esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modeling
### Geometric Model
#### 2.1.3 The Meshing Pair Gap Shape Function  
During the meshing process, the variation range of the screw thread elevation angle \(\alpha\) equals to that of the tooth flank elevation angle \(\beta\) (See Figure 7). Meanwhile, the screw thread length is far longer than the tooth thickness. Therefore, the curvature of the screw thread flank is far smaller than that of the tooth flank and the screw thread flank can be considered as a straight line in the range of tooth thickness \(t_{\rm h}\), as shown in Figure 7.  
Figure 6: The basic geometric parameters of the meshing pair.  
Since the screw thread elevation angle \(\alpha\) varies along both the radial and the axial direction of the screw, the screw thread of leading flank can be expressed as:  
\[\mathrm{A_{a}}\big{(}\phi_{\mathrm{gr}},I\big{)}x+\mathrm{B_{a}}\big{(}\phi_{ \mathrm{gr}},I\big{)}z+\mathrm{C_{a}}\big{(}\phi_{\mathrm{gr}},I\big{)}=0 \tag{2}\]  
where  
\[\left\{\begin{array}{l}\mathrm{A_{a}}\big{(}\phi_{\mathrm{gr}},I\big{)}=\tan \big{[}\alpha_{a}\big{(}\phi_{\mathrm{gr}},I\big{)}\big{]}\\ \mathrm{B_{a}}\big{(}\phi_{\mathrm{gr}},I\big{)}=1\\ \mathrm{C_{a}}\big{(}\phi_{\mathrm{gr}},I\big{)}=-\tan\big{[}\alpha_{a}\big{(} \phi_{\mathrm{gr}},I\big{)}\big{]}x_{K}\big{(}\phi_{\mathrm{gr}},I\big{)}-z_{K }\big{(}\phi_{\mathrm{gr}},I\big{)}\end{array}\right. \tag{3}\]  
where the subscript \"K\" refers to the contact point K on the screw thread.  
The gap between the screw thread flank and the tooth flank can be described by a gap thickness funtion \(h\) and is given by:  
\[h_{a}(z)=\frac{\Big{|}\mathrm{A_{a}}\big{(}\phi_{\mathrm{gr}},I\big{)}x_{a}(z, l)+\mathrm{B_{a}}\big{(}\phi_{\mathrm{gr}},I\big{)}z+\mathrm{C_{a}}\big{(}\phi_{ \mathrm{gr}},I\big{)}\Big{|}}{\sqrt{\mathrm{A_{a}}\big{(}\phi_{\mathrm{gr}}, I\big{)}^{2}+\mathrm{B_{a}}\big{(}\phi_{\mathrm{gr}},I\big{)}^{2}}} \tag{4}\]  
It is noteworthy that the gap shape not only varies along with the gate-rotor rotating angle \(\varphi_{\mathrm{gr}}\) but also varies along with the tooth length parameter \(l\). Therefore, the gap is changing and twisted. Compared to the clearance of journal bearing, the gap between the meshing pair is much more complicated.
## 3 Results
### Helicity-Dependent All Optical Switching (HD-AOS)  
In the present section, we present numerical results trying to reproduce the experimental observation of the HD-AOS by evaluating two different scenarios: the IFE and the MCD, which we evaluated separately in Section 3.1.1 (IFE) and Section 3.1.2 (MCD). We start from a uniformly magnetized state (either up \(\overrightarrow{m}\uparrow\) or down \(\overrightarrow{m}\downarrow\)), and a series of laser pulses with circular polarization (either LC or RC) are applied.
## 4 Results
### Effect Comparison of LSTM and GRU Model Results  
Existing research has proven that RNN models excel in time series forecasting tasks [55; 56; 57]. As the two main variants of RNN, LSTM and GRU each excel at different tasks. Therefore, the focus of this section is to compare the results of LSTM and GRU and to explain the reason that LSTM is selected as the benchmark for developing the multi-ring prediction model in this study.  
Figure 12 shows ten prediction results from the LSTM and GRU models. The results show some randomness, which is likely due to the use of dropout hyperparameters and the random initialization of the model weights. In general, the LSTM model produces higher R\({}^{2}\) and lower RMSE results compared to the GRU model. It is evident that the overall predictive performance of the LSTM model is better than that of the GRU model. Both RMSE and R\({}^{2}\) metrics are employed for evaluating model performance because a model may perform well on one metric but not as well on the other. For instance, in the prediction of HDSH at step 6, the GRU model (0.783) slightly outperforms the LSTM model (0.780) when evaluated using R\({}^{2}\). However, under the RMSE metric at the same step, the LSTM model (19.5 mm) performs better than the GRU model (20 mm).  
The loss distribution for Bayesian hyperparameter optimization is illustrated in Figure 12(a). The optimization paths of the LSTM and GRU models are shown in Figure 12(b). Overall, the distribution of loss values for the LSTM is lower than that for the GRU [Figure 12(a)], indicating that the LSTM model is more effective at extracting temporal information in the task of predicting shield movement performance. Specifically, while the initial loss of the LSTM and GRU models is similar, the LSTM is able to achieve a lower loss more quickly [Figure 12(b)]. Therefore, the LSTM model is more suitable for shield construction parameter prediction tasks due to its higher prediction accuracy and better hyperparameter tuning effectiveness.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**RMSE**} \\ \cline{2-6}  & **HDSH** & **HDST** & **VDSH** & **VDST** & **Total Thrust** \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(km)** \\ \hline Manual tuning & 18.2 & 15.3 & 7.9 & 13.4 & 3195 \\ Bayesian optimization & 8.21 & 8.73 & 4.47 & 8.43 & 2654 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparing manual tuning and Bayesian optimization based on RMSE.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**R\({}^{2}\)**} \\ \cline{2-6}  & **HDSH** & **HDST** & **VDSH** & **VDST** & **Total Thrust** \\  & **HDSH** & **HDST** & **VDST** & **(km)** & **(km)** \\ \hline Manual tuning & 0.87 & 0.84 & 0.53 & 0.61 & 0.76 \\ Bayesian optimization & 0.94 & 0.93 & 0.76 & 0.85 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparing manual tuning and Bayesian optimization based on R\({}^{2}\).
# Research on a Time Series Data Prediction Model Based on Causal Feature Weight Adjustment
## 3 LWA-LSTM Method
### Multifactorial Forecasting  
The daily real-time trading in the stock market generates a large amount of data for analysis. Various types of trading data that reflect changes in stock prices are suitable for stock price prediction, making them the focus of research. Traditional stock prediction methods often rely on single features such as opening and closing prices for forecasts. However, due to the limited number of features, these models struggle to capture the patterns of stock price fluctuations, resulting in limited predictive accuracy. A multifactor model utilizes multiple relevant features, which can improve the accuracy and robustness of stock prediction while enhancing the model's interpretability. Therefore, we have chosen to use a multifactor model for stock forecasting.  
Factors in addition to common ones such as the highest price, lowest price, and trading volume also include some manually constructed composite technical indicators. They can be divided into the following categories: scale-related features, valuation-related features, trading-related features, and price-related features. When selecting features, several factors need to be considered: firstly, the selected features should be representative and able to reflect the stock's trading situation and changing trends; secondly, the selected features should have a strong causal relationship with the predicted value, and for predicting stock prices, they should possess greater stability and interpretability. Based on these requirements, we have selected 33 initial features for the model, which include price-related features, trading-related features, and others, as shown in Table 1.
###### Abstract  
This study reports on the synthesis and characterization of a novel electrode material for supercapacitor applications based on a clustered heterostructure of indium sulfide (In\({}_{2}\)S\({}_{3}\)) and single-walled carbon nanotubes (SWCNTs). The In\({}_{2}\)S\({}_{3}\)-SWCNT (INSC) sample was prepared using the facile successive ionic layer adsorption and reaction (SILAR) method and demonstrated a higher specific capacitance (258 Fg\({}^{-1}\) at 1 Ag\({}^{-1}\)) compared to the bare indium sulfide (In\({}_{2}\)S\({}_{3}\)) electrode. The enhanced electrochemical performance is attributed to the synergistic effect between the In\({}_{2}\)S\({}_{3}\) and SWCNTs, which improves electron transportation, catalytic nature, and specific capacitance. Moreover, the cyclic stability of the INSC electrode was significantly improved, retaining 96.8% of the initial capacitance after 300 cycles. The high voltage holding capacity and high cyclic efficiency of the fabricated INSC-based supercapacitor devices suggest their potential for next-generation energy storage devices. Additionally, the INSC electrode-based supercapacitor devices exhibit excellent flexibility and bendability, retaining similar performance even at a bending angle of 180\({}^{\circ}\), making them suitable for flexible energy storage applications.  
2D materials; In\({}_{2}\)S\({}_{3}\) nanostructure; SILAR deposition; supercapacitor +
Footnote †: journal: Applied Sciences
# Using a GPU to Accelerate a Longwave Radiative Transfer Model with Efficient CUDA-Based Methods
###### Abstract  
Climatic simulations rely heavily on high-performance computing. As one of the atmospheric radiative transfer models, the rapid radiative transfer model for general circulation models (RRTMG) is used to calculate the radiative transfer of electromagnetic radiation through a planetary atmosphere. Radiation physics is one of the most time-consuming physical processes, so the RRTMG presents large-scale and long-term simulation challenges to the development of efficient parallel algorithms that fit well into multicore clusters. This paper presents a method for improving the calculative efficiency of radiation physics, an RRTMG long-wave radiation scheme (RRTMG_LW) that is accelerated on a graphics processing unit (GPU). First, a GPU-based acceleration algorithm with one-dimensional domain decomposition is proposed. Then, a second acceleration algorithm with two-dimensional domain decomposition is presented. After the two algorithms were implemented in Compute Unified Device Architecture (CUDA) Fortran, a GPU version of the RRTMG_LW, namely G-RRTMG_LW, was developed. Results demonstrated that the proposed acceleration algorithms were effective and that the G-RRTMG_LW achieved a significant speedup. In the case without I/O transfer, the 2-D G-RRTMG_LW on one K40 GPU obtained a speed increase of 18.52\(\times\) over the baseline performance on a single Intel Xeon E5-2680 CPU core.  
Keywords:high-performance computing; graphics processing unit; compute unified device architecture; radiation transfer +
Footnote †: journal: Applied
## 7 Experiments Results and Analysis
### Experiment IV: Comparison Results on Personalized Video Recommendation  
In order to analyze these algorithms in fairness, 10 different background users' video browsing history was tracked for a month to model their user preferences. Then, tag ranking, topic video, and our method were used to rank 50 videos, respectively. Finally, in order to calculate precision and recall of each method, the information about the user preference for the 50 videos was collected.  
The results of the final recall and precision are shown in Table 3. Taking k = 20 as an example, the precision of the video topic method [22], the tag ranking method [24], and our method are 0.51, 0.59, and 0.65, respectively. Meanwhile, the recall of the three methods are 0.55, 0.64, and 0.70, respectively. Therefore, it can be seen that the recall and precision of our method are better than the other two methods.  
The data in Table 3 is plotted as a graph shown in Figure 13. The green dashed line is the recall and precision curve of our method, the blue is the video topic method, and the yellow line is the tag ranking method. From the experimental results, our method has the highest precision and recall compared to the others. The reason is that our method models high-level semantic information and visual information simultaneously. The user preference modeling for social tags can capture the semantic information of user preference, while the user preference modeling for deep features can capture the latent representation of user preference.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**User** & **Video Topic [22]** & **Tag Ranking [24]** & **Our Method** \\ \hline User 1 & 0.64 & 0.82 & 0.82 \\ User 2 & 0.55 & 0.79 & 0.86 \\ User 3 & 0.60 & 0.88 & 0.91 \\ User 4 & 0.45 & 0.69 & 0.66 \\ User 5 & 0.67 & 0.69 & 0.76 \\ User 6 & 0.56 & 0.93 & 0.98 \\ User 7 & 0.74 & 0.86 & 0.88 \\ User 8 & 0.78 & 0.79 & 0.88 \\ User 9 & 0.58 & 0.85 & 0.87 \\ User 10 & 0.64 & 0.73 & 0.74 \\ Average & 0.621 & 0.803 & 0.836 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of correlation coefficient of three methods.  
Figure 12: The comparison of correlation coefficient of three methods.
# Effect of Wing Membrane Material on the Aerodynamic Performance of Flexible Flapping Wing
## 5 Conclusions  
In this study, the aerodynamic and deformable characteristics of flapping wings with different wing membrane materials have been experimentally investigated. Three materials (PET, PE, TPU) with similar thicknesses were selected as the wing membrane to fabricate the tested wings. Lift force and power input were measured during the hovering flight. Rotation angles at different wing sections were analyzed to evaluate the deformation process. The following conclusions are drawn from the study:  
1. Flexibility presents a significant influence on lift generation and power consumption. Wings with a higher elastic modulus membrane, which means a more rigid wing, could generate higher lift but at the cost of more power.
2. Wings with a lower elastic modulus membrane present greater twisting deformation along the spanwise direction, especially at a high flapping frequency. Meanwhile, the twisting deformation is more obvious with the increase of flapping frequency.
3. A more flexible wing shows an advantage in translational force for a smaller angle of attack, close to the optimal value. However, the potentially superior rotational force related to higher rotation angular velocity and angular acceleration may be responsible for the higher lift of a more rigid wing, yet a larger angle of attack also forms a larger drag force and greater power consumption.
4. Rotation angle fluctuation occurs after the stroke reversal. Flexibility shows advantages in attenuating the rotation angle fluctuation, which in turn enhances the aerodynamic efficiency.  
Conceptualization, B.S.; Formal analysis, X.L.; Funding acquisition, X.Y.; Writing--original draft, X.L.; Writing--review & editing, W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number11872314, 11902103; the Key R&D Program in Shaanxi Province of China, grant number 2020 GY-154; and the foundation of National Key Laboratory of Science and Technology on Aerodynamic Design and Research.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 15: Angular velocity and angular acceleration of the angle of attack at 0.75\(R\) for 22 Hz. (**a**) Angular velocity; (**b**) Angular acceleration.
## 7 Conclusions  
This paper reviews fault diagnosis technology in the context of artificial intelligence technology and big data. The concept of sensors and sensor networks in Ag-IoT are introduced, and common sensor fault types and fault diagnosis strategies in Ag-IoT are listed. In addition, the research and application status of three sensor fault diagnosis technologies are summarized: model-based, artificial intelligence-based, and deep learning-based. The methods are compared and their advantages and disadvantages are discussed. Through the analysis of this review, the following conclusions are drawn, and suggestions for future work are put forward.  
(1) Presently, sensors are usually deployed in harsh environments, which are extremely vulnerable to environmental impact, resulting in inaccurate data collection and irreversible impact on sensors. In the future, sensors will be further developed, less affected by the environment, and can use limited resources to work for a long time; The output format codes between different sensors have developed from the original disunity to the standardized format.  
(2) Deep learning has unique advantages, although the research in the field of sensor fault diagnosis is still in its infancy, it has a good development prospect. It is necessary to improve the system's robustness to meet the changes in the conditions in practical applications.  
(3) The commonly used single-fault diagnosis methods often encounter many problems: resource limitation problems, data transmission problems, node optimization problems, data security problems, and so on. In the future, fault diagnosis methods based on the integration of multiple technologies can be studied, such as adding edge computing and satellite communication and combining expert systems and deep learning to shorten the training time by using known rules through transfer learning, which could improve the accuracy of sensor fault diagnosis and reduce the frequency of faults to ensure the stability and reliability of intelligent agricultural measurement and control systems.  
Conceptualization, X.Z., Y.Q. and L.S.; Investigation, W.L., S.W.; Methodology, X.Z., W.L., Z.H. and Y.Q.; Project administration, X.Z. and Y.Q.; Visualization, W.L., S.W., Z.C. and C.X.; Writing--original draft, X.Z., W.L., S.W., Z.C., C.X., Y.B. and Z.L.; Writing--review & editing, X.Z., S.W., Z.H., Y.G., Y.Q. and L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jiangsu Agriculture Science and Technology Innovation Fund of China (CX(21)3058), and the Program for International S&T Cooperation Projects of Jiangsu, China (BZ2021022).  
Not applicable.  
We are grateful to [PERSON], [PERSON], and [PERSON] for their assistance with the literature search.  
The authors declare no conflict of interest.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 4 Locata Applications
### Flight Tests  
Augmenting GPS and/or INS for flight navigation is a primary application of Locata technology. Technical issues, such as antenna offsets between different systems, the influence of the number, location and geometric distribution of the LocataLites on ambiguity resolution, positioning accuracy, system reliability, and stability had been analysed via a series of flight tests. At first by simulations [1], and subsequently by real world tests that were carried out at Wedderburn Airfield, Australia [58], and south of Canberra, Australia [59]. A series of Unmanned Aerial Vehicle (UAV) flight tests were also conducted at the Advanced Navigation Technology Center, at the US Air Force Institute of Technology [29]. These analyses confirmed that Locata real-time positioning accuracy was at the centimeter-level, and that augmenting Locata with GPS can significantly improve the vertical positioning accuracy.  
In October 2011 another flight trial was conducted, from Bankstown Airport to Cooma Snowy Mountains Airport, in Australia. Utilising data from airborne sensors, including dual-frequency GPS receivers, GPS/INS integrated systems, and Locata Rover receivers, the investigations were conducted on the positioning performance of a Locata-alone system, Locata/GPS integration systems, and triple-integrated Locata/GPS/INS systems [37; 47; 60].
## 6 Resulting Graph Visualizations
### Star Motif
#### 6.1.1 Visualizing the Reconnaissance Tactic by Connection Count  
Figure 2 depicts the Reconnaissance Tactic radiating from a single vertex, 143.88.2.10, to multiple other vertices in the graph. The number of connections from point to point was generally in the average range of connections, with the exception of a few that were in the extreme range of binning. Looking deeper into the data, it can be seen that each connection generally involves a different port; therefore, this graph is representative of a port scan, typical of a Reconnaissance Tactic. This graph has some areas of interest, represented by the red connections (bin = 6), where considerably more connections occur than the normal connection count (bin = 3), which was 1024 connections. Each of these bin 6 connections was in excess of 1 million. One outlier in the data was a connection between 143.88.5.12 and 143.88.5.1 (bin = 5) with \(\frac{1}{2}\) million connections. Example data points by connection count can be seen in Table 3. For the Reconnaissance Tactic, the maximum connection count was 3,112,192, while the minimum connection count was zero, and the average connection count was 33,927.946.  
It can also be noted from Figure 2 that 143.88.2.10 is mostly pointing to the 143.88.7.* addresses. The graph is actually pointing to the entire range of the subnet from 143.88.7.0-255. The red lines indicate where most of the bytes are being transmitted back and forth. This is highly likely because the four IP addresses belonged to virtual machines running on the victim's network, and a reply from the victim's network is indicative of an open port of a victim's host.
# Random Walker Coverage Analysis for Information Dissemination in Wireless Sensor Networks
## Appendix B Overlapping Surface  
Let \(B\) be the random walker's current position and \(A\) a previous position \(t\) steps before, where \(t\leq 9\) (Figure 1). Distance \(AB\) is on average: \(d_{t}=\frac{2r_{c}}{3}\sqrt{t}\) (Equation (1)). Furthermore, \(A\Gamma=A\Delta=B\Gamma=B\Delta=r_{c}\).  
From the Pythagorean theorem, it holds that: \(\Gamma\Delta=2\sqrt{r_{c}^{2}-\frac{d_{t}^{2}}{4}}=2\sqrt{r_{c}^{2}-\frac{4r_{ c}^{2}t}{36}}=\frac{2r_{c}}{3}\sqrt{9-t}\). Moreover, for the following angles it holds that \(\widehat{\Gamma A\Delta}=\widehat{\Gamma B\Delta}=2\arccos\left(\frac{d_{t}} {2r_{c}}\right)=2\arccos\left(\frac{\frac{2r_{c}}{3}\sqrt{t}}{2r_{c}}\right)= 2\arccos\left(\frac{\sqrt{t}}{3}\right)\).  
The area of the meniscus \(E_{t}\) constituting the common surface of the two circles is given by the following formula, where: \(E_{KT(A\Gamma\Delta)}\) is the surface of the circular sector \(A\Gamma\Delta\) and \(E_{A\Gamma\Delta}\) is the triangle's \(A\Gamma\Delta\) area.  
\[E_{t} = 2\left(E_{KT(A\Gamma\Delta)}-E_{A\Gamma\Delta}\right) \tag{11}\] \[= 2\left(r_{c}^{2}\arccos\left(\frac{d_{t}}{2r_{c}}\right)-\frac{ 1}{2}\Gamma\Delta.A\bar{E}\right)\] \[= 2\left(r_{c}^{2}\arccos\left(\frac{\sqrt{t}}{3}\right)-\frac{ 1}{2}\frac{2r_{c}}{3}\sqrt{9-t}\frac{r_{c}}{3}\sqrt{t}\right)\] \[= 2r_{c}^{2}\left(\arccos\left(\frac{\sqrt{t}}{3}\right)-\frac{ \sqrt{t(9-t)}}{9}\right)\]  
Consequently, two nodes \(t\) steps (\(t\leq 9\)) apart on the random walker's path have on average an area of \(E_{t}\) as a common neighborhood surface.