esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
Additionally, a significant portion of the short-chain fatty acid ethyl esters were branched-chain fatty acid ethyl esters. Research has demonstrated that branched fatty acid ethyl esters are more stable than straight-chain fatty acid ethyl esters and even increase during the aging process [33]. It is believed that the corresponding acids are conversed into branched-chain fatty acid ethyl esters under acid-ester equilibrium, which explains the continuous accumulation of branched-chain fatty acid ethyl esters [34]. Therefore, it can be hypothesized that the effect of bottle storage on fatty acid ethyl esters varied depending on the length of the carbon chain and structural difference. Some aroma compounds, including free ethyl 2-methylbutyrate, accumulated continuously, and their accumulation rate was higher in 'Marselan' than in 'Merlot'. Studies have found that ethyl 2-methylbutyrate has a low boiling point and low log\(P\) value. The release of esters with these characteristics varies with the level of polyphenol content in wine. At low polyphenol content, there is a general retention phenomenon, while at high polyphenol content, there is a tendency of salting-out effects [35]. The experiments conducted by [PERSON] et al. [36] found that the release of ethyl 2-methylbutyrate was found to be facilitated by an upward trend in red wines, likely due to their high total polyphenol content (TPC). In research of [PERSON] et al., they also suggested that the addition of polyphenols during red wine fermentation can increase the content of various esters, including ethyl 3-methylbutyrate [37]. Therefore, it was believed that the difference in accumulation rate in this experiment was caused by the difference in total polyphenol content (TPC) between \"Marselan\" and \"Merlot\". Previous research has shown that the flavonoid content in \"Marselan\" is significantly higher than that of \"Merlot\" [21], which may have led to an enhanced growth rate of aroma compounds in \"Marselan\". The study investigated the evolution of characteristic aromas in dry red wines and their sensory impacts. The dominant aroma difference between the end of the malo-lactic fermentation and the 12-month bottle samples was proposed to be caused by the (\(E\))-\(\beta\)-damascenone from the grape berries. The difference in its contents resulted in the lack of floral and honeyed aromas in the dry red wines bottled for 12 months. However, the accumulation of ethyl esters during the bottle-storage stage enriched the fruity aroma of the end-of-bottle-storage samples to some extent. Overall, although there was a significant difference in (\(E\))-\(\beta\)-damascenone detected in the berries was significantly different between the two varieties, this difference decreased as fermentation and bottle storage progressed. The winemaking process has a greater impact on the aromas of dry red wine compared to the variety.
## 2 SRPC Compensation Principle  
The SRPC system structure is presented in Figure 1, where the main objective is to compensate the NSC of currents, besides the harmonics produced by the electric locomotives [6]. A higher current imbalance ratio indicates a huge difference between the three-phase currents and a higher NSC of currents injected into the three-phase public power grid. The right feeder section is denoted as phase \(x\) and the left feeder section is denoted as phase \(y\). The corresponding phases on the primary side windings of the V/V power transformer are denoted as phase \(A\) and phase \(B\), respectively. By considering the three-phase power grid voltages \(\dot{U}_{A}\), \(\dot{U}_{B}\), and \(\dot{U}_{C}\) are:  
\[\begin{array}{l}\dot{U}_{A}=\ U_{A}\ e^{\int^{0}\dot{U}_{B}}\\ \dot{U}_{B}=\ U_{B}\ e^{-j120^{\circ}}\\ \dot{U}_{C}=\ U_{C}\ e^{-j240^{\circ}}\end{array} \tag{1}\]  
Figure 1: Structure of the half-bridge modular multilevel converter (MMC) based simplified rail power conditioner system (SRPC) used to validate the proposed control strategy.  
From Equation (1), and as shown in Figure 1, phase x and phase y voltages, \(\dot{U}_{x}\), \(\dot{U}_{y}\) of the traction power system correspond to the line-to-line grid voltages of \(\dot{U}_{AC}\), and \(\dot{U}_{BC}\), respectively, and can be expressed as in Equation (2).  
\[\begin{array}{l}\dot{U}_{x}=\frac{U_{AC}}{K_{V}}\ e^{-\beta 30^{\circ}}\\ \dot{U}_{y}=\frac{U_{BC}}{K_{V}}\ e^{-\beta 90^{\circ}}\end{array} \tag{2}\]  
where, \(K_{V}\), denotes the turns ratio of the V/V power transformer. By assuming that both catenary sections have a near unitary power factor (since modern locomotives are normally driven by bidirectional PWM converters, approximately, at the substation, the power factor could be considered unitary) and by considering sinusoidal current waveforms at the catenary sections, the fundamental currents of both catenary sections \(x\) and \(y\) are in phase with the overhead catenary voltages \(\dot{U}_{x}\), and \(\dot{U}_{y}\), and are expressed by:  
\[\begin{array}{l}\dot{I}_{Lx}=\ I_{Lx}\ e^{-\beta 30^{\circ}}\\ \dot{I}_{Ly}=\ I_{Ly}\ e^{-\beta 90^{\circ}}\end{array} \tag{3}\]  
where, \(I_{Lx}\), \(I_{Ly}\) denote the fundamental RMS currents of both catenary sections \(x\), and \(y\), respectively. The three-phase power grid currents in this case are as in Equation (4) and they have the phasors diagram presented in Figure 2a.  
\[\begin{array}{l}\dot{I}_{A}=\frac{I_{Lx}}{K_{V}}\ e^{-\beta 30^{\circ}}\\ \dot{I}_{B}=\frac{I_{Lx}}{K_{V}}\ e^{-\beta 90^{\circ}}\\ \dot{I}_{C}=-\frac{I_{Lx}}{K_{V}}\ e^{-\beta 30^{\circ}}-\frac{I_{Ly}}{K_{V}}\ e^{- \beta 90^{\circ}}\end{array} \tag{4}\]
# Hailstorm Detection by Satellite Microwave Radiometers
## Abbreviations  
AMSU-B Advanced Microwave Sounding Unit-B  
AR Argentina  
ATMS Advanced Technology Microwave Sounder  
BTD Brightness Temperature Difference  
CONUS Conterminous United States  
DPR Dual-frequency Precipitation Radar  
ESSL European Severe Storms Laboratory  
EU Europe  
FOV Field of View  
IFOV Instantaneous FOV  
IR InfraRed  
GMI GPM Microwave Imager  
GPM Global Precipitation Measurement mission  
MHS Microwave Humidity Sounder  
MWCC MicroWave Cloud Classification method  
NOAA National Oceanic and Atmospheric Administration  
PMW Passive Microwave  
PMWCC Perturbation of MWCC  
SPC Storm Prediction Center  
SSM/I Special Sensor Microwave/Imager  
SSMIS Special Sensor Microwave Imager/Sounder
## 3 Experimental Results and Analysis
### Classification Results and Analysis Based on Support Vector Machine
Although the accuracy and recall for vowels \"\(\chi\)\" and \"u\" are high (95% and 94%, respectively), the performance for apical vowels \"\(\mathcal{Z}\)\" and \"\(\mathcal{Z}\)\" is significantly poorer, with accuracy values of 87% and 88% and recall values of 88% and 85%, respectively. This result reflects the pronunciation difficulties faced by elementary learners in apical vowels, possibly related to native language interference or insufficient oral muscle control. Moreover, the recall for vowel \"i\" is only 90%, indicating that elementary learners also exhibit a certain degree of instability in their pronunciation of high front vowels. SVM generally outperforms the ID3 algorithm across all level groups, particularly in terms of accuracy and recall, demonstrating higher stability and precision. SVM can handle high-dimensional data and complex non-linear relationships, which is particularly important for vowel classification in speech recognition.
# Closed-Form Performance Analysis of the Inverse Power Lomax Fading Channel Model
###### Abstract  
This research presents a closed-form mathematical framework for assessing the performance of a wireless communication system in the presence of multipath fading channels with an instantaneous signal-to-noise ratio (SNR) subjected to the inverse power Lomax (IPL) distribution. It is demonstrated that depending on the channel parameters, such a model can describe both severe and light fading covering most cases of the well-renowned simplified models (i.e., Rayleigh, Rice, Nakagami-m, Hoyt, \(\alpha-\mu\), Lomax, etc.). This study provides the exact results for a basic statistical description of an IPL channel, including the PDF, CDF, MGF, and raw moments. The derived representation was further used to assess the performance of a communication link. For this purpose, the exact expression and their high signal-to-noise ratio (SNR) asymptotics were derived for the amount of fading (AoF), outage probability (OP), average bit error rate (ABER), and ergodic capacity (EC). The closed-form and numerical hyper-Rayleigh analysis of the IPL channel is performed, identifying the boundaries of weak, strong, and full hyper-Rayleigh regimes (HRRs). An in-depth analysis of the system performance was carried out for all possible fading channel parameters' values. The practical applicability of the channel model was supported by comparing it with real-world experimental results. The derived expressions were tested against a numerical analysis and statistical simulation and demonstrated a high correspondence.  
Keywords:fading channel; statistical description; inverse power Lomax; error rate; outage; capacity +
Footnote †: journal: _Mathematics_
# Performance of Automated Oral Cancer Screening Algorithm in Tobacco Users vs. Non-Tobacco Users
###### Abstract  
Oral non-neoplastic and neoplastic lesions have similar clinical manifestations, increasing the risk of inaccurate screening decisions that adversely affect oral cancer (OC) outcomes. Tobacco-use-related changes in the oral soft tissues may affect the accuracy of \"smart\" oral screening modalities. Because smoking is such a strong predictor of OC risk, it may overwhelm the impact of other variables on algorithm performance. The objective was to evaluate the screening accuracy in tobacco users vs. non-users of a previously developed prototype smartphone and machine-learning algorithm-based oral health screening modality. 318 subjects with healthy mucosa or oral lesions were allocated into either a \"tobacco smoker\" group or a \"tobacco non-smoker\" group. Next, intraoral autofluorescence (AFI) and polarized white light images (pWLI), risk factors as well as clinical signs and symptoms were recorded using the prototype screening platform. OC risk status as determined by the algorithm was compared with OC risk evaluation by an oral medicine specialist (gold standard). The screening platform achieved 80.0% sensitivity, 87.5% specificity, 83.67% agreement with specialist screening outcome in tobacco smokers, and 62.1% sensitivity, 82.9% specificity, 73.1% agreement with specialist screening outcome in non-smokers. Tobacco use should be carefully weighted as a variable in the architecture of any imaging-based screening algorithm for OC risk.  
oral cancer; screening; artificial intelligence; machine learning; early detection
## 4 Discussion
Additionally, the lack of operator blinding introduced the possibility of bias in the interpretation of the thermographic data. The study was conducted under controlled environmental conditions (fixed temperature, fixed humidity, and an absence of air currents), which might not accurately reflect real-world scenarios where external factors, such as humidity and wind, could affect thermoregulation. The short 8-day observation period also precluded the evaluation of the long-term or cumulative effects of repeated ice boot application, including potential tissue adaptations or damage. Moreover, localized surface temperature changes were not correlated with core body temperature, limiting insights into systemic thermoregulatory responses. Finally, the variation in ice boot temperature (\(-0.4\)-\(7.5\)\({}^{\circ}\)C) might have introduced inconsistencies in cooling efficacy across sessions.
# SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing
###### Abstract  
Task scheduling is key to performance optimization and resource management in cloud computing systems. Because of its complexity, it has been defined as an NP problem. We introduce an online scheme to solve the problem of task scheduling under a dynamic load in the cloud environment. After analyzing the process, we propose a server level agreement constraint adaptive online task scheduling algorithm based on double deep Q-learning (SLA-DQTS) to reduce the makespan, cost, and average overdue time under the constraints of virtual machine (VM) resources and deadlines. In the algorithm, we prevent the change of the model input dimension with the number of VMs by taking the Gaussian distribution of related parameters as a part of the state space. Through the design of the reward function, the model can be optimized for different goals and task loads. We evaluate the performance of the algorithm by comparing it with three heuristic algorithms (Min-Min, random, and round robin) under different loads. The results show that the algorithm in this paper can achieve similar or better results than the comparison algorithms at a lower cost.  
cloud computing; task scheduling; DDQN
## 2 Terpenoids as Semiochemicals
### Insect-Plant Activity
#### 2.1.2. Terpenoids as Attractants  
In some cases, plants produce terpenoids to attract specific insects that play a role in their pollination or seed dispersal. A study by [PERSON] et al. (2018) on the relationship between fig (_Ficus hispida_) and its pollinator, the _Certososlen solmsi marchali_ wasp, concluded that only the female floral stage is receptive to its pollinator and that, in the next stage of figs development, the pollinator response diminished (Table 1). The insect is responsive to both changes in concentration and quality. The volatiles present during the development stage were linalool, limonene, and \(\beta\)-pinene, with the presence of different isomers during the two stages studied. Another study reveals that terpene compounds can be finely adjusted to a certain concentration to repel or attract pollinators, with the volatile compounds' emission being dependent on the daily thermogenic phase, increasing with the increase in temperature ([PERSON], 2018).  
An effective indirect defense against herbivores consists of the emission of volatile terpen and terpenoids that attract the parasitoids of specific herbivores. The monoterpene 1,8-cineole, present in the EO of cultivars of _Brassica oleracea_, apart from their known insecticidal properties, has the characteristic of attracting the parasitoid _Cotesia glomerata_, which lay eggs in the larvae stage of the butterfly _Pieris brassica_, which cause extensive damage to cabbage cultures ([PERSON] et al., 2018).  
It has been observed that some terpenoids act as chemo-attractants to herbivores. In a study, different constitutive and induced terpen (\(\gamma\)-terpinene and \(\alpha\)-pinene) were synthesized in increasing concentrations on _Eucalyptus grandis_ in response to attacks from eucalyptus gall wasp _Leptocyte inwasa_, acting as pest attractants; whereas iso-pinocarveyol, also produced, acted to recruit parasitoids for this wasp ([PERSON] et al., 2018). Also, the cultivation of transgenic plants overexpressing attractants may effectively lure herbivores into entrapments that prevent escape ([PERSON] et al., 2018).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
## 2 Materials and Methods
### Data Pre-Processing  
BCI competition IV dataset was firstly filtered using a 0.5 Hz to 100 Hz band-pass filter to eradicate the effect of non-physiological artifacts in the form of noise, and then a 50 Hz notch filter was applied to reduce the effect of line noise [23]. Moreover, MI signals were sampled at a frequency of 250 Hz. Our own SSMVEP dataset was recorded at a sampling frequency of 250 Hz. A 0.5 Hz to 60 Hz band-pass filter was then applied to reduce the effect noise. A notch filter at a cut-off frequency of 50 Hz was applied to reduce the effect of line noise. Moreover, a common average reference (CAR) was applied to eliminate the effect of noise originating from electrodes [17].  
Figure 1: Our dataset experimental paradigm timing scheme.
# An Improved Method for Named Entity Recognition and Its Application to CEMR+
## 5 Conclusions  
In this paper, we proposed an attention-based ID-CNNs-CRF model for NER on CEMR. Firstly, word representation combined with position embedding was used for the input of our model to capture the word-order information. Secondly, we stacked four dilated CNN blocks to obtain broad semantic information to make up for the shortage of the CNN-based model in the language field. Then, the attention mechanism was applied to pay more attention to the characteristics of the current words and increase the performance of the model. Finally, the CRF was utilized to obtain the optimal tag sequence. The experiments on two CEMR datasets demonstrated that the attention-based ID-CNNs-CRF was superior to state-of-the-art methods with a faster test time. As we know, Bi-LSTM-CRF has a temporal structure, and its output at each step depends on the output of the previous step. This temporal structure is time consuming, especially when dealing with long text. Our model had good parallelism and could make full use of the GPU for parallel computing. Compared with the ID-CNNs-CRF, our model obtained better performance (improvements of 5.95%, 7.48%, and 7.08% in precision, recall, and F1-score, respectively). This demonstrates that the position embedding and attention mechanism had a huge performance improvement for the ID-CNNs model. In addition, our model outperformed Bi-LSTM-CRF, showing that our attention-based ID-CNNs-CRF was also an effective token encoder for structured inference. The model we proposed was 22% faster than the Bi-LSTM-CRF. There was no significant improvement in our model in the number of entities with fewer samples. Therefore, our future work is to study how to improve the recognition of these entities with fewer samples.  
Conceptualization, M.G. and S.W.; Methodology, M.G.; Software, M.G.; Validation, M.G., S.W. and Q.X.; Formal Analysis, K.D.; Investigation, M.G.; Resources, M.G.; Data Curation, M.G.; Writing--Original Draft Preparation, M.G.; Writing--Review & Editing, S.W.; Visualization, S.W.; Supervision, M.G.; Project Administration, M.G.; Funding Acquisition, S.W.  
This research was funded by [National Key Research and Development Program of China] grant number [2017 YFC0907505] and [Shanghai Natural Science Foundation] grant number [18 ZR1414400].  
**Acknowledgments:** The authors thank all the anonymous reviewers for their insightful comments and useful suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: The chart on the left shows the effect of changes in the different embedding dimensions of CCKS2017 on the F1-score of the model (the chart on the right is CCKS2018). The abscissa represents different dimensions, and the ordinate represents the F1-score of the model on the test set.
# A New Online Service for the Validation of Multi-GNSS Orbits Using SLR
## 3 Results
### Discussion on Possible Directions of the System Development for the Future  
The benefits from SLR tracking of the multi-GNSS constellation are constantly being confirmed: improvement of the laser station coordinate determination [59], contribution to GGOS activities [60], improvement of the quality of multi-GNSS satellites, as well as clock synchronization and confirmation of relativistic effects acting on Galileo satellites in eccentric orbits [61]. However, a limited number of ACs routinely process SLR observations to GNSS in their operational products. Currently, GOVUS delivers ready-to-use online tools and the up-to-date dataset of SLR residuals. The major advantage of the system is a favorable ratio of time, which is needed to perform a plethora of multi-GNSS orbit analyses. We are open to ideas of new tools that might potentially be developed into the service. The computational schemes of the GOVUS system are currently focused on precise orbit products delivered by CODE in the frame of the MGEX project; however, other orbit solutions can be included into the validation process, including new satellite types and results provided by MGEX ACs.  
Nowadays, we observe the gradual improvement of MGEX orbit solutions. In the upcoming years, MGEX contributors will probably eliminate critical issues of precise orbit determination such as dependencies in a Sun-Earth-satellite frame or improvement of common multi-GNSS models, e.g., SRP. Finally, a full integration of multi-GNSS will come to the official IGS processing routine. Public users will be increasingly interested in using multi-GNSS products. The GOVUS service may become a great tool for MGEX contributors (especially ACs) as well as for laser stations. The next efforts on the development of GOVUS will be focused on providing necessary real-time functionality for users of multi-GNSS data and products. We plan to implement spatial tools, which could inform the user about the quality of satellite orbits, which are visible in a specific period from a specific place on the Earth. The GOVUS should also monitor the exact spatial coverage of SLR measurements to the GNSS satellites.
## 2 Materials and Methods
### Vegetation Indices (VI)
#### 2.7.2 Accuracy Assessment  
The accuracy of the VIs olive tree canopies models was accessed using AUC (area under the curve) values obtained from ROC (receiver operating characteristics) curves. ROC curve is a graphic diagram that illustrates the diagnostic ability of a certain classifier system, and it is created using TRP (true-positive rate) and FPR (false-positive rate) values of the observed model [64; 65]. ROC curves are commonly calculated by either utilizing the design sample reuse approach or by applying the classification rule to a test set of points with known classes. While the AUC represents the possibility that a randomly selected member of class \(1\) will have a lower estimated chance of being a member of class \(0\) than a randomly selected member of class \(0\)[66]. ROC or AUC are often used wherever it is necessary to verify or visualize the performance of multi-class classifications [67]. The creation of ROC curves and AUC values is a fully automated process using the Calculate ROC Curves and AUC Value tool, which was integrated into ArcGIS within the ArcSDM Tools set.
## 2 Convergence-Confinement Method and Displacement Release Coefficient
### Displacement Release Coefficient Analysis
The displacement release coefficient equation given above has achieved some results in describing the longitudinal deformation curve of the surrounding rock, but it is mostly obtained by empirical speculation and numerical fitting, without considering the influence of the change of surrounding rock parameters during tunnel excavation, so it has certain limitations. [PERSON] and [PERSON] [13], considering the influence of Poisson's ratio, proposed a displacement release coefficient equation, which is more suitable for a practical engineering application (Equation (8)). However, the relationship only considers the influence of Poisson's ratio on the displacement release coefficient and does not explain other parameters of rock mass. The influence of other parameters needs to be further studied. \[\lambda(x)\ =\ \left\{ \begin{array}{l}(0.22v+0.19)\exp\big{[}-(0.73v+0.81) \big{|}\frac{x}{R}\big{|}\big{]}\cdot\cdot\cdot x<0\\ (0.22v+0.19)+(0.81-0.22v)\bigg{\{}1-\bigg{[}\frac{0.39v+0.65}{0.39v+0.65+x/R} \bigg{]}^{2}\bigg{\}}\cdot\cdot\cdot x\geq 0\end{array} \right. \tag{8}\]