esa-sceva/satcom-chunk-collection huggingface dataset:
# Building a Speech Dataset and Recognition Model for the Minority Tu Language
###### Abstract  
Speech recognition technology has many applications in our daily life. However, for many low-resource languages without written forms, acquiring sufficient training data remains a significant challenge for building accurate ASR models. The Tu language, spoken by an ethnic minority group in Qinghai Province in China, is one such example. Due to the lack of written records and the great diversity in regional pronuncations, there has been little previous research on Tu-language speech recognition. This work seeks to address this research gap by creating the first speech dataset for the Tu language spoken in Huzhu County, Qinghai. We first formulated the relevant pronunciation rules for the Tu language based on linguistic analysis. Then, we constructed a new speech corpus, named HZ-TuDs, through targeted data collection and annotation. Based on the HZ-TuDs dataset, we designed several baseline sequence-to-sequence deep neural models for end-to-end Tu-language speech recognition. Additionally, we proposed a novel SA-conformer model, which combines convolutional and channel attention modules to better extract speech features. Experiments showed that our proposed SA-conformer model can significantly reduce the character error rate from 23% to 12%, effectively improving the accuracy of Tu language recognition compared to previous approaches. This demonstrates the effectiveness of our dataset construction and model design efforts in advancing speech recognition technology for this low-resource minority language.  
automatic speech recognition; low-resource language; conformer; Tu language Article Building a Speech Dataset and Recognition Model for the Minority Tu Language  
[PERSON], [PERSON]; [PERSON]; [PERSON] Building a Speech Dataset and Recognition Model for the Minority Tu Language  
* Create a new dataset for the Tu language to provide data and experimental support for subsequent Tu dialect research.
* Design a Tu-ASR system to facilitate communication between elderly people who do not speak Mandarin.
* Protect the culture of the Tu nationality and inherit the language and the civilization of the Tu nationality.
# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
###### Abstract  
We propose a simple and robust HSV color-space-based algorithm that can automatically extract object position information without human intervention or prior knowledge. In manufacturing sites with high variability, it is difficult to recognize products through robot machine vision, especially in terms of extracting object information accurately, owing to various environmental factors such as the noise around objects, shadows, light reflections, and illumination interferences. The proposed algorithm, which does not require users to reset the HSV color threshold value whenever a product is changed, uses ROI referencing method to solve this problem. The algorithm automatically identifies the object's location by using the HSV color-space-based ROI random sampling, ROI similarity comparison, and ROI merging. The proposed system utilizes an IoT device with several modules for the detection, analysis, control, and management of object data. The experimental results show that the proposed algorithm is very useful for industrial automation applications under complex and highly variable manufacturing environments.  
object localization; HSV color space; teaching-less robot; smart factory +
Footnote †: journal: Applied Sciences
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & **Season** & **AI** & **TI** & **HH** \\ \hline \multirow{4}{*}{Evoikos Gulf} & Autumn & 0.297 \({}^{\text{a}\text{b}}\)\(\pm\) 0.068 & 0.359 \({}^{\text{a}}\)\(\pm\) 0.089 & 1.615 \({}^{\text{a}}\)\(\pm\) 0.476 \\  & Winter & 0.365 \({}^{\text{a}}\)\(\pm\) 0.023 & 0.333 \({}^{\text{a}}\)\(\pm\) 0.054 & 1.651 \({}^{\text{a}}\)\(\pm\) 0.199 \\  & Spring & 0.315 \({}^{\text{a}}\)\(\pm\) 0.073 & 0.278 \({}^{\text{a}}\)\(\pm\) 0.094 & 1.650 \({}^{\text{a}}\)\(\pm\) 1.065 \\  & Summer & 0.370 \({}^{\text{a}}\)\(\pm\) 0.048 & 0.374 \({}^{\text{a}}\)\(\pm\) 0.076 & 1.661 \({}^{\text{a}}\)\(\pm\) 0.337 \\ \hline \multirow{4}{*}{Saronic Gulf} & Autumn & 0.306 \({}^{\text{a}\text{b}}\)\(\pm\) 0.005 & 0.446 \({}^{\text{a}}\)\(\pm\) 0.108 & 2.019 \({}^{\text{a}}\)\(\pm\) 0.639 \\  & Winter & 0.108 \({}^{\text{b}}\)\(\pm\) 0.008 & 0.174 \({}^{\text{a}}\)\(\pm\) 0.075 & 5.127 \({}^{\text{b}}\)\(\pm\) 1.047 \\ \cline{1-1}  & Spring & 0.223 \({}^{\text{a}\text{b}}\)\(\pm\) 0.120 & 0.263 \({}^{\text{a}}\)\(\pm\) 0.131 & 3.623 \({}^{\text{a}\text{b}}\)\(\pm\) 1.454 \\ \cline{1-1}  & Summer & 0.278 \({}^{\text{a}\text{b}}\)\(\pm\) 0.083 & 0.317 \({}^{\text{a}}\)\(\pm\) 0.085 & 2.240 \({}^{\text{a}}\)\(\pm\) 0.369 \\ \hline \hline \end{tabular} Values per line (season) that do not have the same exponent (superscripts letters) show statistically significant differences (_p_ < 0.05).  
\end{table} 
Table 3: Dietary lipid quality indices for the Evoikos and Saronic Gulfs over a full year. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The present work is a part of the project \"Commercial exploitation of the pearl oyster _Pinctada imbricata radiata_ by adding value through the development of processed products\" (Code MIS:5010850). Author [PERSON] was employed by the MicroPhykos, Aquaculture Systems & Services. 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.
# Laser-Induced Breakdown Spectroscopy for Determination of Spectral Fundamental Parameters
## 5 Experimental Determination of Spectral Parameters by LIBS
### Determination of Stark Broadening Coefficients
In 2019, [PERSON] acquired LIBS spectra of a pure lead target to determine the Stark widths of two Pb IV lines [90] and the Stark widths and shifts of the two ionic lines of Pb II at 220.35 nm and 438.65 nm [91]. In those papers, the authors confirmed the absence of important self-absorption effects after calculation of the absorption coefficient \(k(\lambda_{0})\). The same year, Stark widths of 18 Mo I and 18 Mo II spectral lines were measured by [PERSON] et al. [92] by laser ablation of a molybdenum sample of high purity (99.9%). The authors used the duplicating mirror technique to evaluate the self-absorption effects. [PERSON] et al. [93] in 2019 calculated the Stark widths of 41 Mn II lines and the Stark shifts of 30 Mn II lines. Fused glass samples with varying Mn concentrations (between 0.01 at% and 0.5 at%) were employed in this experiment. The effects of self-absorption were taken into account using the C-sigma graph method. In 2020, [PERSON] et al. [94] investigated the Stark widths of Al II, Al III and He I 388.86-nm spectral lines. The measurement was done on a flat aluminum sample in a helium-hydrogen gas mixture at reduced pressure. The self-absorption was evaluated using the duplicating mirror technique. Furthermore, in 2020, [PERSON] et al. [61] used the time-independent extended C-sigma approach (TIECS) for the determination of the Stark broadening of 23 tantalum lines (4 neutral and 19 ionic). The LIBS plasma was generated by laser ablation of a pure tantalum foil of 250 \(\upmu\)m thickness. The TIECS method intrinsically includes, in the calculation, the effects of self-absorption.
## 2 Preliminaries
### Elliptic Curve (EC)  
For a prime \(p\geq 5\), the elliptic curve denoted by \(E_{p,a,b}\) defined over a prime field \(\mathbb{F}_{p}\) can be expressed by an equation in the form \(y^{2}\equiv x^{3}+ax+b\pmod{p}\), where \(a\) and \(b\) are integers modulo \(p\), and the discriminant \(4a^{3}+27b^{2}\) is not equal to zero modulo \(p\). The set of points on this curve includes a point at infinity denoted by \(\delta\) and all points \((x,y)\) within \(\mathbb{F}_{p}\times\mathbb{F}_{p}\) that satisfy the equation \(y^{2}\equiv x^{3}+ax+b\pmod{p}\).  
An EC \(E_{p,a,b}\) with coefficient \(a=0\) is known as the Mordell elliptic curve (MEC). We denote MEC by \(E_{p,b}\). According to [PERSON] [34], a MEC having \(p\equiv 2\pmod{3}\) has precisely \(p+1\) distinct points and does not contain any duplicate \(y\) coordinates.  
The group law [35] of an elliptic curve allows the addition of two points on the curve to generate a third point that also lies on the curve. This is performed by using the formula for adding two points, \(\mathrm{P}\) and \(\mathrm{Q}\), on the EC \(E_{p,a,b}\). The resulting point is denoted by \(\mathrm{R}\) and its coordinates are calculated using the formula: \(\mathrm{R}=(\lambda^{2}-x_{\mathrm{P}}-x_{\mathrm{Q}},\lambda(x_{\mathrm{P}}- x_{\mathrm{Q}})-y_{\mathrm{P}})\), where \(\lambda\) is calculated based on whether the points \(\mathrm{P}\) and \(\mathrm{Q}\) are the same or different. If they are different, \(\lambda\) is calculated as \((y_{\mathrm{P}}-y_{\mathrm{Q}})/(x_{\mathrm{P}}-x_{\mathrm{Q}})\). If they are the same, \(\lambda\) is calculated as \((3x_{\mathrm{P}}^{2}+a)/(2y_{\mathrm{P}})\).
## Appendix A Training, Validation, and Test Results of the LSTM Model  
Tables A1-A5 provide the performance evaluation results visualized in Figure 6. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & \multirow{2}{*}{**Performance**} & \multicolumn{9}{c}{**Number of Neurons in the Hidden Layer**} \\ \cline{3-10}  & & **1** & **2** & **4** & **8** & **16** & **32** & **64** & **128** \\ \hline \multirow{4}{*}{RT} & Train & \(R^{2}\) & 0.827 & 0.827 & 0.831 & 0.836 & 0.835 & 0.836 & 0.844 & 0.854 \\  & Train & RMSE & 0.311 & 0.293 & 0.274 & 0.264 & 0.264 & 0.263 & 0.257 & 0.249 \\  & Valid & \(R^{2}\) & 0.589 & 0.594 & 0.602 & 0.579 & 0.565 & 0.551 & 0.525 & 0.482 \\  & Valid & RMSE & 1.126 & 1.095 & 1.060 & 1.055 & 1.072 & 1.071 & 1.098 & 1.142 \\  & Test & \(R^{2}\) & 0.644 & 0.643 & 0.640 & 0.637 & 0.636 & 0.639 & 0.653 & 0.671 \\  & RMSE & 0.460 & 0.453 & 0.448 & 0.451 & 0.451 & 0.451 & 0.443 & 0.431 \\ \hline \multirow{4}{*}{GR} & Train & \(R^{2}\) & 0.822 & 0.824 & 0.833 & 0.829 & 0.834 & 0.837 & 0.839 & 0.851 \\  & RMSE & 0.314 & 0.283 & 0.270 & 0.271 & 0.265 & 0.263 & 0.261 & 0.252 \\  & Valid & \(R^{2}\) & 0.613 & 0.626 & 0.644 & 0.607 & 0.621 & 0.600 & 0.582 & 0.558 \\  & RMSE & 1.097 & 1.030 & 0.992 & 1.015 & 0.988 & 1.013 & 1.035 & 1.065 \\  & Test & \(R^{2}\) & 0.645 & 0.646 & 0.662 & 0.650 & 0.658 & 0.661 & 0.667 & 0.683 \\  & RMSE & 0.458 & 0.445 & 0.434 & 0.442 & 0.439 & 0.439 & 0.438 & 0.422 \\ \hline \multirow{4}{*}{GR} & Train & \(R^{2}\) & 0.702 & 0.827 & 0.823 & 0.832 & 0.837 & 0.836 & 0.839 & 0.843 \\  & RMSE & 0.425 & 0.298 & 0.281 & 0.268 & 0.263 & 0.266 & 0.262 & 0.258 \\  & \(R^{2}\) & 0.259 & 0.607 & 0.585 & 0.633 & 0.618 & 0.631 & 0.604 & 0.523 \\ \cline{1-1}  & \multirow{2}{*}{Valid} & RMSE & 1.365 & 1.080 & 1.062 & 0.981 & 0.996 & 0.981 & 0.995 & 1.080 \\ \cline{1-1}  & & \(R^{2}\) & 0.548 & 0.648 & 0.639 & 0.655 & 0.658 & 0.657 & 0.661 & 0.671 \\ \cline{1-1}  & & RMSE & 0.546 & 0.449 & 0.448 & 0.439 & 0.439 & 0.441 & 0.446 & 0.439 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance for generating synthetic Vp using two input variables.
# A Survey of the Applications of Text Mining for the Food Domain
## 7 Text Mining in Food Safety Regulation  
### Food Safety and Food Fraud Surveillance  
The issue of food fraud, encompassing intentional substitution, adulteration, dilution, falsification, or mislabeling of food products for economic gain, has become a focal point of regulatory attention [112; 113]. This illicit practice not only undermines consumer confidence, but also poses serious risks to food safety and quality. Establishing a robust food-safety-monitoring infrastructure for comprehensive surveillance of the entire supply chain is crucial.  
Textual data play a crucial role in supporting food safety regulatory mechanisms [114]. Publicly accessible articles, government databases, academic research repositories, and Internet sources contain vast amounts of unstructured textual information. Researchers have compiled databases and utilized data from various sources to analyze patterns and trends in food fraud, predict incidents, and proactively monitor food safety issues [61; 115; 116; 117; 118]. Social media platforms have also emerged as valuable data reservoirs for monitoring food safety issues globally [119]. Systems like FoodSIS in Singapore exemplify the use of Internet data retrieval for proactive monitoring [120]. Studies on the role of social media in communication disparities related to food safety incidents further underscore its significance [121].  
The increasing complexity and globalization of the food supply chain have heightened concerns about food safety, posing a significant threat to consumer health [122]. NLP and text mining are deployed for intelligent textual analysis to foster proactive monitoring and risk alerting for food safety events and fraudulent practices [123; 124]. The application of these technologies in analyzing online media data for public health safeguarding and crisis anticipation is advocated [125]. Food safety regulators face a constant influx of reports and complaints in various formats, and the application of multi-class classification techniques aids in monitoring and preventing violations, strengthening consumer rights, public health, and socio-economic stability [126; 127].  
In the realm of social media, insights from user-generated content are valuable for understanding public perception and sentiment regarding food safety [128; 129]. Theconvergence of text classification, sentiment analysis, and web-monitoring technologies proves instrumental in shaping discourse on food safety, enabling regulators and the public to navigate this complex domain more effectively.
# Optical Fiber Current Sensors Based on FBG and Magnetotstrictive Composite Materials
## 2 Working Principle of the Sensor
### Magnetic Circuit Sensing System
#### 2.1.2 Fiber Bragg Grating  
After cleaning the surface of the magnetostrictive material, the fiber Bragg grating (FBG) was bonded to the magnetostrictive material with cyanoacrylate adhesive. When the current generates a magnetic field, the magnetostrictive material in the magnetic field will be deformed, and the deformation will be transferred to the bonded FBG.  
The function of FBG is essentially to form a narrowband (transmission or reflection) filter or mirror in the fiber core, as shown in Figure 3. When a beam of broad-spectrum light passes through the fiber grating, the wavelength meeting the Bragg condition of the  
Figure 1: Magnetostriction phenomenon.  
Figure 2: Schematic diagram of magnetostriction.  
FBG will be reflected, and other wavelengths will continue to transmit through the fiber grating. The formula of center wavelength is [11]:  
\[\lambda_{B}=2n_{eff}\Lambda \tag{1}\]  
where \(\Lambda\) is the grating period, and \(n_{eff}\) is the effective refraction of FBG.  
When the FBG is subjected to axial stress, it will be strained, and the period and refractive index of the FBG will change. At this time, the relationship between the central wavelength change of the FBG and the strain is as follows:  
\[\Delta\lambda_{B}=\lambda_{B}(1\,-\,\text{P}_{\text{C}})\varepsilon \tag{2}\]  
where P\(\varepsilon\) is the effective photo-elastic coefficient, which \(\varepsilon\) is about 0.22 for the silicon fiber medium.
## 2 Optimal Measurement Speed in the Evaluation of Transmission Precision  
### Influencing Factors of the Transmission Error  
The transmission error of precision reducers is defined as the deviation between the actual output angle and the expected output angle of the output shaft and can be expressed as Equation (1) [2; 3]. Figure 1 shows the transmission error curve and the peak-to-peak value is treated as the evaluation basis of the transmission precision of precision reduces.  
\[TE=\theta_{out}-\frac{\theta_{in}}{R} \tag{1}\]  
where \(TE\) is the transmission error; \(\theta_{out}\) is the actual angle of the output; \(\theta_{in}\) is the angle of the input; and, \(R\) is the speed ratio of the precision reducer.  
The influencing factors in the dynamic measurement of a transmission error can be divided into internal and external factors. The internal factors mainly include design tolerance, manufacturing tolerance, and backlash of assembly link. The influences of internal factors are inevitable in the stage of manufacturing and assembly, and improving the manufacturing and assembly precision can decrease them. External factors, including speed, load torque, and temperature, are closely related to measurement conditions. The transmission error measurement is usually done within a short time, so the influence of temperature can be ignored.
# Improving Singing Voice Separation Using Curriculum Learning on Recurrent Neural Networks
## 4 Evaluation
### Experimental Results of ccMixter Dataset  
The ccMixter dataset [31] consists of 50 songs of various genres, the total length of which is approximately 3 hours. This dataset provides a set of songs consisting of a vocal sound, an instrument sound, and their mixture. Each song is sampled at 41 kHz, so it is downsampled to 16 kHz for the separation experiments. Setups for the spectrogram extraction is the same as that of MIR-1K dataset. We used 10 songs with singer names beginning with A to J for test, and the remaining 40 songs for training the models.  
Table 3 shows the results of the experiments performed on the ccMixter dataset [31]. In the music results, the proposed learning improved \(GNSDR_{m}\) by 0.27 dB with the RNN, and 0.14 dB with the U-Net. Interestingly, \(GSIR_{m}\) was degraded with the RNN, while it was improved with the U-Net. In the case of \(GSAR_{m}\), opposite results were observed. One of the reasonable explanations is that, because U-Net focuses more on better reconstruction of the original input, removing interfering sound should be preferred rather than eliminating unwanted artifacts. In the vocal extraction results, most of the values were improved except slight degradation in \(GSIR_{v}\). The final \(GNSDR_{v}\) increments are 0.74 dB with the RNN and 0.41 dB with the U-Net, which is much larger than the \(GNSDR_{m}\) values. We used only two singers in the training set of MIR-1K, but there are up to 40 different singers in the training set of ccMixter. So more significant improvements were obtained with vocal extraction results. However, music extraction results were generally worse with ccMixter than with MIR-1K, because of the size of the dataset (50 songs and 110 songs).
# Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach
###### Abstract  
Low-altitude unstable approach (UA) is one of the crucial risks that threaten flight safety. In this study, we proposed a technical program for detecting low-altitude UA events. The detection logic was to optimize the step-wise regression model with iterative surveys with more than 20 experienced pilots. Accordingly, the frequencies of UA events occurring around each airport in January 2018 were calculated for all the airports within mainland China. Finally, the spatial distribution characteristics of UA events were analyzed via exploratory spatial data analysis. In addition, Pearson's correlation coefficient and the geographically weighted correlation coefficient were used to explore the correlations between UA frequency and the altitude elevation, wind level, and bad weather. The experimental results revealed that the proposed method can accurately detect the occurrence of low-altitude UA and quantitatively characterize risks. It was found that UA exhibits obvious differences in spatial distribution. Moreover, significantly strong correlations were found between UA and altitude elevation, wind level, and bad weather, and correlation differences were also reflected in different regions in China.  
unstable approach; detection method; evaluation model; spatio-temporal analysis Article
## 3 Results and Discussion
### Li\({}_{2}\)CO\({}_{3}\) Splitting and CO\({}_{2}\) Transformation
It seems obvious that molten salt provides a million times more reducible tetravalent carbon sites per unit volume than air. The multiplied reactant concentration contributes to promoting CO\({}_{2}\) conversion via the predetermined reaction path. The transformation process of CO\({}_{2}\) in carbonate melts includes the deposition of carbon on the cathode surface and the formation of oxygen on the anode surface. Because of the intermediate Li\({}_{2}\)O, the molten electrolyte can be recycled and reused repeatedly. The above complete reaction process is described below. Figure 1: Thermodynamic stability of Li\({}_{2}\)CO\({}_{3}\)(or Li\({}_{2}\)CO\({}_{3}\) + Li\({}_{2}\)O) at different temperatures. Carbon deposition reaction:  
\[\text{CO}_{3}^{2-}+4\text{e}^{-}=\text{C}+3\text{O}^{2-} \tag{3}\]  
Oxygen formation reaction:  
\[2\text{O}^{2-}=\text{O}_{2}+4\text{e}^{-} \tag{4}\]  
\(\text{Li}_{2}\text{CO}_{3}\) regeneration reaction:  
\[\text{O}^{2-}+\text{CO}_{2}=\text{CO}_{3}^{2-} \tag{5}\]  
Overall reaction:  
\[\text{CO}_{2}=\text{C}+\text{O}_{2} \tag{6}\]  
As shown in Figure 2, a layer of solid carbon and melts is observed, following the chemical reaction, Equation (3), occurring on the surface of the galvanized iron cathode. There are some carbonates remaining on the surface of carbon products. It also explains why the acid pickling operation is essential for purification of carbon materials. Figure 2b shows that the purified carbon material after pickling--ultrasonic water cleaning--drying post-treatments. An EDS spectrogram is used to analyze the elemental composition and SEM is used to detect the microstructure of the obtained solid products. As displayed in Figure 2d, the molar ratio of the C element accounts for an overwhelming 90%, while only 10% of other elements remain. The obtained solid black powders are indeed carbon-based materials. The Ni element is derived from anodic corrosion during electrochemical reaction. The Cl element is derived from the hydrochloric acid pickling process, and the Au element comes from the gold spraying operation for EDS-SEM analysis. According to the previous reports, O is believed to originate from oxygen functional groups in carbon products [25; 26].