esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 2 Theoretical Background  
### Temporal Convolutional Network  
The combined process of one-dimensional full convolution, causal convolution, and dilation convolution is represented by dilation causal convolution, as shown in Figure 1. In Figure 1, the input sequence is denoted as \(X=\{x_{1},x_{2},\cdots,x_{t-1},x_{t}\}\). The output sequence, denoted as \(Y=\{y_{1},y_{2},\cdots,y_{t-1},y_{t}\}\), is obtained by performing a one-dimensional dilated causal convolution operation with a convolution kernel size of 3 on a three-layer input sequence, which is the same as the input sequence. The dilation factor, denoted as \(d\in N^{*}\), is often set to 2 in convolution calculations.  
The sensory field \(v\) is determined by the dimensions of the convolution kernel, the number of layers of the convolution computation, and the dilation factor, which is calculated as follows:  
\[v=1+\sum_{i=0}^{l-1}(k-1)\cdot b^{i} \tag{1}\]  
where \(k\), \(l\), and \(b\) represent the size of the convolutional kernel, the number of convolutional layers in the network, and the base of the expansion factor, respectively. which is usually set to \(b=2\).  
For the task of TCN, we are provided with a one-dimensional input sequence \(x\in\mathbb{R}^{n}\) and a convolution kernel \(f:\{0,1,\cdots,k-1\}\rightarrow\mathbb{R}\). The dilated causal full convolution at the position of the sequence \(s\) is computed as follows:  
\[F(s)=\sum_{i=0}^{k-1}f(i)\bullet x_{s-di} \tag{2}\]  
where \(x_{s-di}\) is the \((s-di)-th\) element in the preceding layer, and \(f(i)\) is a convolution kernel function that maps an index \(i\) to a real weight. \(x_{s-di}\) denotes the \((s-di)-th\) element of the input sequence \(x\) after it has been adjusted by the expansion causality operation, and the remaining parameters retain their former significance.  
Furthermore, TCN employs residual block connectivity to enhance the depth of the network, which can effectively weaken the gradient problem and further increase the model sensory field by connecting multiple residual blocks together. The structure of each residual block is depicted in Figure 2.  
Figure 1: Illustration of the dilated causal convolution operation.
## 2 Materials and Methods
### Design of Solid Phase Microextraction (SPME)  
Generally, the concentration of exhaled breath is within the range of parts per billion or trillion. As a result, pre-concentration is necessary in order to maximize the concentration of chemical substances. Coated fiber, similar to the one used in solid phase microextraction (SPME), is used to pre-concentrate the chemical compounds from exhaled breath prior to analysis using GC-MS and the exhaled breath sensor systems. The SPME fiber used in this study was obtained from Sigma, St. Louis, MO, US. It was coated with 65 uM polydemethysiloxane/divinylbenzene (PDMS/DVB), and contained a hydrophilic and hydrophobic layer to allow the absorption and desorption, respectively, of chemical compounds.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristic** & **AD N (20)** & **PD N (20)** & **Control N (20)** & \(p\)**-Value** \\ \hline
**Age, (Y)** & 74.9 \(\pm\) 7.53 & 72.3 \(\pm\) 7.55 & 67.6 \(\pm\) 7.03 & - \\
**Male sex-no (\%)** & 30 & 50 & 25 & - \\
**Smoker (\%)** & 10 & 5 & 10 & - \\
**Ex-smoker (\%)** & 5 & 20 & 10 & - \\
**MMSE** & 17.95 \(\pm\) 4.86 & 22.85 \(\pm\) 4.65 & 28.55 \(\pm\) 1.05 & \(\leq\)0.05 \\
**CDR-SOB** & 6.1 \(\pm\) 2.6 & 2.24 \(\pm\) 2.67 & 0.25 \(\pm\) 0.30 & \(\leq\)0.05 \\
**H-Y** & nd & 2.31 \(\pm\) 0.56 & nd & - \\ \hline \hline \end{tabular} Values denoted as mean \(\pm\) standard deviation. Scores on the mini-mental state examination (MMSE) range from 0 (severe impairment) to 30 (no impairment). A score higher than 27 was considered normal. The clinical dementia rating scale sum of boxes (CDR-SOB) scores range from 0 (cognitive normality) to 18 (maximal cognitive impairment). Hoehn-Yahr (H-Y) scores from stage 1 (mil) to stage 5 (maximal disability). The \(p\)-value for variables was computed using one-way analysis of variance (ANOVA). \(p\leq\) 0.05; ns = not significant; nd = not determined; AD, Alzheimer’s disease; PD, Parkinson’s disease.  
\end{table}
Table 1: Demographic and clinical data of the participants in the study.  
Figure 1: Collection and analysis of exhaled breath. (**A**) Collection of exhaled breath into a 3 L Tedlar bag using a mouth-piece; (**B**) Exhaled breath sensor system for clustering exhaled breath samples; (**C**) Gas sensors placed on the metal-oxide semiconductor; (**D**) Pre-concentration of chemical compounds in the exhaled breath samples using solid phase microextraction (SPME) fiber.
## 7 Conclusions  
SER remains a challenging endeavour due to the inherently subjective nature of emotions and the limited availability of comprehensive emotional speech datasets. In this study, we sought to address these challenges by leveraging successful models from different domains, specifically image and speaker recognition, and applying TL to enhance SER performance. The results demonstrate that the TL significantly improves the accuracy of the SER systems, particularly when models are fine-tuned from a domain with closer alignment to speech, such as speaker recognition.  
Our experiments reveal that models utilizing TL outperformed those trained from scratch, validating the approach of transferring knowledge from other domains to SER. Among the models tested, speaker-based models, especially the enhanced r-vector model, demonstrate superior performance, achieving a UA of 71.08% and a WA of 70.97% after cross-validation on the IEMOCAP dataset. This performance not only highlights the effectiveness of TL in overcoming the limitations posed by small and imbalanced emotional speech datasets but also aligns with the outcomes of similar research in this area.  
Although the augmentation technique is helpful, it plays a smaller role in improving the accuracy of SER compared to TL and other enhancement strategies. Overall, the improvements are more pronounced for the IEMOCAP dataset compared to CREMA-D. The difference is likely due to IEMOCAP's combination of scripted and improvised utterances, which presents a more complex task, whereas the structured nature and balanced emotion representation of CREMA-D led to pre-TL performance already being higher.  
The methodologies and solutions presented in this research provide a promising foundation for further advancements in the SER field. The application of TL has proven to be an effective strategy, with significant potential for future innovation. Moreover, integrating multi-task learning (e.g., gender detection) could enhance the model's ability to disentangle emotion from other speech-related features. Additionally, exploring domain shifts, particularly when combining diverse emotional datasets, represents a valuable direction for future research.  
Conceptualization, M.J., E.L. and R.J.; methodology, M.J., E.L. and R.J.; writing--original draft preparation, E.L. and M.J.; writing--review and editing, M.J., R.J. and E.L.; supervision, R.J. and E.L.; data curation, M.S. and P.K.; project administration, E.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Determination and Risk Assessment of Flavor Components in Flavored Milk
## 2 Materials and Methods  
### Reagents and Equipment  
The online survey \"Residents' flavored milk consumption questionnaire\" was created on the Questionnaire website and was available for completion from 1 January 2021 to 30 May 2021. Table S1 has provided details of the questionnaire model. Flavor samples of five brands (Givaudan, MANE, Huacheng, ARTSCI, and Firmenich) were collected from Chinese dairy companies. Twenty-eight flavored milk samples were obtained from Chinese supermarkets, including brands such as Bright Dairy, Mengniu Dairy, Yili Dairy, and Sanyuan Daiy. 2,3,5-trimethylpyrazine (99%), phenol, 2-methoxy-4-(2-propenyl)- (\(>\)99.5%), maltol (99%), benzenemethanol (\(\geq\)99.5%), methyl salicate (\(\geq\)99.5%), benzyl acetate (\(\geq\)99.7%), 5-methylfurfural (98%), linalool (98%), benzaldehyde (\(\geq\)99.5%), furfural (\(\geq\)99.5%), hexanoic acid 2-propenyl ester (98%), 1-hexanol (\(>\)99.5%), ethyl 3-methylbutyrate (\(\geq\)99.7%), and 2-methylpropanal (99%) were purchased from Shanghai Aladdin Biochemical Technology Company (Shanghai, China). Methyleugenol (\(\geq\)98%) was purchased from Shanghai Yuanye Biology Science and Technology Company (Shanghai, China). Methanol (99.9%) was collected from Thermofisher Scientific Technology Company (Boston, MA, USA). C7-C40 N-alkanes (99.5%) was purchased from an American company, O2 si (USA). 50/30 \(\upmu\)m divinylbenzene carbon molecular sieve polydimethylsiloxane (DVB/CAR/PDMS) extraction head was obtained from the American company Supelco (Saint Louis, MO, USA). Gas chromatography-mass spectrometry equipment was purchased from Thermo Scientific. A DB-WAX column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) was obtained from Agilent (Santa Clara, CA, USA).
## 2 Related Work
### Prior Research Efforts on Telecom Fraud Detection  
In the early research, the main method relied on compiling blacklists of fraudulent phone numbers. This method can effectively limit the occurrence of continuous fraud using the same number. However, this approach often takes effect only after the fraud has already happened. Criminals typically exploit the same number to send out widespread information and then engage in targeted fraudulent activities. By the time victims report it, the fraud has usually already occurred, rendering the method less timely and effective for prevention. There are also methods based on machine learning. For example, [PERSON] [14] proposed a method that combines random forest (RF), decision trees (DTs), artificial neural networks (ANNs), naive Bayes (NB), and logistic regression (LR) with a feature selection method based on genetic algorithms (GAs). Additionally, there are rule-based studies, such as that by [PERSON] [15], who used natural language processing to extract the features from text data. They detected telecom fraud further by establishing rules to identify similar content within the same call. Machine learning models rely heavily on the quality of the features selected, which may not fully capture the complexities of telecom fraud patterns. Additionally, rule-based methods, although they are effective in specific scenarios, can be rigid and may struggle to adapt to the evolving nature of fraud tactics.
# A Novel Longitudinal Speed Estimator for Four-Wheel Slip in Snowy Conditions
## 2 Materials and Methods
### Longitudinal Speed Estimator of Four-Wheel Drive Slipping Vehicle on Split Roads
#### 2.1.3 Vehicle Speed Estimator Based on Vehicle Acceleration  
The longitudinal vehicle speed \(v_{E,a}\) is obtained after low pass filtering of the acceleration integration and a linear calibration in the \"vehicle speed estimator based on acceleration\" controller. Although it has drawbacks in low vehicle acceleration during vehicle driving start and drastic changes in acceleration, the estimation can benefit in certain other cases based on the wheel speed encoders. We use this estimated vehicle speed to supplement the estimate based on the wheel encoder speeds in the first layer.  
To accurately estimate longitudinal speed in extreme slip conditions, it is necessary to compensate for the errors of the vehicle speed estimator based on the wheel speed algorithm. However, there may be limits for practical application.  
\[\mathrm{V}_{i,j}\geq\mathrm{V}_{m,m}\geq\mathrm{real\ longitudinal\ vehicle\ speed}\]  
Another acceleration sensor is introduced. Fortunately, its low-cost and stable characteristics bring no extra challenges to the automotive application. One drawback, though, is the lack of output precision during low acceleration. Since speed is derived from integrating the acceleration, noise can be amplified during the calculation process.  
Its inherent advantage in output precision is better than the vehicle speed estimator based on wheel speed, prompting its usage in high-level slip. This method is adopted as another reference in the first layer.  
In the vehicle speed estimator, signal shift is eliminated after filtering the acceleration signal from sensors. Then, a small step iterative integration method is added to integrate the acceleration signal with initial vehicle speed obtained from the estimated vehicle speed based on wheel speeds.  
\[V^{\prime}_{E,a}=\int_{time}^{time+step}a_{sensor}\ dt+\mathrm{V}_{time}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Low Slip** & **Mid Slip** & **High Slip** & **Output** \\ \hline
4 & 0 & 0 & Small Rate \\
3 & 1 & 0 & Mid Rate \\
2 & 1 & 1 & Mid Rate \\
2 & 0 & 2 & Small Rate \\
1 & 0 & 3 & Small Rate \\
1 & 1 & 2 & Mid Rate \\
1 & 2 & 1 & High Rate \\
1 & 3 & 0 & High Rate \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters.  
Although compensation has been performed to obtain an integration result, there are still flaws due to the presence of noise and accuracy of the acceleration sensor. Therefore, a linear compensation method is adopted.  
\[V_{E,a}=V_{E,a}^{\prime}\text{-calibration\_coefficient}\]
## 3 Experimental Setup and Results
### Results from Proposed Stacking Experiment  
After the Base-Level classifiers are trained using training data, we tested their performance on the development data in each dataset. Table 6 presents the F1-score of each Base-Level classifier used in the proposed stacking architecture on the development set. These Base-Level classifiers were employed in the stacking architecture with five different combinations. Table 7 shows the performance of the proposed stacking ensembles on the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Non-Hateful Tweet** & **Hateful Tweet** & **Total** \\  & **(0)** & **(1)** & **Total** \\ \hline Train & 965 (58\%) & 704 (42) & 1669 \\ Development & 106 (57\%) & 80 (43\%) & 186 \\ Test & 249 (54\%) & 215 (46\%) & 464 \\ Total & 1320 (57\%) & 999 (43\%) & 2319 \\ \hline \hline \end{tabular}
\end{table}
Table 4: COVID-HATE dataset distribution of Non-hateful and Hateful tweets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Non-Hateful Tweet** & **Hateful Tweet** & **Total** \\  & **(0)** & **(1)** & **Total** \\ \hline Train & 7935 (68\%) & 3682 (32\%) & 11,617 \\ Development & 877 (68\%) & 414 (32\%) & 1291 \\ Test & 2221 (69\%) & 1006 (31\%) & 3227 \\ Total & 11,033 (68\%) & 5102 (32\%) & 16,135 \\ \hline \hline \end{tabular}
\end{table}
Table 5: ZeerakW dataset distribution of Non-hateful and Hateful tweets.  
test set. The Meta-Level classifier in each proposed stacking ensemble is trained using the development set.  
In our experiment, we tried all different combinations of the seven individual classifiers. Among all combinations of these single classifiers, we present the top five combinations in terms of the F1-score. Examining the five ensembles using the four test sets, we found that the ensemble of SVM, LR, and XGB is the best combination among other ensembles. As Table 7 shows, the best results for all datasets are achieved by the proposed architecture containing SVM, LR, XGB as Base-Level classifiers.  
As Table 6 showed, NB performed worst on the Davidson dataset, while KNN, RF, and E-tree performed worst on COVID-HATE, ZeerakW, and HatEval datasets, respectively. On the other hand, NB and RF performed the best on the HatEval and COVID-HATE datasets, respectively, while SVM achieved the best performance with the Davidson dataset, and LR was the best on ZeerakW datasets. We observed that the stacking ensemble combination of (KNN, SVM, NB) performed the worst on three out of four datasets (HatEval, COVID-HATE, and ZeerakW).
## 3 Discussion  
### Comparison of AGB Estimates for Different Grassmanns  
Xinjiang has a variety of grasslands distributed across different regions. Previous studies have shown that the productivity and coverage of grasslands in Xinjiang generally follow the order meadow \(>\) grassland \(>\) desert [31; 32; 33; 34; 35]. Owing to limited ground sample data and significant vertical differences in Xinjiang's grasslands, there is considerable uncertainty in estimating the grassland AGB in the large region of Xinjiang using remote sensing modeling. Therefore, based on a large number of field observation sample data, this study used an AGB model based on a random forest algorithm to invert the mean value of grassland AGB in Xinjiang to 97.73 g\(\cdot\)m\({}^{-2}\). The mean AGB of desert grassland was 82.27 g\(\cdot\)m\({}^{-2}\) in this study, which was lower than the mean AGB calculated by [PERSON] et al. [36] in Northern Xinjiang (115.42 g\(\cdot\)m\({}^{-2}\)). These differences may be due to the location of the sampling sites, the choice of estimation models, or sources of modeling variation. Furthermore, the grassland AGB was the highest in the meadow, second highest in the steppe, and lowest in the desert [37], which is consistent with the results of this study. This study found that the highest value of AGB from 2000 to 2022 was in the montane meadow (152.77 g\(\cdot\)m\({}^{-2}\)) in 2016, while the lowest value was in the alpine desert (63.50 g\(\cdot\)m\({}^{-2}\)) in 2003.  
This study examined the spatial distribution of grassland AGB in Xinjiang. It found that the mean values of grassland AGB in the Yili Valley, Altay Mountains, and western Tianshan Mountains were high and showed an increasing trend, whereas that of the temperate desert grassland AGB in the Junggar Basin was low and showed a decreasing trend over a large area. [PERSON] et al. [38] noted an increasing trend in grassland productivity in the Altay Mountains and Tianshan Mountains but a decreasing trend in the Junggar Basin, which was similar to the results of this study. Furthermore, the grassland AGB in Xinjiang showed spatial distribution characteristics of being high in the northwest and low in the southeast. The overall trend was upward, which was similar to the results of [PERSON] et al. [39] and [PERSON] et al. [40].  
Figure 8: Statistics data of correlation between biomass and climate factors for different grassland types: (**a**) AGB and temperature and (**b**) AGB and precipitation.
## 9 Mobility Environment
### Scenario 1 Results
When the number of nodes increased, the CS-DSDV scheme drops more packets due to network congestion caused by a network bottleneck. The network overwhelms the receiving nodes due to more data generated by the source node than the receiving node can consume, thus causing many packets to be dropped. The ACO-AODV performs better than CS-DSDV in most cases when the ACO scheme encounters link breakages. It can adapt to changes, such as new routes, though it takes a longer time to do so.
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 2 Experimental Setup and Methodology
### Performance Comparisons  
It is demonstrated in [2] that the TDOA and TOA/AOA localization methods benefit from NLOS mitigation. The process of NLOS mitigation in these cases involve running the LSSVM twice, once for classification and then secondly for regression during mitigation. The direct method discussed herein, also involves running the LSSVM twice, once to obtain the x-coordinate, and second to obtain the y-coordinate. It is therefore of interest to compare the performance of these approaches in different scenarios. It should be noted that the TDOA and TOA/AOA schemes, additionally involve running the localization algorithm itself, after mitigation is applied.  
The data for the city environments, was used to run the TOA/AOA algorithm, with NLOS identification and mitigation. By its design, the TOA/AOA algorithm assumes the availability of a LOS component between the BS and the MS, and that, it is the first arriving path, that is LOS. This means that after NLOS identification, using the method described in [2], only those paths that are determined to be LOS are used for localization, meaning that some MS positions could not be localized and were excluded. The LSSVM direct localization scheme was also not run for those positions that were excluded. Because the TOA/AOA scheme in this case is using identified LOS paths, it is the performance of LOS identification and mitigation which is being compared to the direct localization scheme. For that reason, this comparison is only done for one environment, in this case the dense urban environment.  
TDOA requires each MS position to be able to see at least three BSs. MS positions that could not see at least 3 BSs were excluded. The LSSVM direct localization scheme uses only one BS and it's the BS with the shortest time delay to the MS, which is used. To make the localization performance comparison with TDOA, 2 additional BSs are chosen, with best time delays.  
Figure 6: (a) Dense urban area/city center (sampled color-coded positions: same color means positions with same received signal power). (b) Park/farmland, showing trees and open areas [3].
## Abbreviations  
Abbreviations are used in this paper and listed as follows:  
\(q\)A \(k\)-bit prime number  
\(GF(q)\)Finite group \(q\)  
\(E\)The elliptic curve defined on finite group \(q\)  
\(G\)A generating point based on the elliptic curve \(E\)  
\(ID_{x}\)A name representing identity \(x\)  
\(k_{x}\)A random value on elliptic curve  
\((r_{x},s_{x})\)Elliptic curve signature value of \(x\)  
\(M_{x\cdot y}\)A message from \(x\) to \(y\)  
\(ID_{BC}\)An index value of blockchain message  
\(BC_{x}\)Blockchain message of \(x\)  
\(PK_{X}/SK_{X}\)An asymmetric public/private key  
\(E_{PKX}(M)\)Use \(X\)'s public key \(PKx\) to encrypt the message \(M\)
## 2 Literature Review
### Indirect Measurement Techniques
#### 2.2.1 Acoustic Method  
Pure and salt water detection using the acoustic method employs sonar technology. The sound velocity in pure water and in salty water is different. The measurement can be obtained according to the return time of the ultrasonic wave [14]. This method is suitable for pure and seawater detection. However, it has a high cost and a poor anti-interference ability [15].
## 4 Results
### EEG Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mental State** & **Datapoints per Channel** & **Subject A** & **Subject B** & **Subject C** & **Subject D** \\ \hline \multirow{3}{*}{Concentration} & Real & 15192 & 11364 & 15204 & 11364 \\  & Synthetic & 15192 & 11364 & 15204 & 11364 \\ \cline{2-6}  & Real & 15204 & 15204 & 15204 & 15204 \\ \cline{2-6}  & Synthetic & 15204 & 15204 & 15204 & 15204 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of datapoints for each channel (TP9, AF7, AF8, and TP10) of real and corresponding generated synthetic waves for each mental state table. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Classifier** & **Real Data** & **Synthetic Data** & **Real + Synthetic Data** \\ \hline [PERSON] et al. [30]: GPT-2 & \begin{tabular}{c} Support vector \\ machine (SVM) \\ \end{tabular} & 90.84 & 66.88 & 93.71 \\ [PERSON] et al. [30]: GPT-2 & \begin{tabular}{c} Random forest (RF) \\ \end{tabular} & 88.14 & 70.71 & 96.69 \\ \multirow{2}{*}{Our model: GAN} & \
\begin{tabular}{c} Convolutional neural \\ network (CNN) \\ \end{tabular} & 92 & 85.78 & 94.11 \\ \multirow{2}{*}{Our model: WGAN-GP} &
\begin{tabular}{c} Convolutional neural \\ network (CNN) \\ \end{tabular} & 92 & 98.42 & 98.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of model accuracy(%) between our approach and [PERSON] et al. [30] in classifying real, synthetic, and combined datasets. Overall, our findings indicate that incorporating up to 50% synthetic data enhances classification accuracy for WGAN-GP, establishing it as an effective strategy for improving EEG data analysis.