esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictability of Maximly Expansion with Invisalign(r) First: Treatment Planning vs. Outcome
###### Abstract  
The aim of this study is to retrospectively investigate the predictability of maxillary arch expansion with Invisalign (r) First in mixed dentition. In a sample of 64 treated patients, 3D digital models from the ClinCheck(r) software were compared with post-treatment digital models. Predicted maxillary expansion was related to the final outcome of expansion. At the cuspidal level, there was observed a predictability of 59.68% for deciduous canines, 63.77% for first deciduous molars, 66.99% for second deciduous molars, and 55.61% for first permanent molars. At the gingival level, there was observed a predictability of 49.87% for deciduous canines, 53.11% for first deciduous molars, 53.36% for second deciduous molars, and 45.16% for first permanent molars. The difference between the predicted and achieved expansion was statistically significant (\(p<0.05\)). Within the present limitations, our results suggest that, with Invisalign(r) First, it is possible to expand the upper arch in mixed dentition in an effective and predictable way. Clear aligners could represent a new and valid alternative for arch expansion in growing patients.  
p 2024 14 9871 10.3390/app14219871
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
## 2 Spot Scanning Surface Defect Evaluation System
### Deviations in the System and their Consequences  
However, the practical scanning trace will differ from the ideal scanning trace due to the existence of system deviations, including the following: (1) angle deviation of the rotation axis from the \(Z_{s}\) axis; (2) position deviation of the initial illuminated spot from the origin \(O_{s}\); and (3) deviations of translation velocity, rotation velocity, and sampling frequency from their setting values. Because the optical sample is precisely leveled when it is placed, the surface can be considered as a horizontal plane at the initial system state. In this case, if the ideal scanning trace is still used for image reconstruction, distortions will be introduced. This section will simulate the impacts of these system deviations on the scanning trace and image reconstruction. The specific models involved in the simulation will be given in the next section.  
Figure 3 presents the simulated practical scanning traces (upper row) and the corresponding images (lower row) reconstructed using the ideal scanning trace, where a-d represent the cases with no deviation, angle deviation of the rotation axis, position deviation of the initial illuminated spot, and rotation velocity deviation, respectively. It can be seen that these system deviations can change the scanning traces to some degree and hence yield obvious distortions in the reconstructed images. The distortions will affect the accuracy of detection results in many aspects, such as causing the displacement of the defect's location and the scaling of the defect's size. Line-like defects, such as scratches, may appear bent, leading to an inaccurate extraction of the curvature features. Moreover, the position deviation of the initial illuminated spot will even produce an undetected area that is not scanned at the center of the surface. As shown in Figure 3c, the consequence of this issue is that the point-like defect near the center in the reconstructed image is missing. According to the analysis, the existence of system deviations will severely reduce the reliability of the SS-SDES. It is significant to propose an effective and accurate method for system calibration and image reconstruction.
## 3 Numerical Model  
### Geometric Model and Mesh  
To study the fluid dynamic behavior outside the shock tube, an entire computational domain was modeled, including the shock tube and the larger external space. The three-dimensional geometric model was meshed to discretize the computational domain and governing equations in the numerical simulation. The inner diameter of the shock tube model was 16 mm, which was consistent with the experimental conditions. The external space was 300 mm in diameter and 600 mm in height. The hexahedral mesh of the entire computational domain and shock tube constructed by topological segmentation is shown in Figure 2. The mesh was refined near the tube and particle layer to accurately capture the interaction between the shock wave and solid particles. Three different mesh sizes were tested for the calculation (463,582, 1,223,040, and 2,184,768 elements). It was found that the calculation results with 1,223,040 elements was acceptable.  
Figure 2: Hexahedral mesh of (**a**) entire computational domain and (**b**) shock tube.  
The Jacobian matrix is one of the most important criteria of hexahedral mesh quality. The relative determinant is the ratio of the smallest determinant of the Jacobian matrix divided by the largest determinant of the Jacobian matrix; the mesh quality is better when the value is closer to 1. As shown in Figure 3, the relative determinant for the global mesh in the simulation was above 0.669, which indicates the hexahedral mesh was of high quality.
## 3 Results
### Hue Angle Cutoff Threshold  
The hue angles of different land cover types, including _Suaeda salsa_, mudflats, water bodies, and green vegetation (e.g., reeds), exhibit significant differences. _Suaeda salsa_ generally has the highest hue angle, followed by mudflats, with green vegetation and water bodies displaying lower values.  
To extract _Suaeda salsa_ pixels from UAV visible-light imagery, a hue angle cutoff threshold can be established. While _Suaeda salsa_ typically has a higher hue angle than mudflats, low-coverage _Suaeda salsa_ pixels may have similar values to mudflat pixels. This suggests that the lowest hue angle of low-coverage _Suaeda salsa_ or the highest hue angle of pure mudflat pixels could serve as potential cutoff thresholds. However, accurately identifying low-coverage _Suaeda salsa_ pixels may be challenging. Therefore, using the highest hue angle of pure mudflat pixels as a cutoff threshold may provide a more reliable approach to extracting _Suaeda salsa_ pixel information.  
To determine a suitable cutoff threshold, we randomly selected pure mudflat areas from the UAV visible imagery and analyzed the distribution of pixel hue angles (Figure 6). The cumulative proportion of pixels with a hue angle of 249.01 exceeded 99.99%. Consequently, 249.01 was adopted as the minimum cutoff threshold for _Suaeda salsa_ pixel extraction. Pixels with a hue angle greater than this cutoff threshold were classified as _Suaeda salsa_.  
A cutoff threshold value of 249.01 was applied to extract _Suaeda salsa_ information from the UAV visible imagery. The resulting extraction accurately reflected the actual distribution of _Suaeda salsa_ (Figure 7a). Notably, this method demonstrated exceptional performance in both low-density (Figure 7b) and mixed areas with reeds (Figure 7c), highlighting its robustness and versatility.  
Figure 6: Distribution of mudflat hue angle: pixel count and cumulative proportion.
# TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
## 2 Preliminary  
### Problem Formulation  
The discrete time sequence is a set of chronological observation values, temperature, salinity, dissolved oxygen, and daily precipitation are all sequences of this kind.  
The 3D grid data sets consist of longitude, latitude, and depth. As shown in Figure 1a, in the horizontal plane, each observation point has corresponding observed values. At the same time, as shown in Figure 1b, in each observation point in the vertical direction of different depth also has the corresponding measurements. These points make up the spatial distribution of ocean temperature. On time distribution, each temporal point has a set of observations sorted by time interval. We wanted to solve the problem of how to predict future data according to existing temperature data.  
We look at the observations at each observation point as a time series. If a model can be built to capture the temporal dependencies among data, then the future values can be predicted according to the historical values. Therefore, the prediction problem of each observation point in the 3D grid region can be expressed as the prediction problem of time series. Given the historical observations \(\{\mathbf{X}_{t}|t=0,...,n-1\}\), predict \(\{\mathbf{X}_{n}\}\).
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures
### Distributed Energy Resources  
The decentralized production of electricity is paralleled with the so-called virtual power plants, which are a collection of small and very small decentralized power plants, monitored and controlled by a high-quality management system. Successful operation of a virtual power plant requires an efficient and reliable telecommunication network through which the management system monitors, manages, and optimizes the operation of decentralized units [28; 48].  
In large virtual power stations, control systems based on protocols, such as IEC 61850 and IEC 61400, can be implemented. Given the increasing number of virtual power stations, it is expected that the communication channels and protocols will play a crucial role and the conventional telemetry technique will be gradually replaced by more advanced communication techniques based on TCP/IP protocols. Transmission techniques via the power lines (Power Line Communication-PLC) could also be utilized [44; 48; 52; 53].  
The IEC 61850-7-420 standard is compatible with the majority of modern equipment and dispersed energy production systems. However, there is no complete mapping of the IEC 61850- 8-x communication protocols, as well as the respective protocols developed for wind energy systems in IEC 61400-25 [54; 55]. Furthermore, no system configuration language (SCL) has been developed for the communication between the dispersed energy sources in IEC 61850-6-x, while a major drawback is the fact that the electrical connection points (ECPs) are often of specific configuration and are compatible only with specific systems/regions [7; 14].
###### Abstract  
The sphericity error is a critical form and position tolerance for spheres. We explored the distribution of sphericity errors within the solution space to achieve a high-precision evaluation using the minimum zone criteria. Within local solution spaces, we propose treating the evaluation of sphericity errors as a unimodal function optimization task. And computational geometric methods are employed to achieve highly accurate solutions within the local solution spaces. Subsequently, we integrated the computational geometric method with the differential evolution algorithm (DE algorithm). By centering on individual population members of the DE algorithm, we partitioned the local solution spaces and utilized the best solutions within them to optimize the population. With the gradual convergence of the DE algorithm, we successfully achieved the high-precision resolution of sphericity errors. The experimental results demonstrate a significant order-of-magnitude improvement in precision compared to traditional algorithms in the field of sphericity error evaluation, with uncertainty levels reaching magnitudes of \(10^{-14}\) mm. Moreover, this method enhances the accuracy of sphericity error evaluation by approximately 10% for three-coordinate measuring machines. Additionally, we conducted ablation experiments to validate the effectiveness of the proposed computational geometric method. In summary, this approach enables the high-precision evaluation of sphericity errors and provides a practical methodology for applying ultra-precision spheres in precision engineering.  
high precision; sphericity error; minimum zone criteria; computational geometric method; differential evolution algorithm +
Footnote †: journal: Applied Sciences  
Article  
A Novel Approach for High-Precision Evaluation of Errors Using Computational Geometric Method and Differential Evolution Algorithm
## 2 Automatic Geometric Corrections
### Geometric Modelling
#### 2.2.2 Gross Error Removal  
The RFM implementation is straightforward. Although the RPC coefficients are quite accurate, they have to be bias-corrected first with at least few GCPs that ensure high accuracy throughout the resulting image. The correction starts with all of the extracted GCPs. Despite the automatic detection being composed of many steps and intermediate accuracy checks, some GCPs can exhibit the gross errors that need to be removed before the model refinement. Gross error removal is performed in two steps. First, the erroneous GCPs are eliminated based on the RANSAC algorithm [33]. Roughly 10% of the GCPs are discarded according to the magnitude of their residuals that are obtained through the geometric model. Additionally, a simple gross error detection procedure is used that eliminates GCPs with residuals that differ by over 2\(\sigma\) from the mean value. The implementation of gross error removal in two steps is possible due to the high number of automatically detected GCPs.
## 3 The Proposed Methods  
In this section, we provide an approximation method that can transform the ML estimator into a GTRS problem. Without loss of generality, we assume that noise \(n_{i}\) is sufficiently small, and let \(d_{0}=1\,\mathrm{m}\)[12; 15]. From Equation (1), we have the following approximation expression  
\[\|\mathbf{x}-\mathbf{s}_{i}\|\approx 10^{\frac{P_{0}-P_{1}+\alpha_{f}}{10\gamma}}10^{- \frac{\alpha_{f}\|\mathbf{x}-\mathbf{s}_{i}\|}{10\gamma}}. \tag{7}\]  
Let \(u=-\frac{\alpha_{f}\|\mathbf{x}-\mathbf{s}_{i}\|}{10\gamma}\) be the exponent of the second factor on the right hand side of Equation (7). It was shown in [12; 18] that, when the absorption coefficient \(\alpha_{f}\) is sufficiently small, the absorption term \(\alpha_{f}\|\mathbf{x}-\mathbf{s}_{i}\|\) can be relatively small (\(\alpha_{f}\|\mathbf{x}-\mathbf{s}_{i}\|\ll\frac{10\gamma}{\ln 10}\) ), especially in deep water; then, \(0<|u|\ll 1\)Therefore, for a sufficiently small \(\alpha_{f}\), it is reasonable to approximate this factor by using the first-order Taylor expansion about \(u=0\) as follows:  
\[10^{-\frac{\alpha_{f}\|\mathbf{x}-\mathbf{s}_{i}\|}{10\gamma}}\approx 1-\ln 10\cdot\frac{ \alpha_{f}\|\mathbf{x}-\mathbf{s}_{i}\|}{10\gamma} \tag{8}\]  
where the higher-order terms are omitted.  
By substituting Equation (8) into Equation (7), the distance between the \(i\)-th anchor and the target node can be further approximated in the following form:  
\[\lambda_{i}\|\mathbf{x}-\mathbf{s}_{i}\|\approx\eta, \tag{9}\]  
where \(\lambda_{i}=\beta_{i}+\frac{\eta\alpha_{f}\ln 10}{10\gamma}\)\(\beta_{i}=10^{\frac{P_{i}-\alpha_{f}}{10\gamma}}\), and \(\eta=10^{\frac{P_{0}}{10\gamma}}\).
## 1 Introduction
The prediction findings can be utilized as the foundation for evaluating trends in transfer passenger aggregation and implementing the monitoring feature of cooperative multimodal transportation system operation in the urban agglomeration. The model suggested in this study can accept input series in parallel and without the idea of the time step, in contrast to classic forecasting models that typically pass input time series data one after another. By creating a deep learning-based transformer architecture, this study assesses the suitability of an LSTM-based transformer for short-time transfer passenger flow prediction. The Beijing-Tianjin-Hebei urban agglomeration's air-rail transfer corridor between Beijing Capital Airport and Beijing South Railway Station was chosen as the study's research object. It used a deep learning model transformer to perform a short-term prediction of the transfer passenger flow using analysis of data from cell phone signals. In the Beijing-Tianjin-Hebei urban agglomeration, the forecasting results are used as a reference base for monitoring and managing multimodal traffic operations by placing the historical data. The following are the research contributions made by this work. 1. For short-term prediction of inter-hub transfer passengers in the urban agglomeration, the transformer framework based on attention mechanisms is applied. A more valuable and adaptable prediction method is created for large-scale time series prediction of interchange passengers with notable temporal characteristics. 2. To resolve the long-time dependence in the time series prediction process and to turn the historical traffic passenger sequences into vectors that the transformer can recognize, the LSTM is utilized to pre-process the historical transfer passenger data. 3. The passenger transfer between two significant passenger hubs in the Beijing-Tianjin-Hebei urban agglomeration is forecasted using the LSTM-based transformer based on 10-23 May 2021, and the forecast results serve as a reference point for increasing the operational effectiveness of the multimodal transportation system. The following structure describes how this essay is set up. Section 2 introduces the pertinent theories, models, research developments and applications of deep learning to temporal prediction and traffic modeling. The LSTM-based transformer model architecture and techniques, and the metrics that can be used to assess the model's ability to predict outcomes are introduced in Section 3. Section 4 predicts the transfer passengers' short time between Beijing Capital Airport and Beijing South Railway Station (railway station) in the urban agglomeration of Beijing-Tianjin-Hebei and analyzes the findings. The research discussion and conclusion are presented in Section 5.
# New Miniature Narrow Band Microstrip Diplexer for Recent Wireless Communications
## 2 Related Work
With frequency-contiguous channels for the differential mode and strong common-mode rejection, a new type of balanced dual-band planar diplexer was projected in [14]. It uses a diplexer's symmetry plane, which contains filtering cells loaded with two stubs and coupled to one another through resistors. Each diplexer channel can operate as a differential-mode dual-passband filter with severe rejection at both passbands. Low common-mode in-band input power reflection and improved common-mode suppression are further benefits. Using microstrip open-loop coupled resonators, a dual-channel diplexer with two operational bands per channel was presented using a first channel of (1.424/1.732 GHz) in transmit and receive ends of (2.014/2.318 GHz) for a second channel [15]. When using load 1, the insertion loss is 1 dB, whereas when using load 2, it drops to 1.5 dB. The effective isolation of 35 dB between channels generates return loss magnitudes greater than 10 dB. The presented design is very good for wireless applications due to its straightforward topology, compact circuit size, and narrowband frequency responses.
## 4 Conclusions  
This paper proposed the SSFF process for the production of the connecting rod for a HICE. It experimentally investigated the effect of the operating parameters on the quality of the produced connecting rod. The main conclusions are provided below.  
1. The 7075 aluminum alloy with a diameter of 90 mm used in this work can be successfully deformed by the RFPD process, with an FR of up to 75%. The FR of 75% is identified as a reasonable process parameter for the SSFF process proposed;
2. In terms of the preparation of the 7075 aluminum alloy SSB, reasonable process parameters are obtained as two sets. One set is an FR of 75%, ST of 620 \({}^{\circ}\)C, and DT of 9 min. Another set is an FR of 75%, ST of 630 \({}^{\circ}\)C, and DT of 6 min. With reasonable working parameters, the 7075 aluminum alloy SSB can be prepared with the AGS of 41.48-42.57 \(\upmu\)m and the ASF of 0.80-0.81;
3. When the connecting rod is produced with an FR of 75%, ST of 590 \({}^{\circ}\)C, and DT of 6 min in the SFF process, the big end of the connecting rod is not fully filled, although the rod shaft and the small end of the connecting rod can be fully formed;
4. An improved shape profile is obtained for the AACR formed by the SFF process compared to that from the SFF process. The improvement ratios of yield strength and tensile strength by the SSFF process are 47.07% and 20.89%, respectively.  
Conceptualization, Y.W.; methodology, Y.W. and H.J.; validation, Y.W., C.Z. and H.J.; formal analysis, H.J., C.Z. and D.D.; investigation, Y.W., Y.G. and S.Z.; writing--original draft preparation, M.Z. and Y.W.; writing--review and editing, Y.G. and S.Z.; visualization, M.Z. and D.D. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (Grant No. 52105397) and the Open Foundation of the National Key Laboratory of Metal Forming Technology and Heavy Equipment (Grant No. S2208100.W01).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Result** & **AGS (\(\upmu\)m)** & **SF** \\ \hline Semi-solid 7075 aluminum alloy billet & 41.48–42.57 & 0.80–0.81 \\ Connecting rod produced by SFF process & 42.46–43.59 & 0.68–0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Microstructure analysis of the experimental result of the SSB and the connecting rod.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mechanical** & **The Connecting Rod Formed** & **The Connecting Rod Formed** & \\
**Property** & **by the SFF Process** & **by the SSFF Process** & **Improvement Ratio** \\ \hline Yield strength (MPa) & 229.95 & 338.18 & 47.07\% \\ Tensile strength (MPa) & 347.75 & 420.40 & 20.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mechanical property of the connecting rod formed by SFF and SSFF process.
# Experimental Investigation into the Influence of Plasma Technology on Seed Surface Wettability
###### Abstract  
The influence of atmospheric plasma discharge (APD) of the Gliding Arc type and low-pressure microwave plasma discharge (LPMD) on the class of various seeds was investigated. Pea, wheat, and sunflower seeds, representing legumes, cereals, and oilseeds, respectively, were selected for the treatment. Our study aimed to verify the effect of plasma treatment on the water contact angle and the wettability of the seeds. Treatment time, working gas flow and microwave power were varied to determine their impact. All treated and untreated variants were used to conduct the water contact angle measurements to compare their wettability. APD treatment seemed to be utterly ineffective for improving the wettability for most process parameters. On the contrary, LPMD manifested a much more efficient impact. The maximum effect was found for the 800 W microwave power applied to the pea seed for 30 s. The contact angle achieved decreased by approximately 50% compared to the untreated sample. These results indicate that LPMD may be an effective alternative to traditional pre-sowing seed treatments used in agriculture for water intake enhancement. Still, it is strongly dependent on the seed's type and the used process parameters.  
plasma treatment; seeds; contact angle; wettability +
Footnote †: journal: selected topics