esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 6 Detail Analysis of Track Condition Using Time-Frequency Analysis
### Track Condition before and after Track Maintenance  
It is known that when trains run on tracks with faults, characteristic vibrations corresponding to the types of track faults occur. It may be possible to identify the types and degradation levels of track faults and their locations by using the time-frequency plane. From the measured data, a time-frequency analysis was performed to identify and evaluate the detailed track conditions before and after track maintenance.  
Figure 18 shows the time-frequency analysis of the car-body vertical acceleration before track maintenance. The top section of Figure 18 shows the measured car-body acceleration (Measurement date: 16 December 2021, Train speed: V = 37 [km/h]). The middle section of Figure 18 shows the time-frequency plane with the CWT. The bottom section of Figure 18 shows the time-frequency plane with the HHT.  
Figure 19 shows the time-frequency analysis of the car-body vertical acceleration after track maintenance. The top section of Figure 19 shows the measured car-body acceleration (Measurement date: 28 June 2022, Train speed: V = 36 [km/h]). The middle section of Figure 19 shows the time-frequency plane with the CWT. The bottom section of Figure 19 shows the time-frequency plane with the HHT.  
In December 2021, vibrations due to longitudinal track irregularities were detected at 1-2 Hz in the 3750-3800 m section but were no longer detected in July 2022, owing to the track maintenance carried out in May 2022. It should be noted that the time-frequency  
Figure 19: Time-frequency analysis of car-body vertical acceleration after track maintenance. The top section shows measured car-body acceleration (Measurement date: 28 June 2022, Train speed: V = 36 [km/h]). The middle section shows the time-frequency plane with CWT. The bottom section shows the time-frequency plane with HHT.  
Figure 18: Time-frequency analysis of car-body vertical acceleration before track maintenance. The top section shows measured car-body acceleration (Measurement date: 16 December 2021, Train speed: V = 37 [km/h]). The middle section shows the time-frequency plane with CWT. The bottom section shows the time-frequency plane with HHT.  
analysis with the CWT and the HHT gives detailed information about the track conditions, which is useful for evaluating the quality of track maintenance.  
The vertical vibration acceleration of the car-body mainly consisted of vibration components between 1 and 2 Hz. In the CWT, the time resolution was widely spread due to uncertainty in lower frequency band. On the other hand, the HHT had a higher time resolution than the CWT. Thus, it is considered that more effective identification of track faults is possible using the HHT.  
As indicated above, a time-frequency analysis of the vertical acceleration of the car-body can be used to evaluate longitudinal irregularities. It is also known that by focusing on higher frequency bands, joint depression can also be identified [41].
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 1 Introduction
Generative adversarial networks have emerged as a powerful class of deep learning models that can generate novel and realistic synthetic data. These models consist of two neural networks, a generator, and a discriminator, that are trained in an adversarial fashion. The methodology enhances both data augmentation and feature extraction processes. The use of GANs allows for the generation of synthetic voice samples, effectively expanding the dataset and improving model training. A key innovation of this approach lies in the iterative reduction of synthetic samples, which balances the contribution of real and GAN-generated data to mitigate overfitting and maintain robust performance. The CNN model is specifically tailored for voiceprint recognition tasks, employing MFCC features to capture critical speech characteristics. Designed with advanced techniques such as dropout layers and adaptive optimizers, the architecture is optimized to achieve high accuracy in resource-constrained environments. The methodology's effectiveness is demonstrated through comprehensive experiments involving diverse datasets, varied sample sizes, and different ratios of synthetic and real samples, showcasing its adaptability and reliability. This approach also highlights its practical applications, including multilingual speaker verification, biometric authentication, and forensic analysis. By reducing data collection costs while maintaining high performance, the methodology offers a resource-efficient solution that is well-suited for real-world implementations. These contributions distinguish the proposed method from existing approaches, addressing the common limitations of data scarcity and overfitting in voiceprint recognition systems.
## 2 Materials and Methods
### Description of the Tested Soil Specimens  
The tested primary sand specimen, east coast sand (ECS), is commercially mined from near the shore of Pakiri Beach, Mangawhai, north of Auckland, in the North Island of New Zealand. The sand is an alluvial product of the mechanical weathering of the Pakiri Group sandstone. The artificially created clayey invariants of the sand were derived by mixing the ECS with 0, 5, 10, 15, 20, and 30 percent by weight of kaolinite clay. The adopted naming nomenclature of the reconstituted model soil specimens are ECS00, ECS05, ECS10, ECS15, ECS20, and ECS30.  
The scanning electron microscope (SEM) results obtained at the \(\times 50\) magnification scale indicated that the shape of ECS is sub-angular to angular, according to the visible geometric description (refer to Figure 5). The executed classification tests include the ECS's particle size distribution (PSD) and hydrometer tests for the kaolinite clay. The corresponding derived unified soil classification system (USCS) of ECS is a poorly graded sand (SP) material, the kaolinite is classified as a lean clay (CL), and other soil mixtures are mostly classified as clayey sands (SC), as shown in Table 1.
## 1 Introduction  
Wastewater treatment is a key process in ensuring public health and environmental well-being. The environmental aspect of this process is based on removing pollutants, such as organic matter and nutrients (N, P), before disposal. The energy cost of the activated sludge process, the most widely used municipal wastewater treatment process, is quite high (0.69-3.01 kWh/kg COD) [1]. The increase in the cost of energy has led to research on more environmental and economically efficient methods. The new approaches of domestic wastewater management are based on circular ways to recover valuable nutrients and energy, as opposed to the traditional linear methods [2]. Currently, the benchmark approach to municipal wastewater management consists of sever collection, treatment in a facility aimed at removal of suspended solids through primary sedimentation, biological oxidation of organic matter under aerobic conditions, biological nutrient (N and P) removal, and disposal of the clarified effluent following disinfection by chlorination. The process generates a mixture of primary and excess secondary sludge, which are typically mixed, stabilized by anaerobic digestion, and dewatered before disposal [3]. The key operating costs lie in the aeration and in sludge (biosolids) management [4]. Anaerobic digestion emerges as a feasible solution that aims to reduce the energy requirements of WW treatment and is already globally implemented in WW treatment plants. However, AD is most commonly used as a side process in WWTPs, mostly for stabilization of the sludge generated during primary and secondary sedimentation. The efficiency of direct AD of MWW has been proposed by many researchers, and notable is the installment of a full-scale expanded granular sludge bed reactor (EGSBR) in a WWTP in Ireland, during the operation of which a high BOD removal rate (85%) was accomplished [5]. Another management scheme that is a substantial departure from the conventional activated sludge process is the low-energy mainline (LEM) process [6]. This scheme is based on direct low-strength anaerobic digestion of municipal wastewater after primary sedimentation and use of the reactor's effluent as irrigation water, as no significant amount of nitrogen and phosphorus would be consumed in the anaerobic process, based on the same concept by examining the capabilities of an innovative high-rate anaerobic system to treat municipal wastewater.  
The aim of this work is to examine the suitability of a novel high-rate anaerobic system--the periodic anaerobic baffled reactor (PABR)--to treat municipal wastewater directly.
# A Survey of the Applications of Text Mining for the Food Domain
## 4 Data Sources in the Food Domain
### Government and Science Data

Table 6: Literature dataset sources. governmental bodies. This corpus of information provides a comprehensive macro-level view of the state of affairs in the entire food domain, overseeing the quality and safety of food and shaping food policies [59; 60; 63; 75]. For example, scholars have utilized data from the European Rapid Early Warning System for Food and Feed (RASFF) to predict food deception and anticipate risks to food safety [61; 62; 63; 64]. Notification data on mycotoxins from the RASFF database have been employed for statistical analyses to decipher the risk associated with mycotoxins in food [56]. Governmental databases house repositories of national food safety standards, results from food sampling, monitoring of foodborne pathogens, and licenses for food production. These databases aggregate food standards, assay results, and surveillance data, serving as foundational infrastructure for comprehensive monitoring and risk assessment of food quality and safety. They provide crucial information support for the decision-making processes of food regulators [76]. For instance, the creation of a food safety information database within the Greater China region empowers governmental entities with the capacity to analyze and compare food safety, facilitating the formulation of effective policies [76]. Computational toxicologists at the Indian Institute of Toxicology created FOCUS-DB, a comprehensive repository compiling detailed information on 2885 food additives, serving as a valuable tool for scientific inquiry and public awareness [77]. Scientific literature exploration is another source of textual information, where researchers leverage scientific abstracts to glean insights into associations among plant-based foods, phytochemical constituents, and human diseases. For instance, \"NutriChem\", a scholarly resource, connects the chemical makeup of plant-based foods with the phenotypes of human diseases, providing a foundation for well-informed nutritional and therapeutic strategies [65]. Scientific literature research is crucial for scrutinizing relationships between foods, genetics, and diseases [66; 67], fostering a deep understanding of these intricate relationships. It is crucial to emphasize that repositories sourced from governmental agencies, international bodies, and the scientific realm adhere to rigorous data integrity standards, enhancing their data trustworthiness. Most publicly available datasets are also derived from government and science data.
## 2 Principle
### Neural Network Nonlinear Equalizer  
In fiber-wireless communication systems, nonlinear noise is difficult to mitigate using traditional polarization demultiplexing techniques. Considering the excellent nonlinear fitting capabilities of neural networks, we propose applying neural network algorithms as an adaptive algorithm in polarization demultiplexing systems.  
In our experiment, we employ a DNN with three fully connected layers (FCLs), which include batch normalization (BN), the rectified linear unit (ReLU) activation function, the mean squared error (MSE) loss function, and the Adam optimizer. The batch normalization layer normalizes the output of the fully connected layers to prevent distribution skewing and gradient vanishing. Normalization is achieved by calculating the mean and variance of each mini-batch of data, followed by scaling and shifting the data. The ReLU activation function performs a nonlinear transformation on the batch-normalized data, with the following formula:  
\[f(x)=\begin{cases}x,x>0\\ 0,x\leq 0\end{cases} \tag{5}\]  
Figure 2: Schematic diagrams of 2 \(\times\) 2 MIMO wireless transmission systems. (**a**) Traditional 2 \(\times\) 2 MIMO. (**b**) Polarization multiplexed 2 \(\times\) 2 MIMO.  
The mean squared error (MSE) loss function is used to measure the discrepancy between the network's predicted values and the actual values. The formula for MSE is as follows:  
\[MSE=\frac{1}{N}\sum_{n=1}^{N}(d(n)-y(n))^{2} \tag{6}\]  
where \(d(n)\) represents the actual value, \(y(n)\) is the output data from the neural network, and \(N\) is the batch size. Adam (Adaptive Moment Estimation) is an adaptive learning rate optimization algorithm that adjusts the learning rate for each parameter during training to improve convergence speed and stability.  
This three-layer DNN neural network, through the backpropagation algorithm and the Adam optimizer, continuously adjusts the weights and biases during training to minimize the MSE loss function, thereby enhancing the prediction accuracy. By introducing neural network algorithms for THz-wave channel equalization, we can leverage the powerful fitting capabilities of neural networks to overcome the limitations of traditional algorithms under complex channel conditions, compensate for channel distortion, and achieve higher transmission performance.
# Antifungal Activity of Quaternary Pyridinium Salts Against _Fusarium culmorum_ in Wheat Seedlings
## 2 Materials and Methods
### Synthesis of Quaternary Pyridinium Salts  
Nicotinamide and isonicotinamide derivatives (Figure 1, Table 1) were employed as nucleophiles in reactions with various electrophiles, including alkyl halides (methyl iodide), alkyl dihalides (dihomopropane, diiodopropane), and differently substituted 2-bromoetophenone (4-Cl, 4-Br, 4-H, 4-CH\({}_{3}\), 4-F, 4-OCH\({}_{3}\), 4-Ph, 2-OCH\({}_{3}\), 4-NO\({}_{2}\)). Quaternary pyridinium salts were obtained through conventional quaternization synthesis in ethanol and alternative methods, including microwave-assisted synthesis [39; 44] and synthesis in DES [40].  
In the conventional approach, the reactants were refluxed in ethanol, yielding the desired products in moderate amounts. In contrast, the microwave-assisted method utilized microwave irradiation in either ethanol or acetone as the solvent, significantly accelerating the reaction. Microwave dielectric heating in this method led to substantially improved yields--up to eight times higher--and reduced reaction times to just 10-20 min [44].  
The conventional synthesis of isonicotinamide derivatives involves prolonged heating of the reaction mixture under reflux, while the MW method significantly reduces the reaction time to just 10 min using microwave energy. In both cases, the products are obtained by cooling, washed with ether, and recrystallized from methanol. The MW method enables more efficient and faster synthesis with similar yield and product [39]. The syntheses of quaternary salts were performed in deep eutectic solvents (DES) using three synthetic methods: conventional (2-6 h), microwave (20 min), and ultrasonic (3 h), all at 80 \({}^{\circ}\)C. These solvents offer environmentally friendly advantages, including biodegradability, ease of preparation, and simplified purification. The highest yields, up to 98%, were achieved with  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Chemical Name** & **Label** \\ \hline
**Nicotinamide derivatives** & \\
3-carbamoyl-1-1-methylpyridinium iodide & **(1)** \\
3-carbamoyl-1-(2-(4-chlorophenylphenyl)-2-oxoethyl)pyridinium bromide & **(2)** \\
1-(2-phenyl-2-oxoethyl)-3-carbamoylpyridinium bromide & **(3)** \\
3-carbamoyl-1-(2-(4-nitrophenyl)-2-oxoethyl)pyridinium bromide & **(4)** \\
1-(3-bromopropyl)-3-carbamoylpyridinium bromide & **(5)** \\ \hline
**Isonicotinamide derivatives** & **Label** \\ \hline
4-carbamoyl-1-(2-(4-methoxyphenyl)-2-oxoethyl)pyridinium bromide & **(6)** \\
1-(2-([1,1’-biphenyl]-4-yl)-2-oxoethyl)-4-carbamoylpyridinium bromide & **(7)** \\
4-carbamoyl-1-(2-(2-methoxyphenyl)-2-oxoethyl)pyridinium bromide & **(8)** \\
4-carbamoyl-1-1-[2-(4-nitrophenyl)-2-oxoethyl)pyridinium bromide & **(9)** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected derivatives: chemical names and labels.  
the microwave method. DES based on choline chloride with urea, oxalic acid, or levulinic acid proved to be the most effective [40].
## 3 The End-to-End Instrument Performance Simulation System (EIPS) Framework  
The EIPS framework has been designed following the process discussed in Section 2 (as shown in Figure 2). It incorporates a sensor model, an atmospheric radiative transfer model and an inversion algorithm. To make the framework applicable to whole EM spectrum, we decided to incorporate a state-of-the-art radiative transfer model, the Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC) [19] for fast radiative transfer simulation capabilities (see Section 3.2.2 for details). The basic aim behind EIPS was to design a framework around the HT-FRTC model to obtain a complete and flexible end-to-end simulation tool for instrument performance analysis purposes. For sensor related computational simulations, a sensor parameter model has been developed (see Section 3.2.1), and for atmospheric retrievals, an information and error analysis model has been developed (see Section 3.2.4).  
In this way, the EIPS will provide combined functionality of sensor parameter modelling, fast radiative transfer simulation (by the HT-FRTC), and geophysical parameter retrievals. Since the HT-FRTC is applicable to whole EM spectrum, the EIPS can be applied to any remote sensing instrument operating in any spectral range (UV, IR and MW) with appropriate tuning in the component models.
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 8 Experiments and Discussions
### Path Planning Performance
In the first scenario, the position on the trajectory that has arc length L(u,i) \(\approx 4\) m has the corresponding curvature is \(-0.048\) m\({}^{-1}\) and the position has arc length \(L(u,i)\approx 4.4\) (m) has the corresponding curvature is \(0.048\) m\({}^{-1}\), The average rate of change of curvature respect to arc length is approximately \(0.24\) m\({}^{-2}\). The robot does not meet the desired velocity at this point, so it slips off the original trajectory. We can easily see the deviation in position and heading of the robot with respect to the desired position and heading at this point on the position error and heading error graphs. However, by the third and fourth re-paths, the robot gradually takes control of the error. In the second scenario, In the initial position, the robot has an initial heading in the opposite direction of the path, it is easy to see that the robots cannot meet the angular speed at this point, leading to large heading errors and position error in the beginning. By using the re-path algorithm, the robot quickly returns to its original trajectory by first and second re-paths. It illustrated in Figure 20.
## 6 Simulation Results
Figure 13 depicts the harmonic spectra of \(v_{\text{gabc}}\) and \(i_{\text{ggabc}}\), confirming the system's compliance with IEEE Std. 519-2014. The PCC voltage exhibits a THD of 3.59%, remaining well within the allowable 8% limit, while the grid-injected current achieves an exceptionally low THD of 0.74%, far below the 4% threshold. These results highlight the effectiveness of the GFMC APF in harmonic suppression, ensuring high power quality by significantly reducing harmonic distortions compared to the unmitigated case shown in Figure 9.  
Figure 14 illustrates the harmonic spectra of the capacitor voltage \(v_{\text{fgabc}}\) in the GFMC APF during operation, emphasizing supraharmonic content. The graph shows the magnitude of the fundamental and harmonic components as a percentage of the fundamental  
Figure 12: Active and reactive power dynamics during GFMC APF operation. Figure 13: Harmonic spectra of PCC voltages and grid currents under GFMC APF. voltage, extending up to 12 kHz. The THD is 3.68%, reflecting effective harmonic suppression. Focusing on supraharmonics, the spectrum reveals that harmonic magnitudes decrease sharply beyond 1 kHz, with minimal components between 2 kHz and 12 kHz. This indicates that the GFMC APF successfully limits supraharmonic emissions in this frequency range, maintaining low amplitudes relative to the fundamental. Figure 15 illustrates the response of the GFMC APF to non-stationary harmonic disturbances induced by variations in nonlinear load parameters. The top graph shows \(v_{\text{gabc}}\), which undergoes a voltage sag followed by a voltage swell. Despite these transient variations, the waveform remains sinusoidal, demonstrating the GFMC APF's capability to maintain voltage quality under time-varying operating conditions. The bottom graph presents \(i_{\text{ggabc}}\) under the same scenario, which also remains stable and sinusoidal. These results demonstrate the effectiveness of the GFMC APF in suppressing harmonic distortion and maintaining high power quality in the presence of dynamic load changes. Figure 16 illustrates the active and reactive power dynamics of the GFMC APF under non-stationary harmonic conditions. The top graph displays the active power, which settles at approximately 6000 W after an initial transient, indicating effective power regulation in the presence of harmonic disturbances. The bottom graph shows the reactive power, which decreases from around 600 VAR to nearly zero, with transient fluctuations reflecting the impact of time-varying harmonic content. These results demonstrate the GFMC APF's capability to regulate power flow and contribute to system-level stability under dynamic operating conditions. Figure 14: Harmonic spectra of capacitor voltages under GFMC APF. Table 1.0 PCC voltages and grid-injected currents under non-stationary harmonic conditions.