adopted two types of approaches to handle these textual variations i.e., word-to-word substitution (WtWS) and word-to-phrase substitution (WtPS). The WtWS was adopted to deal with OOV words which result from character-level errors, whereas the WtPS is used for word-level errors such as word deletion, word abbreviations etc. We ranked the list of valid words based on the context of the sentence to select the most appropriate substitution. For context estimation we adopted an n-gram-based approach. Algorithm 3 shows the stepwise approach for spell-checking process. The block diagram of the spell-checker is shown in Figure 3. In this method we aim to deal with three types of normalizations, i.e., word enlargement, word shortening and typ spelling errors. The input text is passed from the spell-checker, where WEP(D[i], dict) resolves the word enlargement problem by removing the consecutive repeated characters. The WSP(D[i], Sdict) function is used to resolve the problem of word shortening. We made a new list of valid words \(Sdict\) by removing all the vowels from the valid words in the original dictionary leaving only starting vowels. The function WSP(D[i], Sdict) uses the edit-distance algorithm to find the nearest neighbors for the misspelled words in the new dictionary \(Sdict\). The nearest neighbors are then compared with the actual dictionary to extract and return its corresponding word as a candidate. ```
Result: Normalized_text
1 Preprocessed test data (\(PTsD\));
2 whilei<ndo
3 ifWtPS(PTsD)then
4 User-generated abbreviations problem \(Cand\_Normalized=UGAP(PTsD)\) ;
5 Dialectal/informal usage problem \(Cand\_Normalized=DIUP(PTsD)\) ;
6 Word-deletion problem \(Cand\_Normalized=WDP(PTsD)\);
7 else
8 Resolve word enlargement problem, word shortening, and typo spelling errors as discussed in Algorithm 3\(Cand\_Normalized=\)
9\(Spell\)**Correction(\(PTsD\), dict);
10\(Cand\_Normalized=\)**Resolve phonetic-based words substitution using Metaphone algorithm [72];
11 end while
12 Normalized_text= \(CandidateRanking(Cand\_Normalized, tri-gram(dict))\)
13
14 end while
```  
**Algorithm 2**Proposed social media textual variations handler (TVH) algorithm  
The typo spelling correction TypoCorrection(D[i], dict) is a combination of two widely used string-proximity estimation methods, i.e., edit distance and bi-grams. The edit distance provides a list of correct words from the dictionary which are the nearest neighbors of the misspelled word. The bi-grams of each word, in the list of nearest neighbors, and the misspelled word are matched to return the nearest neighbor with maximum bi-grams matched with the misspelled word. The word returned is the correct replacement for the misspelled word. Figure 2: Block diagram for the proposed SM textual variations handler (TVH) and its integration with current text analysis. The algorithm is repeated until the last word D[n] in the document, where \(n\) is the number of words in the document.
# Earthquake-Induced Pounding of Medium-to-High-Rise Base-Isolated Buildings
## 2 Materials and Methods
### Details of Dynamic Analysis  
For the purposes of the dynamic analysis, seven different earthquake records, summarized in Table 2, have been considered. For the comparison purposes, all records have been scaled so as to attain the peak acceleration value of 0.5 g (g is the acceleration of gravity). The bounding-involved seismic response has been determined using the time-stepping Newmark method (see [39]). Based on the preliminary computations, the time step of 0.001 s has been found to be small enough to satisfy the numerical stability and accuracy conditions during collisions between analyzed buildings. Therefore, this time step has been applied in the non-linear time history dynamic analysis. A large number of results for different ground motions have been obtained. Due to the limitation of space, the representative examples are presented in the paper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Isolator Characteristics**} & \multicolumn{2}{c}{**Building**} \\ \cline{2-3}  & **Five-Story** & **Eight-Story** \\ \hline Effective stiffness, \(K_{\text{eff}}\) (kN/m) & 208.6 & 313.81 \\ Effective damping, \(C_{\text{eff}}\) (kN-s/m) & 49.9 & 75.15 \\ Initial stiffness, \(K_{1}\) (kN/m) & 1351.85 & 2039.7 \\ Yield force, \(f_{y}\) (kN) & 23.54 & 35.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the seismically isolated structural models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Year of**} & \multirow{2}{*}{**Earthquake**} & \multirow{2}{*}{**M\({}_{\text{W}}\)**} & \multirow{2}{*}{**Station**} & \multicolumn{1}{c}{**Duration**} & \multirow{2}{*}{**PGA (g)**} & \multirow{2}{*}{**PGD (cm)**} \\ \cline{1-1}
**Occurrence** & & & & & \\ \cline{1-1}
1992 & Big Bear & 6.4 & Desert Hot Spr. (New Fire Stn.) & 60 & 0.23 & 4.53 \\
1994 & Northridge & 6.7 & Century CCC & 60 & 0.26 & 7.85 \\
1994 & Northridge & 6.7 & Saturn Street School & 32 & 0.43 & 23.04 \\
1971 & San Fernando & 6.6 & Castaic, Old Ridge Route & 30 & 0.27 & 4.87 \\
1994 & Northridge & 6.7 & Buena-Park & 35 & 0.14 & 1.62 \\
1992 & Landers & 7.3 & Baker & 50 & 0.11 & 5.76 \\
1994 & Northridge & 6.7 & Moorpark (Ventura Fire Stn.) & 60 & 0.29 & 5.48 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Earthquake records considered in the study.
# A Wideband Metal-Only Patch Antenna for CubeSat
## 3 Experimental Results
To evaluate the broadband characteristics of the proposed antenna, the measured and simulated gains versus frequency is presented in Figure 15. The measured 3 dB gain bandwidth of the proposed antenna is 61.22% with a peak gain of 8.5 dBi at 2.45 GHz, indicating that an acceptable gain can be maintained within the operating frequency range. The measured and simulated efficiency of the proposed antenna can be seen in Figure 16, which is highly correlated with ohmic losses in the metallic structure of the antenna as well as impedance mismatching between the folded patch and the probe feed. The efficiency fluctuates between 82% and 88% from 2.2 GHz to 3.7 GHz. Therefore, the proposed patch antenna can achieve a high radiation of 85% at 2.45 GHz, while it can maintain an efficiency higher than 71% from 1.5 GHz to 4 GHz.
## 4 Discussion  
### Controls on the EOS and EOS Turning Points  
This study is the first to demonstrate pixel-scale spatial heterogeneity of the EOS turning points and explain the turning point controls. The results indicate that the joint effects of climate variables and human activities are the main controls of the EOS turning points. The response of the EOS to environmental changes is complex. Some previous studies indicated that temperature plays a crucial role in EOS regulation [49] however, we show that the temperature control over the EOS is regulated by precipitation and insolation in the meadow and grassland ecotones. The cause of the turning points in most subregions is the abrupt change of temperature and precipitation. The results also reveal that insolation contributed considerably to the EOS changes, which is consistent with some previous reports that the EOS and its relation with GPP is mainly limited by insolation [50]. Other studies have reported that meadow shrinkage, decreased land cover, land albedo changes, and permafrost and seasonal frozen soil dynamics intersect with climate change, which alters the EOS trends [51].  
Grazing is the most important human activity that affects grassland dynamics on the Qinghai-Tibetan Plateau [52]. The spatial heterogeneity of community increases, community function alteration, and biodiversity loss are considered to be some of the key disturbances that result in grassland degradation [53; 54]. The pika population could also increase the effects of animal distribution on vegetation [55]. Overgrazing reduces the vegetation biomass and height, and restricts the regrowth ability of grassland. Our analysis shows that grazing activities in Qinghai notably decreased around 1998, coinciding with the implementation of national conservation policies (e.g., ecological compensation, restoration of degraded grassland). Grazing in Tibet was not active before 1995 and then rapidly increased, however grazing decreased after 2005 due to the late implementation of ecological conservation projects. The primary industry (mostly agriculture and animal husbandry) increased by nearly a factor of five in 1996-2015 compared with that in 1982-1994, which is also consistent with the EOS change turning points. The tertiary industry in Qinghai and Tibet quickly increased after the turning points, which indirectly reflects the intensification of human activities on the Qinghai-Tibetan Plateau.
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 3 Results
### Single Tank Control Results
#### 3.1.2 The Dahlin Algorithm Results  
Let the chosen sampling period be \(T=1\) s. Since the transport delay for the single-tank system is \(\tau=4.3\) s and the sampling period is \(T=1\) s, from (16) it follows that \(m=0.7\) and \(N=4\).  
Let the time constant of the desired aperiodic response be \(1/\lambda=5\) s.  
Based on the parameters presented in Table 2, relation (17) is written as follows:  
\[D(z)=\frac{0.181\cdot(1-0.904\cdot z^{-1})}{51.6\cdot(1-0.819\cdot z^{-1}-0.18 1\cdot z^{-5})\cdot(0.068+0.028\cdot z^{-1})}. \tag{30}\]  
Figure 9 shows the Simulink model of the tank system with the Dahlin controller. The height of the liquid in the ST system with the Dahlin controller is shown in Figure 10.  
Figure 8: The comparison of the ST system and the linearized ST model responses.  
Figure 10: The output of the ST system with the Dahlin controller.  
Figure 9: The Simulink model of the ST system with the Dahlin controller.  
The obtained output has a desired response time of 5 s and an expected delay time of 4 s. The expected aperiodic output with no oscillation and no overshoot was achieved, with a rise time of \(T_{r}=10.8\) s and a setting time of \(T_{s}=20\) s, Table 3.  
The transfer function of the modified Dahlin controller with \"no ringing\" effect of the control signal, based on (19), is:  
\[D(z)=\frac{0.1813z^{5}-0.1639z^{4}}{4.958z^{5}-4.059z^{4}-0.8986}. \tag{31}\]  
Control signals from the Dahlin controller with the effect of \"ringing\" and from the modified Dahlin controller with \"non-ringing\" behavior are shown in Figure 11. The control signal from the Dahlin PI control is presented in Figure 12.  
Figure 11: Control signals from: (**a**) The Dahlin controller with ringing; (**b**) The modified Dahlin controller with non-ringing, for ST system.  
Figure 12: A control signal from the Dahlin PI controller.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Dahlin Algorithm** \\ \hline Rise time & 10.8 s \\ \hline Overshoot & 0 \\ \hline Setting time & 20 s \\ \hline Steady-state error & 0 \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of the transient response with the Dahlin controller.  
The transfer function of the Dahlin PI controller, based on (20) and (21), is:  
\[D(z)=0.0192\bigg{(}1+\frac{0.1063}{1-z^{-1}}\bigg{)}. \tag{32}\]  
Figure 13 shows the responses obtained from the designed controllers. The response obtained with the modified Dahlin controller matches the response obtained with the Dahlin controller. However, the response obtained with the Dahlin PI controller somewhat deviates from these responses. It has an overshoot of \(P\%\) = 3.8%, rise time of \(T_{r}\) = 11 s, and a setting time of \(T_{s}\) = 29 s.
## 2 Methodology
Each temporal slice contains points from a short time interval of data collection. The individual slices are sequentially registered to the static point cloud using the ICP (Figure 1). The functional relationship between a point in the cloud captured by the MMS in slice k, \(\mathbf{r}^{MMS}\), and the closest point in the reference TLS point cloud, \(\mathbf{r}^{TLS}\), is thus given by:  
\[\mathbf{r}^{TLS}=\mathbf{R}_{k}\mathbf{r}^{MMS}+\mathbf{t}_{k} \tag{2}\]  
where \(\mathbf{t}_{k}\) is the translation vector and \(\mathbf{R}_{k}\) is the rotation matrix between the two point clouds. Note that the mapping frame superscript has been omitted for clarity. The objective of the ICP is to minimize the sum of squares of differences between the two point clouds, \(\varepsilon^{2}\), i.e.,  
\[\varepsilon^{2}=\sum\left\|\mathbf{R}_{k}\mathbf{r}^{MMS}+\mathbf{t}_{k}-\mathbf{r}^{TLS} \right\|^{2} \tag{3}\]  
It is assumed that the initial position and orientation of the kinematic point cloud provided by the direct georeferencing is sufficiently accurate enough to ensure convergence of the ICP. The result of applying this procedure to all slices is a time-series of transformation parameter sets that provides an accuracy measure of the kinematic data at different temporal points and spatial locations. Note that navigation quality changes rapidly in urban areas and, accordingly, the spatial quality of the kinematic point cloud may vary at different temporal points. Therefore, it is hypothesized that a shorter temporal slice will result in a more realistic accuracy assessment. Point cloud data collected within a short time interval from a moving mobile mapping system will have homogeneous spatial accuracy. Nevertheless, one should bear in mind that a very short time interval may not contain a sufficient number of points or enough objects/features to perform the ICP registration. On the other hand, a very long time interval will provide a large size point cloud with inhomogeneous spatial accuracy and may lead to a false accuracy assessment. Spatial errors in a few locations may not be detected through the registration of large point clouds. The errors may be distributed throughout the larger dataset and will not contribute significantly to the estimation of the transformation parameters. Since the transformation parameters are used to quantify data quality, a long time interval must be avoided. A few factors should be taken to account for determining the time interval length, for instance: (1) the mobile mapping system speed, (2) the laser scanner range, (3) the scanning frequency, (4) the quality of the GNSS/IMU system, and (5) the structure of the scanning area.
# Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study
## 7 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Measures**} & \multicolumn{1}{c}{**Mean (SD)**} & \multicolumn{1}{c}{**Mean (SD)**} & \multicolumn{1}{c}{**\(\chi^{2}\)**} & \multicolumn{1}{c}{**gdl**} & \multicolumn{1}{c}{\(p\)**} & \multicolumn{1}{c}{\(\epsilon^{2}\)} \\  & **ADHD Group** & **TD Group** & **\(\chi^{2}\)** & **gdl** & **\(p\)** & **\(\epsilon^{2}\)** \\ \hline Block 1 mean RT & 342.0606 (29.866) & 347.2353 (41.281) & 0.0797 & 1 & 0.778 & 0.00121 \\ Block 1 mean RT & 209.1212 (186.121) & 153.5882 (142.344) & 1.8910 & 1 & 0.169 & 0.02865 \\ Block 2 mean RT & 351.8485 (68.258) & 363.2353 (35.863) & 0.00665 & 1 & 0.935 & \(1.01\times 10^{-4}\) \\ Block 2 mean RT & 273.5758 (291.567) & 90.8235 (129.341) & 10.24573 & 1 & 0.001 \({}^{*}\) & 0.155 \\ Block 3 mean RT & 317.9394 (204.911) & 319.2647 (289.348) & 0.321 & 1 & 0.571 & 0.00486 \\ Block 3 mean RT & 22.4242 (85.457) & 67.7941 (158.605) & 0.945 & 1 & 0.331 & 0.01432 \\  & 259.2121 (218.735) & 283.5588 (145.812) & 1.627 & 1 & 0.202 & 0.02465 \\ Block 4 mean RT & 18.8788 (79.474) & 48.0882 (148.029) & 0.145 & 1 & 0.703 & 0.00220 \\ \hline \multicolumn{1}{c}{\({}^{*}\)Significant.} & & & & & & \\ \end{tabular}
\end{table} 
Table 6: Mean reaction time (RT) for correct and wrong answers in the four blocks of the Go/NoGO task. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Measures**} & \multicolumn{1}{c}{**Mean (SD)**} & \multicolumn{1}{c}{**Mean (SD)**} & \multicolumn{1}{c}{**\(\chi^{2}\)**} & \multicolumn{1}{c}{**gdl**} & \multicolumn{1}{c}{\(p\)**} & \multicolumn{1}{c}{\(\epsilon^{2}\)} \\  & **ADHD Group** & **TD Group** & **\(\chi^{2}\)** & **gdl** & **\(p\)** & **\(\epsilon^{2}\)** \\ \hline Block 1 commission errors & 0.9091 (1.156) & 0.6176 (0.888) & 1.4457 & 1 & 0.229 & 0.02191 \\ Block 2 commission errors & 1.0303 (2.604) & 0.2647 (0.567) & 6.3012 & 1 & 0.012 \({}^{*}\) & 0.09547 \\ Block 3 commission errors & 0.4848 (1.093) & 0.5000 (0.826) & 0.8132 & 1 & 0.367 & 0.01232 \\ Block 4 commission errors & 0.5758 (1.370) & 0.4118 (0.821) & 0.0875 & 1 & 0.767 & 0.00133 \\ \hline \multicolumn{1}{c}{\({}^{*}\)Significant.} & & & & & & \\ \end{tabular}
\end{table} 
Table 7: Commission errors in the four different blocks of the Go/NoGO task.
# Neural Network-Assisted Interferogram Analysis Using Cylindrical and Flat Reference Beams
## 2 Materials and Methods
### Theoretical Foundations
\[A_{n}\ =\ \sqrt{\frac{n+1}{\pi}}. \tag{3}\]1 The transmission function of a cylindrical reference beam (\(p\) = 2) for a cubic interferogram depending on different beam tilt angles \(\alpha\): (**a**) is \(2\pi\); (**b**) is \(4\pi\); (**c**) is \(6\pi\); (**d**) is \(8\pi\); (**e**) is \(10\pi\). where \(\overline{S}_{p}\ =\ \sum\limits_{q\ =\ 1}^{Q}S_{pq}\), \(p\) is the interferogram type, \(q\) is the aberration type, and \(Q\) is the number of considered aberrations (\(Q\) = 8) for different types and levels of aberration. If \(\Delta\geq 0\), then the sensitivity of the cylindrical interferogram is higher than the linear one; otherwise, the sensitivity is lower. For clarity, the information content parameter is introduced as  
\[A_{n}\ =\ \sqrt{\frac{n+1}{\pi}}. \tag{3}\]2  
for cylindrical and linear interferograms for different types and levels of aberrations with a variation of the parameter \(\alpha\), which shows how many times one type of interferogram is more effective for recognizing aberrations from interferograms using convolutional neural networks.
## 3 Simulation Analysis
### Constitutive Models of Concrete and Steel  
The concrete damaged plasticity (CDP) model in ABAQUS was chosen to model the concrete material thanks to its simple modelling and stable numeric calculation. In the CDP model, concrete's uniaxial tension and compression responses were fundamental components. The compressive behavior of concrete was defined based on the formula proposed by [PERSON] (1965) as follows  
\[\frac{\sigma}{f_{c}^{\prime}}=\frac{\varepsilon}{\varepsilon_{c}^{\prime}} \frac{n}{n-1+(\varepsilon/\varepsilon_{c}^{\prime})^{n}} \tag{2}\]  
where \(f_{c}\) is the compressive strength of concrete and \(\varepsilon^{\prime}_{c}\) is the corresponding compressive strain, \(n\) is the power index and can be expressed as an approximate function of the compressive strength of normal-weight concrete as:  
\[n=0.4\times 10^{-3}f_{c}^{\prime}+1.0 \tag{3}\]  
and compressive strain of concrete at peak compressive stress is given by:  
\[\varepsilon_{c}^{\prime}=2.7\times 10^{-4}\sqrt[4]{f_{c}^{\prime}} \tag{4}\]  
The tensile behavior of concrete is characterized by the uniaxial tensile stress \(f_{t}\) and fracture energy per unit area as follows:  
\[f_{t}=0.3\big{(}f_{c}^{\prime}-8\big{)}^{2/3} \tag{5}\]  
\[G_{F}=0.073\big{(}f_{c}^{\prime}\big{)}^{0.18}\quad(N/mm) \tag{6}\]  
where Equation (5) are retrieved from Eurocode 2 ([PERSON] _et al._, 2010) and Equation (6) is defined in _fib_ 2010 ([PERSON] _et al._, 2010). The elastic modulus of concrete \(E_{c}\) is taken as the empirical equation suggested in ACI 318 ([PERSON] _et al._, 2010), and the Poisson's ratio of concrete is 0.2.  
\[E_{c}=4700\sqrt{f_{c}^{\prime}} \tag{7}\]  
Figure 4: Mesh of a simulation mode: (**a**) quarter of specimens, (**b**) quarter of concrete, and (**c**) quarter of steel bar.  
Other parameters of the CDP model are taken as follows as suggested by [PERSON] et al. [40] and [PERSON] et al. [41].  
* Dilation angle \(\psi=40^{\circ}\).
* Flow potential eccentricity \(e=0.1\).
* Ratio of initial equiaxial compressive yield stress to initial compressive yield stress \(f_{b0}/f_{c0}=1.16\).
* Ratio of second stress invariant on the tensile meridian \(K=0.667\).
* Viscosity parameter \(\mu=0.001\).  
It was noted herein that the current problem does not consider the reloading stage, and only the bond strength is of interest. Hence, the damage parameters are not involved in the model.  
The elastic-perfect plastic model with isotropic hardening was adopted to model the steel bar, the elastic modulus of the steel bar was taken as \(E_{\rm s}=200\),000 MPa, and the corresponding Poisson's ratio is 0.3.