esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Low-Height Railway Noise Barriers with Porous Materials
## 6 Results
Figure 18 shows the insertion loss calculated at the receivers illustrated in Figure 14. For this purpose, the energetic average of the sound pressure in the receivers was calculated for the cases with and without a barrier and the insertion loss was then computed. In what concerns reflective barriers, the analysis shown in Figure 18 corroborates what was already observed in the proximity of the noise source. The curved barrier presents a substantially better performance than the vertical one, particularly from the frequency 630 Hz on. From 200 Hz up to 630 Hz, the insertion loss varies and the vertical barrier is slightly superior at some frequencies. From 630 Hz onwards, the insertion loss of the curved barrier stabilises and the difference between the two is always more than 3 dB. From the analysis of Figure 18, it is highlighted that the barrier with porous material has a better performance in comparison with the purely reflective barriers for the whole frequency range under analysis. The performance of the curved barrier with porous material translates into an insertion loss greater than 10 dB in the whole frequency range, reaching 15 dB in a large part of those frequencies and for the frequency of 3150 Hz the insertion loss exceeds 25 dB. In practical terms, for the lowest frequencies (200-630 Hz), the difference between the three barriers under analysis is not more than 2/3 dB between them; however, from 630 Hz on, the performance of the curved barrier with porous material is clearly superior in relation to the other solutions presented. Figure 17: Illustration of barrier geometry and porous material. Figure 18: Insertion loss calculated for the three proposed noise barriers. As observed, the barriers studied have satisfactory levels of insertion loss. Taking into consideration the maximum insertion loss values recorded for the frequencies 630 Hz, 1250 Hz and 3150 Hz, we tried to understand what role the porous material would have, which allowed the mitigation level to be considerably higher at the frequencies mentioned. Figure 19 is divided into three distinct parts: the vertical barrier, Figure 19a-c; curved barrier, Figure 19d-f; and curved barrier with porous material, Figure 19g-i. In Figure 19, the influence of the porous materials on the higher frequencies (1250 Hz and 3150 Hz) is shown, namely in the area between the vehicle and the barrier where the red and yellow colouration is less intense; therefore, less energy is sent to the receivers and there are higher mitigation levels, corroborating the results presented in Figure 18. Regarding the frequency of 630 Hz, as illustrated in Figure 18, the behaviour of the curved barriers in relation to the vertical barrier is not as superior as in the cases of the other frequencies under analysis, partly due to the minor influence of the porous material in controlling reflections between the barrier and the vehicle (Figure 19a,d,g).
# Halophytes as Medicinal Plants against Human Infectious Diseases
## 3 Halophytes as a Source of Antimicrobial Compounds
### Antibacterial Effects
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bacteria** & **Plant Species** & **Reference** \\ \hline \multirow{6}{*}{_Microococcus luteus_} & _Salicornia europaea_ & [80] \\ \cline{2-3}  & _Cressa cretic_ & [89] \\  & _Mesembryanthemum edulis_ & [90] \\  & _Retama nateam_ & [91] \\  & _Retama sphareocarpa_ & [91] \\  & _Tamariz gallica_ & [86] \\ \hline \multirow{6}{*}{_Mycobacterium tuberculosis_} & _Citrullus colocyntthis_ & [82] \\ \cline{2-3}  & _Mesembryanthemum edulis_ & [88] \\  & _Ziziphus spina-christi_ & [92] \\ \hline _Protein_ sp. & _Exocaria agallocha_ & [34] \\ \hline \multirow{6}{*}{_Pseudomonas_ sp.} & _Cressa cretic_ & [89] \\  & _Mesembryanthemum edulis_ & [90] \\  & _Stueda monoica_ & [84] \\  & _Tamariz gallica_ & [86] \\ \hline \multirow{6}{*}{_Pseudomonas aeruginosa_} & _Arthrocenum macrostachum_ & [88] \\  & _Eryngium barrelieri_ & \\ \cline{2-3}  & _Eryngium glomeratum_ & [93] \\ \hline \multirow{6}{*}{_Salmonella_ sp.} & _Cressa cretic_ & [89] \\  & _Mesembryanthemum edulis_ & [88] \\  & _Suaeda monoica_ & [84] \\  & _Tamariz gallica_ & [86] \\ \hline \multirow{2}{*}{_Salmonella typhi_} & _Exocaria agallocha_ & [81] \\ \cline{2-3}  & _Salmonella typhimuriumurium_ & _Salicornia europaea_ & [80] \\ \hline \multirow{6}{*}{_Serratia marcescens_} & _Salicornia europaea_ & [80] \\ \cline{2-3}  & _Arthrocenum macrostachym_ & [88] \\ \cline{2-3}  & _Exocaria agallocha_ & [81] \\ \cline{2-3}  & _Staphylococcus aureus_ & _Cressa cretic_ & [89] \\  & _Mesembryanthemum edulis_ & [88] \\  & _Suaeda monoica_ & [84] \\  & _Tamariz gallica_ & [86] \\ \hline \multirow{6}{*}{_Staphylococcus aureus_} & _Acanthus licifolius_ & [94] \\  & _Exocaria agallocha_ & [95] \\  & _Rhizophora mucronata_ & [96] \\ \cline{2-3}  & _Sonneratia caseolaris_ & [94] \\ \cline{2-3}  & _Aegiceras cornicultum_ & [97] \\ \cline{2-3}  & _Avicentia marina_ & [98] \\ \cline{2-3}  & _Ceriops decandra_ & [97] \\ \cline{2-3}  & _Eryngium tharoafolium_ & [99] \\ \cline{2-3}  & _Kochia scoparia_ & [34] \\ \cline{2-3}  & _Lumnitzera racemosa_ & [97] \\ \cline{2-3}  & _Rhizophora mucronata_ & [96] \\ \cline{2-3}  & _Tamariz gallica_ & [86] \\ \hline \multirow{6}{*}{_Staphylococcus epidermidis_} & _Salicornia europaea_ & [80] \\ \cline{2-3}  & _Vibrio cholerae_ & _Exocaria agallocha_ & [81] \\ \cline{2-3}  & _Vibrio cholerae_ & [81] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
## 2 Discrete Element Parameter Model and Parameter Calibration of Soil-Soil Interaction
### Simulation Model  
#### 2.4.1 Contact Model  
Utilizing an appropriate contact model is a crucial prerequisite for accurately modeling soil in EDEM. The soil samples in this study exhibited macroscopically high adhesion and cohesion characteristics. Therefore, simulation was conducted using the Hertz-Mindlin model with JKR contact model [27]. Due to contact induced by particle motion, the surfaces of the particles gradually deformed during the contact process, resulting in contact forces. The implementation of normal elastic contact force, F\({}_{\text{JKR}}\), in this model depends on the overlap distance (\(\delta\)) and interaction parameters, namely surface energy (\(\gamma\)), defined by the following equation:  
\[F_{JKR}=-4\sqrt{\pi\gamma E^{*}}\alpha^{\frac{3}{2}}+\frac{4E^{*}}{3R^{*}} \alpha^{3} \tag{2}\]  
Figure 3: Soil particle pile.  
Figure 2: Physical test of soil accumulation Angle.  
\[\delta=\frac{\alpha^{2}}{R^{*}}-\sqrt{\frac{4\pi r\kappa}{E^{*}}} \tag{3}\]  
In the equation, F\({}_{\text{JKR}}\) represents the JKR normal elastic force, measured in Newtons (N), \(\delta\) and \(\alpha\) denote the overlap distance and radius of the two contacting particles, measured in meters (m), \(\gamma\) represents the surface energy, measured in Newton-meters per square meter (N\(\cdot\)m\({}^{-1}\)), E* is the equivalent elastic modulus, measured in Pascals (Pa), and R* signifies the equivalent radius, also measured in meters (m). The following formula can be employed for the determination:  
\[\frac{1}{E^{*}}=\frac{1-v^{2}}{E_{1}}+\frac{1-v^{2}}{E_{2}} \tag{4}\]  
\[R^{*}=\frac{1}{R_{1}}-\frac{1}{R_{2}} \tag{5}\]  
where \(\
u_{1}\) and \(\
u_{2}\) are the Poisson's ratios of the two particles, E\({}_{1}\) and E\({}_{2}\) represent the shear moduli of the two contacting particles in Pascals (Pa), and R\({}_{1}\) and R\({}_{2}\) denote the radii of the two contacting particles in meters (m).
# Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
## 2 Methods  
### Study Area  
The study area is located on Vancouver Island, BC, Canada, near the city of Campbell River, in the Oyster River area about 16 km from the east coast of the island (49 deg52'7.8\"N, 125 deg20'6.3\"W, 350 m above mean sea level). The area is a plantation forest consisting of 80% Douglas-fir (_Pseudotsuga menziesii_ spp. _menziesii_ ([PERSON].) [PERSON]), 17% western redcedar (_Thuja plicata_ [PERSON]. [PERSON] D. [PERSON]), and 3% western hemlock (_Tsuga heterephthalylla_ ([PERSON].) [PERSON]) [36; 37; 38] with main understorey species, salal (_Gaultheria shallon_ Pursh.), dull Oregon grape (_Mahonia nervosa_ (Pursh.) [PERSON]), vanilla-leaf deer foot (_Achlys triphyllla_ ([PERSON]) [PERSON]), and various ferns and mosses. The site is located on a northeast facing 5 deg-10 deg slope and has a stand density of 1100 stems ha\({}^{-1}\) with tree heights ranging between 30 and 35 m [39], a mean diameter at 1.3 m height of 31 cm [40] and a mean annual increment of 12.1 m\({}^{3}\cdot\)ha\({}^{-1}\)[38]. The predominant age of the trees was 60 years old in 2009 and the leaf area index was 7.3 m\({}^{2}\cdot\)m\({}^{-2}\)[41]. The soil is classified as a humo-ferric podzol and has a gravelly sandy loam texture and a surface organic layer ranging in depth between 1 and 10 cm [38]. The region belongs to the dry maritime Coastal Western Hemlock biogeoclimatic subzone (CWHxm) and has a mean annual precipitation and temperature of 1470 mm and 8.6 deg\({}^{\circ}\)C, respectively [40]. Approximately 110 ha of the study area, extending in the prevailing wind directions (NE and SW), was aerially fertilized with urea at 200 kg\(\cdot\)N\(\cdot\)ha\({}^{-1}\) on 13 January 2007 and simulation of the footprint climatology has demonstrated that this extent contributes to over 80% of the eddy-covariance flux measured at the flux tower [40; 42].
## 4 Experiment Verification
### Experimental Results
#### 4.2.2 Comparison with Other Models with Transfer  
To further demonstrate the transmission performance of the DTLCL model and explain the reason why this method is better than other fault diagnosis methods under different working conditions and faults of different degrees, the above training models of CNN, AE as TCNN, TAE are retained. The well-known deep models such as Xception, InceptionV3, D- CORAL, DANN can also be transferred. A model for transfer learning in SOTA models called DistilBERT_transformers is also used in comparative experiments [47]. Thus, TCNN, TAE, Xception, InceptionV3 are presented for comparison on dataset A and dataset B. A single Gaussian kernel is used on TCNN, TAE, TANN, TSVM. On DTLCL, a multi-tier adapter with a Gaussian combination of a linear kernel is used. D-CORAL and DNN share the same architecture but adapt to the properties of fully connected layer 1 and fully connected layer 2 by using CORAL loss. DANN has the same structure as DNN for fault classification but adds a domain classifier for domain adaptation that inputs flat layer features into the domain classifier.  
In Test 3 and Test 4, the number of training samples is the same as in Test 1 with significant and small errors. Table 6 shows the accuracy and training time of the different models in the training dataset, validation dataset and test dataset among ten experiments. The following two effects can be intuitively deduced from the analysis of the results in Tables 5 and 6. First, the overall accuracy of the models with transfer is better than those without transfer and the training time is shortened. The average accuracy rates of the test data set TCNN, TAE, InceptionV3 and Xception with transfer are 0.843, 0.524, 0.630, 0.812 in Test 1 and 0.934, 0.752, 0.715, 0.864 in Test 2, respectively, the time taken is 19 s, 22 s, 80 s, 17 s and 20 s, 24 s, 81 s, 17 s. This shows that DTLCL with a Gaussian combination of linear kernels reduces the distribution discrepancy and gives better results.
###### Abstract  
To investigate the influence of multiple cycles of aging and rejuvenation on the rheological, chemical, and morphological properties of styrene-butadiene-styrene (SBS)-modified asphalt-binders, the asphalt-binders were aged using two laboratory simulation methods, namely a rolling thin film oven (RTR) test for short-term aging and pressure aging vessel (PAV) for long-term aging. The asphalt-binders were then rejuvenation with three types of rejuvenations (Type I, II, and III) with different dosages (i.e., \(\theta\)%, 10%, and 14% for the first, second, and third rejuvenation, respectively). A dynamic shear rheometer (DSR) was then used to analyze the effect of rejuvenators on the rheological properties of all the asphalt-binders. The changes in the functional groups and microscopic morphology in the process of multiple aging and rejuvenation cycles were studied using Fourier transform infrared (FTIR) and atomic force microscopy (AFM). The results indicated that the three rejuvenators could soften the stiffness and restore the microstructures of the aged asphalt-binders in the process of repeated aging and rejuvenation from DSR and AFM testing. Considering the rutting and fatigue properties, the Type I rejuvenator exhibited the potential to achieve the desired rejuvenation effects under multiple rejuvenation cycles. During the multiple aging and rejuvenation cycles, the aging resistance of SBSMA decreased gradually from the FTIR results. This inherently limited the number of repeated rejuvenation cycles. This research is conducive to promoting the application of repeated penetrating rejuvenation.  
multiple aging and rejuvenation cycles; SBS modified asphalt; morphological properties; DSR; FTIR; AFM +
Footnote †: journal: Applied Sciences
## 5 Case Studies
### Engineering Experiment A: A Series Industrial System  
We consider a roof truss structure in this experiment, shown in Figure 4, whose tie as well as bottom end rods are steel rods, and its press as well as top rods are concrete members. Taking the safety and availability of the roof truss into account, the vertical deflection of the node cannot exceed the threshold; hence, the limit state function of the roof truss structure can be expressed as two failure modes, and the two limit state functions are established as \(g_{1}(\mathbf{X})=4.0x_{3}+4.0x_{1}-3.9998x_{2}-x_{4}\), \(g_{2}(\mathbf{X})=0.2299x_{3}+3.2425x_{2}-x_{4}\), whose parameters refer to the literature [41]. Furthermore, the functional function of the system is \(g(\mathbf{X})=\min\{g_{1},g_{2}\}\), where \(\mathbf{X}=(x_{1},x_{2},x_{3},x_{4})\) denotes the basic random variable, \(x_{1}\sim N(83.5,10.02)\), \(x_{2}\sim N(83.5,10.02)\), \(x_{3}\sim N(83.5,10.02)\) and \(x_{4}\sim N(150,37.5)\). Analogous to the numerical experiment A, two comparative methods, the Monte Carlo and importance sampling methods, are employed to compare with the proposed approach  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Number of Iterations** & **Reliability Index** & **Probability of Failure** & **Relative Error\%** \\ \hline Monte Carlo & \(10^{8}\) & 2.341625 & 0.0096 & - \\ Response Surface & 30 & 2.388398 & 0.008461 & 1.83 \\ Kriging model & 7 & 2.330912 & 0.009879 & 0.62 \\ Proposed approach & 3 & 2.346199 & 0.009483 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the numerical example result.  
when calculating the failure probability. The calculation results and the process of sample point addition during the iteration are shown in Table 2 and Figure 5, respectively.
# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 3 Methodology
### Pre-Defined Affinity Graph Representation
#### 3.2.2 SimRank Graph  
SimRank similarity between metro points is calculated as follows [24]:  
\[A_{Sim,\ ab}\ =s(a,b)=\frac{C}{|I(a)||I(b)|}\ \ \sum_{i=1}^{|I(a)|}\sum_{j=1}^{ |I(b)|}s\big{(}I_{i}(a),I_{j}(b)\big{)} \tag{2}\]  
where \(C\) is a constant between 0 and 1. We iterate over all in-neighbor pairs \(I_{i}(a),I_{j}(b)\) of \((a,b)\), and sum up the similarity \(s\big{(}I_{i}(a),I_{j}(b)\big{)}\) of these pairs. Then we divide by the total number of in-neighbor pairs, \(|I(a)||I(b)|\), to normalize. That is, the similarity between \(a\) and \(b\) is the average similarity between in-neighbors of \(a\) and in-neighbors of \(b\). Note that the similarity between an object and itself is defined to be 1. This method can be used to encode similar relationships between more distant stations in the subway network.
## 7 Numerical Simulations and Data Analysis
### Numerical Simulations Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \((n,m)\) & \(\theta_{1}=0.5\) & \(\theta_{1}=1\) & \(\theta_{1}=1.5\) & \(\theta_{1}=2\) & \(\theta_{1}=3\) \\ \hline  & 0.3879(0.90) & 0.4205(0.91) & 0.4074(0.90) & 0.3880(0.93) & 0.3377(0.89) \\  & 0.3894(0.90) & 0.4206(0.92) & 0.4079(0.90) & 0.3907(0.93) & 0.3403(0.90) \\ \((10,10)\) & 0.4044(0.89) & 0.4526(0.92) & 0.4303(0.91) & 0.4042(0.92) & 0.3469(0.91) \\  & 0.5179(0.90) & 0.4526(0.90) & 0.5425(0.90) & 0.5240(0.90) & 0.3939(0.90) \\  & 0.3997(0.95) & 0.4113(0.95) & 0.4028(0.93) & 0.3931(0.95) & 0.3546(0.94) \\ \hline  & 0.3201(0.92) & 0.3480(0.92) & 0.3427(0.92) & 0.3176(0.92) & 0.2862(0.91) \\  & 0.3205(0.93) & 0.3480(0.93) & 0.3430(0.94) & 0.3186(0.93) & 0.2873(0.91) \\ \((20,15)\) & 0.3305(0.92) & 0.3657(0.93) & 0.3577(0.94) & 0.3275(0.93) & 0.2915(0.92) \\  & 0.3810(0.90) & 0.4089(0.91) & 0.4020(0.94) & 0.3814(0.91) & 0.3531(0.90) \\  & 0.3281(0.95) & 0.3427(0.93) & 0.3390(0.95) & 0.3207(0.96) & 0.2953(0.96) \\ \hline  & 0.2501(0.93) & 0.2729(0.94) & 0.2634(0.93) & 0.2509(0.96) & 0.2244(0.95) \\  & 0.2502(0.93) & 0.2729(0.95) & 0.2637(0.94) & 0.2512(0.95) & 0.2245(0.95) \\ \((25,25)\) & 0.2510(0.93) & 0.2774(0.94) & 0.2696(0.92) & 0.2555(0.96) & 0.2250(0.96) \\  & 0.2756(0.93) & 0.2961(0.93) & 0.2765(0.92) & 0.2755(0.93) & 0.2354(0.95) \\  & 0.2537(0.96) & 0.2700(0.96) & 0.1884(0.95) & 0.2512(0.95) & 0.2286(0.96) \\ \hline  & 0.2278(0.96) & 0.2496(0.94) & 0.2437(0.94) & 0.2261(0.96) & 0.2059(0.95) \\  & 0.2279(0.96) & 0.2496(0.95) & 0.2436(0.95) & 0.2263(0.96) & 0.2062(0.94) \\ \((30,40)\) & 0.2296(0.95) & 0.2522(0.95) & 0.2482(0.95) & 0.2275(0.97) & 0.2050(0.94) \\  & 0.2482(0.94) & 0.2682(0.94) & 0.2588(0.95) & 0.2458(0.92) & 0.2267(0.93) \\  & 0.2284(0.97) & 0.2470(0.95) & 0.2423(0.97) & 0?2272(0.97) & 0.2090(0.95) \\ \hline  & 0.1788(0.96) & 0.1948(0.96) & 0.1887(0.95) & 0.1780(0.97) & 0.1553(0.96) \\  & 0.1788(0.96) & 0.1948(0.96) & 0.1887(0.96) & 0.1781(0.96) & 0.1554(0.95) \\ \((50,50)\) & 0.1789(0.96) & 0.1952(0.95) & 0.1890(0.94) & 0.0812(0.96) & 0.1598(0.96) \\  & 0.1848(0.94) & 0.2013(0.94) & 0.1942(0.92) & 0.1845(0.95) & 0.1588(0.95) \\  & 0.1784(0.97) & 0.1935(0.96) & 0.1880(0.96) & 0.1778(0.96) & 0.1620(0.97) \\ \hline \hline \end{tabular} In each cell, the average confidence lengths are provided and the corresponding coverage probabilities are given within brackets. The first to the fifth row corresponds the results for MLEs, AMLEs, Boot-\(p\) method, Boot-\(t\) method and Bayes estimates respectively.  
\end{table} 
Table 1: Biases and mean square errors(MSEs) of the maximum likelihood estimate (MLE), approximate maximum likelihood estimate (AMLE) and Bayes estimate of \(R\), when \(\alpha=2\), \(\theta_{2}=1\) and for different values of \(\theta_{1}\).
## 4 Simulation, Experiment, Analysis
### Controllable Behavior Comparison Experiment  
To verify the behavior controllability of BCAMP, AMP was used as the control group for comparison experiments. The experiments involved training both BCAMP and AMP with consistent reference trajectory libraries and hyperparameters, and a stepped obstacle was added to the simulation environment for analysis.  
The controller trained with BCAMP received both command parameters and user commands, while the AMP-trained controller only accepted command parameters. Analyzing Figure 8, when the robot wanted to overcome the obstacle, the AMP controller, upon receiving command parameters to increase the height of the CoM, chose the bipedal standing action but could not actively switch to the jumping action, eventually leading to a fall. In contrast, Figure 9 shows that the BCAMP controller, when approaching the obstacle, received the same command parameter to increase the height of CoM along with a user command representing the jumping action. In this case, the robot successfully used the jumping action to clear the obstacle.  
The AMP controller, when faced with both jumping and bipedal standing actions that involve raising the height of CoM, failed to select the appropriate action for obstacle clearance. On the other hand, the BCAMP controller actively switched between actions using user commands, successfully completing the obstacle-crossing task. In conclusion, this experiment demonstrates that training with BCAMP enables controllable behavior in quadruped robots, significantly improving their traversability and adaptability.  
Figure 8: AMP obstacle-crossing action snapshot.  
Figure 7: Active switching of quadruped robot actions.  
To measure whether both algorithms performed well during the training of the quadruped robot, a comparison of the training effects was conducted. Combining Figures 12 and 13, it can be observed that when training the bipedal standing action using only the PPO, after the front feet leave the ground, the robot rear feet are separated front and back to maintain balance. When the rear feet are standing apart, the distribution of the CoM and stability are affected, resulting in an unnatural overall bipedal standing posture compared to the BCAMP.  
In summary, the experiments demonstrate the effectiveness of the method proposed in this paper for generating actions in quadruped robots, showing improvements in training speed and the naturalness of movements when compared to the PPO.
## Oil Shale Chemical Characteristic  
### Oil Shale Retorting Chemistry Characteristic  
Utilizing oil shale comprehensively is a cutting-edge technique that maximizes both the chemical and energy potential while reducing environmental consequences. To achieve a high yield of shale oil and process shale char effectively, several aspects must be taken into account, including retorting temperature, residence time, particle size, and heating rate.  
Chemically speaking, the retorting temperature has the most substantial impact on shale oil yield. Increased temperatures lead to improved yields; however, it also results in higher input heat, heat loss, and costs associated with retorting. Furthermore, elevated temperatures can cause the cracking and coking of shale oil. Thus, it is vital to maintain an optimal temperature range (preferably between 460 and 490 \({}^{\circ}\)C) for the retorting process.  
The duration of residence time also affects oil yield, with an increase in time resulting in improved yield. However, excessively extending the residence time may lead to increased cracking and coking of residual organic matter inside shale char particles, which is not desired. As a result, it is crucial to balance both retorting temperature and residence time for the best outcome.  
Particle size is another critical element since both larger and smaller sizes can cause secondary decomposition of shale oil, reducing the yield. A medium particle size is best for enhancing the shale oil yield.  
Finally, the rate of heating influences the yield of shale oil. A higher average heating rate of 10 \({}^{\circ}\)C/min is generally advantageous and consistent with most previous studies.  
In summary, refining the retorting factors, particularly focusing on retorting temperature, is crucial for optimizing the yield of shale oil from a chemical perspective.
## 1 Introduction
Therefore, this study used an accelerometer to measure the vibration signals to extract the gearbox fault features and proposed a hybrid algorithm of the SDP and CNN to identify the fault type of the gearbox. The gearbox fault types in this study included a normal gearbox (Type 1), a rusted gearbox (Type 2), a stripped gearbox gear (Type 3), a worn gearbox gear (Type 4), and an aged gearbox (Type 5). First, the accelerometer was installed on the gearbox to acquire a vibration signal. The captured signals were then filtered and imported into the SDP to draw a snowflake diagram [22; 23; 24]. Next, the snowflake diagram was led as a feature map in the CNN to identify the fault type of the signals. The SDP can visualize data and is highly sensitive. Even with a subtle change in data, the differences could be displayed in the exported image.