esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Results for Volatile Compound Identification Via HS-SPME-GC-TOF-MS  
The GC-TOF-MS analysis of volatile compounds emitted from waste shiiitake FBs identified six prominent compounds, which were categorized into ketones, alcohols, and siloxanes based on their molecular structure and chemical properties (Figure 6 and Table 1).  
The results of HS-SPME-GC-TOF-MS analysis of VCs emitted from WSFB profiled six compounds, which were categorized into ketones, alcohols, and siloxanes based on their molecular structure and chemical properties (Table 1 and Figure 6). Siloxanes may be coming from the coating materials of SPME fiber assembly (Table 1 and Figure 6, compound **1** and **6**).  
Figure 5: Effects of shitiake fungi bed age on rice seedling growth and biomass accumulation. (**A**) Representative images of rice seedlings exposed to volatile compounds (VCs) from young (YSFB), mature (MSFB), and waste (WSFB) shitiake fungi beds compared to the control (scale bar = 5 cm is shown as white bar at the left). (**B**) Dry weight (mg) of rice seedlings. (**C**) Plant height (cm) of rice seedlings. (**D**) Root length (cm) of rice seedlings. Different letters indicate statistically significant differences among treatments (Tukey’s test, \(p<0.05\)). Data are presented as mean \(\pm\) SD (n = 5). Error bars indicate the standard deviation (\(\pm\)SD).  
Specific compounds, such as 1-Octen-3-one and 3-octanone (Table 1 and Figure 6, compounds **2** and **3**), have been directly linked to increased seedling biomass and plant height observed in earlier experiments [34; 35]. 1-octen-3-ol (Table 1 and Figure 6, compounds **4**), commonly referred to as the \"mushroom alcohol\", has been shown to trigger both systemic resistance and growth modulation in plants [36]. Similarly, 3-octanone (Table 1 and Figure 6) is recognized for its potential in agricultural applications, given its abundance and diversity of effects on plant metabolism, including inducing resistance to biotic and abiotic stresses [3; 37].
# Convergence and Stability of a New Parametric Class of Iterative Processes for Nonlinear Systems
## 4 Bifurcation Analysis of Free Critical Points
### Dynamical Planes  
The tool with which we can graphically visualize most of the information obtained is the dynamical planes; in these, we represent the basins of attraction of the attracting fixed points for several values of parameter \(\alpha\). The above mentioned can only be done when the nonlinear system has a dimension of 2, although the results of the dynamical analysis are valid for any dimension.  
To calculate the dynamical planes for the systems, a grid of points is defined in the real plane, and each point is used as an initial estimate of the iterative method for a fixed \(\alpha\). If the iterative method converges to some zero of the polynomial from a particular point of the grid, then it is assigned a certain color; in particular, if it only converges to the roots of \(q(x)\), the predominant colors are orange if it converges to \(x=(1,1)\), blue if it converges to \(x=(-1,1)\), green if it converges to \(x=(-1,-1)\) and brown if it converges to \(x=(1,-1)\). If, as an initial estimate, a point has not converged to any root of the polynomial in 100 iterations at most, it is assigned as black. The colors in certain basins are darker or not, indicating that the orbit in certain initial estimate will converge to the fixed point of the basin with greater or fewer iterations, with the lighter colors causing fewer iterations.  
For \(\alpha\) with stable behavior, we elaborate our graph that has a grid with 500 \(\times\) 500 points, with 100 as the maximum number of iterations and limit of \([-5,5]\) for both axes (see Figure 11), for values of \(\alpha\) in unstable regions the interval is \([-40,40]\) in both axes in Figure 12 and \([-30,30]\) in Figure 13. Periodic orbits are also observed in Figure 13.  
In Figure 11a, we see four basins of attraction that correspond to the roots of polynomial system, with a very stable behavior. However, each basin of attraction has more than one connected component for \(\alpha=-5\) and \(\alpha=-40\), as can be seen in Figures 11b,c, respectively. This performance increases for lower values of \(\alpha\) close to the instability zones seen in the parameter lines Figure 4b, as seen in Figure 11d.  
In Figure 12, we can see a chaotic behavior (chaos) when we take initial estimations in the black zones, producing orbits with random behavior that do not lead to the expected solution. Finally, in Figure 13, the phase space for \(\alpha=-73\) is plotted. In them, the following 3-period orbits are painted in yellow:  
* \(\{(-21.0469,1),(-0.368161,1),(0.361236,1),(17.9769,1)\}\),
* \(\{(1,17.9769),(1,-0.368161),(1,0.361236),(1,-21.049)\}\),
* \(\{(-21.0469,-21.0469),(-0.368161,-0.368161),(0.361236,0.361236),\)  
\((17.9769,17.9769)\}\),  
We can observe three attracting orbits, whose coordinates are symmetric.
## 5 Results and Discussion
### Simulation Results
#### 5.1.1 Scenario1: Only Ocean Current Information

Table 2: Results comparison using PSO, FFA and SHH algorithms in Scenario 1. Figure 8: Comparison of the path planning results of PSO, FFA and SHH algorithms in scenario 1.
## 5 Field Application and Effect Analysis
The average floor heave across the four monitoring stations was 82 mm, the average side convergence was 127 mm, and the average roof subsidence was 176 mm. By analyzing the measured data from the four monitoring points, a comparison is drawn between the calculated roadway deformation results from the numerical simulation and the average measured values. The comparison of deformation between numerical simulation and field application is illustrated in Figure 19. The figure shows that although the optimized scheme does not fully achieve the expected results from the numerical simulation in practical engineering applications, its overall effectiveness exceeds 50%. Since the implementation of the optimized scheme, the deformation of the roadway has been significantly reduced, the stability of the surrounding rock has been notably improved, and the safety and protection efficiency of the roadway have been ensured. Figure 17: Monitoring station location distribution.
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 2 Materials and Methods
### Feature Extraction Module
#### 2.4.2 Multi-Head Dilated Convolutional Layer (MDL)  
Although the multi-head self-attention mechanism excels at handling long-range dependencies, its performance in feature extraction for time-series data with hierarchical or multi-scale characteristics is not always satisfactory. Dilated convolution expands the receptive field by inserting holes (that is, skip sampling) within the convolutional kernels, allowing for the effective capture of local features at different scales without increasing computational complexity. This study introduces a novel multi-head dilated convolution module based on the transformer-encoder architecture, as shown in Figure 4. The goal is to better capture local fault features of different scales in the input sequence.  
Figure 4: Multi-head dilated convolutional layer.  
The proposed multi-head dilated convolution layer includes a multi-head dilated convolution, concatenation layer, and linear mapping layer. Through the multi-head mechanism, it simultaneously learns multiple feature subspaces to capture information at different scales. Firstly, the multi-head mechanism divides the last dimension of the output \(X_{o2}\in\mathbb{R}^{M^{2}\times D_{embed}}\) from the previous layer into h heads, obtaining \(D_{1},D_{2},D_{3}\in\mathbb{R}^{M^{2}\times d_{k}^{t}}\) in each head, where \(d_{k}^{t}=\frac{D_{embed}}{h}\). These are then input into the multi-scale dilated convolution, where dilated convolutions with dilation rates of 1, 2, and 3 are performed separately. Then, the output by the three different scales of dilated convolution is added with equal weights to obtain the multi-head output, which is calculated as follows:  
\[\text{Conv}_{h}=\text{conv}_{h,1}(D_{1},W_{h,1})+\text{conv}_{h,2}(D_{2},W_{h,2 })+\text{conv}_{h,3}(D_{3},W_{h,3}) \tag{11}\]  
where \(\text{Conv}_{h}\) represents the output of the \(h\)-th head through multi-scale dilated convolution. The \(\text{conv}_{h,1},\ \ \text{conv}_{h,2},\text{ and }\ \text{conv}_{h,3}\) are two-dimensional dilated convolution operations with expansion rates of 1, 2, and 3 in the \(h\)-th head, respectively. The size of the convolution kernel is set to 3, and \(W_{h,1}\), \(W_{h,2}\), and \(W_{h,3}\) are three dilated convolution parameters in the \(h\)-th head, respectively. Next, concatenate the outputs, and finally obtain \(X_{oH}^{d}\) through linear mapping.  
\[X_{oH}^{d}=\text{Concat}([\text{Conv}_{1},\text{Conv}_{2},\dots,\text{Conv}_{h} ])\cdot W_{H}^{d} \tag{12}\]  
where \(W_{H}^{d}\) represents the weight parameters for linear mapping. Then, the output of this module is obtained through layer normalization and residual connection. LN represents Layer Normalization, and the calculation is as follows:  
\[{X_{oH}^{d}}^{\prime}=\text{LN}(X_{oH}^{d}) \tag{13}\]  
Then, the residual connection is performed to obtain the output \(X_{o3}\in\mathbb{R}^{M^{2}\times D_{embed}}\).  
\[X_{o3}=X_{o2}+{X_{oH}^{d}}^{\prime}. \tag{14}\]
# Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data
## 5 Conclusions  
A new calibration method for correcting the air temperature measured by low-cost sensors under natural conditions was proposed. We used an artificial neural network (ANN) to balance the effect of multiple environmental factors on the air temperature measurements. Data were collected over 305 days, at three different locations in Japan, and used to evaluate the performance of the method. As a preliminary experiment, we conducted an indoor experiment and confirmed that any measurement errors arising in the outdoor experiments could be mainly attributed to environmental factors. We then conducted two further analyses; calibration using data measured at a single location and calibration using data measured at nearby and distant locations. The results of these analyses showed that our method works well when training and test data were collected under similar environments, even if they were different locations. However, there was a negative effect when data from to distant locations, where environmental variations were totally different, were used. In a future study, we need to consider preceding environmental conditions, observation frequencies and other environmental information such as rain levels and wind speed to improve our method. We also need to prepare datasets obtained from a range of environments for the practical applications in future work.  
We thank the technical support staff at the Institute of Sustainable Agro-ecosystem Services, the University of Tokyo, for their help in the field experiments. We also thank the staff at Tea Experiment Station of Saga Prefecture for providing us the experimental field. This work was partially supported by the India-Japan Joint Research Laboratory Programme \"Data Science-based Farming Support System for Sustainable Crop Production under Climatic Change\" funded by Japan Science and Technology Agency and Department of Science and Technology, Ministry of Science and Technology.  
[PERSON] designed this study and conducted all of the analyses. [PERSON] provided helpful comments and suggestions on the data analysis. [PERSON] and [PERSON] supported the setup of the sensors in the experimental fields to collect environmental data.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Synthesis of ML-WS\({}_{2}\) and ML-MoS\({}_{2}\)  
ML-MoS\({}_{2}\) and ML-WS\({}_{2}\) were both synthesized in an open-type double-temperature-zone tube furnace (MTI OTF-1200X-II, HF-Kejing, Hefei, China). Prior to the synthetic procedure, a SiO\({}_{2}\)/Si wafer (SiO\({}_{2}\) thickness is 300 nm) with dimensions of 20 mm \(\times\) 20 mm was subjected to ultrasonic cleaning in ethanol, isopropanol, and deionized water for 15 min each in sequence. Afterward, the SiO\({}_{2}\)/Si wafer was dried with N\({}_{2}\) gas flow and served as the substrate for the growth of the 2D materials. For the synthesis of ML-MoS\({}_{2}\), 0.5 g of S powder and 10 mg of MoO\({}_{3}\) powder were placed in Zone I and Zone II, respectively. The distances between the S and MoO\({}_{3}\) were 30 cm, and the as-cleaned SiO\({}_{2}\)/Si wafer was placed upside down on the quartz boat with MoO\({}_{3}\) powder. The system was evacuated to 10 Pa and then backfilled with N\({}_{2}\) to atmospheric pressure at a flow rate of 70 sccm. Zones I and II were separately heated to 350 \({}^{\circ}\)C and 760 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C min\({}^{-1}\) and maintained at stable temperatures for 20 min to facilitate growth. Then, the system underwent natural cooling to room temperature.  
To synthesize ML-WS\({}_{2}\), a growth source of 20 mg of WS\({}_{2}\) powder (99.9% metallic base) was positioned near the central section of the tube. The pre-cleaned SiO\({}_{2}\)/Si wafer was then placed 2.2 cm behind the WS\({}_{2}\) powder. The tube was sealed and first evacuated to a pressure of 10 Pa, followed by the introduction of Ar gas until atmospheric pressure was reached. This process was repeated three times to ensure the complete removal of air. Zone I was heated to 1000 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C min\({}^{-1}\) and kept for 1 h under the protection of 100 sccm of Ar gas flow to grow large ML-WS\({}_{2}\) triangles. Finally, the tube furnace was naturally cooled to room temperature to prevent the triangle from breaking.
# Advanced Automatic Welding System for Offshore Pipeline System with Seam Tracking Function
## 5 Application of the Automatic Welding System
### Welding Seam Tracking
The weaving width is fixed to avoid the abrupt weaving motion. Finally, the correction value for deviations (\(V_{L}\), \(V_{R}\)) of the welding seam tracking is transmitted to the weaving motion. The calculation of the correction value is performed in Equation (5). The welding torch from left to right seam tracking function compares the average of the measured signals between the start location and the end location and determines the extent of the left and right deviation of the welding torch according to the signs (\(+\), \(-\)) and the value of the result. The maximum correction value should be modified according to the oscillation frequency in order to implement a reliable seam tracking system. In case of high oscillation frequency in root pass welding, the number of corrections can be increased. When the oscillation frequency is low in fill pass welding, the correction frequency is reduced. Therefore, the maximum correction value in the root pass is limited as 0.1 mm and fill pass is limited as 0.2 mm. \[\text{D}=\frac{V_{Left}-V_{Right}}{k} \tag{5}\]  
where \(k\) is a constant in the weld database. \(V_{Left}\) is the averaged voltage value in data processing range, and \(V_{Right}\) is the averaged voltage value in the data processing range. Figure 16 shows a schematic of the U-groove geometry and welding sequence. Table 2 summarizes the experiment condition. The welding time is cut in half compared with the single carriage welding system, and the preparation of weld time for installation is reduced by 1/3. The fusion zone shape was shown in Figure 17a, and the value for the deviation of the weld centerline is \(\pm\)0.3 mm in Figure 17b. Figure 15: _Cont._  
Figure 16: Schematic of U-groove shape without root gap: (**a**) Joint detail and (**b**) welding sequence. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pass** & **Current \&** & **Current (A) \&** & **Voltage (V)** & **Travel** & **Ocill.** & **Ocill.** \\
**No.** & **Polarity** & **Lead/Trail** & **\& Lead/Trail** & **Speed** & **Width** & **Frequency** \\ \hline Root & DC (+) & Lead: 252 & Lead: 25.5 & & & \\  & & Trail: 246 & Trail: 24.5 & 63 & 0.6 & 220 \\ Hot & DC (+) & Lead: 252 & Lead: 25.5 & & & \\  & & Trail: 246 & Trail: 24.5 & 63 & 0.6 & 220 \\ Fill 1 & DC (+) & Lead: 247 & Lead: 25 & & & \\  & & Trail: 233 & Trail: 24.5 & 62 & 1.8 & 200 \\ Fill 2 & DC (+) & Lead: 243 & Lead: 25 & 63 & 2.7 & 200 \\ Strip & DC (+) & Trail: 237 & Trail: 25.5 & 62 & 2.7 & 200 \\ Cap & DC (+) & Lead: 247 & Lead: 24.5 & 24.5 & 66 & 2.7 & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Welding condition. Figure 15: Moving averaged signal (voltage).
# A Novel Approach to Enhance Crude Oil Recovery Ratio Using Selected Bacterial Species
## 3 Results
### Oil Viscosity Reduction  
Oil viscosity was used as an indicator of microbial-induced compositional change in the crude oil after microbial treatment. Table 4 represents the change in viscosity of crude oil after microbial treatment at different shear rate values. Two temperatures were covered in this study (37 and 55 \({}^{\circ}\)C) to examine the effect of temperature on the flow behavior of the heavy crude oil. The heavy crude oil viscosity decreases significantly from 13.827 to 3.597 Pas when the temperature changes from 37 to 55 \({}^{\circ}\)C.  
The viscosity of crude oil treated with _Bacillus_ species was significantly decreased from 22.47 Pa.s at 0.8481 s\({}^{-1}\) to 4.127 Pa.s at 98.73 s\({}^{-1}\) at a temperature of 37 \({}^{\circ}\)C; comparing with the efficiency of the _Pseudomonas putida_, the viscosity reduction was from 3.806 Pa.s at 0.7652 s\({}^{-1}\) to 1.226 Pa.s at 98.64 s\({}^{-1}\). _Nocardia cylriciegorica_ reduced oil viscosity from 0.9786 Pa.s at 0.8226 s\({}^{-1}\) to 0.3224 Pa.s at 98.79 s\({}^{-1}\) (Figure 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & **Average Viscosity, Pa.s** & **Average Viscosity, Pa.s** & **Average Viscosity, Pa.s** \\  & _Pseudomonas putida_ & _Bacillus_ & _Nocardia cylriciegorica_ \\ \hline
55 & 0.682388 & 1.39256 & 0.481724 \\
37 & 1.52084 & 6.22 & 0.428794 \\ \hline \hline \end{tabular} Effect of Bacterial Isolates on Viscosity of Bouri Crude Oil  
Specific quantitative lab procedures using a Rotational Viscometer (CV100) were carried out to measure the shift in rheological properties in treated (inoculated) samples. The reduction of oil viscosity to enhance the flow properties occurs due to effects of bacterial degradation of oil or excreting of components such as surfactants into the oil phase. Figure 1 at 37 \({}^{\circ}\)C and Figure 2 at 55 \({}^{\circ}\)C show a sharp decrease in viscosity at a shear rate below 3 s\({}^{-1}\), and after 50 s\({}^{-1}\) the viscosity remains steady.  
\end{table}
Table 4: Comparing average viscosity of crude oil after using bacterial species at different temperature conditions.  
Figure 1: Reduction in viscosity of crude oil treated with _Bacillus_ species, _Pseudomonas putida_, and _Nocardia cylriciegorica_ at 37 \({}^{\circ}\)C.  
At a temperature of 55 \({}^{\circ}\)C, the viscosity of crude oil treated with _Bacillus_ species was significantly decreased from 7.61 Pa.s at 0.885 s\({}^{-1}\) to 1.11 Pa.s at 96.74 s\({}^{-1}\). The result further showed that the _Bacillus_ species can be successfully used at high temperatures. The reduction in viscosity was decreased from 1.565 Pa.s at 0.7622 s\({}^{-1}\) to 0.5415 Pa.s at 98.67 s\({}^{-1}\) with _Pseudomonas putida_. _Nocardiia cyriacigeorgica_ reduced viscosity from 0.9406 Pa.s at 0.885 s\({}^{-1}\) to 0.1998 Pa.s at 96.74 s\({}^{-1}\) (Figure 2).  
There was no significant change of pH, indicating that no acidic byproducts were produced during the incubation period.
# Analyzing and Controlling Inter-Head Diversity in Multi-Head Attention
## 5 Inter-Head Similarity Analysis
### Experimental Details for Similarity Analysis  
* **Data and Setups:** We choose De\(\rightarrow\)En IWSLT17 machine translation task [14] for our analysis in this section. Training set consists of 223,162 sentences, development set consists of 8130 sentences, and test set consists of 1116 sentences. To tokenize the corpus, we use Byte Pair Encoding [30] with a vocabulary size of 16,384. We use Transformer [1] architectures with various numbers of heads and hidden dimension sizes for comparison. For all models, we use 6 layers for encoder's self-attention, decoder's self-attention, and encoder-decoder attention modules.
* **Performances of trained models:** Table 1 shows BLEU scores of models with various hidden dimension sizes and numbers of heads. As represented in Table 1, increasing hidden size \(d\) results in higher BLEU performances with a fixed number of heads,although increasing the number of heads does not always assure higher performance with fixed hidden size.
## 7 Experimental Validation  
To experimentally validate the designed system, the Control Hardware-in-the-Loop (CHiL) process is adopted in this section. The hardware platform of the CHiL setup is shown in Figure 19. The hardware configuration of the designed system, consisting of the asymmetric half-bridge (AHB) converter, a Switched Reluctance Generator (SRG), and a battery pack, is emulated on the OPAL-RT platform (OP4510), which operates on four cores based on the Intel Core Xeon processor at 3.5 GHz and RAM 2 \(\times\) 8 GB. The communication port between the host PC and the OPAL-RT platform is based on Gigabit Ethernet LAN. The system controller is uploaded on a 150 MHz DSP (Digital Signal Processor) control board labeled as (TMS320F28335 ZJZA). The DSP represents the closed-loop controller of the designed system. The controller interface (OP8665) is used as a development tool to provide a quick and easy interface for the DSP controller with an OPAL-RT platform without worrying about any interface issues.  
The CHiL has been carried out for the constant current PID controller for turn-off and turn-on angle control. The tuned PID parameters from the PSO approach are used in the experimental test. The DSP controller receives signals from the OPAL-RT platform, such as the actual constant current, phase currents, and actual speed, and based on these values, it generates the gating signals to operate the AHB converter on the OPAL-RT platform. The control-hardware-in-the-loop platform is shown in Figure 20.  
Output current responses at different reference signals and their corresponding turn-off angles are validated by the OPAL-RT simulator and shown in Figure 21. The response  
Figure 19: Block diagram of the (ChiL) process.  
Figure 20: Control-hardware-in-the-loop platform: (1) Opal-RT real-time simulator, (2) OP8665 controller interface, (3) opal interface and monitoring, (4) DSP interface, (5) Oscilloscope.  
is similar to the Matlab/Simulink response. The maximum current generation is validated and shown in Figure 22a. Dash curves show the Simulink response compared to opal RT simulator solid curves. Validation has been performed for the turn-on control angle, where the output response for a constant current at different reference values is shown as solid curves in Figure 23a, which are compared with Matlab/Simulink response dashed curves. Good validation has been achieved.  
Figure 21: Opal-RT real-time simulator turn-off angle control on constant current response (V\({}_{\text{battery}}\) = 250 V, \(\theta_{\text{on}}\) = 0\({}^{\circ}\)), (**a**) the constant current at different reference values, (**b**) the corresponding turn-off angle.  
Figure 22: Opal-RT real-time simulator response for turn-off angle control; (**a**) effect of initial turn-on angle on maximum current (solid traces for experimental results and dashed traces for simulation), (**b**) effect of battery voltage on turn-off angle at different reference currents.