esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
The overall methodology to study the mineralogical and geochemical changes of carbonate rocks when exposed to SC CO\({}_{2}\) rich brine consisted of the following steps:  
* Selection of a study area with real potential for CO\({}_{2}\) geological storage (Figure 1).
* Identification of reservoir rocks in this area and their sampling for laboratory experimentation (Figure 1).
* Preparation of rock samples for laboratory experiments providing sample pairs in order to conduct comparative studies before and after SC CO\({}_{2}\) exposure. The sample pairs are prepared to have one matching surface as close to each other in the original block as possible (Figure 2).
* Mineralogical and chemical characterization of rock samples and definition of brine composition before the experiment.
* Rock sample exposure to SC CO\({}_{2}\) in a hyperbaric chamber simulating conditions of CO\({}_{2}\) injection in a geological reservoir (\(P\approx 75\) bar, \(T\approx 35\)\({}^{\circ}\)C, \(970\) h exposure time and no CO\({}_{2}\) flow) (Figure 2).  
Figure 1: Location of study area with basic geological information. Modified after [33].  
* Mineralogical and chemical characterization of rock samples and analysis of brine composition after the experiment.
* Comparison of analytical results obtained in the two stages of the experimentation (before and after SC CO\({}_{2}\) exposure).Mass balance calculation and numerical modelling for validation of obtained results.
###### Abstract  
One of the main factors that limit the lifetime of the Hall effect Thrusters (HETs) is the erosion of the acceleration channel caused by the flux of energetic ions. The magnetic field that is curved and convex towards the anode has been widely used in HETs because of its role in reducing the divergence angle of the ion beam and the channel wall erosion. However, the mechanism of the influence of the magnetic field line curvature on the wall erosion is not clear. Therefore, in this paper, a 2D3V numerical model based on the immersed-finite-element and particle-in-cell (IFE-PIC) method is established to simulate the radial-azimuthal plane near the exit of the Hall thruster. The effect of the tilt angle of the magnetic field line on the wall sputtering erosion rate is analyzed. The results show that compared to the case with the electric field \(E\) perpendicular to the magnetic field \(B\), the energy of the ions hitting the channel wall is smaller and the wall erosion is weaker when the magnetic field lines are convex to the anode. As the tilt angle of the magnetic field lines increases from \(0^{\circ}\) to \(60^{\circ}\), the erosion rate is reduced by \(90\%\). Conversely, when the magnetic field lines are convex to the exit plane of the channel, the wall erosion is much more serious compared to the case with the orthogonal electric field \(E\) and the magnetic field \(B\). As the tilt angle of the magnetic field line changes from \(0^{\circ}\) to \(60^{\circ}\), the erosion rate is enhanced by \(171\%\). The results in this paper are instructive for the design and optimization of the magnetic field of the HETs.  
ball effect thruster; immersed-finite-element; particle-in-cell method; magnetic field line curvature; tilt angle; wall erosion +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] The Influence of the Magnetic Field Line Curvature on Wall Erosion near the Hall Thruster Exit Plane. _Appl. Sci._**2023**, 13, 3547. [[https://doi.org/10.3309/appl3063547](https://doi.org/10.3309/appl3063547)]([https://doi.org/10.3309/appl3063547](https://doi.org/10.3309/appl3063547))
## 3 Results and Discussion
### Alternative Detection Method for the Field Application
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Battery No.**} & \multirow{3}{*}{**String No.**} & \multirow{3}{*}{\(R_{i}\)**Fitted**} & \multicolumn{3}{c}{\(R_{i,\text{calc}}\) **Based on 100 Hz**} & \multicolumn{3}{c}{\(R_{i,\text{MAX,calc}}\) **Based on \(\alpha\)E(100 Hz, 1 Hz)**} \\  & & & \multicolumn{3}{c}{**—120 DC**} & & \multicolumn{3}{c}{**—120 DC**} \\ \cline{3-10}  & & **Abs. in mT** & **Norm.** & **Abs. in mT** & **\textgreater{}Norm.** & **Abs. in mT** & **Norm.** \\ \hline
2-014 & 2 & 25.8 & +8.28\% & 31.06 & +6.44\% & 52.6 & +49.39\% & 44.66 & +47.81\% \\
2-018 & 2 & 25.9 & +13.44\% & 31.05 & +13.67\% & 56.5 & +66.64\% & 46.74 & +51.73\% \\
2-024 & 2 & 24.9 & +11.29\% & 30.06 & +10.69\% & 49.2 & +47.94\% & 42.34 & +51.43\% \\
3-004 & 3 & 22.1 & +3.23\% & 26.57 & +1.4\% & _n.yf._ & _n.yf._ & 28.99 & +22.71\% \\
3-008 & 3 & 26.6 & +13.37\% & 33.60 & +15.33\% & _n.yf._ & _n.yf._ & 26.57 & +17.28\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of absolute (abs.) and normalized (norm.) resistance values for \(R_{i}\) and \(R_{\text{ct}}\) either fitted from the impedance spectra (fitted) or calculated based on single-frequency measurements @100 Hz and 1 Hz. n.y.f. = not yet fitted. Figure 9: Change of the calculated parameters based on two impedance measurements (100 Hz and 1 Hz) for the batteries of string 2. Solid lines are R (Z) and dashed lines \(\mid\)Z\(\mid\). (**a**) normalized change of \(R_{i,\text{calc}}\) based on values measured @100 Hz. (**b**) normalized change of \(R_{\text{ct,NAM,calc}}\), which is the delta of impedances @100 Hz and 1 Hz. the made assumptions. At this point of the investigation it can be said that the measurement of two distinct frequencies can gain comparable results to the fitting of a full impedance spectrum. Thus, this methodology can be used and extended to distinguish the aging mechanisms, which are attributed to different frequency domains.
## 4 Results  
The results for positioning accuracy under forest canopies are shown in Table 7. As regards NVS and NovAtel, both the real-time autonomous single-point solution and the post-processed differential solution are given. As can be seen, the real-time accuracy of single-frequency devices is superior to that of geodetic dual-frequency device equipped with a less sensitive antenna-receiver combination optimized for differential solution under better satellite visibility. High-sensitivity receivers are able to track weaker signals, which results in a larger number of tracked satellites, but the signals are subject to interference and multipathing due to disturbance caused by forest canopies. Geodetic receivers are also subject to interference and multipathing, but the tracked signals are of better quality in terms of frequency spectrum and measurement noise thanks to the purpose-designed antenna and signal-processing techniques. High-sensitivity receivers are mostly used in navigators and smartphones, and geodetic receivers have been adopted by professionals for geodetic purposes. When post-processing is applied, the accuracy of NovAtel improves notably and is superior to real-time solutions. With IMU data also available, the accuracy was 0.7 m (reference trajectory). During the data acquisition along Trajectory 2, there were five visible satellites more than were available for Trajectory 1, and this probably partly explains the better accuracy obtained with the real-time systems.  
On analyzing accuracy under different forest conditions, it appeared that the accuracy of single-frequency GNSS devices is less dependent on forest conditions, whereas the performance of the tested geodetic dual-frequency receiver is very sensitive to satellite visibility (Table 8). This can also be seen in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Device (No. of Observations Traj.1/Traj.2)**} & \multicolumn{2}{c}{**RMSE (m)**} & \multirow{2}{*}{**Combined**} \\  & **Traj.1** & **Traj.2** & \\ \hline Garmin eTrex 30 (823/690) & 10.2 & 6.6 & 8.4 \\ u-blox MAX-M5Q (823/690) & 4.9 & 4.6 & 4.7 \\ NVS single point (642/N/A) & 6.2 & N/A & N/A \\ NVS post-proc. GPS only (660/N/A) & 6.7 & N/A & N/A \\ NovAtel single point (768/654) & 45.6 & 17.5 & 31.5 \\ NovAtel post-proc. (556/400) & 1.7 & 1.9 & 1.8 \\ \hline \hline \end{tabular}  
* Traj.: trajectory.  
\end{table}
Table 7: The positioning accuracy under forest canopies.  
post-processed solution for NovAtel; the accuracy deteriorated with increasing basal area, mean height, and stem volume.
## 5 Conclusions  
In this study, we investigated the effect of different beam-forming strategies on the accuracy of displacement estimates in four different transducers (L7-4, 12L4 VF, L12-5 and MS250). We developed an easy-to-implement method to evaluate the displacement estimation accuracy based on the displacement gradients estimated in a rotating phantom. A line density of multiple lines per pitch seemed to outperform the accuracy of the estimates compared to one line per pitch for all beam-forming strategies and transducers. Lu's-fk beam-forming seemed to outperform Stolt's-fk and resulted in similar accuracy as DaS for multiple lines per pitch. It can therefore be used interchangeable in displacement estimation without affecting accuracy, with the added advantage that the computational load is lower. In future work, an inhomogeneous phantom or ex vivo tissue might be used to improve extrapolation of results to in vivo conditions and to further investigate the effect of contrast enhancement and side lobe reduction by apodization or weighting strategies on displacement estimation.  
This research is supported by Siemens Healthineers and by the Dutch Technology Foundation STW (Project 13290) which is part of the Netherlands Organization for Scientific Research (NWO) and is partly funded by the Ministry of Economic Affairs. Furthermore, the authors wish to thank [PERSON] in our group for all constructive discussions and [PERSON], [PERSON] and [PERSON] of Siemens Healthineers for their technical support.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] implemented the beam-forming and displacement estimation algorithms; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Fractional-Order Sliding Mode with Active Disturbance Rejection Control for UAVs
## 4 Experiments and Results
### Analysis of Comparative Experiments of FOSM-ADRC and PID in Case of Strong Interference  
In order to make the performance of this controller persuasive, a conventional PID controller was designed to serve as a comparison. As shown in Figure 7, the PID controller is able to control the quadrotor in flight normally under the parameters (Table 5) in a disturbance-free environment.  
Figure 6: Experimental results with strong interference.  
In the case of the same controller parameters, we add the same strong interference signal to the model as before, as shown in Figure 8. The PID controller and the controller proposed in this paper as a comparison can be clearly seen. In the initial moment, the PID controller in the attitude angle control has been dispersed and cannot form an effective control of the quadrotor. In the three graphs of position information, it is obvious that the quadrotor is not under control.  
It can be seen that the controller proposed in this paper has a certain degree of anti-interference ability, and its anti-interference ability in the simulation experiments is more powerful than that of the traditional SMC and traditional PID controller.  
Combining the above experimental results, the FOSM-ADRC proposed in this paper can operate stably in the case of no interference, and in the case of strong interference, it has a stronger anti-interference ability and more robustness compared with other comparative controllers. While obtaining superior performance, the more limited
## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **AI Models** & **Input Parameters** & **No. of Dataset** & **Performance** \\ \hline
[20] & ANN & PLS, V\({}_{\text{P}}\), SHR, P\({}_{\text{n}}\) & 30 & R\({}^{2}\) = 0.93 \\
[21] & GP & P\({}_{\text{n}}\), r, V\({}_{\text{P}}\) & 72 & RMSE = 12.3 \\
[24] & ANN & r, CC, QC & 138 & R\({}^{2}\) = 0.76 \\
[25] & ANN & P\({}_{\text{n}}\), SD, SH, PLS & 39 & R\({}^{2}\) = 0.93 \\
[28] & FIS & SDI\({}_{\text{2}}\), SDI\({}_{\text{4}}\), CLC & 65 & RMSE = 2.767 \\
[29] & FIS & BPI, PLS, SHR, V\({}_{\text{P}}\), P\({}_{\text{n}}\), r & 60 & R\({}^{2}\) = 0.98 RMSE = 8.21 \\
[30] & FIS & SH, r, P\({}_{\text{n}}\) & 75 & R\({}^{2}\) = 0.9437 \\
[31] & SVM & P\({}_{\text{n}}\), V\({}_{\text{P}}\), SD & 47 & R\({}^{2}\) = 0.7712 \\
[32] & SVM & V\({}_{\text{P}}\), P\({}_{\text{n}}\), SHR & 85 & R\({}^{2}\) = 0.9516 RMSE = 2.14 \\
[33] & SVM & V\({}_{\text{P}}\), SHH, CSS & 90 & R\({}^{2}\) = 0.867 \\
[34] & SVM & SH, P\({}_{\text{n}}\), V\({}_{\text{P}}\), PLS & 170 & R\({}^{2}\) = 0.9363 RMSE = 1.097 \\
[35] & RF & PLS, P\({}_{\text{n}}\), V\({}_{\text{P}}\), SHR & 30 & R\({}^{2}\) = 0.93 \\
[36] & RF & V\({}_{\text{P}}\), SH, P\({}_{\text{n}}\), PLS & 93 & R\({}^{2}\) = 0.488 RMSE = 8.071 \\
[40] & MLP & RC, r, P\({}_{\text{n}}\), V\({}_{\text{P}}\), WA, PLS & 197 & R\({}^{2}\) = 0.90 RMSE = 0.289 \\
[41] & GEP & UPV, WA, Dd, Sd, Bd & 167 & R\({}^{2}\) = 0.877 \\
[43] & GA & QC, r, P\({}_{\text{n}}\), CI, SSH & 44 & R\({}^{2}\) = 0.63 \\
[101] & ANN & WS, r, P\({}_{\text{n}}\) & 83 & R\({}^{2}\) = 0.96 \\
[102] & ANN & V\({}_{\text{P}}\), r, P\({}_{\text{n}}\) & 105 & R\({}^{2}\) = 0.95 \\  & ELM KELM & & & \({}^{2}\)\({}_{\text{ELM}}\) = 0.8225 R\({}^{2}\)\({}_{\text{KELM}}\) = 0.8803 \\ This study & KELM-GWO DELM & SH, P\({}_{\text{n}}\), V\({}_{\text{P}}\), PLS & 271 & R\({}^{2}\)\({}_{\text{KELM-GWO}}\) = 0.9152 R\({}^{2}\)\({}_{\text{DELM}}\) = 0.7019 R\({}^{2}\)\({}_{\text{BPNN}}\) = 0.8849 \\ \hline \hline \end{tabular} Note: QC: quartz content; SH: shore hardness; SHR: Schmidt hardness rebound; CL: cone indenter hardness; CC: concavity/convexity; SD: slake durability; SDI: slake durability index; SDI\({}_{\text{2}}\): two-cycle slake durability index; SDI\({}_{\text{4}}\): four-cycle slake durability index; CLC: clay content; CSS: cubic sample sizes; RC: rock class; WA: water absorption; WS: water saturation; UPV: ultrasound pulse velocity; Dd: dry density; Sd: saturated density; Bd: bulk density; SSH: shore scleroscope hardness; GWO: grey wolf optimizer.  
\end{table} 
Table 6: Comparison between the current and previous works by using AI algorithms for UCS of rock prediction. Figure 12: Graphical Taylor diagrams for the comparison of all models: (**a**) training phase; (**b**) testing phase.
# Accounting for Energy Cost When Designing Energy-Efficient Wireless Access Networks
## 3 Results and Discussion  
In this section, the evolution of the total cost for the necessary energy to supply the base stations during one week is investigated, applying the methodologies described in the previous section.  
As already mentioned, the worst week of the year in terms of solar power generation is considered: from 3-9 January.  
Parameters are set in the following way (unless mentioned otherwise), as suggested in [14]:  
* PV panel capacity: 100 kWp;
* storage capacity: 50 kWh;
* time window TW, when present: 5 h.  
The reference scenario corresponds to the current situation: neither an energy-reducing action, nor a cost-reducing action are used (_Undertake no action_ and _No action_ of Strategy 1) in the wireless access network equipped with a PV panel system and energy batteries described above.
## 4 Experimental Validation and Results  
Images of bridge abutments and bridge decks were captured using a DJI Mavic 3 Cine drone equipped with a 20-megapixel 4/3-inch CMOS imaging system. In order to simulate the damage state of the bridge under munitions strikes, MATLAB R2014a was used to generate strike craters with random locations and sizes. In the experiment, the dimensions of the bridge were 30 m long and 12 m wide, and the height of the abutment was 3 m. Based on the lane width of 3.6 m, the number of original passable lanes of the bridge is calculated to be three. Penetrating strikes are simulated by randomly laying black circles of varying numbers, sizes, and locations on the bridge deck and abutments. The experimental process and results are shown in Figures 7 and 8  
From the above experimental results, it can be calculated that the residual bearing capacity class of the bridge abutment is 1 and the residual bearing capacity class of the bridge deck is 2. According to Equation (4), the residual bearing capacity class of the bridge can be obtained as 2; the bridge can pass the number of lanes for 0, and according to Formula (9), the bridge can pass the number of lanes of the eigenvalue of (10). In summary, the above results, substituted into Formula (10) can be calculated so that the bridge is destroyed for the level of 10. In Table 2, it can be found that the bridge is destroyed under severe damage. Since the number of passable lanes on the bridge deck is zero, it is no longer passable for vehicles, so the bridge has lost its passable function and is a total destruction. The above experiments can verify that the bridge damage assessment algorithm based on the image features proposed in this paper is real and effective, and the damage assessment indexes given in this paper can reflect the damage information of the bridge more comprehensively, and the assessment results can reflect the damage situation of the bridge more accurately.
# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 2 Materials and Methods
### Statistics
#### 2.6.3 Interpolation  
The use of geo-referenced information in models (satellite pixels and yield monitors) makes it possible to construct potential yield maps. However, the output of this type of model is a vector with no spatial dimensions, which produces a map of points, leaving areas of the map without information. To facilitate the interpretation of the information by the end user, a continuous map without gaps should be created, and the most common technique for that is an interpolation. The \"ordinary kriging\" method was used to interpolate the data. For that, a semi-variogram was fitted using the rational quadratic function. The interpolation showed a strong autocorrelation (<0.25) [29], which indicates a good quality of the map. The higher the spatial autocorrelation, the better the spatial estimation [30].
# Review on Soft Mobility Infrastructure Design Codes
## 2 Codes for Soft Mobility Infrastructure
### Introduction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Country** & **Area of Application** & **Code** & **Document Type** & \begin{tabular}{c} **Document** \\ **Reference** \\ \end{tabular} & \begin{tabular}{c} **Total Road** \\ **Network Length *** \\ **(km)** \\ \end{tabular} & \begin{tabular}{c} **Road Length per** \\ **Capita * (m/person)** \\ \end{tabular} & \
\begin{tabular}{c} **Road Length per** \\ **per km2 * (m/km2)** \\ \end{tabular} \\ \hline Kingdom of Denmark & Nationwide & DK & Handbook & [16] & 74,558 & 12.92 & 1757.0 \\ \hline Republic of Austria & Nationwide & AT & Guidelines & [17,18] & 137,039 & 15.50 & 1662.2 \\ \hline Hellenic Republic (Greece) & Nationwide & GR & Instruction & [19] & 117,000 & 10.90 & 895.5 \\ \hline New Zealand & Nationwide & NZ & Standard and Guidelines & [20] & 94,000 & 19.61 & 355.3 \\ \hline Republic of Singapore & Nationwide & SG & Design Guidelines & [21] & 3500 & 0.62 & 4935.1 \\ \hline Japan & Nationwide & JP & Government Order & [21] & 1,218,772 & 9.59 & 3343.8 \\ \hline Republic of South Africa & Nationwide & ZA & Guidelines & [23] & 750,000 & 13.48 & 617.6 \\ \hline United Republic of Tanzania & Nationwide & TZ & Design Manual & [24] & 87,581 & 1.80 & 98.9 \\ \hline Russian Federation & Nationwide & RU & Rules & [25] & 1,283,387 & 9.01 & 78.4 \\ \hline \multirow{3}{*}{People’s Republic of China} & Nationwide & CN & Design Standard & [26] & 4,900,600 & 3.58 & 531.9 \\ \cline{2-7}  & Local & Beijing & BJ & Design Standard & [27] & 22,559 [28] & 1.03 * & 1374.7 * \\ \cline{2-7}  & Local & Hong Kong & HK &
\begin{tabular}{c} Design Standard and \\ Guidelines \\ \end{tabular} & [29] & 2107 & 0.29 & 1963.7 \\ \cline{2-7}  & Local & Taiwan & TW & Design Guidelines & [30] & 43,206 & 1.83 & 1339.3 \\ \hline United States of America & Local & Washington D.C. & WA & Design Manual & [31,32] & 2293 [33] & 3.22 * & 12,954.8 * \\ \hline \multirow{2}{*}{Republic of India} & Local & New Jersey & NJ & Design Guidelines & [34,3] & 62,683 [36] & 6.60 * & 2775.0 * \\ \hline Republic of India & Local & Delhi & DL & Guidelines & [37] & 33,198 [38] & 1.98 * & 22,360 * \\ \hline United Arab Emirates & Local & Abu Dhabi & AD & Design Manual & [29] & 26,997 [40] & 7.10 * & 401.0 * \\ \hline \multicolumn{7}{l}{\({}^{*}\)Except individual ranks, road length data are cited from [11]. Numbers with \({}^{*}\) means they are calculated by population and area.} \\ \end{tabular}
\end{table} 
Table 1: Document type of countries, regions, and cities reviewed and road length data.
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Artistic Use**} & \multicolumn{6}{c}{**Artistic Activities**} \\ \hline
**Groups** & **N** & **AVG** & **SD** & **Min.** & **Max.** & **N** & **AVG** & **SD** & **Min.** & **Max.** \\ \hline Pilot group & 193 & 27,719.85 & 68,252.91 & 9.86 & 531,629.10 & 199 & 164.26 & 359.32 & 1 & 2673 \\
1 st Cycle & 90 & 33,505.70 & 79,639.93 & 9.86 & 531,629.10 & 93 & 206.16 & 431.50 & 1 & 2673 \\
2 nd Cycle & 93 & 24,898.82 & 58,927.72 & 16.65 & 410,792.62 & 96 & 138.57 & 290.05 & 1 & 1910 \\ Special Needs & 10 & 1882.84 & 2204.86 & 158.60 & 7370.25 & 10 & 21.10 & 33.17 & 3 & 113 \\ \hline \multicolumn{10}{c}{**Emotion Use**} & \multicolumn{6}{c}{**Emotion Activities**} \\ \hline
**Groups** & **N** & **AVG** & **SD** & **Min.** & **Max.** & **N** & **AVG** & **SD** & **Min.** & **Max.** \\ \hline Pilot group & 157 & 4446.52 & 8812.83 & 9.98 & 55,829.48 & 161 & 26.55 & 51.61 & 1 & 397 \\
1 st Cycle & 77 & 5332.82 & 9412.81 & 15.97 & 50,824.00 & 81 & 30.62 & 50.09 & 1 & 284 \\
2 nd Cycle & 69 & 3926.84 & 8723.41 & 9.98 & 55,829.48 & 69 & 24.90 & 56.79 & 1 & 397 \\ Special Needs & 11 & 1502.27 & 1819.15 & 34.63 & 5552.54 & 11 & 6.91 & 6.139 & 1 & 19 \\ \hline \multicolumn{10}{c}{**Logic Use**} & \multicolumn{6}{c}{**Logic Activities**} \\ \hline
**Groups** & **N** & **AVG** & **SD** & **Min.** & **Max.** & **N** & **AVG** & **SD** & **Min.** & **Max.** \\ \hline Pilot group & 218 & 48,472.53 & 83,509.39 & 22.76 & 419,728.11 & 223 & 196.58 & 329.86 & 1 & 2289 \\
1 st Cycle & 101 & 64,579.17 & 93,543.72 & 24.69 & 419,728.11 & 104 & 249.01 & 352.87 & 1 & 1536 \\
2 nd Cycle & 104 & 36,945.43 & 74,620.57 & 22.76 & 395,131.96 & 106 & 162.01 & 318.12 & 1 & 2289 \\ Special Needs & 13 & 15,553.14 & 29,776.27 & 42.60 & 111,901.73 & 13 & 59.00 & 60.78 & 1 & 201 \\ \hline \multicolumn{10}{c}{**Science Use**} & \multicolumn{6}{c}{**Science Activities**} \\ \hline
**Groups** & **N** & **AVG** & **SD** & **Min.** & **Max.** & **N** & **AVG** & **SD** & **Min.** & **Max.** \\ \hline Pilot group & 199 & 65,525.20 & 131,669.22 & 5.05 & 981,210.48 & 205 & 232.30 & 523.24 & 1 & 4649 \\
1 st Cycle & 101 & 75,372.20 & 128,321.35 & 5.05 & 919,615.53 & 102 & 280.31 & 587.11 & 1 & 4649 \\
2 nd Cycle & 89 & 60,165.53 & 140,741.02 & 56.15 & 981,210.48 & 94 & 199.13 & 467.54 & 1 & 3528 \\ Special Needs & 9 & 8021.09 & 10,176.30 & 1008.89 & 33,795.67 & 9 & 34.67 & 44.89 & 2 & 148 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Descriptive statistics of the Pilot Group 2 at platform worlds. The use is measured in seconds and the activities done are collected as numbers.