esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Model Creation  
No ethics approval was required because no animal experiments or human studies were involved in this research. To simulate maxillary molar intrusion with MMEA, FE models were created using an integrated FE analysis software (Femap with NX Nastran: Siemens PLM, Plano, TX, USA) on a personal computer (ThinkCentre M92p; Lenovo, Hong Kong). The models consisted of the maxillary right first molar and its surrounding tissues, including periodontal ligament (PDL), cortical bone, trabecular bone, and palatal mucosa. These models were created as three-dimensional structures, but only the palatal mucosa was created as a two-dimensional contour.  
Figure 1: A treatment approach for anterior open-bite patients who need molar intrusion. **(A)** An example of malocclusion with an anterior open bite. **(B)** The mechanics of maxillary molar intrusion with midpalatal miniscrew-attached extension arm. An intrusive load was provided by elastic chain (shown with white open arrowhead) from an extension arm attached to midpalatal miniscrews to maxillary first molars (shown with white arrowhead). Transpalatal arch was attached between first molars to avoid tipping movement.  
The geometry of the maxillary first molar was created based on published data [21]. The size of the crown of the molar was fixed as 10.0 mm meso-distally, 12.0 mm bucco-palatally, and 7.0 mm occluso-apically. The buccal and palatal roots were represented as conical structures, 12.0 mm in height. The maximum radius of the palatal root was designated as 4.0 mm and that of the buccal roots was 3.0 mm. These roots partially overlapped in the trifurcation area (Figure 2A). A layer structure was formed in the order of PDL, cortical bone, and trabecular bone from the root surface. The PDL models had a thickness of 0.3 mm [22]. The cortical and trabecular bone models had a rectangular parallelepiped shape, with dimensions of 18.0 mm meso-distally, 16.0 mm bucco-palatally, and 15.0 mm occluso-apically. The surface layer of 1.0 mm in thickness was defined as the cortical bone [23], and the others were defined as the trabecular bone (Figure 2B). The palatal mucosal contour was drawn on the plane that is perpendicular to palatal cortical bone surface and through the meso-distal midline of the surface. Three landmarks were plotted at 6.0 mm intervals in the occluso-apical direction according to a mucosal thickness that measured with computed tomography (CT) images [24], and these values were 2.3 mm, 3.0 mm, and 4.7 mm in the order from cervical region. These landmarks were connected with curves and the palatal mucosal contour was created (Figure 2C). The three-dimensional XYZ coordinates were constructed as follows: the center of the occlusal surface of the molar was defined as the origin, the meso-distal direction as the \(x\)-axis, the bucco-palatal direction as the \(y\)-axis, and the occluso-apical direction as the \(z\)-axis (Figure 2).
## 3 Parameter Analysis
### Influence of seawater on the Site Seismic Response  
One of the most significant differences between marine sites and land sites is that the site response is affected by the dynamic coupling effect with seawater. Therefore, it is necessary to study the impact of seawater on the seismic response of the reef island. The land site model (i.e., the slope-platform model) used in this section is obtained by deleting the fluid elements and the fluid artificial boundaries of the reef island-seawater system model. The comparison results of the two models are shown in Figures 10 and 11.  
Figure 11: Vertical ground motion peak acceleration distribution on the seabed and reef island surface.  
Figures 12 and 13 show the distribution of horizontal and vertical peak accelerations on the reef island surface and seabed, respectively. It can be seen from the figure that, on the slope surface of the reef island, the horizontal peak acceleration of the reef-island-seawater model is significantly greater than that of the slope-platform model, while the vertical peak acceleration is smaller than that of the slope-platform model. This is because for vertical input motions, the seawater layer has a strong suppression effect on vertical ground motion, so the vertical peak acceleration on the slope surface is smaller than that of the slope-platform model.  
Figure 12: Comparison of the horizontal ground motion peak acceleration distribution on reef island and seabed with and without seawater (\(\theta=45^{\circ}\)).
## 2 Unified Electric Machine Model
 \tag{10}\]  
For the correct solution of the equations, all the terms must be referred to the same reference frame, conventionally the rotor is chosen. It is therefore necessary to introduce a transformation to bring stator quantities into rotor frame and vice versa. A rotation of the rotor angle \(\theta(t)\) is needed and, since the equations use for elements vectors, a slightly modified Park transformation is used. The matrices that operate the transformation are shown in (11). To simplify the writing, time dependency will be omitted. \[[T(\theta(t))]\equiv[T]= \begin{bmatrix}\cos(\theta)&-\sin(\theta)&0&0\\ \sin(\theta)&\cos(\theta)&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}  \tag{11}\] \[[T(\theta(t))]^{-1}\equiv[T]^{-1}= \begin{bmatrix}\cos(\theta)& \sin(\theta)&0&0\\ -\sin(\theta)&\cos(\theta)&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} \]  
The (6) and (7) are brought into a canonical Input-State-Output form with the following choices:  
* Input: voltage \(\mathbf{v}\);
* Output: current \(\mathbf{i}\);
* State: flux \(\mathbf{\varphi}\)  
Using Park (11) to keep the measurements in the natural reference frame, but operating the necessary conversion to rotor frame, the complete machine equations become (12) and (13). \[\dot{\mathbf{\varphi}} =\mathbf{v}-[R][T]^{-1}[L]^{-1}[T]\mathbf{\varphi}+[R][T]^{-1}[L]^{-1}\bm {\varphi}_{e} \tag{12}\] \[\mathbf{i} =[T]^{-1}[L]^{-1}[T]\mathbf{\varphi}-[T]^{-1}[L]^{-1}\mathbf{\varphi}_{e} \tag{13}\]  
These equations are completely general for any type of traditional electric machine such as IM, SPM-SM, IPM-SM, R-SM, and AR-SM as shown also in [16; 17].
# Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas
###### Abstract  
Multi-source uncertainties yielded by randomness, spatial variability and cross-correlation of soil parameters severely affect the realization of random fields. However, current studies rarely account for these simultaneously, leading to inevitable bias in random field simulation and subsequent structure analysis. In this paper, copula-based cross-correlated random fields for transversely anisotropic soil slope are proposed. Firstly, based on the traditional probabilistic method and random field theory, the effect of the cross-correlation of soil parameters on the random field is comprehensively analyzed. Then copulas, which mainly characterize the dependent structures of random variables, are further expanded to connect multivariate random fields. Four special algorithms associated with Gaussian, Frank, Plackett and No. 16 copulas are subsequently developed. At last, the performance and effectiveness of copula-based cross-correlated random fields are illustrated by means of assumed and engineering slope cases. The results show that the proposed method is amenable to characterizing spatial variability comprising multiple cross-correlated soil parameters of transversely anisotropic slope. Soil profiles can be represented with a relatively high accuracy. Moreover, the performance of copula-based CCRF is simultaneously governed by margins, cross-correlated coefficients and copulas. The proper selection of these crucial factors can considerably reduce multi-source uncertainties. Overall, the proposed method could provide a useful guideline for accurately modeling cross-correlation random fields of soil slope.  
spatial variability; cross-correlated random fields; transverse anisotropy; copula +
Footnote †: journal:
## 3 Results and Discussion
### Building the Stacking Models
While the stacking approach demonstrated strong predictive capabilities, the validation metrics alone were insufficient to conclude that the stacking models were superior to individual \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Validation \(MAE\)** & **Validation \(R^{2}\)** & **Validation RMSE** \\ \hline GA-DT (20) & 0.4570 \(\pm\) 0.1479 & 0.4853 \(\pm\) 0.4181 & 0.6227 \(\pm\) 0.1966 \\ AGA-RF (80) & 0.4194 \(\pm\) 0.1227 & 0.6183 \(\pm\) 0.2213 & 0.5563 \(\pm\) 0.1577 \\ GA-XGB (20) & 0.4315 \(\pm\) 0.1202 & 0.6098 \(\pm\) 0.2263 & 0.5619 \(\pm\) 0.1582 \\ AGA-SVR (20) & 0.3537 \(\pm\) 0.1006 & 0.7158 \(\pm\) 0.1689 & 0.4738 \(\pm\) 0.1385 \\ AGA-ANN-1 (20) & 0.3579 \(\pm\) 0.1036 & 0.7215 \(\pm\) 0.1749 & 0.4688 \(\pm\) 0.1348 \\ GA-ANN-2 (50) & 0.3648 \(\pm\) 0.0913 & 0.7270 \(\pm\) 0.1543 & 0.4659 \(\pm\) 0.1203 \\ AGA-ELM-3 (20) & 0.3535 \(\pm\) 0.0908 & 0.7450 \(\pm\) 0.1496 & 0.4494 \(\pm\) 0.1160 \\ GA-ELM-4 (50) & 0.3534 \(\pm\) 0.0914 & 0.7466 \(\pm\) 0.1468 & 0.4479 \(\pm\) 0.1177 \\ Stacking 1 & 0.3476 \(\pm\) 0.0952 & 0.7454 \(\pm\) 0.1570 & 0.4483 \(\pm\) 0.1230 \\ Stacking 2 & 0.3500 \(\pm\) 0.0923 & 0.7445 \(\pm\) 0.1497 & 0.4501 \(\pm\) 0.1206 \\ Stacking 3 & 0.3554 \(\pm\) 0.0936 & 0.7379 \(\pm\) 0.1487 & 0.4572 \(\pm\) 0.1195 \\ Stacking 4 & 0.3583 \(\pm\) 0.0965 & 0.7340 \(\pm\) 0.1575 & 0.4593 \(\pm\) 0.1244 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean validation metrics for the best ML-based models and stacking models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Train \(MAE\)** & **Test \(MAE\)** & **Train \(R^{2}\)** & **Test \(R^{2}\)** & **Train \(RMSE\)** & **Test \(RMSE\)** \\ \hline GA-DT (20) & 0.3015 & 0.3499 & 0.7579 & 0.6369 & 0.4920 & 0.6202 \\ AGA-RF (80) & 0.2515 & 0.3408 & 0.8810 & 0.6334 & 0.3448 & 0.6232 \\ GA-XGB (20) & 0.0396 & 0.4082 & 0.9321 & 0.5730 & 0.0576 & 0.6726 \\ AGA-SVR (20) & 0.2685 & 0.3307 & 0.8371 & 0.6375 & 0.4036 & 0.6197 \\ AGA-ANN-1 (20) & 0.2986 & 0.1882 & 0.8457 & 0.9508 & 0.3928 & 0.2283 \\ GA-ANN-2 (50) & 0.2868 & 0.2282 & 0.8698 & 0.9059 & 0.3609 & 0.3158 \\ AGA-ELM-3 (20) & 0.3145 & 0.1940 & 0.8262 & 0.9544 & 0.4169 & 0.2197 \\ GA-ELM-4 (50) & 0.3145 & 0.1940 & 0.8262 & 0.9544 & 0.4169 & 0.2197 \\ Stacking 1 & 0.3023 & 0.2219 & 0.8435 & 0.9356 & 0.3956 & 0.2613 \\ Stacking 2 & 0.2886 & 0.2437 & 0.8514 & 0.8924 & 0.3855 & 0.3376 \\ Stacking 3 & 0.2889 & 0.2177 & 0.8582 & 0.9246 & 0.3766 & 0.2826 \\ Stacking 4 & 0.2881 & 0.2227 & 0.8574 & 0.9241 & 0.3777 & 0.2835 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training and testing metrics for the best ML-based models and stacking models. neural networks. Therefore, further statistical testing is necessary to determine whether the observed differences in performance are statistically significant.
# Air Quality Assessment of a School in an Industrialized Area of Southern Italy
## 3 Results and Discussion  
### Total Volatile Organic C Concentration  
The real time TVOC concentrations, monitored over the entire investigated period, both inside and outside the school, showed indoor concentrations systematically larger than outdoor ones, with a mean value of 0.10 \(\pm\) 0.04 ppm for the outdoor sites and 0.14 \(\pm\) 0.07 ppm for indoor, and a mean value of the I/O ratio equal to 1.41. As expected, the analysis by day of the week shows that the mean value of TVOC concentrations during school hours (0.16 \(\pm\) 0.07 ppm) was higher than that calculated for Sunday (0.11 \(\pm\) 0.05 ppm). Differences between indoor and outdoor profiles are shown in Figure 2, which reports, for example, the TVOC data collected from Monday 18 to Sunday 24 February. Indoor TVOC profiles showed more frequent and higher peak values (up to 0.76 ppm) with respect to the outdoor profiles. Both profiles presented a decrease on 23 and 24 February, related both to the increase of wind speed intensity in the two days (Figure S3), and to the emission reduction on Sunday. This is particularly evident in the indoor profile where no peak was registered. The daily profiles (Figure 2) show the presence of two peaks of TVOC concentrations during the morning and afternoon, not directly linked to outdoor intrusion. Moreover, considering the outdoor temperature trend shown in Figure S2, a decrease during 23 and 24 February can be observed, such as the RH trend. The higher TVOC concentrations registered through the daytime school hours were probably due to student occupancy (acrylic/tempera paints, permanent markers containing aromatic- and alkane-containing solvents and adhesive, use of perfumes and deodorants after gym activities) and poorly-ventilated classrooms in the cold investigated period. In the same way, taking into account that after-school activities were carried out on Monday and Tuesday, the highest peaks registered during these two afternoons would be due to student occupancy and activities. On the other weekdays, the afternoon TVOC peaks showed about the same duration and time slot, suggesting a link between VOC concentrations and cleaning activities.  
A comparison between outdoor TVOC concentrations and NO2 (Figure S5) measured at the air quality monitoring station shows a correlation of 0.53 between the two pollutants (r = 0.53; \(n\) = 169; \(p\) = 0.03), suggesting the hypothesis that only a portion of outdoor TVOCs is attributable to vehicle-traffic emissions, which is the main source of NO2 in the examined area.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 4 Propulsion Systems in Nanosatellites
### Pulse Plasma Thruster  
Pulsed plasma thrusters (PPTs) discharge a high-current pulse through a solid or liquid propellant, ionizing and vaporizing it to produce a plasma. The plasma is then accelerated by electromagnetic forces (Lorentz force) to generate thrust [69]. PPTs are simple, reliable, and capable of operating with various propellants, but they provide low thrust and are typically used for attitude control and small satellite applications [69; 70; 71]. The schematic of a pulse plasma thruster propulsion system is shown in Figure 8. Pulsed plasma thrusters require power to generate high-current pulses that vaporize and ionize the propellant. The power (\(P\)) is typically stored in capacitors, released in pulses, and calculated as shown in Equation (11).  
Electric propulsion systems, particularly ion and Hall effect thrusters, are often deemed optimal for most nanosatellite missions due to their high specific impulse and efficiency. These attributes render them well suited for extended missions necessitating precise control and substantial delta-v (\(\Delta\)v). Conversely, chemical propulsion systems or cold gas thrusters may be more suitable for short-term missions or those requiring rapid maneuvers because  
Figure 8: Pulse Plasma Thruster Propulsion System.  
Figure 7: Hall Thruster Propulsion System.  
of their simplicity and robust thrust capabilities. Moreover, selecting a propulsion system for nanosatellites should be contingent upon specific mission requirements, encompassing mission duration, required delta-V, available power, and budgetary constraints.  
The PPTCUP (Pulsed Plasma Thruster for CubeSat Propulsion), developed by Mars Space Ltd. in collaboration with Clyde Space and the University of Southampton, is an example of pulsed plasma technology used in nanosatellites. The system uses Polytetrafluoroethylene (PTFE) as a propellant, providing a thrust of 0.04 mN at only 2 W of power. The PPTCUP has been successfully flight-qualified, making it one of the few PPT systems designed explicitly for nanosatellites [58]. Additionally, newer developments in PPT technology, such as those reported in recent experimental trials, focus on multi-thruster modules for enhanced attitude and translational control for small satellites [66].
# Slope Stability Analysis of Open-Pit Mine Considering Weathering Effects
## 2 Laboratory Tests of Weathering Effects
### Experimental Methodologies  
A comprehensive suite of tests, including uniaxial compressive strength tests, direct shear tests, and Brazilian splitting tests, were conducted on the specimens (Figure 2). For the uniaxial compressive strength tests the specimen was placed between the platens of a compressive testing machine. A gradually increasing compressive load at 0.5 MPa/s was applied to the specimen at a controlled rate. For direct shear tests, the specimen was placed between the upper and lower shear boxes. A predetermined normal load was applied to the specimen to simulate the in situ stress conditions. A gradually increasing horizontal load at 0.1 MPa/s was applied to cause shear deformation. For the Brazilian splitting tests, the specimen was placed between two loading platens. A compressive load at 0.5 MPa/s was applied along the diameter of the specimen. Prior to testing, the specimens were carefully placed in the actual mining environment to undergo natural weathering. The specimens were placed in the areas near the slope edge where significant diurnal temperature range co-occurs during day and night, which is a prominent feature of the plateau climate. To understand the evolution of physical and mechanical parameters during weathering, the specimens were periodically retrieved, and examined for relevant physical and mechanical parameters. The testing programs are outlined in Table 1. A total of 15 Brazilian splitting tests, 15 uniaxial compression tests, and 15 direct shear tests were conducted. During the tests, since the weathering process occurred in the actual mining environment, the temperature as well as the precipitation were different each day. It was recorded that the  
Figure 1: Examples of the specimens: (**a**) Brazilian splitting test, (**b**) uniaxial compression test, and (**c**) direct shear test.  
maximum surface temperature reached 75.2 \({}^{\circ}\)C, while the minimum surface temperature was 16.4 \({}^{\circ}\)C, resulting in a temperature difference of 55.8 \({}^{\circ}\)C. The large diurnal temperature variation not only affected the physical properties of rocks, such as causing micro-cracks on the surface of rocks, but also promoted the freeze-thaw weathering process of rocks. When the temperature dropped at night, moisture on the surface of rocks easily condensed into frost or ice, exerting a freezing force on the rocks, thereby accelerating the weathering process. The area also has abundant precipitation, and weathering was even more intense. It was recorded that during the 28 days of weathering, there were 9 days of rain.
## 4 Case Study: MIMO Magnetic Levitation System
### System Analysis and Results
#### 4.2.3 Comparison with an Existing TES Method  
The bound generation quadratic equality of [PERSON] et al. [4] method (Appendix in Reference [4]) involves \"pairing\" of two plants \(p_{111}\) and \(p_{112}\) from the plant set \(p_{11}\) (channel-11). The \"pairing\" has to be done for all possible pairs of plants from \(p_{11}\). Thus, it can become a computational bottleneck, when \(p_{11}\) is represented by a large number of plants, such as when the number of uncertain plant parameters is large. The number of plants in \(p_{11}\) considered is \(3^{8}=6561\) for a grid size of three and the plant pairs to be considered in quadratic inequality becomes \(3^{8}\times 3^{8}=43,046,721\). This requires very large computational time in method [4] due to \"pairing requirement\" in the specifications. The proposed method does not require \"pairing\" and it takes 0.5 sec for generating performance bounds in both the channels. Another drawback in Reference [4], the bound balancing values and the tolerance tightening  
Figure 11: Comparison of controller magnitudes for both the equivalent single-input single-output (SISO) loops; (**a**) channel-11 and (**b**) channel-22.  
factors are selected, such that performance bound reaches an optimal configuration. These values were chosen by tuning at each performance design frequency and it needs an experienced designer, so the [PERSON] et al. method [4] is not simple. The proposed method does not require any tuning and is simple, computationally efficient. The [PERSON] et al. method [4] can still be applied after manipulating the plant template using the special methods such as the interval math, the affine plant representation, or methods to compute the outer border of the template but this is beyond the scope of the paper.
# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 3 Methodology
### Step 2: Shipper/Consignee Clustering  
K-means is used to assign each record in the dataset to one initial cluster. Each record is assigned to the cluster to which it is most similar, using a measure of distance or similarity as the Euclidean distance measure, as per Equation (3)  
\[\text{\it Euclidean distance measure}\ \ (d)=\sqrt{(a_{1}-b_{1})^{2}+(a_{2}-b_{2})^{2}+ \ldots+(a_{n}-b_{n})^{2}} \tag{3}\]  
A suitable number of groups of the dataset is referred to as K-optimal, determined by K-means clustering. Validation is conducted by the elbow method, which runs k-means clustering on the dataset for a range of values of k and each value of k. The average distance within the centroid is calculated using Equation (4), where n is the number of dataset points and d is the distance between clustering centroid and dataset point.  
\[\text{\it Average within centroid distance}\ \ =\frac{d_{1c}+d_{2c}+d_{3c}}{n} \tag{4}\]  
A line chart of the average within the centroid distance is then plotted for each value of k. If the line chart looks like a knee, then the 'elbow' of the knee represents the optimal value of k. This idea is similar to a sum of square error [62].  
This step divided shippers of Y and consignees of X==>Y into groups with similar R, F, M, NC, NP, W, and D values. Data were partitioned into k clusters using the K-means technique [86]. This involved four sub-steps:  
1. Determining R, F, M, NC, NP, W, and D variables for each shipper and consignee
2. Correlating variables to investigate relationships
3. Determining optimal numbers of clusters, with the K-optimal method [62]. The K-means technique was used to cluster a group of Y shippers and X==>Y consignees.
4. Analyzing different clusters of Y shippers and screening potential X==>Y consignee clusters.
###### Abstract  
The underwater wireless sensor network is an important component of the underwater three-dimensional monitoring system. Due to the high bit error rate, high delay, low bandwidth, limited energy, and high dynamic of underwater networks, it is very difficult to realize efficient and reliable data transmission. Therefore, this paper posits that it is not enough to design the routing algorithm only from the perspective of the transmission environment; the comprehensive design of the data transmission algorithm should also be combined with the application. An edge prediction-based adaptive data transmission algorithm (EP-ADTA) is proposed that can dynamically adapt to the needs of underwater monitoring applications and the changes in the transmission environment. EP-ADTA uses the end-edge-cloud architecture to define the underwater wireless sensor networks. The algorithm uses communication nodes as the agents, realizes the monitoring data prediction and compression according to the edge prediction, dynamically selects the transmission route, and controls the data transmission accuracy based on reinforcement learning. The simulation results show that EP-ADTA can meet the accuracy requirements of underwater monitoring applications, dynamically adapt to the changes in the transmission environment, and ensure efficient and reliable data transmission in underwater wireless sensor networks.  
underwater wireless sensor networks (UWSNs); underwater monitoring applications; edge computing; intelligent routing algorithm; reinforcement learning (RL); auto regressive and moving average (ARMA) +
Footnote †: journal: Sensors