esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
Key metrics included a positive predictive value of 0.786 for up- and down-titrating \(\text{FiO}_{2}\), 0.574 for extubation, 0.139 for intubation, 0.533 for starting noradrenaline, 0.441 for fluid challenge, and 0.315 for death, indicating the TCNs' effectiveness in predicting various critical events. DataToCare is complementary to the above research. DataToCare targets shorter prediction horizons (2-4 h) over a wide range of treatments and interventions. Unlike [PERSON] et al. 's model, DataToCare does not yet make detailed dosage or drug administration route recommendations, which remains an area for future development. **Generic Treatment Recommendation Systems. ** [PERSON] (2018) uses a generic predictive model based on a recurrent neural network (RNN). Using diagnosis, medication or procedure codes, Doctor AI's RNN predicted (with high prediction performance) the diagnosis and medication categories for a subsequent visit. Analogously to DoctorAI, [PERSON] et al. (2018) proposed a framework based on LSTM, which uses temporal information of disease conditions, laboratory results, and treatment records of the patient to predict next-period prescriptions, where the next period is at least a day later. [PERSON] et al. (2019) uses a supervised reinforcement learning with an RNN approach to predict prescriptions to be given a day into the future based on a patient's state. [PERSON] and [PERSON] (2018) predict the probable curative classes of medications that a patient is taking based on billing/diagnosis codes. [PERSON] and [PERSON] (2018) proposed neighbor-based recommendation approaches where two patients are neighbors based on longitudinal non-treatment features such as lab results, clinical notes, and demographics. Unfortunately, their F1-scores were low. The neighborhood/similarity patient network approach is nevertheless intuitively attractive (see also the review by [PERSON] et al. (2018)). [PERSON] et al. (2018) developed a framework that used temporal and heterogeneous doctor order information as an input for treatment pattern discovery. That study regards cerebral infarction and is evaluated on traditional Chinese medicine. Their approach shows great promise for the diseases they treat. Using electronic health record (EHR) data and a drug-drug interaction data source, [PERSON] (2018) proposed a method to recommend safe combinations of medications. [PERSON] et al. (2018) proposed a pre-training model named G-BERT, which uses medical code representations and the diagnosis of a patient visit to predict the prescriptions for that stay. [PERSON] (2018) learns dependencies from disease diagnoses to medications to recommend treatments. These systems are designed to predict prescriptions for a time interval that is at least 24 h in the future or, in some cases, the next hospital visit. By contrast, DataToCare's concern is to predict treatments to be given in the next few hours, a critical need in the ICUsetting based on abnormal measurements. In this way, DataToCare complements the related work.
## 1 Introduction
The worst user cooperative jamming scheme has certain limitations, because it is limited by the transmitting power of the worst user and, therefore, the interference SNR is low and the interference effect on eavesdroppers is not obvious. The Gamma-Gamma distribution is used for the FSO links in the above security performance studies of the mixed RF/FSO communication system with multi-user diversity. This distribution is mainly applicable to moderate and strong turbulence in the FSO link. Recently, an increasing amount of attention has been paid to the \(\mathcal{F}\) distribution [16]. [PERSON] et al. proposed to use the \(\mathcal{F}\) distribution to simulate the atmospheric turbulence on the FSO links. The results showed that the \(\mathcal{F}\) distribution was more suitable for FSO fading models under all turbulence conditions, and the simulated experimental data matched closely with those in the actual communication scenario [17]. The \(\mathcal{F}\) distribution is used to simulate the FSO link fading. Compared with the Gamma-Gamma and M distributions, it is more suitable for all turbulence conditions and has a higher computational efficiency. Furthermore, the experimental data are more suitable for actual communication scenarios; therefore, the \(\mathcal{F}\) distribution can be used to evaluate the performance of communication systems [18]. The above research does not consider the optimal user jamming scheme when studying the multi-user mixed system, which has some limitations on the interference of eavesdroppers. There is also no literature on the application of \(\mathcal{F}\) distribution to FSO links to study the physical layer security performance of mixed systems. In recent years, it is found that the simulation results of FSO link fading using F-distribution have a good fit compared with the experimental data. To overcome the limitations of the existing techniques, this paper presents a multi-user mixed RF/FSO system with optimal user interference. The RF link experiences the Rayleigh fading distribution and the FSO link experiences the \(\mathcal{F}\) fading distribution. SWIPT and multiple users are considered in the system. Optimal user interference, equal-gain-combining (EGC) receiving and decode-and-forward (DF) relaying schemes are invoked. In the first place, the probability density function (PDF) and cumulative distribution function (CDF) of the signal-to-interference noise ratio (SINR) of eavesdroppers under interference signals are studied, in which the interference signal is transmitted by the optimal user. Second, we obtain a unified CDF based on the system signal-to-noise ratio (SNR) of the DF trunking scheme. Based on CDF, we derive closed-form formulas for the mixed system's SOP and ASC under the help of the Meijer G-function and the generalized Laguerre integration method, which are verified by Monte Carlo simulation. Through simulation, we analyzed the impact of factors such as the optimal number of interference-transmitting antennas, average interference SNR, energy conversion efficiency, time allocation factor, and atmospheric turbulence on the system's safety performance.
## 4 Discussion
### Discussion of Forest Fire Protection Using Sentinel Data  
The gap in the Canadian method for calculating the FWI for Croatian conditions is a poor gradation during the summertime (a constantly very-high FWI) and during the wintertime (a constantly very-low FWI) when there are also many forest fires. Forest fires during winter are often intentionally caused (e.g., burning of weeds) and monitored. Still, they also can be widely spread under the power lines and negatively influence it (e.g., decrease the DLR or cause physical damages).  
Combining the Canadian FWI and/or the FWI adjusted to Croatian conditions with the detected FRP values and their involvement in the contingency analysis calculation gives a precise view of power network segments that may be out of function or on segments that could be additionally burdened. As described earlier, the cloud cover, in this case, is a significant challenge (Section 2.3.1). Because of that, for now, the optimum solution is to combine the Canadian FWI calculation method and the newly obtained FWI method because the new method may isolate areas of interest. Future research may investigate better options for solving the cloud coverage challenge.  
In Section 3, it was mentioned that FRP values can be recorded out of a fire polygon. The reason may be that the smoke and ash spread relatively quickly through the atmosphere, and their cooling is slow. The FRP product enables forest fire detection and determination of fire-spreading areas. Because of the Sentinel 3 revisit time, which is twice per day, fire detection by recording FRP is impossible to use at the intra-day or hourly operational level. The analysis of the FRP values recorded over a longer time period enables a view into fire behavior in the area of interest that can be used in power system planning and maintenance.
## 4 Conclusions  
In summary, this paper provides a method for producing nitrogen-doped carbon quantum dots with a high mass yield using ordinary poplar wood as the carbon source, coupled with diluted ammonia and hydrogen peroxide, under UV radiation in a mild manner. After ten experiments under the same conditions, the mass yield of CQDs prepared by this scheme ranges from 37 to 50.6 wt%, but mainly concentrates on about 42 wt%. The mass yield attained in this study exceeds that documented in the majority of previous research employing biomass mixes as carbon source materials. Characterization using TEM, FTIR, and XPS revealed that the N-CQDs had superior water solubility, the average particle size of 5.02 nm, and the presence of nitrogen elements on their surface. Fluorescence spectrometry and UV spectrophotometry tests demonstrated that the N-CQDs solution exhibited significant UV absorption and tunable fluorescence emission capabilities; both fluorescence intensity and wavelength were dependent on N-CQDs concentration and excitation wavelength. The fluorescence emission band of N-CQDs was predominantly located within green spectrum, with a quantum yield (QY) of 3.6%. In evaluating Fe (III) detection in water, N-CQDs exhibited a strong linear correlation over a Fe (III) concentration range of 0-100 \(\upmu\)mol/L, attaining a detection limit of 4.1 \(\upmu\)mol/L. This  
Figure 5: (**a**) Fluorescence spectra of N-CQDs solutions with varying concentrations of Fe (III); (**b**) the relationship curve depicting the ratio \(F_{0}/F\) against Fe (III) concentration, with the inset illustrating the linear fitting graph for Fe (III) concentrations ranging from 0 to 100 \(\upmu\)mol/L; (**c**) fluorescence visualization of N-CQDs solutions at different Fe (III) concentrations under both natural and ultraviolet light.  
study's technique effectively accomplished the hydrothermal synthesis of N-CQDs, distinguished by high mass yield, cheap cost, and simple operational procedures, offering novel perspectives for the large-scale production and modification of N-CQDs with abundant fluorescence characteristics.  
Funding acquisition, J.W.; Investigation, Y.H.; Methodology, P.M.; Project administration, J.W.; Supervision, J.W.; Validation, P.M.; Visualization, P.M.; Writing--original draft, P.M., Y.H. and J.W.; Writing--review and editing. P.M., Y.H. and J.W. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by the Fundamental Research Funds for the Central Universities, grant number 2572023 DJ03.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to they are the part of an ongoing study.  
Author [PERSON] was employed by the company Northeast Forestry University Engineering Consulting Design and Research Institute Co., Ltd. But 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.
## 2 Methodology
### Model Validation and Evaluation  
After processing the imbalanced algorithm FC-SMOTE, four types of pathological voice class-balanced datasets and normal and pathological voice samples are obtained from the initial class-imbalanced dataset. In this process, in order to ensure that our results are not biased towards specific machine learning models and reduce the risk of overfitting, we validated and evaluated a set of standard representative machine learning classifiers (logistic regression(LR), naive Bayes (NB), decision tree (DT), support vector machine (SVM), K nearest neighbours (KNN), random forest (RF), XGBoost, and gradient tree boosting (GBDT)), as well as a deep-learning-based classification model (multi-layer perceptron (MLP) and convolutional neural network (CNN)).  
```
0:\(\mathbf{X}=\{\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{n}}\}\), \(j\)(a specify a number of clusters), \(k\)(number of nearest neighbors to be found by SMOTE), \(c\)(membership matrix dimensions). Output: a class-balanced dataset. Step 1: The input elements \(\mathbf{X}\) is clustered into \(j\) clusters by Fuzzy C-means. Step 2: Select the safe areas used for oversampling based on fuzzy set, according to Formula (2). Step 3: for safe areas \(j^{{}^{\prime}}\)do  (a): Calculate the distance matrix \(\mathbf{D}_{j^{{}^{\prime}}}\) between the membership value of each minority sample point \(S_{min}\) and the cluster center point, according to Formulas (3)-(5).  (b): Obtain the mean distance of minority samples. \(sum\)\(uclidean\)\(distance\)\(\leftarrow\)\(\sum_{S_{min}\in j^{{}^{\prime}}}\mathbf{D}_{j^{{}^{\prime}}}\),  mean \(minority\)\(distance\)\(\leftarrow\) mean(sum \(uclidean\) distance)  (c): Calculate the sampling weight. \(density\)\(\leftarrow\)\(\frac{\|U_{i}-V_{i}\|}{mean\)\(uclidean\)\(distance\)\(\leftarrow\)\(\frac{1}{density}\)  (d): Calculate the number of new minority samples that need to be generated by SMOTE around each minority sample, according to the sampling weight. \(number\)\(of\)\(samples\)\(\leftarrow\)\(|(S_{maj}-S_{min})\times sampling\)\(weight|\), \(generated\)\(samples\)\(\leftarrow\)\(S_{min}\cup\{SMOTE(u,k)\}\), \(u=(1,2,...,S_{min})\)  (e): Combine \((S_{maj}-S_{min})\) and \(\sum_{u=1}^{S_{min}}N_{min}^{u}\) to construct a class-balanced dataset, according to Formulas (9) and (10). endfor
```  
**Algorithm 1** The algorithm of the FC-SMOTE  
Figure 3: The flowchart of FC-SMOTE.
## 3 Results and Discussion  
### FT-IR Analysis  
Figure 1 shows the FT-IR spectra obtained for the biomass of _Staphylococcus epidermidis_ CECT 4183 before and after the biosorption step. As can be seen, the biomass initially had a large number of available functional groups, which were affected after contact with Cu(II)ions. The spectrum obtained after Cu(II) biosorption showed a significant loss of intensity, and several functional groups were displaced or disappeared. In this sense, there is a clear involvement of methyl groups (-CH\({}_{3}\)), as indicated by the shifts of the peaks located at 3267 cm\({}^{-1}\) (which changes to 3074 cm\({}^{-1}\)) and at 2853 cm\({}^{-1}\) (which changes to 2875 cm\({}^{-1}\)), both involving vibrations due to asymmetric stretching of C-H bonds. This involvement of C-H bonds is confirmed in the region between 1300 and 1400 cm\({}^{-1}\), characteristic of bending vibrations for this type of bond, where a band shift from 1398 cm\({}^{-1}\) to 1386 cm\({}^{-1}\) occurs together with a band disappearance at 1335 cm\({}^{-1}\)[21].  
Likewise, the involvement of carbonyl and amino groups linked to amide I and amide II is demonstrated by the shifts 1628-1634 cm\({}^{-1}\) and 1539-1518 cm\({}^{-1}\), respectively, which are related to stretching of -CO and -CN bonds and bending vibrations of N-H bonds. On the other hand, a strong shift in the 1218 cm\({}^{-1}\) band (which shifted to 1229 cm\({}^{-1}\)) was observed and could indicate the participation of carbonyl groups but also the participation of phosphate groups due to asymmetric P=O bond strains. This participation of phosphate groups could be confirmed by the disappearance of three bands in the region between 800 and 1000 cm\({}^{-1}\) and by the shift of the band located at 525 cm\({}^{-1}\) after the biosorption stage. [22,23]. Other authors have reached similar conclusions, [PERSON] et al. [24] identified the participation of carboxyl, hydroxyl, and phosphate groups in the biosorption of Cu(II) by the green alga _Neochloris oleoabundans_. Similarly, [PERSON] et al. identified the participation of hydroxyl, carboxyl, amino, carbonyl, and phosphate groups in the biosorption of Cu(II) by bacterial biofilms [25].
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 2 Materials and Methods
### Discriminant Factor
#### 2.3.1 Approximate Entropy (ApEn)  
The general idea of approximate entropy is to decompose a time series into \(k\) series, with \(m\) as the window size. The distance between each series and all series needs to be calculated, which is defined as the absolute value of the difference between two series. Then, the distance values are tabulated for subsequent operations. Here, a threshold F is usually defined as  
\[F=r\times SD \tag{11}\]  
where \(r\) is the similarity tolerance, which is usually 0.1-0.25. \(SD\) means the standard deviation of the series.  
Then, the number of the distance value in each row of the table which is greater than the threshold \(F\) needs to be counted, which is used to calculate the ratio to the total number. Next, the logarithmic average is calculated according to the ratio. Then, the window size is increased by 1, and the operation is repeated to obtain another logarithmic average. Finally, the approximate entropy can be obtained through calculating the difference between the logarithmic averages.
## 2 Materials and Methods
### Gas Chromatography/Mass Spectrometry (GS-MS) Analysis  
To further determine antagonistic potential of _L. plantarum_ colonies, they were grown on MRS agar in special glass jars with elastic septum seal at their top. A glass jar with sterile MRS served as control. After incubation at 37 \({}^{\circ}\)C for 24, 48, or 72 h, SPME fiber (solid-phase microextraction (SPME) using stable-flex fibers, 1 cm in length, coated with a 50/30-\(\upmu\)m layer of divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) (Supelco, Bellefonte, PA, USA)) was inserted into the jar, and this complex was incubated in 50 \({}^{\circ}\)C for 30 min in order to allow the adsorption of the volatiles into the fiber. After incubation, manual injection took place, and the fiber was desorbed for 1 min at 250 \({}^{\circ}\)C in the splitless inlet of the GC (Agilent 7890A, Palo Alto, CA, USA) equipped with an HP-5 column (30 m \(\times\) 0.25 mm ID, 0.25 \(\upmu\)m film thickness, J&W Scientific, Folsom, CA, USA). The oven was programmed for initial temperature of 40 \({}^{\circ}\)C for 1 min, and then ramped to 150 \({}^{\circ}\)C at 5 \({}^{\circ}\)C min\({}^{-1}\), and to 270 \({}^{\circ}\)C at 15 \({}^{\circ}\)C min\({}^{-1}\) and held at that temperature for 1.3 min. The helium carrier gas flow was set at 0.8 mL min\({}^{-1}\). The effluent was transferred to an MS detector (Agilent 5975C, Palo Alto, CA, USA) that was set to scan from mass 40 through 300 at 7.72 scans/s in the positive-ion mode, and mass spectra in the electron impact (EI) mode were generated at 70 eV. Chromatographic peaks were identified by comparing the mass spectrum of each component with the US National Institute of Standards and Technology (NIST) library of mass spectra, 2006 version. Volatile's composition was similar in samples incubated for 7 days.
## 4 Simulation and Discussion
### Composite Scattering of Plain and Valley Composite Rough Surface  
This subsection investigates the bistatic composite scattering characteristics of plain and valley composite rough surfaces. The wind speed \(v_{19.5}=0.2\) m/s, and the RMS height \(h_{\text{rms}}=0.2\lambda\). For plain environment, the \(\alpha_{1}=\alpha_{3}=0^{\circ}\); for valley environment, the \(\alpha_{1}=\alpha_{3}=30^{\circ}\). The illustration of simulation geometry is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Polarization**} & \multirow{2}{*}{**Memory (GB)**} & \multirow{2}{*}{**Time (s)**} & \multirow{2}{*}{**Maximum Error (dB)**} & \multirow{2}{*}{**Average Error (dB)**} \\  & & & & & \\ \hline \multirow{2}{*}{MLFMA} & HH & 15.236 & 1251 & - & - \\  & VV & 15.373 & 1264 & - & - \\ \hline \multirow{2}{*}{SBR-TSM} & HH & 2.663 & 87.6 & 26.60 & 3.75 \\  & VV & 2.679 & 88.1 & 27.21 & 4.18 \\ \hline \multirow{2}{*}{MSBR-FBSSA} & HH & 2.703 & 89.7 & 9.13 & 1.63 \\  & VV & 2.730 & 91.3 & 10.41 & 1.85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Efficiency and accuracy of different algorithms.  
Figure 9: Bistatic RCS of a flare above cosine-anyon composite rough surface: (**a**) HH polarization; (**b**) VV polarization.  
In Figure 11, the incident angle is \((\theta_{i},\varphi_{i})=(-30^{\circ},90^{\circ})\), the observation angles are \((\theta_{s},\varphi_{s})=(-90^{\circ}\sim 90^{\circ},90^{\circ})\). The plain and valley RCS curves have a similar trend in most of the angular range. They all peak at \(\theta_{s}=30^{\circ}\), which is the specular reflection direction of the plain and the valley's bottom. On both sides of \(\theta_{s}=30^{\circ}\), the RCSs decrease gradually with the observation angles varying. Differently, for the scattering of the valley, the curves have other peaks at \(\theta_{s}=30^{\circ}\), which are caused by the structure of the right slope. When the incident angle \(\theta_{i}=-30^{\circ}\), the electromagnetic waves hit the right slope vertically, and the waves are reflected vertically to the source point, causing the peak at \(\theta_{s}=-30^{\circ}\). Moreover, due to the multiple reflections of incident EM waves inside the semi-enclosed cavity structure of the valley composite rough surface, the RCSs at near \(\theta_{s}=90^{\circ}\) are higher than those of plain, which means the scattering power is redistributed because of the unique structure. In a real detection scenario, the number of peaks can help to recognize the plain and valley.
# English-Welsh Cross-Lingual Embeddings
## 2 Background
### Welsh Language NLP  
Recently there has been much research in the space of applying NLP to non-English minority languages such as Welsh. The defining characteristic of a minority language is that the amount of corresponding data available for that language is significantly less than that available for the English language. Most state-of-the-art NLP models use deep learning where performance scales with the amount of available data. Given this, achieving performance on NLP tasks for minority languages is on par with that achieved for the same tasks for the English language represents a significant challenge. The Welsh Natural Language Toolkit (WNLT) is a Welsh-Government-funded project which focuses on the development of NLP tools for the Welsh language ([[https://hypermedia.research.southwales.ac.uk/kos/wnlt/](https://hypermedia.research.southwales.ac.uk/kos/wnlt/)]([https://hypermedia.research.southwales.ac.uk/kos/wnlt/](https://hypermedia.research.southwales.ac.uk/kos/wnlt/)), accessed on 15 July 2021). The tools in question are distributed under GNU Lesser General Public License (LGPL) and include tools for tokenization, lemmatization, POS tagging, and Named Entity Recognition (NER) ([[https://sourceforge.net/projects/wnlt-project/](https://sourceforge.net/projects/wnlt-project/)]([https://sourceforge.net/projects/wnlt-project/](https://sourceforge.net/projects/wnlt-project/)), accessed on 15 July 2021). [PERSON] et al. [11] developed a rule-based part-of-speech (POS) tagger for the Welsh language entitled CyTag. Although state-of-the-art POS taggers for the English language use deep learning, the authors argue there is insufficient Welsh language data to use such an approach for the Welsh language. The same authors later developed a rule-based semantic tagger, entitled CySemTagger [15]. Both of these tools are available under a free software (GPL version 3) licence ([[https://github.com/CorCenCC](https://github.com/CorCenCC)]([https://github.com/CorCenCC](https://github.com/CorCenCC)), accessed on 15 July 2021). [PERSON] et al. [16] developed a statistical machine translation model for the English and Welsh language pair. [PERSON] et al. [17] developed a statistical method for multiple word term recognition in Welsh. This method builds on a previously proposed term-recognition method known as FlexiTerm [18].