esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
## 3 Cycloid Gear Profile Model
### Cycloid Gear Tooth Profile Model Based on Dyadic Quaternion  
In the case of fixed pin tooth, the process of profile formation of cycloid teeth can be divided into five processes as shown in Figure 3. They are rotation \(\alpha=-\pi+\theta\), translation e, rotation \(\beta=\pi-\theta_{b}\), translation Rb, and rotation \(\gamma=\pi-\theta_{e}\). The five processes corresponding to the dual quaternions comprise Equation (6).  
\[\left\{\begin{array}{l}P_{r1}=\left[\cos\frac{\alpha}{2},0,0,\sin\frac{ \alpha}{2},0,0,0,0\right]\\ P_{t1}=\left[1,0,0,0,0,\frac{\varepsilon}{2},0,0\right]\\ P_{t2}=\left[\cos\frac{\beta}{2},0,0,\sin\frac{\beta}{2},0,0,0,0\right]\\ P_{t2}=\left[1,0,0,0  
After completing the five-step coordinate transformation process above, the coordinate of point k (r\({}_{\text{p}}\), 0, 0) in the coordinate system \(X_{3}Y_{3}Z_{\text{p}}\) is rewritten in a dual quaternion form k (1, 0, 0, 0, 0, r\({}_{\text{p}}\), 0, 0). The cycloidal gear tooth profile equation can be obtained by multiplying as in Equation (7).  
\[\left\{\begin{array}{l}q=P_{r1}P_{r1}P_{r2}P_{t2}P_{r3}\\ p_{a}=q^{*}p_{b}q\end{array}\right. \tag{7}\]  
Point k in the coordinate system \(X_{3}Y_{3}Z_{\text{p}}\) is expressed in the dual-quaternion space as follows:  
\[p=\left[1,0,0,0,0,r_{p},0,0\right]^{\text{T}} \tag{8}\]  
After coordinate transformation, in the XYZ\({}_{a}\) coordinate system, the parameterized expression of the point \(k\) is shown in Equation (9).  
\[\left\{\begin{array}{l}x=-e\cos\theta+R_{b}\cos(\theta-\theta_{b})-r_{p}\cos (\theta-\theta_{b}-\theta_{e})\\ y=-e\cos\theta+R_{b}\sin(\theta-\theta_{b})+r_{p}\sin(\theta-\theta_{b}-\theta _{e})\end{array}\right. \tag{9}\]  
The computational efficiency of this model is compared with that of the popular model in Table 1. The mean time spent on producing 100, 1000, 10,000, and 100,000 scattered points of cycloid tooth profiles with 1000 cycles as the comparison target. The results are shown in Figure 4. The new tooth profile model proposed by the authors has improved computation speed compared with other models. Specifically, the minimum improvement is 27.75801% over Model 1, and the computation time is improved by at least 3.33333% compared with Model 2. In the subsequent studies of 3D tooth profile characteristics, the new model had a significant effect when the number of scatter points increased sharply.  
\begin{table}
\begin{tabular}{c} Contrast Model 1: such models are mainly derived based on the inversion method [5; 11; 35] \\ \hline Contrast Model 2: tooth profile model is derived based on the method of velocity transient centers [36; 37; 38; 39] \\ \hline \end{tabular}
\end{table}
Table 1: Cycloid tooth profile model for comparison.  
Figure 3: Cycloidal profile single-point coordinate transformation trajectory.
## 5 Parametric Analysis of Negative Stiffness of the 3-3-Type HCMS
### Validation of the Analytical Model by Finite Element Modeling  
In order to validate the analytical formulas of magnetic force and stiffness in Equations (23) and (24), COMSOL Multiphysics(r) software (v6.3, COMSOL, Inc., Stockholm, Sweden) is utilized for finite element simulations on magnetic forces (stiffness cannot be directly simulated). The finite element model (FEM) of the 3-3-type HCMS in Figure 5 is built as shown in Figure 11a, where the inner and outer Halbach cylinders are automatically meshed by using the tetrahedral elements. Then, parametric-sweep simulations are carried out over the distance \(z\) and the corresponding forces are simulated and recorded. At the same time, magnetic force and stiffness curves of the same 3-3-type HCMSare calculated by using the analytical formulas. Next, the analytical and simulated magnetic force curves are compared in Figure 11b, and the stiffness curve is shown in Figure 11b. The following can be seen: (i) As for the magnetic force, the analytical and simulation results are almost consistent. This indicates that the proposed analytical model is accurate and feasible. (ii) The magnetic force is equal to zero at \(z=0\). That is to say, the force equilibrium position is located on the horizontal axis. The magnetic force curve has a sine-like shape near the equilibrium position. (iii) The stiffness curve is symmetrical in terms of the axis of \(z=0\). In particular, the stiffness is negative near the equilibrium position, which proves that the 3-3-type HCMS can generate a negative stiffness.
## 3 Experiment Dataset and Evaluation  
### Experiment Data  
The first dataset used in this study is the WHU building dataset [38] from the New Zealand Land Information Service website. The WHU dataset covers 450 km\({}^{2}\) of land on the ground and was selected from approximately 22,000 individual buildings in the Christchurch area with a spatial resolution of 0.3 m. The dataset provides shapefile format data of buildings as well as rasterized data. Figure 8 is an example of the original images and their corresponding ground truths. White represents buildings, and black represents the background. The second dataset used in this study is the China typical cities (CHN) building dataset [39]. The original data were derived from Google's Class 19 satellite imagery with a ground resolution of 0.29 m. The sample dataset covers a total area of approximately 120 km\({}^{2}\). This dataset contains a sample of 7260 image areas, with a total of 63,886 buildings, distributed among four cities: Beijing, Shanghai, Shenzhen and Wuhan. Figure 9 shows the original image from the dataset and its corresponding ground truth.  
Figure 8: Image and label example selected from the WHU dataset: (**a**) Original image; (**b**) Ground truth. Black and white pixels mark non-building and building, respectively.
## 5 Conclusions  
To further develop the list flip decoder of polar codes, a new ALF decoder with high-order error correction capability was designed in this paper. Although an existing D-SCLF decoder can achieve high-order error correction capabilities by employing a flip metric, this flip metric introduces new exponential and logarithmic operations. However, the number of these operations increases exponentially as the number of non-frozen bits and the order of error correction increase. To overcome this problem, we designed a new, simplified flip  
Figure 14: Comparison of the average complexity performance of the D-SCLF2 and the ALF2, for \(PC(256,128+24)\), \(PC(512,256+24)\), and \(PC(1024,512+24)\).  
Figure 13: Comparison of the FER performance of the D-SCLF2 and the ALF2, for \(PC(256,128+24)\), \(PC(512,256+24)\), and \(PC(1024,512+24)\).  
metric, which replaces the logarithmic and exponential operations in the original flip metric with multiplication and addition operations. Simulation results prove that the new flip metric does not reduce the error-correction performance of the existing D-SCLF decoder. In addition, we adopted the adaptive list to further reduce the complexity based on in-depth analyses of the combination of the adaptive list and the list flip decoder. Simulation results show that our ALF decoder can effectively reduce the average complexity (energy) of the existing D-SCLF decoder without losing error-correction performance.  
To further exploit the performance potential of list flip decoders, a more accurate flip metric for high-order error correction is worth exploring in future research work.  
Validation, Y.L., H.Y., Z.Y., Y.W. and J.D.; data curation, Y.L.; formal analysis, Y.L., H.Y., Z.Y., Y.W. and J.D.; methodology, Y.L.; software, Y.L. and J.D.; writing--original draft, Y.L.; writing--review and editing, Y.L., H.Y., Z.Y., Y.W. and J.D.; project administration, H.Y. and Z.Y.; funding acquisition, H.Y. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities (CUC22 GZ013).  
Not applicable.  
The authors declare no conflict of interest.
## 6 Discussion on Clinical Relationship Extraction
### Unsupervised Learning Methods  
Unsupervised Learning is defined as an ML technique where users are not required to supervise the model, but it allows the model to run and learn by itself to excavate interesting patterns that were earlier undetected. These methods do not require annotated texts as they are capable of working on unlabelled data on their own. The level of processing needed for these kind of tasks is very high, but due to their simplicity, these algorithmsare more suitable for simpler tasks and, hence, unsupervised learning algorithms can be unpredictable for RE. The different types of unsupervised learning techniques are Clustering, Anomaly Detection, and Association. In clinical RE, unsupervised learning algorithms have been used to identify the different types of relations in the text that needs to be later reviewed and annotated by domain specialists to evaluate the performance of the model. In real life, the text contains a lot of noise and unsupervised learning is not always effective in identifying the different relations with a high level of accuracy. However, this method is less time expensive and is preferred in some cases.  
Unsupervised learning has been the least popular in RE on clinical texts because of the limitations of the unsupervised algorithms to identify relation patterns from complex textual data. Without proper clinical annotations by the clinicians, this learning task is far more ambiguous, which might result in the decreased accuracy of these models. Out of the very few works, [PERSON] et al. (2014) [101] were the pioneers in proposing an unsupervised text mining method for RE on clinical data. The unsupervised clustering-based method that is a combination of dependency and phase structure parsing for RE performed moderately with respect to the previous models but their proposed semi-supervised model surpassed its performance to become the second-best model on this dataset. [PERSON] et al. (2016) [102] used unsupervised methods for entity and relation extraction from Italian clinical records. The performance of the unsupervised clustering algorithm in the space of entity pairs, being represented by an ad hoc feature vector, is found to be promising in labeling the clinical records by using the most significant features. Since the dataset was not annotated here, similarity measures such as Manhattan, Binary, and Cosine similarities are used to measure the goodness of the clustering models. Not many other unsupervised methods have been proposed for RE on clinical notes. Some clinical RE tasks using unsupervised learning-based methods are tabulated in Table A6.
## 3 Experimental Results and Discussion
### Engineering Problems
#### 3.7.2 Lennard-Jones Potential Problem (LJ)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **GSLEO-AEFA** & **AEFA** & **PSO** & **DE** & **JAYA** & **SCA** & **CS** \\ \hline x(1) & 0.673 & 1.338 & 0.000 & 0.319 & 0.551 & 0.000 & 2.140 \\ x(2) & 0.329 & 3.307 & 4.000 & 0.567 & 0.035 & 0.000 & 2.789 \\ x(3) & 0.847 & 2.115 & 3.142 & 1.947 & 0.745 & 0.000 & 1.473 \\ x(4) & 0.316 & 3.639 & \(-4.000\) & \(-3.980\) & \(-0.237\) & 1.184 & 0.508 \\ x(5) & \(-0.608\) & \(-3.723\) & \(-0.687\) & 3.761 & 0.661 & 0.008 & \(-0.682\) \\ x(6) & 0.884 & 0.427 & 4.000 & 3.855 & 0.820 & 0.341 & 0.722 \\ x(7) & \(-0.292\) & 0.706 & \(-1.507\) & \(-3.970\) & \(-0.992\) & 0.337 & 0.393 \\ x(8) & \(-0.419\) & 1.807 & \(-3.925\) & 2.768 & 1.094 & \(-1.074\) & 0.265 \\ x(9) & \(-0.750\) & \(-3.380\) & \(-0.706\) & 4.250 & 0.559 & 0.005 & 0.443 \\ x(10) & \(-0.533\) & \(-1.841\) & \(-1.260\) & \(-4.250\) & \(-0.219\) & \(-0.909\) & \(-1.221\) \\ x(11) & \(0.298\) & \(-2.964\) & \(-4.109\) & 4.500 & 0.658 & 0.382 & 0.694 \\ x(12) & \(-0.148\) & \(-3.440\) & 0.286 & 4.453 & 1.910 & \(-4.500\) & \(-1.556\) \\ x(13) & \(0.359\) & 2.205 & \(-1.105\) & 4.500 & 2.766 & 0.220 & 0.327 \\ x(14) & \(-0.092\) & 2.806 & \(-4.750\) & \(-3.053\) & 0.004 & 0.575 & \(-0.435\) \\ x(15) & \(-0.019\) & 1.921 & \(-1.239\) & \(-1.506\) & \(-4.750\) & 0.921 & \(-0.238\) \\ x(16) & \(-0.390\) & 2.510 & \(-3.925\) & \(-4.357\) & \(-0.384\) & 0.172 & \(-2.438\) \\ x(17) & \(-0.681\) & 2.646 & \(-0.770\) & 3.520 & 0.064 & \(-0.396\) & 0.519 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results Lennard–Jones Potential Problem. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **GSLEO-AEFA** & **AEFA** & **PSO** & **DE** & **JAYA** & **SCA** & **CS** \\ \hline x(1) & 1.00 & 0.43 & \(-1.00\) & 1.00 & \(-0.63\) & \(-1.06\) & \(-1.04\) \\ x(2) & 5.00 & 0.15 & \(-5.00\) & 5.00 & 0.00 & 0.00 & \(-5.02\) \\ x(3) & \(-1.50\) & 2.03 & 1.50 & 1.50 & 4.29 & 0.69 & \(-1.36\) \\ x(4) & 4.80 & 4.98 & 4.80 & \(-4.80\) & 4.87 & 0.00 & \(-4.75\) \\ x(5) & 2.00 & 0.83 & \(-2.00\) & \(-2.00\) & 0.18 & \(-4.45\) & 2.01 \\ x(6) & 4.90 & \(-4.41\) & \(-4.90\) & 4.90 & 0.08 & \(-4.88\) & \(-4.90\) \\ f(v) & 0 & \(1.963\times 10^{1}\) & \(1.950\times 10^{-27}\) & \(7.796\times 10^{-21}\) & \(1.164\times 10^{1}\) & \(1.220\times 10^{1}\) & 1.888 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of parameter estimation for frequency-modulated sound waves.
## 4 The Used Simulation Environment and Our Model Extensions
### Our Proposal: A Novel, CBR-Based Dynamic Channel Selection Model
This can range from DP0 up to DP3. It puts the DP0 request on the primary channel exclusion, while in some cases, it goes through the channel pool and checks the CBR values. If the current CBR parameter exceeds the threshold assigned to that channel, it continues to the next one. When it finds a channel with a CBR value below the allowed level, it returns the interface assigned to that channel for sending the message. The message will not be sent if there is no channel with a CBR level lower than the required threshold. Since both CAM and CPM have a DP2 profile, further distinctions are used. If the message profile to be sent is DP2 (DP1-DP3), we also look at the message type. If it is a CAM (AID = 36), then the CBR values for the CAM defined in _channelChange_.xml will be the thresholds. If it is a CPM (AID = 146), the CBR values will be related to the CPM. Using this method, different CBR values for different message types can be set. If we want a message to stay longer with a higher-priority channel, we need to specify higher CBR threshold values. When the ITS-AID is 36, another distinction is used, namely the Service Announcement Service (SAS). This means that vehicles can get SAMs with channel information regarding which channel will be the next CAM in the queue transmitted. For the sake of simplicity, it was implemented only for CAM. The complete algorithm is depicted in (Figure 4).
## 3 Results
### Null Hypothesis
#### 3.7.2 Marginal Bone Loss  
Marginal bone loss showed a significant development over time and was influenced by several factors. Smoking was associated with increased bone loss (\(p\) = 0.04), leading to rejection of the null hypothesis. The presence of systemic disease also showed a negative  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Independent Variable** & **Outcome** & **OR** & **95\% CI** & \(p\)**-Value** \\ \hline Smoking & Implant failure & 3.25 & 1.02–10.34 & 0.04 \\ \hline Systemic disease & Implant failure & 2.89 & 1.08–7.75 & 0.04 \\ \hline Implant site (post. maxilla) & Implant failure & 3.6 & 1.16–11.17 & 0.03 \\ \hline Rehabilitation type (bridge) & Implant failure & 1.85 & 0.89–3.85 & 0.09 \\ \hline Smoking & Marginal bone loss & 2.76 & 1.05–7.21 & 0.04 \\ \hline Systemic disease & Marginal bone loss & 2.58 & 1.10–6.01 & 0.03 \\ \hline Implant site (post. maxilla) & Marginal bone loss & 2.93 & 1.12–7.67 & 0.03 \\ \hline Rehabilitation type (bridge) & Marginal bone loss & 1.72 & 0.93–3.16 & 0.07 \\ \hline Smoking & Peri-implant PPD & 2.45 & 1.06–5.69 & 0.04 \\ \hline Implant site (post. maxilla) & Peri-implant PPD & 2.9 & 1.18–7.13 & 0.02 \\ \hline Smoking & Supuration & 3.75 & 1.22–11.56 & 0.02 \\ \hline Systemic disease & Supuration & 3.42 & 1.14–10.29 & 0.03 \\ \hline Smoking & Surgical complications & 3.08 & 1.15–8.26 & 0.03 \\ \hline Systemic disease & Surgical complications & 3.4 & 1.24–9.28 & 0.02 \\ \hline Rehabilitation type (bridge) & Surgical complications & 2.65 & 1.06–6.64 & 0.04 \\ \hline Implant site (posterior) & Prosthetic complications & 2.98 & 1.10–8.11 & 0.03 \\ \hline Rehabilitation type (bridge) & Prosthetic complications & 2.78 & 1.04–7.42 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multivariate logistic regression analysis according to the OR, CI, and \(p\)-values.  
influence on peri-implant bone maintenance (\(p\) = 0.03), thus rejecting the null hypothesis. Similarly, the implant site proved to be decisive, with the highest resorption values observed in the posterior maxilla (\(p\) = 0.03), confirming a significant correlation. Conversely, the type of rehabilitation did not show a statistically significant association with MBL (\(p\) = 0.07), and therefore, the null hypothesis was accepted for this variable.
# Surface Discharge Mechanism on Epoxy Resin in Electronegative Gases and Its Application
## 4 Discussion
### Surface Discharge Mechanism
Since suppression and relaxation are the direct factors influencing gas breakdown, a good insulation performance can also be ensured with these engineering techniques. Therefore, when insulation gas selection and engineering insulation techniques are simultaneously applied to high-voltage apparatus, the surface insulation performance of the apparatus can be improved, owing to a synergistic effect that imbibes physical and engineering elements. Therefore, the selection of insulating gas based on the physics of gas discharge (i.e., electron swarm parameters) is more important than engineering insulating design techniques. This is because engineering insulation design techniques are based on physical knowledge (i.e., physical mechanisms and physical analysis), while the insulation performance of the high-voltage apparatus is significantly dependent on the insulating gas. To date, single or mixed insulating gases that can completely replace SF\({}_{6}\) in terms of insulation, arc quenching, and liquefaction have not yet been reported in the fields of applied physics and high-voltage engineering. The authors believe that a new insulating gas as an alternative to SF\({}_{6}\) can be discovered or developed, based on the physics-based knowledge of gas discharge.
## 3 Results and Discussion
### Effects of Divergent Length on Cavitation Characteristics
#### 3.2.3 Analysis of Flow Field Parameters  
Several significant flow field parameters at three different pressure conditions were collected in Table 3 to assess the impact of divergence section length on the cavitation flow field more accurately. The table shows that for the inlet pressure of 0.4 MPa, the variation of the cavitation volume of Venturi with different divergence section lengths is very tiny. However, at the inlet pressure of 0.8 and 1.2 MPa, Venturi tubes with shorter divergence section lengths obviously have higher cavitation volumes, which is consistent with the analysis in Figure 8. Furthermore, the maximum velocity of the throat increases slightly as the length of the divergence section decreases under varied inlet conditions. Another fact we may observe is that with the same inlet pressure, the outlet mass flow rate of Venturi tubes with various diffusion lengths is nearly identical, indicating that the diffusion length has no significant impact on the outlet mass flow rate.  
Figure 9: Vapor volume fraction contour of Venturi tubes at various inlet pressures and divergence lengths: (**a**) 0.4 MPa, (**b**) 0.8 MPa, and (**c**) 1.2 MPa.  
Figure 11: Variations of turbulence kinetic energy peaks at two locations (throat and divergence) in a Venturi tube under different pressures.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & \multirow{2}{*}{
\begin{tabular}{c} **P\({}_{\text{in}}\)** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\(\boldsymbol{\sigma}\)} & **L\({}_{\text{d}}\)** & **Q** & **V\({}_{\text{t}}\)** & **Vol** & **Vap\({}_{\text{m}}\)** \\  & & & **(mm)** & **(kg\(\cdot\)s\({}^{-1}\))** & **(m\(\cdot\)s\({}^{-1}\))** & **(mm\({}^{3}\))** & **Vap\({}_{\text{m}}\)** \\ \hline
1 & & & 0.2595 & 140 & 1.88831842 & 27.29931 & 17,694.4 & 0.8916025 \\
2 & 0.4 & 0.2603 & 190 & 1.88298337 & 27.26803 & 18,965.5 & 0.8933302 \\
3 & & 0.2608 & 240 & 1.88450754 & 27.23960 & 18,932.1 & 0.8932120 \\ \hline
6 & & & 0.1294 & 140 & 2.67930270 & 38.68253 & 106,551 & 0.9799927 \\
7 & 0.8 & 0.1298 & 190 & 2.67167545 & 38.62886 & 85,491.1 & 0.9747809 \\
8 & & 0.1299 & 240 & 2.67394078 & 38.60078 & 85,072.4 & 0.97477983 \\ \hline
11 & & & 0.0860 & 140 & 3.2972403 & 47.45741 & 268,358.5 & 0.9915485 \\
12 & 1.2 & & 0.0863 & 190 & 3.28465412 & 47.39365 & 162,727.6 & 0.9926510 \\
13 & & 0.0865 & 240 & 3.28479553 & 47.33728 & 149,088.7 & 0.9912778 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Venturi tubes’ flow field characteristics at various pressures and diffusion lengths.  
Figure 10: The variation of turbulent kinetic energy along the axis of the Venturi tube under varying pressure conditions: (**a**) 0.4 MPa, (**b**) 0.8 MPa, and (**c**) 1.2 MPa.