esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Structure and Working Principle  
### Structure and Composition of Axial Flux Permanent Magnet Synchronous Motor  
The axial flux permanent magnet motor designed in this paper was a three-phase motor, with each phase having 10 turns connected in series. The motor was designed with 10 poles and a rated speed of 1080 rpm, corresponding to a frequency of 180 Hz. The double-sided internal rotor structure (AFIR) consisted of two stators and one intermediate rotor, forming a symmetrical double air gap configuration. As shown in Figure 1, the stator included a core and windings. The core, made of silicon steel sheets, provided the magnetic circuit, while the windings, made of copper or aluminum wire, generated the magnetic field. The stator teeth and slots adopted a fractional-slot structure with a pole-slot combination of 20 poles and 24 slots. The stator slot depth was 5.2 mm, and the slot opening width was 4.8 mm. The windings were implemented using a multi-strand parallel winding scheme, with 6 strands in parallel per slot. The rotor consisted of a core and permanent magnets, where the core was also made of silicon steel sheets to form the magnetic circuit, and the permanent magnets, made of materials such as neodymium-iron-boron, generated the magnetic field. The intermediate rotor was subjected to axial magnetic forces from both the upper and lower stators, which counteracted each other, thereby reducing the bearing load and mechanical losses. This contributed to an increase in the motor's power density and efficiency. The windings were placed on both stators, while the permanent magnets were mounted on the intermediate rotor. The magnets on the rotor can be arranged in two configurations: surface-mounted or embedded. In the surface-mounted configuration, the magnetic flux passes through the rotor bracket, requiring the use of magnetically conductive materials. To prevent the rotor bracket's magnetic circuit from becoming saturated, the axial size of the bracket must be increased. This can increase the length of the magnetic path and reduce the magnetic reluctance of the path, thereby enhancing the magnetic conductivity and avoiding saturation of the magnetic circuit. This approach is not conducive to the flat design of the electric drive joint. In this paper, an embedded structure was adopted, where the magnetic flux path passed only through the permanent magnets. The rotor bracket can be made of non-magnetic materials, which helps to reduce the overall weight, shorten the axial dimension of the motor, and further enhance the torque density of the motor.
## 2 Materials and Methods
### Model Performance Evaluation  
Various metrics were used to evaluate the performance of ML prediction models. The correlation coefficient is a key measure that indicates how close the model's predictions are to the actual data, with high values expected in strong linear relationships. RMSE calculates the model's prediction errors, showing how accurate the model's predictions are. The lower this value is, the higher the accuracy of the model. RAE evaluates the model's predictions in relation to the average error rate of the dataset. MAPE measures the percentage error between the predicted and actual values, providing insight into the relative accuracy of the model, especially for datasets with varying scales. Additionally, execution time measures  
the total time spent by the model while making predictions, an important parameter for efficiency, as faster processing times enhance the model's applicability in large datasets. Each of these metrics is critical to understanding the model's accuracy, efficiency, and generalization ability.  
We conducted \(t\)-tests to evaluate whether the differences in the R values between optimal and suboptimal conditions were statistically significant. Specifically, the means of the R values under optimal conditions were compared to those under suboptimal conditions for each model. The \(t\)-test formula was applied as follows in Equation (4):  
\[t=\frac{\overline{x}_{1}-\overline{x}_{2}}{\sqrt{\frac{\overline{x}_{1}^{2}}{ n_{1}}+\frac{\overline{x}_{2}^{2}}{n_{2}}}} \tag{4}\]  
where \(\overline{x}_{1}\) and \(\overline{x}_{2}\) are the means of the two groups, \(s_{1}^{2}\) and \(s_{2}^{2}\) are their respective variances, and \(n_{1}\) and \(n_{2}\) are the sample sizes of the two groups.  
[PERSON]'s d was used to measure the effect size of the differences observed in the R values between optimal and suboptimal conditions, using the formula in Equation (5).  
\[d=\frac{\overline{x}_{1}-\overline{x}_{2}}{\sqrt{\frac{\overline{x}_{1}^{2}+ \overline{x}_{2}^{2}}{2}}} \tag{5}\]
## 7 Conclusions  
The purpose of this research was to propose a selection methodology for composite material for the manufacturing of a retractable main landing gear strut for a given lightweight aircraft. For this purpose, four uni-directional fiber-reinforced composite materials, two each from carbon- and glass-fiber families, were considered. Initially classical lamination theory was used on thin laminated composite plates to categorize these four different materials based on the strength-to-weight ratio. Initial categorization of these materials was as follows:  
AS carbon fiber/epoxy \(>\) S-glass fiber/epoxy \(>\) T300 carbon fiber/epoxy \(>\) E-glass fiber/epoxy  
Figure 14: Variation of Tsai-Wu failure criterion factor with co-efficient of friction.  
Figure 15: Mesh convergence study for landing gear strut analysis.  
In the next phase of analysis, thick laminated composite beams were subjected to the same bending load under the stringent requirement of the Tsai-Wu failure criterion. The categorization of these materials based on the strength-to-weight ratio was as follows :  
T300 carbon fiber/epoxy \(>\) AS carbon fiber/epoxy \(>\) S-glass fiber/epoxy \(>\) E-glass fiber/epoxy  
For the design and analysis of the main landing gear strut of the given aircraft, the maximum landing loads for one point and two point landing conditions were calculated using FAA FAR 23 airworthiness requirements. After that, the main landing gear strut was modeled using recommended materials from carbon and glass fiber families and analyzed computationally against the calculated landing loads using Abaqus/CAE(r). In the first phase of analysis, only the landing gear strut without mounting bracket and axle was analyzed, and the T300 carbon fiber/epoxy strut just meeting the Tsai-Wu failure criterion (0.98) and having a minimum mass of 7.56 kg was recommended for further analysis with a mounting bracket and axle. In the second phase of analysis, the same landing gear strut could not meet the Tsai-Wu failure criterion due to stress concentration around the bolt holes of the strut when the mounting bracket and axle were assembled. For qualification of the composite strut with mounting bracket and axle, different options of joints were considered, along with change in dimensions (width) of the recommended struts within the given design constraints. Finally, the option of a hybrid joint (bolted and bonded) was applied to the landing gear strut design with the safest Tsai-Wu failure criterion value having dimensions of (52.8 mm \(\times\) 100 mm) and (59.4 mm \(\times\) 120 mm) for T300 carbon fiber/epoxy and S-glass fiber/epoxy, respectively, and the desired results were obtained. CATIA V5(r) was used to perform a collision detection test of these qualified struts to ensure smooth retraction on the aircraft. It was concluded that the main landing gear strut of T300 carbon fiber/epoxy, having a mass of 10 kg for the one point landing condition and 7.4 kg mass for the two point landing condition, was 1.5 times lighter than S-glass fiber/epoxy and was recommended for the manufacture of the main landing gear strut of a aircraft having an all up mass of 1600 kg.
## 1 Introduction
Therefore, using longer measurement periods such as 24 h, to obtain more accurate representation of noise levels in urban environments was recommended. On the other hand, regarding the prediction of the equivalent sound level using short-term measurements, [PERSON] et al. [26] focused on the stabilization time for road traffic noise measurements and concluded that a time of at least 10 min is necessary for reliable estimation of the equivalent sound pressure level of 1-h period; in addition, factors such as traffic volume, traffic composition, and road type can affect the required stabilization time. An environmental acoustic pattern refers to the distribution and variation of sound levels in a specific environment. These patterns can be affected by a variety of factors, such as land use, weather conditions, and human activity. In urban environments, the environmental acoustic pattern is typically characterized by high levels of noise pollution from sources such as traffic, construction, and industrial activities. However, these noise sources create a complex and dynamic acoustic environment which is highly dependent on the time of day and location. In this research, the environmental acoustic pattern of a location refers to the classification of a location using the equivalent sound pressure level during the day, evening, and night periods over a year, as recommended by the Directive [2] and defined in Section 2.2. The contribution of the current research is to use an unsupervised learning algorithm to estimate the corresponding environmental acoustic pattern of a location among the recognized long-term behaviors based on one-year acoustic data. This is carried out by using one-hour equivalent acoustic data to design and test algorithms based on ANNs, which are trained using shorter periods of time with a large amount of available data and require parallel processing to optimize the data pipelines. This rest of this paper is structured as follows. The datasets, algorithms, and methodology used for training and testing the models are presented in Section 2. Then, in Section 3, the results obtained from the analysis are displayed and discussed. Finally, Section 4 summarizes the main conclusions of this work.
## 4 Speech Databases and the ASR System  
The ASR experiments of the study were conducted using two openly available speech databases. The adults' speech data that were used in training was taken from the WSJCAM0 British English speech corpus [44]. The children's speech data that were used for testing were taken from the PF-STAR British English speech corpus [49]. Detailed information about the two databases are given in Table 2. The children's speech data were split into two parts: the validation set and the test set. The former consisted of 2.5 h of speech from 62 speakers with an age range of 6-14 years. The latter consisted of 1.1 h of speech from 60 speakers with an age range of 4-13 years.  
To build ASR systems, the Kaldi toolkit [50] was used. Deep neural network (DNN)-based context-dependent hidden Markov models (HMM) were used for acoustic modeling of the cross-word tri-phones. Decision-tree-based state tying was performed with the maximum number of tied-states (senones) fixed at 2000. Prior to learning the parameters of the DNN-HMM-based ASR system, the fMLLR-normalized feature vectors were time-spliced considering a context size of 9 frames. The number of hidden layers in the DNN was set to 5, with 1024 hidden units in each layer. The non-linearity in the hidden layers was modeled using the tanh function. The initial learning rate was set to 0.005, which was reduced to 0.0005 in 15 epochs for training the DNN-HMM system. The minibatch size of 512 was used in the DNN training. In decoding the test set for children's speech, a 1.5 k domain-specific bigram language model (LM) was used. This bigram LM was trained on the transcripts of the speech data in PF-STAR after excluding the test set. In total, 1969 words were used, including pronunciation variations in the lexicon for decoding the children's test set.
# Improved ISR for IEEE 802.11 ah Nonlinearity Compensation via Adjustable Constellation Borders
## 3 Improved ISR
### Proposed Scheme  
To allow the received symbols located at the decision border to return to their original decision region, it is necessary to design a room for reconstruction before employing a hard decision. As the channel noise added in each subcarrier is a two-dimensional Gaussian distribution, we proposed the decision region in a circle with an adjustable radius \(R\). For standard \(\mathcal{M}\)-ary QAM, the Euclidean distance between two adjacent constellation points is \(d=2/\sqrt{K}\), where \(K=2(\mathcal{M}-1)/3\) is the normalization factor. The radius of the decision circle with scaling ratio \(\alpha\) can be defined as  
\[R=\alpha\cdot\frac{d}{2}=\frac{\alpha}{\sqrt{K}},0<\alpha\leq 1 \tag{9}\]  
Therefore, the area of the decision-free region (the grey region in Figure 2b) can be determined by the value of \(\alpha\). When \(\alpha\to 0\), the decision circle shrinks to a constellation point, where the process of ISR-II becomes the same as that of ISR-I. As there is a trade-off between BER improvement and convergence rate, the proposed scheme helps in designing a flexible structure so that the performances of converged BER and convergence by the parameter \(\alpha\) can be adjusted.
# A Thermopile-Based Colorimetric Temperature Measurement Method for Arbitrary Bandwidth
## 1 Introduction
However, this method demands intricate optical system design to obtain narrow-band wavelength ranges at specific wavelengths. Additionally, it requires meticulous attention to the infrared detector's spectral responsivity in the selected narrow-band wavelength ranges, which limits detector selection. Moreover, excessively narrow wavelength bands can cause the detector to receive less energy, which might affect the accuracy of the measurement. A comparison of several radiation thermometry methods is shown in Table 1.  
This article first introduces the principles of several main radiation thermometry methods and analyzes the advantages and limitations of each method. The remaining structure of the article is as follows: Section 2 explains the temperature measurement principle of traditional colorimetric thermometry. Section 3 uses the monotonicity principle to prove the monotonic relationship between the ratio of radiative energies in two bands and temperature, proposing a thermopile-based colorimetric temperature measurement \begin{table}
\begin{tabular}{c c c} \hline
**Temperature Measurement Method** & **Advantages** & **Limitations** \\ \hline Total radiation thermometry & Simple optical structure, low cost & Difficult to obtain emissivity, low accuracy \\ \hline Single-wavelength thermometry & Simple structure, high cost-effectiveness & Significantly affected by emissivity and atmospheric attenuation, low accuracy \\ \hline Dual-wavelength thermometry & Minimally affected by emissivity and atmospheric attenuation, high accuracy & Relatively complex structure, limited band selection \\ \hline Multi-wavelength thermometry & Negligibly affected by emissivity and atmospheric attenuation, very high accuracy & Dependent on emissivity model, high cost, complex structure and algorithms, limited bandwidth selection \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of several radiation thermometry methods. method for arbitrary bandwidth. Section 4 validates this method through simulation and analyzes the simulation results. Section 5 presents the conclusions of this article.
# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 1 Introduction
Different from the existing methods for oceanic phenomena detection, MFNN realizes the detection of various oceanic phenomena and outlines them. MFNN also improves the detection accuracy of linear oceanic phenomena (such as oceanic fronts and ship wakes) that are difficult to detect in SAR images by improving the extraction of scale features and using weights in the parameter optimization process. We use the sample dataset to train and test the MFNN. The experimental results show that the network can detect the location and class information of multiple oceanic phenomena and achieves an average detection accuracy of 91%, which proves the effectiveness of the network. The remainder of this paper is organized as follows. In Section 2, we provide a detailed description of the oceanic phenomena detection network--the MFNN. In Section 3, we describe the methods of establishing, expanding, and labeling the SAR image dataset of oceanic phenomena. In Section 4, the experimental results are given and analyzed. The discussions and conclusions are given in Sections 5 and 6, respectively.
# Design of a Dual-Path Speech Enhancement Model
## 4 Experimental Setup
### Implementation Details  
All signals and noises had a sampling rate of 16 kHz, with a window length, hop size, and FFT length of 32 ms, 16 ms, and 512 samples, respectively. The Adam optimizer was used with a learning rate of 0.00075. The chunk size is 2 s, and the batch size is 8.  
The kernel size of all convolution layers in the encoder/decoder is (2 x 3) on the (time axis \(\times\) frequency axis) except for INCCONV, OUTCCONV and the CCTFA blocks. The kernel size of INCCONV and OUTCCONV is set as 1.
## 3 Results and Discussion
### Selection of Optimal Refrigerant  
Figure 9 shows the variation of optimal system performance with evaporation temperature for different R744 mixed refrigerants applied in the transcritical heat pump (T\({}_{\text{g-o}}\) is 313 K). From Figure 9, it can be observed that as the evaporation temperature increases, the optimal COP of the system increases, while the optimal pressure decreases. Meanwhile, among numerous mixed refrigerants, only the performance parameters of R744/R1234 yf (90/10) are comprehensively higher than those of R744; therefore, it is determined as the preferred refrigerant to replace R744. In addition, compared to R744, the system COP of R744/R1234 yf (90/10) increased by 1.16%, and the optimal pressure decreased by 12.83%.  
Figure 10 shows the variation of system performance with condensation temperature for R515B, R1234 ze, and R515B-1 applied in the subcritical heat pump. From Figure 10, it can be observed that as the condensation temperature increases, the COP of the system decreases. Meanwhile, compared to R1234 ze and R515B, the system COP of R515B-1 increased by 2.65% and 4.79%, respectively. Therefore, R515B-1 is identified as the preferred refrigerant to replace R515B.  
Figure 8: Model validation of two high-temperature heat pumps. (**a**) T\({}_{\text{e}}\) is 273–293 K, T\({}_{\text{g-o}}\) is 313 K, P\({}_{\text{g}}\) is 9.5 MPa; (**b**) T\({}_{\text{e}}\) is 273–293 K, T\({}_{\text{co}}\) is 358 K.
## Materials and Methods
### Analysis of mRNA Expression  
RNA was isolated using the RNeasy protect Mini kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Concentration of the isolated RNA was determined using the Nano Drop ND-2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). For cDNA synthesis, 500 ng of RNA was reverse transcribed using the iScript(tm) Select cDNA Synthesis Kit (Bio-Rad Laboratories, Munich, Germany) according to the manufacturer's instructions. Real-time PCR was used to detect the expression of the target mRNA, using 1 uL of cDNA, 2.5 uL of specific primers (Ki67, PCNA, GAPDH; QuantiTect Primer Assay, Qiagen), 12.5 uL of SYBR Green QPCR Master Mix (Bio-Rad) and 9 uL of deionised water in a 25 uL reagent mixture. Here, the following protocol was used: initial denaturation and thermal activation of polymerase (95 \({}^{\circ}\)C, 5 min), 40 cycles of denaturation (95 \({}^{\circ}\)C, 10 s), and a combined annealing/elongation (60 \({}^{\circ}\)C, 30 s). Quantification of mRNA was performed using the comparative threshold cycle method.
## 2 Diversity and Distribution of Rice Phytoalexins
### Diterenoid Phytotalexins in Rice  
Terpenes and terpenoids, also known as isoprenoids, are the largest and most diverse group of natural products, most of which are derived from plants. Of the more than 18,000 plant terpenoids known to date, about 10,000 are diterpenoids (composed of four isoprene units to form a 20-carbon backbone) [22; 23]. Monocot diterpenoids almost invariably belong to the large family of labdane-related compounds [24]. This group of diterpenoids is characterized by a labdane-type bicyclic core structure or more complex ring systems derived from labdane-type skeletons, such as abietane, kaurane, pimarane, beyerane, cassava, atisane, stemodane, and manoyl oxide [25]. To date, more than 7000 different labdane-related diterpenoids have been identified in plants [26]. Although they include the plant hormones gibberellins (GAs) as primary metabolites, the vast majority of labdane-related diterpenoids are secondary or rather specialized metabolites, acting as inducible antimicrobial compounds (phytoalexins) and/or plant growth inhibitors involved in antagonistic plant-plant interactions (allelochemicals).  
Rice ditterpenoid phytoalexins can be classified into four groups based on the structure of their hydrocarbon precursors, namely momilactones A and B [27; 28] (Figure 1A,B), phytocassanes A-G [29; 30; 31] (Figure 2A-G), oryzaeaxins A-F [32; 33; 34; 35; 36; 37; 38] (Figure 3A-F), and oryzalexin S [39; 40] (Figure 3G). Recently, [PERSON] et al. [41] isolated seven diterpenoids from rice hulls, three of which have never been described previously (i.e., 3,20-epoxy-3-hydroxy-8,11,13-abietatrien-7-one; 4,6-epoxy-3-\(\beta\)-hydroxy-9\(\beta\)-pimara-7,15-diene; 2-((E)-3-(4-hydroxy-3-methoxyphenyl)] allylidene) momilactone A). All isolated compounds showed antifungal activity against four crop pathogenic fungi (i.e., _Magnaporthe grisea_, _Rizzoctonia solani_,  
Figure 4: Phenolic compounds that accumulate in rice after exposure to biotic and/or abiotic stresses. (**A**) is a flavanone compound, while (**B–G**) are phenylamides.  
Fusarium oxysporum_, and _Blumeria graminearum_), and phytotoxicity against the major rice weed _Echinochla crus-galli_ (barnyard grass).
## 3 Channel Model
### FSO Channel
#### 3.1.4 AOA Fluctuations  
The beam no longer maintains orthogonality with the receiver plane due to the vibration of the hovering UAV. When the incident laser reaches the receiving plane at an angle \(\theta_{a,j}\), the Airy pattern may exceed the detector range due to the significant deviation in direction. The AoAs are defined as  
\[\theta_{a,j}\simeq\begin{cases}\sqrt{\theta_{rx}^{2}+\theta_{ty}^{2}},&j=SR_{i }\\ \sqrt{\theta_{rx}^{2}+\theta_{ty}^{2}},&j=R_{i}D\end{cases}, \tag{22}\]  
where \(\theta_{tx}\) and \(\theta_{rx}\) represent directional deviations in the horizontal plane of Tx and Rx. \(\theta_{ty}\) and \(\theta_{ry}\) are directional deviations in the vertical plane of Tx and Rx. \(\theta_{a,j}\) follows the Rayleigh distribution as  
\[f_{\theta_{a,j}}\big{(}\theta_{a,j}\big{)}=\frac{\theta_{a,j}}{ \sigma_{\theta}^{2}}\exp\left(-\frac{\theta_{a,j}}{2\sigma_{\theta}^{2}} \right),\theta_{a,j}\geq 0,j\in\{SR_{i},R_{i}D\}, \tag{23}\]  
where \(\theta_{FoV,j}\) is the field-of-view (FOV) angle of Rx. The link outage when \(h_{j}^{nat}=0\) occurs for \(\theta_{a,j}>\theta_{FoV,j}\), and \(h_{j}^{aa}=1\) for \(\theta_{a,j}\leq\theta_{FoV,j}\) otherwise. Therefore, the PDF of \(h_{j}^{nat}\) can be expressed as  
\[f_{h_{j}^{nat}}\Big{(}h_{j}^{nat}\Big{)}=\exp\left(-\frac{\theta_{FoV,j}^{2}}{2 \sigma_{\theta}^{2}}\right)\delta\Big{(}h_{j}^{aaa}\Big{)}+\left[1-\exp\left(- \frac{\theta_{FoV,j}^{2}}{2\sigma_{\theta}^{2}}\right)\right]\delta\Big{(}h_{ j}^{aaa}-1\Big{)},j\in\{SR_{i},R_{i}D\}, \tag{24}\]  
where \(\delta(\cdot)\) is the Dirac delta function [16].