esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Methodology of Test Specimens' Design  
First, 3D models of the test specimens were created using the software PTC Creo Parametric 9.0.1.0. The basis of creating 3D models of the test specimens was a block of \(35\times 35\times 7\) mm. The cuboid's volume was filled with a lattice and a gyroid structure. In both specimens, and in all three directions of the X, Y, and Z axes, the unit cell size was 7 mm. The basic cell's position was identical for both experimental specimens, i.e., the whole cell was multiplied and patterned in all coordinate systems' directions. The 3D models of the test specimens and detailed views of the porous structures' basic cells are shown in Figure 1.  
The printing parameters of the 3D model test specimens are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Filled** \\ **Specimen** \\ **Volume [mm\({}^{3}\)]** \\ \end{tabular} & \begin{tabular}{c} **Sample** \\ **Designation** \\ \end{tabular} & \begin{tabular}{c} **Cell Size [mm]** \\ \end{tabular} & \begin{tabular}{c} **Created Volume** \\ **Share [\%]** \\ \end{tabular} & \begin{tabular}{c} **Wall Thickness** \\ **[mm]** \\ \end{tabular} & \
\begin{tabular}{c} **Strut Diameter** \\ **[mm]** \\ \end{tabular} &
\begin{tabular}{c} **Specimen** \\ **Volume [mm\({}^{3}\)]** \\ \end{tabular} \\ \hline \multirow{2}{*}{8575.000} & lattice\_25 & \multirow{2}{*}{7} & 25.170 & - & 1430 & 2158.370 \\ \cline{2-6}  & gyroid\_25 & & 25,080 & 0.500 & - & 2150.770 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Printing parameters of 3D model test specimens.  
Figure 1: Dimension and shape of porous structure 3D model filled with (**a**) lattice structure and (**b**) gyroid structure.  
Two types of specimens were produced for our research: a lattice structure specimen and a gyroid structure specimen. The technological parameters of the experimental specimens can be seen in Table 2.  
A total of 6 experimental specimens were assessed and compared, as shown in Figure 2.
## 4 Conclusions  
In this study, an enhanced method is proposed for analyzing the mesh characteristics of spiral level bears (SBGs), taking into account assembly errors and tooth tip chipping. This method is primarily based on tooth contact analysis (TCA) and loaded tooth contact analysis (LTCA) and has been validated using existing literature. The study explores the effects of assembly errors and tooth tip chipping and the conclusions can be summarized as follows:  
Figure 16: TVMS of different chip areas under healthy installation and E = 0.5 mm: (**a**) healthy installation; (**b**) E = 0.5 mm.  
Figure 17: Unloaded transmission errors and contact stress of different chip areas under E = 0.5 mm: (**a**) unloaded transmission errors; (**b**) contact stress.  
1. The enhanced method has been validated through the consistency of variation trends observed between this paper and previously published literature, considering different assembly errors.
2. When the contact area moves to the tip, the peak-to-peak value of time-varying meshing stiffness (TVMS) will reduce. In addition, the TVMS has different kinds of increases or decreases with the increase of assembly errors.
3. Assembly errors lead to an escalation in contact stress at the tooth tip, amplifying the risk of tooth tip chipping. Tooth tip chipping decreases the TVMS and unloaded transmission error within the affected region, whilst concurrently increasing the maximum contact stress. Furthermore, for identical chips, the TVMS considering assembly errors exhibits a more pronounced decline compared to a healthy installation.  
Conceptualization, Z.L. and R.L.; Software, J.Z.; Formal analysis, Y.W.; Investigation, J.Z.; Resources, Z.L.; Project administration, Y.W.; Funding acquisition, Z.L., R.Z. and H.M. 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. 52205108), Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, Northeastern University (Grant No. VCAME202201), Natural Science Foundation of Jiangsu Province of China (Grant No. BK20220897), National defense technology basic research project of China (JSZL2022110A074) and Postdoctoral research merit-based funding project of Zhejiang Province (ZJ2022129).  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
Author [PERSON] was employed by the company AECC Harbin Dongan Engine Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 1 Introduction  
The aim of the paper is to auto simulate complex systems with multi-dimensions and multi-granularities driven by multi-relational data resources for better classification and causal relationships of a system.  
Multi-Relational Data Mining (MRDM) [1,2] is able to discover knowledge directly from multi-relational data tables, not through connection and aggregation of multiple relational data into a single data. Multi-relationship data mining can effectively prevent the problems of information loss, statistical deviation and low efficiency, _etc._ These methods [3, 4, 5] such as CrossMine, MI-MRNBC and Graph-NB are fitting for multi-relational data mining, but cannot obtain causality in a multi-relational system.  
In 1986, FCM [6, 7, 8] is introduced by [PERSON], suggesting the use of fuzzy causal functions taking numbers in [-1, 1] in concept maps. FCM, as a kind of graph model, combines some aspects from fuzzy logic, neural networks and other techniques, and is fitting for modeling system from data resources. Compared to other techniques, FCM exhibits a number of highly appealing properties. In particular, FCM can directly show the multi-relationships between different concepts and the inference is easy and intuitive. FCM learning algorithms use learning algorithms to establish models from historical data (simulations of concept values), which have been widely used applied to various fields [9, 10, 11, 12, 13] of society, engineering, medicine, environmental science, _etc._ However, none of them consider the data characteristics of multi-relationships.  
Multi-relational FCM discussed in the paper refers to two-levels FCM. There is one FCM of each dimension in low-level. There is only one FCM in high-level. The state value of each concept in high-level actually is a summary evaluation of low-level FCM in the dimension, which is inferred based on the weight vector, obtained by multi-instances oriented NHL, in low-level FCM. RCGA of high-level FCM aims to mine high-level FCM based on summary evaluations of low-level FCNs for high-quality classification and causality. Thus, the proposed multi-relational FCM and the integrating algorithm seem a rather realistic approach to solve the complex model.  
The remainder of this paper is organized as follows. Section 2 describes FCM, existing learning algorithms of FCM, and problems to solve. Then, Section 3 proposes the multi-relational FCM and integrating NHL and RCGA based multi-relational FCM learning algorithm. In Section 4, the experiment and its results are represented and analyzed. Finally, we briefly conclude this paper in Section 5.
## Appendix A Nmi
### Bland-Altman Plot  
Figure 12 shows the Bland-Altman plot for the ensemble model with multiple annotations compared to other models using the NMI metric; each row in the graph represents the implementation of a separate dataset. The SA and MA NMI are scored almost equally for all the datasets. The SA model performs slightly better for the Knee and Heart datasets, and the MA model performs slightly better for the SJJ and Lung datasets. However, the Lung dataset scored the narrowest LoA, and the Heart dataset scored a wide LoA (poorer agreement). Previously, the Heart dataset achieved a very tight LoA (strong agreement) for GDC metrics. The correlation plot for both metrics confirms this conflict in which the points are very tight to the line for the GDC, and for the NMI, the points are spread all around. Furthermore, when the results of the MA model were compared with different versions of MC dropout, MA consistently scored higher NMI regardless of the dropout rates. However, the different range depends on the datasets. For example, for the SJJ dataset, the MD is +2%, meaning the MA model performs better on average by 2%. For the Lung and Knee dataset, MD is approximately +10%. For the Heart, the MD is above +15%, and the number increases when the dropout rate increases; for instance, for Lung segmentation with 80% dropout, the MD reaches +30%. This result is completely opposite of the result of the GDC metric in which MC models with 40% and 60% dropout rates always achieve better (positive) MD (The result of the RMMSE metric is in between NMI and GDC, discussed in the next appendix). Also, the LoA is very wide for all comparison models. Similar results appeared when the MA model was compared with the regression model. The MA model scores +21%, +5%, +26%, and +24%, and +22% MD for the Knee, SJJ, Lung, HHS, HLS, respectively. The conclusion is that the NMI metric always shows superiority toward the MA model compared to MC dropout and regression models.  
Figure A1: Shows box plots for the NMI metric.
## 2 G-LiHT Design and Instrumentation  
### Scientific Objectives and Design Considerations  
G-LiHT was designed as a relatively inexpensive, robust and portable research tool for evaluating the potential benefits of data fusion for studies of terrestrial ecosystems. Table 1 lists the objectives and related measurement requirements for G-LiHT. One of the major obstacles to the development of science-based data fusion algorithms is the availability of accurately co-registered data of similar grain size for different information types. This is often the case when instruments are flown on different platforms and acquired on different dates. We believe that \"instrument fusion\" is a prerequisite to \"data fusion\" and conceived G-LiHT as a multi-sensor airborne imaging system that would simultaneously map the composition, structure and function of terrestrial ecosystems.  
In addition, G-LiHT was designed to simplify worldwide deployment and minimize collection and data processing costs. As a result, G-LiHT features eye-safe lasers, a portable, low-power payload (37 kg; 30 \(\times\) 30 \(\times\) 60 cm; 210 W), a single-solution GPS-INS, compatibility with common, civilian-use aircraft (e.g., Cessna, Piper, Twin Otter; 12/28 VDC compatibility) using a standard camera port or custom wing-mounted pod and commercial off-the-shelf (COTS) instruments that are easy to replace and not regulated by ITAR (International Traffic in Arms Regulation). Both G-LiHT science and deployment objectives are traced to instrument and design requirements in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline
**Objective** & **Requirement** \\ \hline Direct computation of at-sensor reflectance and record of solar & \({}^{\star}\) downwelling irradiance spectrometer (VNIR) \\ illumination conditions & \({}^{\star}\) clear sky solar irradiance model \\ Mapping species composition and variations in biophysical & \({}^{\star}\) VNIR imaging spectrometer \\ variables (e.g., photosynthetic pigments, nutrient content) & \\ Mapping forest health and photosynthetic responses to & \({}^{\star}\) spectral resolution \(\leq\)5 nm \\ environmental conditions & \({}^{\star}\) low-and-slow data acquisition (\(\sim\)335 m AGL, 110 kts) \\ Tree-Scale measurements with minimal atmospheric & \({}^{\star}\) surface temperature observations \\ interference & \({}^{\star}\) scanning airborne lidar \\ \end{tabular}
\end{table}
Table 1: Traceability of [PERSON]’s LiDAR, Hyperspectral and Thermal (G-LiHT) science and deployment objectives. VNIR, Visible and Near Infrared; PALS, Portable Airborne Laser System; VDC, Volts Direct Current; GPS-INS, Georeferenced Positioning System and Inertial Navigation System.  
internally to the aircraft over an appropriately sized view port or externally attached to aircraft using a custom fabricated pod. A custom pod was designed and fabricated by NASA for any Cessna 206, using mounting points that are standard on this platform (Figure 3).  
Specifications of the individual G-LiHT instruments are provided in the sections below.
# Improved ADRC-Based Autonomous Vehicle Path-Tracking Control Study Considering Lateral Stability
## 2 Vehicle Model
### Vehicle Path-Tracking Model  
Autonomous vehicle path tracking is to control the vehicle wheels to actively steer along the desired path, so as to achieve the purpose of automatic driving. How to eliminate the tracking deviation of lateral displacement and heading angle is the fundamental problem of designing the controller. Selecting the lateral deviation and heading error as the evaluation index of the path-tracking effect, the path-tracking model is established, as shown in Figure 1b, where the X and Y axis represent the longitudinal and horizontal axis of the geodetic coordinate system, respectively; \(\varphi_{d}\) indicates the desired heading angle; \(y_{e}\) represents the tracking deviation of lateral displacement, which can be used as the path-tracking performance evaluation index; and \(\rho\) represents the radius of curvature of the desired path.  
The tracking deviation of heading angle and its derivative can be defined as  
\[\left\{\begin{array}{l}\varphi_{e}=\varphi-\varphi_{d}\\ \dot{\varphi}_{e}=\dot{\varphi}-\dot{\varphi}_{d}\end{array}\right. \tag{5}\]  
where \(\varphi_{e}\) indicates the heading error and \(\dot{\varphi}_{d}\) is the derivative of desired heading angle, and can be expressed as  
\[\dot{\varphi}_{d}=\frac{v_{x}}{\rho} \tag{6}\]  
By using the Serret-Frenet equation and assuming that the heading angle is relatively small, the tracking deviation of lateral displacement can be simplified as  
\[\dot{y}_{e}=v_{y}+v_{x}\varphi_{e} \tag{7}\]  
Combining Equations (4)-(6) and substituting into Equation (7), we can derive the vehicle path-tracking model as follows:  
\[\frac{d}{dt}x=A_{1}x+B_{1}u+E_{1}\frac{1}{\rho} \tag{8}\]  
\[\text{where}\ \ A_{1} = \left[\begin{array}{cccc}0&1&0&0\\ 0&-2\frac{C_{f}+C_{r}}{m\alpha_{r}}&2\frac{C_{f}+C_{r}}{m}&2\frac{-l_{f}C_{f}+ l_{r}C_{r}}{m\alpha_{r}}\\ 0&0&0&1\\ 0&2\frac{-l_{f}C_{f}+l_{r}C_{r}}{l_{z}\alpha_{r}}&2\frac{l_{f}C_{f}-l_{r}C_{r}} {l_{z}}&-2\frac{l_{r}^{2}C_{f}+l_{r}^{2}C_{r}}{l_{z}\alpha_{r}}\end{array} \right],\ \ B_{1} = \left[\begin{array}{cccc}0&0\\ 2\frac{C_{f}}{m}&0\\ 0&0\\ 2\frac{l_{f}C_{f}}{l_{z}}&\frac{1}{l_{z}}\end{array}\right],\] \[E_{1} = \left[\begin{array}{cccc}0&0\\ 2\frac{-l_{f}C_{f}+l_{r}C_{r}}{m}-v_{x}^{2}\\ -2\frac{l_{r}^{2}C_{f}+l_{r}^{2}C_{r}}{l_{z}}\end{array}\right],x=\left(\begin{array} []{cccc}y_{e}&\dot{y}_{e}&\varphi_{e}&\dot{\varphi}_{e}\end{array}\right)^{T},u=\left(\begin{array}{cccc}\delta_{f}&\Delta M_{z}\end{array}\right)^{T}.\]
## 3 Results
### Local Sensitivity--One-at-a-Time (OAT) Approach
#### 3.1.1 Boundary Conditions  
The results concerning the DR type and individual price developments (PD) are presented in Figure 3. The sensitivity index depicted on the y-axis of the figure is calculated using Equation (2), where \(\Delta y\) for each case is calculated based on the financial conditions of the baseline, i.e., a decreasing real DR, a 50-year calculation period, and by disregarding specific price developments. Given this consideration, the positive sign index represents scenarios with a greater global cost than the baseline. As evident from Figure 3, the variation from the baseline due to PD is relatively small (<0.1). When comparing the two different PD to the baseline model (decreasing DR), it can be noticed that, when applied individually, the PD for DH has a higher impact than the PD of electricity for all DR types. The combined effect on the result when the PD for both energy sources are applied is slightly lower for real DR types and additive for fixed nominal DR.  
Figure 4 shows the sensitivity index for the DR rates calculated for different periods disregarding the PD. The calculation period affects the results nearly linearly, which was recognized due to a proportional change in the sensitivity index with a change in the calculation period. The difference between the successive calculation steps is nearly equal, but an increase in the index is noticed for the shortest calculation periods. Overall, it can be stated that the calculation period is the boundary condition that will have the greatest influence on the final result, compared to the DR and the application of PD. A 10-year difference in the calculation period results in greater change in the output than applying different DR or PD.
## 4 Results and Discussion
### Influence on the Load Curve  
The scheduling of extensive loads, which is related with the trend of forecasted wind power availability, will influence the load curve of system. The results are shown in Figure 5.  
From Figure 5, it is obvious that the daily peak load is cut, and the daily valley load is increased after optimization. It promotes the daily load curve leveling and is conducive to the system security and stability. This kind of impact is reasonable because wind power owns anti-peak characteristics, which means the trough periods of forecasted wind power are the peak periods of daily load and the peak periods of forecast wind power are the trough periods of daily load. And to maximize the wind power utilization, the scheduling of responsive loads should make the valley load increased and the peak load decreased, leading the daily load curve consistent with the forecasted wind power curve. Contrasting the 3 wind power curves, it is easy to find that the optimal wind power utilization is larger than the conventional wind power utilization during 2:00 to 6:30 and 14:00 to 15:00, which verifies the effect of responsive loads.  
To sum up, the combination of HLs and SLs in the optimization model is of great significance, for it can not only lead better effect in maximizing wind power utilization and minimizing system operation cost, but also contribute to leveling the load curve and maintaining the system security.
## 10 Comparison between MFCC and Spectrogram Features  
The spectrogram calculation considers the length of the window of 81.92 ms. A Blackman window is moved along the time axis with an overlap of 81.44 ms. The resulting  
Figure 14: Boxplots representing the comparison of the accuracy for each cross-validated model and for each vibration signal channel recorded in the reciprocating compressor. The blue box represents the inter-quartile range located between the quartile Q1 (25%) and the third quartile Q3 (75%). The red line in the interquartile range is the median and the red plus signs are the outliers.  
Figure 13: Accuracy as a function of the fault class for each cross-validated model and for each vibration signal channel for the reciprocating compressor: (**a**) accuracy for the CNN model, (**b**) accuracy for the LSTM model, (**c**) BiLSTM model.  
bidimensional array representing the spectrogram includes 64 frequencies and 750-time bins. However, only half of the frequencies are considered, and the array has dimension 32 \(\times\) 750. For this, features array both derivatives \(\Delta_{m,t}\) and \(\Delta\Delta_{m,t}\) are calculated and appended along the frequency axis, resulting in a features array of size \(96\times 750\) where the time axis is common for the three sets of features. Each array (the spectrogram and derivatives) is standardized using the Z-score normalization before concatenation.  
A comparison of accuracy concerning the classification of vibration signals for the centrifugal pump is shown in Table 9. In this case, the classification accuracy is slightly higher for the models trained with the MFCC features than those trained with the spectrogram features. The average accuracy attained using the MFCC features is 99.85% over all channels processed and 99.42% using the spectrogram. In contrast, apparent differences are observed in the reciprocating compressor, where the number of faults is 17, higher than in the pump case. The average accuracy attained using the MFCC features is 99.64% to 96.66% attained using the spectrogram features as shown in Table 10.
## 3 GPS SDR Design for CRPA Using General-Purpose Parallel Processors  
### Hardware Setup  
The entire receiver processing after the analog-to-digital converters in Figures 2 and 3 is performed on general-purpose processors in our SDR implementation. Four Trimble Zephyr antennas are used as an _ad hoc_ CRPA array (Figure 4). GPS L1 signals from four antennas are down-converted to baseband and digitized by four commercial digitizers (Universal Software Radio Peripheral 2, or USRP2, from Ettus Research LLC).  
For raw data recording, the sampling rate of each USRP2 is set to 40 Msps (20 Msps inphase and quadrature samples). Each sample size is 2 byte with 14-bit resolution. Although a 40 Msps rate is adequate for GPS L5 signal processing, only one GPS satellite is transmitting a healthy L5 signal as of July 2011. Thus, it is not possible to demonstrate 12-channel GPS L5 SDR processing with live signals. As an alternative, we process L1 signals with 40 Msps rate to demonstrate the SDR's computational capability for future real-time L5 processing. Otherwise, it is unnecessary to process L1 signal with 40 Msps rate because L1 C/A-code bandwidth is only 2 MHz. However, it is slightly optimistic to directly transfer the 40 Msps L1 C/A-code results in this paper to L5 because L5 has data and pilot signals, longer codes, and a secondary code, which are not considered in the L1 C/A-code tracking.  
The digitized samples are streamed via gigabit Ethernet to solid state disks (SSDs) within computers. SSDs are used because streamed data rate from each antenna is 80 MB/s which is higher than the writing rate of conventional hard disks. After storing four-antenna data in SSDs, the real-time computational capability of our GPS SDR for CRPA is demonstrated by replaying the stored data faster than real time. The COTS general-purpose parallel processors (CPU and GPU) used for the GPS SDR are shown in Figure 4.