esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Causality Inference Using Imess
### Multivariate Effective Source Selection  
In a multivariate time series, the causality between variables are influenced by other parameters, resulting in a binary transfer entropy that cannot reflect the true flow of information between the two parameters. [PERSON] [27] proposed an algorithm named Multivariate Effective Source Selection (MESS), which can identify causality in multivariate time series.  
Let \(\mathbf{D}\) be all variables in system,\(\mathbf{V}_{X}\) be the set of source parameters of \(X\) (\(\mathbf{V}_{X}\in\mathbf{D}\)), which is called the information contribution set. We can decompose \(H(X)\) into the collective transfer entropy of the previous \(t\) time-step information contribution set \(\mathbf{V}_{X}\) (denoted by \(T_{\mathbf{V}_{X}\to X}\)), the historical information provided by \(X\) itself (called Active Information Storage and denoted by \(A_{X}\)), and the inherent uncertainty or randomness information \(U(X)\) in \(X\), as shown in Equation (5):  
\[H_{X}=A_{X}+T_{\mathbf{V}_{X}\to X}+U_{X} \tag{5}\]  
Equation (5) is called the information decomposition formula. According to the information decomposition formula, the source variables of a target variable is the set of variables that can incrementally provide enough information to reduce the uncertainty of the target variable. The steps of MESS are as follows:  
1. Initialize \(\mathbf{V}_{X}=\emptyset\).
2. For each possible source parameter \(Z\in\mathbf{D}\backslash\mathbf{V}_{X}\), calculate \(T_{Z\to X|\mathbf{V}_{X}}\).
3. Choose the \(Z\in\mathbf{D}\backslash\mathbf{V}_{X}\) that provides the largest incremental information contribution \(T_{Z\to X|\mathbf{V}_{X}}\) for the next state of \(X\).
4. Repeat steps 2 and 3 until there is no \(Z\) that can be added to \(\mathbf{V}_{X}\). The reason for the termination of the algorithm is that there is no more information in \(X\) left to account for, or no source \(Z\) provides a statistically significant information contribution \(T_{Z\to X|\mathbf{V}_{X}}\) which could account for part of the remaining uncertainty in \(X\).
5. When no source \(Z\) can be found to provide a statistically significant information contribution \(T_{Z\to X|\mathbf{V}_{X}}\), consider joint pair of sources \(\mathbf{Z}{=}\left\{Z_{a},Z_{b}|Z_{a},Z_{b}\in\mathbf{D}\backslash\mathbf{V}_ {X}\right\}\) instead of a single source \(Z\), and repeat steps 2 and 3.
6. Once we have finalized the determination of \(\mathbf{V}_{X}\) after the above, then check whether each \(Z\in\mathbf{V}_{X}\) adds a statistically significant amount of information \(T_{Z\to X|\mathbf{V}_{X}\backslash Z}\) given all of the other sources selected for \(\mathbf{V}_{X}\). Where the source fails this test, it is removed from \(\mathbf{V}_{X}\).
## 3 Results and Discussion  
### Test Verification of HMCVT  
#### 3.1.1 Test Verification of Clutch Control System  
Due to the nonlinear, time-varying and hysteresis characteristics of the wet clutch pressure control, this study uses PID control. The simulation of the control method is shown in Figure 9. It can be seen that the clutch system has a good control effect in step and sine wave signals. A single clutch test bench was established to verify the control effect as shown in Figure 10a. Because of the delay of the hydraulic system, the actual test is delayed for a short time under the control of step and sinusoidal signals (Figure 10b,c), but the overall control performance is in close agreement with the experimental measurements. The test results show that PID can satisfy the control requirements.
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## 6 Discussion
### Sensitivities of ET to Errors in Key Parameters of the BEPS Model  
Uncertainties in the global ET estimation resulting from errors in key model parameters might be as large as the impacts of leaf clumping mentioned above. To investigate the robustness of our results on the impacts of leaf clumping on global ET estimation, sensitivity analysis was conducted for four key parameters such as LAI, \(\Omega\), V\({}_{\text{cm}}\), and the threshold of relatively available soil moisture under which soil water stress occurs (RASM\({}_{\text{th}}\)). If LAI is uniformly increased or reduced by 20% (assessed LAI error), ET is increased by 6.0% or reduced by 7.0% (Table 2). Nevertheless, the relative differences (RD) between Case II and Case I and between Case III and Case I are affected slightly (less than 0.5%) by the variations in LAI. This indicated that all cases are influenced by the LAI changes in the  
Figure 12: Average annual ET simulated by four remote sensings (RS) based physical models, two machine-learning algorithms, and 14 land surface models (LSM) during 2001 to 2011 and the BEPS model during 2013 to 2016 in this study.  
same direction. Similar to LAI, if \(V_{cmax0}^{25}\) is increased or reduced by 20% in the BEPS model, global terrestrial ET would be increased or decreased in similar magnitudes (around 3.0%), but RD between the cases representing leaf clumping effects changed very little (<0.5%). When \(RASM_{th}\) is increased or reduced by 20%, global terrestrial ET is decreased by 1.6% or increased by 2.1%, but the RD between the cases changed very little (<0.5%). Generally, if other parameters in the BEPS model (i.e., N\({}_{0}\) and \(m\) in Table 1) are modified, the three cases would be moved in the same direction, and the leaf clumping effects quantified as the RD in global terrestrial ET between the cases would not be considerably influenced by these changes. These sensitivity tests support the justification of the spatially consistent leaf clumping effects (Figures 6 and 7).  
In line with expectations, if \(\Omega\) is increased or reduced by 20%, the RD among the cases would be changed substantially (Table 2). In the initial evaluation with field measurements, the average bias error of the CAS-CI product was around 0.01 (-2%) and nearly all CAS-CI values were located within \(\pm\)0.1 of the field observations [27]. Therefore, the 20% variation is generous for most PFTs. The variation range has been made large for covering the high uncertainty in CI estimation of tropical forests [23]. Although leaf clumping is decreased by 20% (\(\Omega\) is increased), there is still a 2.7% difference between Case II and Case I, 7.4% difference between Case III and Case I, indicating that leaf clumping effects should not be neglected even though substantial uncertainties still exist in \(\Omega\) measurements.
## 1 Introduction
Moreover, with the improvement of people's requirements for the ecological environment, it is becoming increasingly important to understand the sensibility of NDVI in response to climate change. Therefore, to better manage grassland ecosystems under global climate change, long-term time series of grassland NDVI and climate (1982-2010) were used to analyze the dynamic effect of climate on NDVI in Inner Mongolia. With satellite and climate data, our main purpose is to explore the interannual variation of NDVI and its driving mechanism. More specifically, we aim to address the following questions: (1) Is it temperature or precipitation that dominates the dynamic of NDVI? (2) How does the sensitivity between NDVI and climate vary temporally? In this study, we innovatively used the linear regression model to find the variable relationships between NDVI and climate from 1982 to 2010. We not only revealed the dynamic effect of climate on NDVI temporally and spatially, but also quantified the contribution rates of temperature and precipitation on the changing of NDVI.
## 3 Background
### Other Key Components Selection
#### 3.5.1 Communication Sub-System  
The purpose of the CubeSat's communication system is to provide a data link between the CubeSat and ground control on Earth, as well as presenting the ability to communicate with other orbiting CubeSats (Inter Satellite Link). Communication between Earth and Orbiting Space Craft is transmitted in the radio band of the electromagnetic spectrum (30 MHz to 40 GHz) [23].  
The CubeSat platform usually handles data in the Very High-Frequency (VHF) and Ultra High-Frequency bands. The operating frequency operating bands are shown in Table 13.  
The selected Antenna is developed by ISIS (Figure 29) and is capable of handling communications in the VHF/UHF band with a dipole configuration. It has a built in thermal knife deployment system to meet the launch requirement stating all deployable must release 30 min after launch. It is compatible with the Pumpkin structure [47].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Radio Band** & **Operating Frequency** \\ \hline Very High-Frequency (VHF) & 30 to 300 MHz \\ Ultra High-Frequency (UHF) & 300 MHz to 3 GHz \\ L & 1 to 2 GHz \\ S & 2 to 4 GHz \\ C & 4 to 8 GHz \\ X & 8 to 12 GHz \\ Optical Laser Communication & 100 to 800 THz \\ \hline \hline \end{tabular}
\end{table}
Table 13: Radio Frequency Operating Bands [23].  
Figure 28: 2100 J CubeSat Thermal Storage Unit [46].  
The nominal power consumption of this component is 40 mW. During the 3 s deployment phase it consumes 2 W [47]. The weight of the component is 77-85 g with envelope stowed dimensions of 98 \(\times\) 98 \(\times\) 7 mm (length by width by height). The device is rated at 5 W and has a qualified temperature range of \(-\)20 \({}^{\circ}\)C to 60 \({}^{\circ}\)C. The cost of the product is \(\ell\)4553.26 [47].  
Incorporating a UHF downlink/VHF uplink full Duplex Transceiver will allow the system to gain telemetry and tele command capabilities in the simple integration of a single board (Figures 30 and 31) [47]. The component has a mass of 75 g and dimensions of 96 \(\times\) 90 \(\times\) 15 mm. The power consumption of the device is 4 W when the transmitter is on and 0.480 W when only the transceiver is operating. The operating temperature range of this product is \(-\)20 \({}^{\circ}\)C to 60 \({}^{\circ}\)C.  
The cost of this product is \(\ell\)7371.95 [23] as of March 2017.  
Figure 31: Typical CubeSat Telecommunication Systems [7].  
Figure 30: UHF Downlink/VHF Uplink Transceiver [47].  
Figure 29: ISIS VHF/UHF Antenna [47].
## 4 Experiment Results and Discussion
### Evaluation Metrics  
The performance of the model was evaluated as a multiclass classification scenario. Consequently, there were four different prediction states described as true positive (TP), true negative (TN), false positive (FP) and false negative (FN). All of the classes were successfully predicted with positive values, which meant that the true value of the class was positive, and the predicted value of the class was also positive. For example, if the density level for the real value of the class was medium density (M), while the predicted class was the same, this was identified as a true positive (TP). Moreover, a true negative (TN) was defined similarly as all the successfully predicted samples with negative values for which the true value of the class was negative and the predicted value of the class was also negative. For example, if the density level for the real value of the class was not medium density (M), and the predicted class was the same, then a false positive (FP) occurred when the actual class differed from the expected class, or when the expected class was positive and the actual class was negative. When the actual class was positive while the projected class was negatively defined, this was considered a false negative (FN).
###### Abstract  
_Objectives_: This study aimed to evaluate the effectiveness of a novel hybrid aligner system, Geniova Technologies(tm) (GT), for arch expansion and to compare the predictability of its virtual setup with a conventional clear aligner system (CA) after the first treatment phase. _Materials and Methods_: Forty (mean age: 31.3 years for GT, 38.4 years for CA) adult patients with maxillary dentoalveolar compression and anterior crowding >3 mm were enrolled and assigned to GT and CA groups. Transverse changes for canines and premolars were measured at the cusp and cervical levels. _Results_: No significant baseline differences in transverse dimensions were found between groups. Treatment duration (4.25 months for GT vs. 9.75 months for CA) and the number of aligners (4.25 in GT vs. 28.25 in CA) significantly differed (\(p<0.001\)). At the cusp level, mean transverse expansions for the maxillary first premolars were 2.78 mm (GT) and 2.44 mm (CA). However, effectiveness comparisons revealed no significant differences in expansion outcomes, with both groups showing similar accuracy. _Conclusions_: The GT group achieved comparable dentoalveolar expansion of canines and premolars in significantly less time and with fewer aligners than the CA group. The predictability of virtual setup measurements was similar for both systems, confirming their comparable performance in achieving planned expansion.  
_Article_  
1  
Academic Editors: [PERSON] and [PERSON]  
22 November 2024  
Received: 20 December 2024  
Accepted: 23 December 2024  
Published: 24 December 2024
# Basins of Convergence in a Multi-Perturbed CR3 BP +
## 2 Description of the Dynamic Model
### Effects of Natural Satellites  
In this section, the effect of the presence of natural satellites in the CR3 BP model is studied in a similar way to that shown in [29]. The modification of the potential is based on the original equations of classical mechanics (Equations (A2) and (A3)). It is assumed that the satellites are perfectly spherical bodies, that they are not a source of radiation, and that their mass is significantly lower than the mass of the primary bodies so that they do not affect the dynamics of the primaries. For each satellite \(i=[1,n_{s}]\), its mass is denoted by \(m_{si}\), and it is considered to orbit circularly with a radius \(a_{si}\) around one of the primary bodies, with a mean motion of \(n_{Dis}\). Employing this notation, the position of each satellite at any time is given as follows:  
\[\begin{array}{l}x_{i}=x_{j}+\frac{a_{si}}{R}\cos((n_{Disi}-n_{D}^{*})\,t)\\ y_{i}=y_{j}+\frac{a_{si}}{R}\sin((n_{Disi}-n_{D}^{*})\,t)\end{array} \tag{16}\]  
where subscript \(j\) refers to the primary mass around which satellite \(i\) orbits. The modifications to the potential are deduced from the equations of classical mechanics and the mathematical development set forth in Section 2 and Appendix A. The effective potential considering the effect of natural satellites is then  
\[\begin{array}{rcl}\Omega^{*}(x,y,\tau)&=&\frac{n^{2}}{2}(x^{2}+ y^{2})+\frac{1-\mu}{r_{1}}\left(q_{1}+\frac{f_{1_{1}}}{2r_{1}^{2}}+\frac{3y^{2}f_{ 2_{1}}}{2r_{1}^{4}}\right)+\\ &+&\frac{\mu}{r_{2}}\left(q_{2}+\frac{f_{1_{2}}}{2r_{2}^{2}}+ \frac{3y^{2}f_{2_{2}}}{2r_{2}^{4}}+\frac{\epsilon}{r_{2}^{2}}\right)+\sum_{i=1 }^{n_{s}}\frac{\alpha_{i}}{r_{s_{i}}}\end{array} \tag{17}\]  
where \(\alpha_{i}=m_{si}/(m_{1}+m_{2})\), and \(r_{s_{i}}\) is the distance between the infinitesimal body and satellite \(i\).  
The positions of the satellites, unlike the positions of the primary masses, are not fixed in the rotating reference system in which the equations are defined. This means that the potential function depends not only on the coordinates of the infinitesimal mass but also on time. However, their presence does not affect the average movement of the primaries, so this modification does not imply any extra term within \(n\), given by (15).
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 5 Modification Processes of Kaolin: Enhancing Industrial Applicability
### Acid-Base Chemical Treatment
#### 5.3.2 Alkaline Activation  
Alkali activation significantly modifies the acidity, surface area, pore size, and volume, as well as the adsorption strength of kaolinite, making it an ideal precursor for solid basic catalysts [59]. This process involves the deprotonation of aluminol and silanol groups, resulting in the simultaneous dealumination and desilication of the kaolinite material [60; 61]. Alkali activation is also effective in developing various basic zeolites with low Si/Al ratios, including K-F zeolite, 13X zeolite, A, P, and X zeolites, zeolite N, Na-Y zeolite, MCM-41, and zeolite NaA [62; 63; 64; 65; 66; 67]. The alkali activation of kaolinite and other clays has received significantly less research attention than acid treatment. Given the solubility of silica and aluminates in alkaline solutions, the gradual leading of silicic and aluminate layers can be expected during alkali activation. Recent studies, however, have reported the selective removal of silicon sheets to create y-alumina with intriguing properties [68]. During these alkaline treatments, concentrated solutions were used both at ambient temperature and under reflux conditions. As a result, metakaolin was broken down and transformed into highly crystalline K-F zeolite. Treatments were performed using 1 M and 5 M potassium hydroxide (KOH) solutions, with the strength of the treatment influencing the formation of zeolite crystals and the breakdown of metakaolinite layers. This strong crystallinity led to extremely low surface areas in the treated samples [58].  
Interestingly, aluminum is more readily dissolved in acidic treatments than silicon, whereas the opposite is observed in alkali treatments. X-ray diffraction (XRD) patterns reveal significant alterations in the crystal structure after acid treatments, while XRD peaks generally become more distinct following alkali treatments. This suggests that alkali treatments have a smaller impact on mineral structures, although they often create new mineral phases. The dissolution of amorphous mineral particles during alkali treatment enhances basal reflections, effectively \"cleaning\" the minerals [69]. Considering the high concentrations of silicon dissolved in alkaline solutions, the exterior silica sheets of the minerals may also be removed. In kaolin, the presence of SiO2 undergoes a conversion process resulting in the formation of free SiO2, which readily reacts with alkaline substances [70]. Sodium hydroxide modification facilitates the leaching of silicon from calcined kaolin, increasing the small pore structure [71]. As the duration of alkali treatment increases, the pore size distribution of calcined coal-series kaolin becomes wider, its specific surface area decreases, its pore volume increases, and its cracking activity and selectivity improve.
# High-Fold 3D Gaussian Splitting Model Pruning Method Assisted by Opacity
## 3 Methods
### Gaussian Quantity Compression
#### 3.1.2 Proportional Contribution Pruning with Learnable Masks
Finally, the Gaussianmodel is subsequently pruned according to the mask to yield a preliminary de-redundant Gaussian model. During the pruning process of 3D Gaussian points, we found that due to the phased pruning strategy applied in the original 3 DGS model, the number of 3D Gaussian points fluctuates in the early stages of pruning, which interferes with the reduction in quantity. The work in [39] proposed a method to eliminate such fluctuations in the number of 3D Gaussian points, where the opacity is reduced by a small fixed value after each cloning or splitting operation. To maximize the trade-off between density and quality, the fixed value was empirically determined at 0.01. We incorporated this method into our Gaussian point reduction module. This effectively eliminates the fluctuations in the number of Gaussian points, meanwhile enhancing the compression of the Gaussian model, and the loss in reconstruction quality is negligible. When training to a certain number of times, the number of Gaussian points retained by the mask gradually tends to stabilize, and then calculate the value of the contribution of each retained Gaussian point according to the following formula:  
\[Con_{n}=\partial_{n}\cdot\left(\frac{4}{3}\pi a_{n}b_{n}c_{n}\right)\cdot\left[ \sum_{i=1}^{NHW}1(G_{n},p_{i})\right] \tag{10}\]  
\(Con_{n}\) represents the contribution of a Gaussian point. Given that the Gaussian point is represented as an ellipsoid in three-dimensional space, the scale parameter \(s_{n}\) comprises the three components of the ellipsoid: \(a_{n},b_{n}\) and \(c_{n}\). Consequently, the volume of a 3D Gaussian point can be calculated by \(\frac{4}{3}\pi a_{n}b_{n}c_{n}\). The product \(NHW\) indicates the total number of pixels in the training image set and \(p_{i}\) refers to the \(i\) pixel point. The function \(1(G_{n},p_{i})\) determines whether the Gaussian point \(G_{n}\) contributes to the rendered pixel \(p_{i}\), counting as 1 if there is an intersection during rendering. This process iterates over the entire training dataset to count the number of times each Gaussian point is rendered on all pixels. Then, multiply the opacity parameter \(\partial_{n}\) processed by the mask to obtain the contribution value parameter of the Gaussian point. After quantifying and ranking the contribution of each Gaussian point, we proportionally pruned the low-contribution Gaussian points. Finally, we complete the reduction of the number of Gaussian points in the Gaussian model. The loss function of the contribution pruning module combined with the learnable mask is as follows:  
\[L=(1-\lambda)L_{1}+\lambda L_{D-SSIM}+\frac{1}{N}\sum_{n=1}^{N}m_{n} \tag{11}\]  
Incorporating a mask loss term into the loss function of the 3D Gaussian Splatting model promotes the minimization of the sum of \(m\), thereby guiding the model to mask as many redundant Gaussian points as possible. This approach aids in reducing the total number of Gaussian points.
## 5 Conclusions  
With the development of large-scale integrated circuits and semiconductor technology, the digital systems for critical data storage suffer from the damage of SEUs. In this paper, a novel radiation-hardened TMR circuit design based on STT-MTJ devices is proposed. Based on an equivalent circuit model for MTJ devices, the whole TMR module is simulated. The simulation results validate the anti-radiation reinforcement qualities of the proposed circuit. On the one hand, the STT-MTJ devices constitute a correct logic pattern of majority voters and achieved basic TMR functionality. On the other hand, the STT-MTJ devices contribute to a pre-charge sense amplifier with the ability to recover the storage cell. Improved anti-radiation properties and robustness of the TMR circuit against SEUs is achieved. The designed TMR circuit can be used in irradiated environments to control and reduce radiation-related operation failures. Further study can focus on the speed and power performance of this TMR circuit.  
Figure 7: Transient simulation waveforms of corrected output from the proposed TMR circuit when SEU occurs with \(Q_{inj}\) = 0.2 pC (a) read “1” and (b) read “0”.  
**Author Contributions:** Conceptualization, S.Z. and Y.C.; Data Curation, P.D. and N.L.; Formal Analysis, S.Z.; Funding Acquisition, S.Z.; Investigation, P.D.; Methodology, S.Z. and P.D.; Project Administration, Y.C.; Resources, S.Z.; Software, P.D. and N.L.; Supervision, Y.C.; Validation, S.Z.; Visualization, N.L.; Writing--Original Draft, P.D. and N.L.; Writing--Review and Editing, S.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 62204100; the China Postdoctoral Science Foundation, grant number 2021M691360; and the Postdoctoral Research Foundation of Zhejiang Province, grant number ZJ2020101.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** Special thanks for the technical support from IGEN Tech Co., Ltd.  
**Conflicts of Interest:** The authors declare no conflict of interest.