esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Studies on the Effects of _M. oleifera_ Leaf Extracts on Male Reproductive Function
### Obesity  
Obesity affects the male reproductive function by increasing the leptin hormonal level, which activates the HPG axis by stimulating the secretion of GnRH, LH and FSH. However, leptin can also negatively affect the male reproductive function by suppressing testosterone secretion through its receptor isoforms in the gonads [7].  
The peripheral adipose tissue in obese individuals can increase the aromatisation of serum testosterone into oestradiol, suppressing the HPG axis through negative feedbackinhibition [99]. Increased oestradiol is commonly seen in obese people, and reduces sex hormone-binding globulin (SHBG), FSH and inhibin B [100]. Hence, obesity is positively correlated with reduced testicular volume, semen quality, spermatogenesis, hypogonadism and erectile dysfunction [100; 101]. Testicular damage in patients with high body mass index (BMIs) includes alterations in sperm parameters, notably reduced sperm count, concentration, motility, progressiveness, disturbed sperm morphology and sperm DNA fragmentation [7; 101].  
Some damaging effects of obesity on male reproductive functions may be attributed to the accumulation of ROS. Increased levels of ROS in obese people and obesity-induced animal models are correlated with impairment of testicular function and the HPA-axis [7]. In induced obese rats, _M. oleifera_ leaf extracts reduced the damaging effects of oxidative stress by lowering MDA concentration, which is a marker of lipid peroxidation. In addition, they increased antioxidant enzymes such as SOD, CAT and GSH [86].  
Additionally, _M. oleifera_ leaf extract (300 mg/kg) increased serum testosterone, FSH and LH, sperm count and sperm motility. In contrast, immotile spermatozoa and primary and secondary sperm abnormalities decreased in induced obese rats [86]. Therefore, _M. oleifera_ leaf extracts could be a potential solution to fertility problems that are associated with obesity.
## Materials and Methods
### Chemicals  
For headspace solid-phase-microextraction (HS-SPME), calcium chloride and the alkane standards C\({}_{6}\)-C\({}_{25}\) (100 \(\upmu\)g/mL) in diethyl ether were obtained from Sigma-Aldrich (now Merck; Poole, UK). For ultra-high performance liquid chromatography mass spectrometry (UPLC-MS), authentic compounds of glucoiberin (GIB; 99.61%, HPLC), progoitrin (PRO; 99.07%, HPLC), sinigrin (SIN; 99%, HPLC), glucoarphanin (GRA; 99.86%, HPLC), glucoarphanin (GAL, 98.8%, HPLC), glucoarphanin (GNP, 98.66%, HPLC), 4-hydroxyglucoberossicin (4 HB; 96.19%, HPLC), glucoarbassicanipin (GBN; 99.22%, HPLC), glucoortapealin (GTP; 99.61%, HPLC), glucoercin (GER; 99.68%, HPLC), glucoorbassicin (GBR; 99.38%, HPLC), and glucoasturtin (GNT; 98.38%, HPLC) were purchased from PhytoPlan (Heidelberg, Germany). Methanol (HPLC grade), formic acid (LC-MS grade), and acetonitrile (LC-MS grade) were purchased from VWR (Leicestershire, UK).
## 2 Related Works  
In this section, we mainly introduce the key module of ECAPA-TDNN and its advantages and disadvantages. ECAPA-TDNN is a novel speaker embedding extractor that uses the Time Delay Neural Network (TDNN) for speaker verification. This model builds on the original x-vector architecture and places additional emphasis on channel attention, propagation, and aggregation. ECAPA-TDNN demonstrates substantial improvements in speaker verification performance, despite its relatively low number of parameters. ECAPA-TDNN mainly consists of SE-Res2 Block modules, multi-level inputs, multi-scale aggregation, and attention pooling modules.  
The SE block in SE-Res2 Net models the dependency between channels, while the Res2 Net module enhances feature processing by constructing a hierarchical residual connection within the convolution layer. The multi-scale input and multi-scale aggregation aim to leverage features at different levels of the neural network. Attention pooling assigns different weights based on their importance to the task, allowing the network to focus more on speech features that are discriminative.  
Although the ECAPA-TDNN architecture has the above-mentioned advantages, there are still some shortcomings in its application to dialect recognition. Firstly, the fixed convolution kernel in the SE-Res2 Block may not capture broader contextual information, which could have a negative impact on model performance for dialect recognition tasks that require more contextual information. Secondly, although the attention mechanism in the SE-Res2 Block can help the model focus on important features, it does not consider local channel information, which is also important for more informative features in dialect recognition tasks.  
To address these issues, we propose the MSCA-Res2 Block. This module consists of two parts: multi-scale dilated convolution and multi-scale channel attention. The multi-scale dilated convolution allows the network to learn features at different scales, better adapting to changes in speech speed; by comparison, the multi-scale channel attention can dynamically adjust the weights between different feature channels at different scales, allowing the network to better capture subtle feature information related to pronunciation and tone differences. The detailed structure design is introduced in Section 3. The following subsection provides a description of the main modules of ECAPA-TDNN.
# Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
## 3 Construction of Dataset and Training of Models
### Linear structural system  
Figure 4: LSTM model for structural response prediction.  
Figure 5: Process of solving structural response.  
The nonlinear component consists of two parts: the linear model \(m_{b}\) and nonlinear model \(m_{d}\). Its governing equation can be expressed as:  
\[\left\{\begin{array}{l}m_{s1}\ddot{u}_{s1}+c_{s1}(\dot{u}_{s1}-\dot{u}_{b})+k_{ s1}(u_{s1}-u_{b})=-m_{s}\ddot{a}_{g}\\ m_{b}\dot{u}_{b}+c_{b}\dot{u}_{b}+k_{b}u_{b}-[c_{s1}(\dot{u}_{s1}-\dot{u}_{b})+k_ {s1}(u_{s1}-u_{b})]-k_{d}(u_{d}-u_{b})=-m_{b}\ddot{a}_{g}\\ m_{d}\ddot{u}_{d}+f(\dot{u}_{d})+k_{d}(u_{d}-u_{b})=-m_{d}\ddot{a}_{g}\\ \end{array}\right. \tag{4}\]  
where the subscript \(s\) represents the superstructure, \(i\) is the floor number, \(b\) and \(d\) represent the linear and nonlinear models of the nonlinear component, \(m\), \(c\), and \(k\) are the mass, damping, and stiffness, respectively, \(\ddot{a}_{g}\) is the ground acceleration, and \(u\), \(\dot{u}\), and \(\ddot{u}\) are the displacement, velocity, and acceleration of the structure, respectively. The Rayleigh damping of the superstructure is 0.05. The detailed parameters of the structure are shown in Tables 2 and 3.  
The above two systems are solved by the Newmark-\(\beta\) method, and the structural responses \([u,\dot{u},\ddot{u}]\) under different seismic waves are obtained. The governing equation of structural vibration can be expressed as:  
\[\begin{array}{l}\dot{\mathbf{M}}\ddot{\mathbf{x}}+\dot{\mathbf{C}}\dot{\mathbf{x}}+\mathbf{K}\mathbf{ x}=\mathbf{F}\\ \end{array} \tag{5}\]  
where \(\mathbf{M}\), \(\mathbf{C}\), \(\mathbf{K}\), and \(\mathbf{F}\) represent the mass, damping, stiffness, and force, respectively, and \(\mathbf{x}\), \(\dot{\mathbf{x}}\), and \(\ddot{\mathbf{x}}\) represent the displacement, velocity, and acceleration, respectively. Based on the Newmark-\(\beta\) method, the structural response can be expressed as:  
\[\begin{array}{l}\ddot{\mathbf{x}}_{t+\Delta t}=\frac{1}{\beta\Delta t^{2}}(\mathbf{x }_{t+\Delta t}-\mathbf{x}_{t})-\frac{1}{\beta\Delta t}\ddot{\mathbf{x}}_{t}-(\frac{1} {2\beta}-1)\Delta t\ddot{\mathbf{x}}_{t}\\ \dot{\mathbf{x}}_{t+\Delta t}=\frac{\gamma}{\beta\Delta t}(\mathbf{x}_{t+\Delta t}- \mathbf{x}_{t})+(1-\frac{\gamma}{\beta})\dot{\mathbf{x}}_{t}+(1-\frac{\gamma}{2\beta })\Delta t\ddot{\mathbf{x}}_{t}\\ \end{array} \tag{6}\]  
where \(\beta\) and \(\gamma\) are parameters that control the accuracy and stability of numerical analysis, respectively, \(\beta=0.5\) and \(\gamma=1/6\); \(\Delta t\) represents the time step, \(\Delta t=0.02\) s. The structural response can be solved by combining the initial conditions and the governing equation.
## 5 Conclusions
Future research could consider incorporating these factors into the optimization framework and conduct multi-objective optimization to achieve more comprehensive performance improvements. Furthermore, with the rapid development of artificial intelligence and machine learning technologies, future research could attempt to combine these advanced technologies with traditional optimization algorithms. Methods like deep learning could be used to enhance the accuracy and intelligence of the optimization process. Regarding the optimization algorithm, although the PSO algorithm has shown good performance, it also has certain limitations. Future studies could attempt to combine PSO with other optimization methods, such as genetic algorithms or ant colony optimization, or even develop new hybrid optimization algorithms to improve solution efficiency and algorithm robustness. Additionally, with the advancement of computational power, the scale of the problem can be expanded, allowing for the resolution of more complex optimization problems. Conceptualization, H.L. and L.S. ; methodology, C.W. and L.S. ; software, C.W. and S.Y. ; validation, H.L., C.W., S.Y. and L.S. ; formal analysis, S.Y. ; investigation, H.L. and C.W. ; resources, H.Z. ; data curation, S.Y. ; writing--original draft preparation, C.W. and S.Y. ; writing--review and editing, C.W. and S.Y. ; visualization, C.W. ; supervision, H.L. and L.S. ; funding acquisition, H.L. and L.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the key technology research project on long-endurance hydrogen-powered hybrid UAVs for air-ground-space collaborative power inspection, grant number J2024028. **Data Availability Statement:** Data will be made available on request. **Acknowledgments:** Sincere thanks to State Grid Jiangsu Electric Power Company for their financial support of the scientific research project, project number J2024028. **Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the company \"State Grid Changzhou Power Supply Company\". 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.
# Analytic Model for U-Nb Liquidus and U-6 Nb Melting Curve
## 3 Application to the Uranium-Niobium System
### The U-Nb Liquidus  
We now apply Formula (8) to the U-Nb system, as the values of two \(T_{\ell}\)s and two \(T_{\ell}^{\prime}\)s are available from Equations (11) and (13). As noted before, in the case of U-Nb, we determine the value of \(C\) from fitting to the most reliable experimental data of Ref. [14] on the U-rich portion of the U-Nb liquidus. The fitting brings up the value of \(C=-0.6\). Therefore, as \(|C|<1\), there is no local extremum of the U-Nb liquidus (see Ref. [15] for more detail); i.e., such local extrema are at the two endpoints.  
The liquidus curve of the U-Nb system is determined by Equation (8) with \(C=-0.6\) and the values of two \(T_{\ell}\)s and two \(T_{\ell}^{\prime}\)s from (11) and (13). It is shown in Figure 2, along with the experimental data of Refs. [13; 14] and the best fit by [PERSON] et al. [11] to the two sets of the experimental data shown in Figure 2. We note that the Calphad-type modeling of the U-Nb system by [PERSON] [37] results in the U-Nb liquidus being very close to that of [PERSON] shown in Figure 2. However, such a Calphad-type modeling requires the adjustment of the values of too many free parameters, which makes its practical use more complicated and more time-consuming.  
Figure 2: The liquidus curve of the U-Nb system: the new analytic model (thick black curve) vs. the experimental data of [PERSON]. [14] ([PERSON], 1982), [13] ([PERSON] et al. 1958), and the best fit to the two sets of experimental data of [PERSON]. [11] (thin black curve, [PERSON] (2022)).  
Again, just like in the Mo-W case considered above, we determined that for the U-Nb system, a variation in \(C\) by as much as 50%, i.e., \(C=-0.6\pm 0.3\), causes a shift in the model's liquidus within \(\sim 100\) K, or \(\sim 4\%\). Specifically, the largest deviations from the \(C=-0.6\) liquidus are +93.6 K at \(x=0.414\) with \(C=-0.9\), and \(-89.2\) K at \(x=0.412\) with \(C=-0.3\), compared to \(T_{\ell}=2265.0\) K at \(x=0.413\) with \(C=-0.6\). Thus, the exact knowledge of the value of \(C\) may not be really necessary to obtain the liquidus of a mixture in good agreement with experiment.  
For the specific case of U-6 Nb considered below, the new analytic model gives, via Equations (8) and (9), respectively,  
\[T_{\ell}^{\rm U-6 Nb}(0)\,=\,1577.6\;{\rm K},\quad T_{\ell}^{\rm\
u U-6 Nb}(0)\, =\,49.167\;{\rm K}/{\rm GPa}. \tag{15}\]
## 1 Introduction
These rules are the basis for the product module's ability to realize and determine the connection [24; 25]. The importance for modular products of a proper interface design has been outlined by various authors [24; 25; 26; 27; 28]. To deal with the module interface problem, some design tools and methods have been proposed. [PERSON] et al. introduced a method that uses a tool--the Interface Evaluation Matrix (IEM) --to determinate the relationships among modules [29]. [PERSON] et al. developed a novel approach to evaluate the design effort complexity of an interface and proposed a procedure to design products using existing modularity methods, presenting a novel redesign effort complexity metric that defines module boundaries (interfaces) so that changes in the modules require minimum redesign effort [30]. [PERSON] et al. constructed an interface complexity measure that takes into account interaction complexity, which is an important aspect of a component-based system [31]. [PERSON] et al. proposed a novel interface design approach to complexity management in assembly systems, with interface design focusing on structuring interface connections rather than designing the specifications of an interface [32]. However, there are few studies that focus on the criteria for assessing interface connectivity, especially in a bus truss-type chassis assembled with many non-standard structural components. A bus chassis is typically made of a truss frame, a welded structure manufactured using various sizes of rectangular cross section steel tubes; the interaction between these parts influences the chassis' behavior [33]. The complexity of technical systems depends on their heterogeneity, the quantity of different elements, and their connectivity pattern, which are measurable system characteristics. This internal product architecture can be represented using graph-theoretic representations of complex systems [34]. The components representing parts of the subsystems are connected by interfaces, if there is a direct interaction between any pair of subsystems [35]. In the bus chassis modules, the structural type of the interface is variable, the spatial position of the matching structural components of the interface is difficult to locate, and the connection section of the structural components is adjustable. These non-standard characteristics make it difficult to assess interface connectivity and to realize a standardized design of a bus chassis module interface. Therefore, the purpose of this research is to propose criteria for assessing the interface connectivity in bus chassis modules using the digital expression of the module interface. Combined with actual product redesign methods, a module redesign method oriented to this benchmark is proposed to further improve the modular design of the bus chassis. Materials and Methods: Construction of the Digital Expression Model for the Bus Chassis Module Interface
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 5 Improvement in Train Position Accuracy Based on Yaw Angular Velocity  
### Overview  
A map-matching method that corrects the latitude and longitude information acquired by the GNSS on the track was used as the position identification method.  
However, this method causes an error of several tens of metres, depending on the GNSS reception conditions. In this study, to improve the position identification accuracy, we used the point at which changes were observed in the curvature, estimated from the yaw angular velocity of the vehicle, to correct the position information obtained via the map-matching method; subsequently, we evaluated the effectiveness of the approach and diagnosed the track condition.
# A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV
## 2 Materials and Methods
### Two-Step Ensemble Method Based on MetaCost Framework
#### 2.3.2 LightGBM  
A fact that cannot be ignored is that the recorder on UAVs can generally store more than 20 flight data points per second, which made us pay more attention to the performance of the diagnostic model for processing large-scale data. Microsoft proposed the LightGBM (Light Gradient Boosting Machine), an exceedingly fast gradient boosting framework, to improve the model training speed when processing large-scale data [31]. In fault diagnosis, LightGBM has been used in diagnosing shipboard medium-voltage DC power system faults [32], rotating machinery faults [33], and classic bearing faults [34].  
As an improved method of GBDT, LightGBM employs Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB), known as the histogram-based algorithm, to reduce the cost of calculating the gain, to speed up training, and to occupy less memory usage [35]. It was verified on UAV test flight data that the lightning LightGBM-based diagnosis model could attain a decent performance without occupying excessive computational resources [27]. As an ensemble learning model, the LightGBM still has the great potential to be employed as a meta-classifier in complicated ensemble frameworks.
# A New GNSS Single-Epoch Ambiguity Resolution Method Based on Triple-Frequency Signals
## 3 GNSS Single-Epoch Ambiguity Resolution  
Single-epoch AR has been a continuing challenge for GNSS dual-frequency bands, even under a short-range baseline. Fortunately, triple- or even multiple-frequency signals form an extra-wide-lane and wide-lane combination, the ambiguities of which can be fixed much more easily based on the relatively large wavelength. BDS is currently a unique system that provides triple-frequency signals. However, the visible BDS satellites may be not sufficient to estimate the position of the rover in a high-rise-intensive city. Another situation would be that the geometry of the visible triple-frequency satellites constellation may be not sufficiently strong to obtain a precise position. For instance, if all of the visible BDS satellites were the GEO satellites, the constellation geometry would be too weak to compute a precision position. To fix the ambiguities of GNSS signals and provide a positioning and navigation service for the rover in a short time, a new single-epoch GNSS AR method for a short-range baseline based on triple-frequency signals is developed in this study. The theory and steps of this method are (Figure 1) as follows:  
Step 1. As the first operational system with triple-frequency bands, the ambiguities of BDS signals can be fixed faster than the other GNSS signals. In the first step, we fix the ambiguities of BDS EWL(Extra Wide Lane) and B1 signals; the detailed methodology is introduced in Section 3.1.  
Step 2. After the ambiguities of the BDS signals, we estimate the float solution by using the BDS carrier phase (B1) observation with fixed ambiguity and other satellites' carrier phase observation (L1). This step is discussed in detail in Section 3.2.  
Step 3. Finally, to realize reliable AR for the GNSS carrier phase observation, robust estimation is involved (Section 3.3).
# Predictive Quantization and Symbolic Dynamics
## 4 Representation
The input \(X\) and latent code \(Z\) can then be seen as jointly distributed random variables \(Z\sim p(Z),X\sim p(X|Z)\). The VAE consists of an encoder probability \(q_{\lambda}(Z|X)\), which approximates the posterior probability \(p(Z|X)\), and a decoder probability \(p_{\theta}(X|Z)\), which parametrizes the likelihood \(p(X|Z)\). In practice, the approximate posterior and likelihood distributions are parametrized by the weights of connections between neurons in the VAE network. Posterior inference is performed by minimizing the KL divergence between the encoder and the true posterior. It can be proved that this optimization problem is same as maximizing the evidence lower bound (ELBO):  
\[ELBO=E[\log p_{\theta}(X|Z)]-KL(q_{\lambda}(Z|X)||p(Z))\leq\log p(X) \tag{14}\]  
Another important insight about VAE is that it is a generative model, where the latent variables \(Z\) are used to \"drive\" the decoder into producing novel samples of data \(X\). In a statistical sense, VAE is used to learn the distribution \(P(X)\) rather then simply encode the observations \(X\).
## 3 Analysis of Structural Dynamic Response under Pedestrian Load Excitation
### Human-Induced Vibration Response Analysis  
Combined with the sensitive frequency range evaluation guidelines of the German and French codes, the sensitive frequency range for vertical vibration was 1.40-2.40 Hz, and for lateral vibration was 0.50-1.20 Hz. The first order vertical frequency of this bridge was 1.43 Hz and the 1 st order lateral vibration frequency 1.15 Hz. It is necessary to check whether the acceleration response under pedestrian loads meets the walking comfort conditions.  
The pedestrian traffic class of footbridges was divided into five classes, namely TC1, TC2, TC3, TC4, and TC5. The corresponding pedestrian density (\(d_{0}\)) is a group of 15 p (\(d_{0}=15\) p/BL, B = width of deck, L = length of deck), 0.2, 0.5, 1.0, and 1.5 p/m\({}^{2}\), respectively [40]. Considering that this bridge has a larger pedestrian density during holidays, a high crowd density of \(d=1.5\) (P/m\({}^{2}\)) and a bridge deck area of \(S=496\) m\({}^{2}\) were chosen. The total number of passengers was \(n=d\times S=744\), which can be equivalent as 50 people walking on the bridge evenly:  
\[nt=1.85\sqrt{n}/S=0.1017(\mathrm{P/m}^{2}) \tag{9}\]  
The simple harmonic walking force loads at equivalent crowd synchronization are the following:  
\[p(t)=P\times\cos(2\pi f_{s}t)\times nt\times\kappa \tag{10}\]  
Figure 2: Mode shapes of the case bridge. (**a**) First order vibration pattern, (**b**) second order vibration pattern, (**c**) third order vibration pattern, (**d**) fourth order vibration pattern.  
where \(f_{s}\) is the step frequency (assumed to be equal to the footbridge fundamental frequency), \(P\) is the vertical and lateral load amplitude for a step frequency equal to the walking force of a single person, taken as 280 N in the vertical direction and 35 N in the lateral direction, and a discount factor introduced to take into account the probability of the step frequency being close to the critical value of the variation range of the fundamental frequency [39].  
The vertical and lateral simple harmonic force walking loads were loaded into the model, and the results are shown in Figures 3 and 4.  
It is indicated in Figures 3 and 4 that the maximum lateral acceleration of the pedestrian arch bridge was of 0.511 m/s\({}^{2}\) at mid-span under lateral simple harmonic loading, and the maximum vertical acceleration of 2.528 m/s\({}^{2}\) at 1/4 L under vertical simple harmonic loading; all these vibrations were within the risk ranges [32].
## 3 Results and Discussion
### Tubular and Annular Plasma Elements' Intrinsic Radar Countermeasure Capability
For HH-polarized radar signals in the S and C bands, the RCS value decreases further with an increase in plasma density. This indicates that these two types of high-density plasma elements have excellent potential as radar countermeasure antennas.