esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Limitations and Strengths  
Limitations: The principal limitation of the study is that only eight recommendations have been made with a high quality of evidence because there are important gaps in methods for assessing nutritional status, the impact of muscles in the process, nutritional requirements, and the most appropriate interventions in terms of nutrition, specific nutrients, and physical activity. The opinion of people with COPD and their caregivers has not been taken into account, nor are there pulmonology specialists in the group. Lastly, the final document was not sent to an external group for validation.  
Strengths: A systematic review has been carried out with a methodology following the PRISMA criteria and applying grading of the evidence according to the SIGN (Scottish Intercollegiate Guidelines Network) methodology. In addition, an approximation has been made to the degree of consensus among experts in the area. Questions have been addressed with an eminently practical orientation.
## 3 Materials and Methods
### GC-MS and GC-O Analysis
#### 3.4.2 Gas Chromatography-Olfactometry (GC-O)  
The GC-olfactometry (GC-O) system was composed of a Varian 3800 GC (Varian Instrument Group, Walnut Creek, CA) coupled to a sniffing port (ODO II, SGE International, Ringwood, Australia) and a flame ionization detector (FID). Analytical conditions were performed following our published paper [27]. In brief, the conditions of volatile compounds extracted by both SAFE and HS-SPME were the same as GC-MS. Two different columns, namely, DB-wax column (30 m length, 0.25 mm i.d., 0.25 \(\upmu\)m film thickness, J&W Scientific, Folsom, CA, USA) and DB-5 ms column (30 length, 0.25 mm i.d., 0.25 \(\upmu\)m film thickness, J&W Scientific, Folsom, CA, USA),respectively, were used for GC-O. Each SPME fiber was injected into the GC injector for 10 min. The GC conditions were the same as GC-MS:overn temperature program was from 40 \({}^{\circ}\)C to 200 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min with initial 5 min and final 10 min, respectively. The flow rate of the carrier gas helium was 1.4 mL/min. Injector and detector temperatures were 280 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. GC-O was performed in triplicate.  
To determine the most aroma-active components, aroma extract dilution analysis (AEDA) was carried out by serially diluting (1:3) the SAFE extracts using the same diluent. The flavor dilution (FD) factor is determined as the highest dilution where a volatile odor can still be perceived [27]. The HS-SPME extracts were stepwise diluted (10 mL DDW mixed with 5 g, 2.5 g, 1.25 g and 0.625 g of doubanjiang, respectively, were put into a 20-mL vial) and then balanced at 40 \({}^{\circ}\)C for 30 min. The sample dilution (SD) factor is determined as the highest sample dilution where an odorous substance can be perceived [28].  
Compound Identification, Quantitation of the Key Odorants and Calculation of Odor Activity Values (OAVs)  
Compounds were identified based on a comparison of the WILEY 7.0 mass spectral library, retention indices (RIs) and authentic standard compounds [26]. The retention indice (RI) values were calculated on DB-wax and DB-5 ms columns using C\({}_{10}\)-C\({}_{22}\) and C\({}_{8}\)-C\({}_{20}\) as the external reference, respectively.  
The selective ion monitoring (SIM) mode was used to quantitate the concentration of a compound in the SAFE concentrate mentioned above with 8-point external standard curves. The identified key aroma compounds were confirmed by authentic chemicals, and the standard curve for individual aroma compounds was obtained by calculating the response factor of known concentrations on GC-MS. The unit of concentration in the SAFE concentrate (ug/L) was converted to micrograms per kilogram (ug/kg, doubanjiang) according to the weight of sample used. For each compound, the OAVs were calculated by dividing the compound concentration in the samples by the mean values of its estimated odor threshold. Relative quantification measurement was also performed for all the volatile compounds, using 3-heptanol as the internal standard.
## 4 Experimental Analysis
### Presentation of Analysis on a Practical Application Related to Human-Machine Interaction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Extracted Term**} & \multirow{2}{*}{\begin{tabular}{c} **TF-IDF** \\ **Score** \\ \end{tabular} } & \begin{tabular}{c} **Frequent** \\ **Co-Occurrence** \\ **Words** \\ \end{tabular} & \
\begin{tabular}{c} **Machine Learning Model** \\ **Prediction** \\ \end{tabular} &
\begin{tabular}{c} **Flagged as** \\ **Suspicious?** \\ \end{tabular} \\ \hline
1 & Bombing & 0.072 & Explosion, Attack &
\begin{tabular}{c} Extra Trees (94.31\% accuracy) \\ \(\rightarrow\) High Risk \\ \end{tabular} & Yes (Flagged) \\ \hline
2 & Radicalization & 0.037 & \
\begin{tabular}{c} Extremism, \\ Recruitment \\ \end{tabular} &
\begin{tabular}{c} XGBoost (90.21\% accuracy) \(\rightarrow\) \\ Medium Risk \\ \end{tabular} & Yes (Flagged) \\ \hline
3 & \begin{tabular}{c} Security \\ Checkpoint \\ \end{tabular} & 0.045 & Surveillance, Monitor & \
\begin{tabular}{c} Random Forest (90.89\% \\ accuracy) \(\rightarrow\) Low Risk \\ \end{tabular} &
\begin{tabular}{c} No (Not \\ Flagged) \\ \end{tabular} \\ \hline
4 & Hostage & 0.029 & Abduction, Threat &
\begin{tabular}{c} Decision Tree (91.47\% \\ accuracy) \(\rightarrow\) Medium Risk \\ \end{tabular} & Yes (Flagged) \\ \hline
5 & Public Gathering & 0.066 & Civilians, Casualties & \
\begin{tabular}{c} Bootstrap Aggregating \\ (87.81\% accuracy) \(\rightarrow\) Low Risk \\ \end{tabular} &
\begin{tabular}{c} No (Not \\ Flagged) \\ \end{tabular} \\ \hline
6 & Cyberterrorism & 0.03 & \
\begin{tabular}{c} Hacking, Network \\ Attack \\ \end{tabular} &
\begin{tabular}{c} Extra Trees (94.22\% accuracy) \\ \(\rightarrow\) High Risk \\ \end{tabular} & Yes (Flagged) \\ \hline
7 & \begin{tabular}{c} Intelligence \\ Report \\ \end{tabular} & 0.061 & \begin{tabular}{c} Database Record, \\ Analysis \\ \end{tabular} & \
\begin{tabular}{c} XGBoost (90.01\% accuracy) \(\rightarrow\) \\ Low Risk \\ \end{tabular} &
\begin{tabular}{c} No (Not \\ Flagged) \\ \end{tabular} \\ \hline
8 & Military Target & 0.064 & Attack, Strategy &
\begin{tabular}{c} Random Forest (90.42\% \\ accuracy) \(\rightarrow\) Medium Risk \\ \end{tabular} & Yes (Flagged) \\ \hline
9 & Fake News & 0.038 & \begin{tabular}{c} Misinformation, \\ Propaganda \\ \end{tabular} & \
\begin{tabular}{c} Decision Tree (91.47\% \\ accuracy) \(\rightarrow\) Low Risk \\ \end{tabular} &
\begin{tabular}{c} No (Not \\ Flagged) \\ \end{tabular} \\ \hline
10 & Suicide Attack & 0.075 & Casualty, Martyr &
\begin{tabular}{c} Extra Trees (94.31\% accuracy) \\ \(\rightarrow\) High Risk \\ \end{tabular} & Yes (Flagged) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Mapping predictions for flagging suspicious content.
## 3 Interrogation Techniques
### Polarimetric Detection  
#### 3.1.1 Basic Polarimetric Scheme  
Different signal analysis techniques can be used to quantify the Faraday rotation of the azimuth of the output light from an optical current sensor. One way to detect this rotation is by using a polarimetric detection scheme, which consists of two polarizers, one at the input of the sensor and the other at the output, arranged as shown schematically in Figure 2. The first polarizer has the objective of defining the initial polarization state of the light wave and the second polarizer is used to adjust the sensor sensitivity and transforms the polarization rotation into a light intensity modulation that can be measured using a photodetector [1].  
According to Malus law, when two polarizers are in such arrangement, the intensity of the output light is given by [2]:  
\[I_{out}{=}I_{P1}{\cdot}cos^{2}(\alpha) \tag{2}\]  
where \(I_{P1}\) is the light intensity after the first polarizer and \(\alpha\) is the relative angle between the transmission axes of the polarizers. As we can see from Figure 3, maximum sensitivity is achieved when this angle is 45\({}^{\circ}\). This means that with the polarizers having a relative angle of 45\({}^{\circ}\), any small change in the plane of polarization of the light, in the path between the two polarizer's, induced by an external magnetic field, will be transformed in a larger change of intensity at the output.  
Figure 2: Polarimetric detection scheme.  
Adjusting the polarizers for the maximum sensitivity, the sensor transfer function is [1]:  
\[I_{out}{=}\frac{I_{in}}{2}[I{+}sin(2\theta_{\beta})] \tag{3}\]  
In order to eliminate the dependence of the sensor response to the input light intensity fluctuations, the final processed output signal can be obtained by dividing the AC component by the DC component [1]. That is:  
\[S_{AC}{=}sin(2\theta_{\beta}) \tag{4}\]
# Syntax-Informed Self-Attention Network for Span-Based Joint Entity and Relation Extraction
## 2 Related Works  
### Syntax-Based Relation Extraction Model  
Syntactic features are beneficial for RE tasks because they can capture dependencies between different components in a sentence that may be ambiguous on the surface. In the past, at the core of relation extraction approaches are statistical classifiers, many of which find that syntactic features play an important role. For traditional methods, [PERSON]. [23] proposed a tree-based kernel approach and introduced kernels defined over shallow parse representations of text. [PERSON]. [24] introduced the simplified dependency path to tree kernels. They proved that such feature can improve the performance of extraction. For neural-based models, [PERSON]. [25] first proposed to learn relation representations from Shortest Dependency Path (SDP) through Convolutional Neural Network (CNN), and [PERSON]. [26; 27] tried to employ LSTM to encode SDP, which proved the effectiveness for syntactic feature encoding with neural models. In addition, they removed irrelevant information by trimming the trees and achieved better results. In addition to these preliminary attempts, some works modeled syntax trees by improving feature extractors. [PERSON]. [6; 28] used Tree-LSTM [29] and Tree-GRU models, respectively, which were an extended form of RNNs over dependency trees and had stronger modeling capabilities. After that, since the dependency tree structure is inherently graph-like, with the emergence of Graph Convolution Network (GCN) [30], [PERSON]. [11; 31] employed GCN to generate syntactic representations. Experimental results proved that sequence models had complementary strengths to syntax-based models and combining them can achieve impressive performance on their benchmarks. Ref. [32] used dependency trees to extract the syntax-based importance scores for the words with Ordered-Neuron Long-Short Term Memory Networks [33] (ON-LSTM) and injected these features into the model. Their experimental results showed that capturing the syntactic importance of the words was beneficial for RE task and had greater generalization ability.  
In addition, with the introduction of Transformer [14], although the multi-head self-attention mechanism has been proven to have excellent performance in some NLP tasks, it lacks a hierarchical structure of the input sentence. Refs. [34; 35] employed absolute structural position and relative structural position to encode structural features, and integrated them into self-attention, respectively, which had better performance on machine translation tasks. In summary, it is feasible to incorporate syntactic feature into self-attention and provide more possibilities for the expansion of syntax-based model in other NLP tasks.
## 4 Results
### Accuracy of COMS/MI SST and Spatial Distribution of Errors  
The MCSSTs and NLSSTs during the daytime and nighttime were compared with the in situ SSTs of a drifting buoy for a period of 3 years, i.e., from April 2011 to March 2014 (Figure 5). Both MCSSTs and NLSSTs showed good agreement with the in situ SSTs, with correlation coefficients of more than 0.98. Relative to the in situ SSTs, the MCSSTs had a very small negative bias of \(-\)0.02 \({}^{\circ}\)C and \(-\)0.04 \({}^{\circ}\)C in the daytime and nighttime respectively. The NLSSTs in both the daytime and nighttime were also slightly lower than the in situ temperatures, with a bias of \(-\)0.01 \({}^{\circ}\)C and \(-\)0.08 \({}^{\circ}\)C, respectively. This could be due to the effect of undetected high cirrus, as well as sub-pixel clouds and abnormal atmospheric conditions [60]. The RMSEs of the NLSSTs (0.58 \({}^{\circ}\)C in the daytime and 0.71 \({}^{\circ}\)C in the nighttime) were smaller than those of the MCSSTs (0.67 \({}^{\circ}\)C during the daytime and 0.79 \({}^{\circ}\)C during the nighttime), which agrees with results in previous studies [17; 61]. The accuracy of SST derived from this study is similar to that of GOES of NOAA and MTSAT (Multifunctional Transport Satellites) of JMA [7; 62].  
Figure 6 illustrates the spatial distribution of differences between satellite-derived SSTs (MCSST and NLSST) and in situ temperatures for 5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\) bin for daytime and nighttime. In some of the tropical regions, the mean bias was not calculated due to persistent cloud cover. Overall, there are biases within \(\pm\)0.5 \({}^{\circ}\)C throughout the full-disk area except where negative biases exceeding \(-\)0.5 \({}^{\circ}\)C are identified at low latitudes. The satellite-derived SSTs tend to be overestimated in the seas around Australia. On the other hand, there is a small bias in the Northwest Pacific. The spatial pattern of mean bias is similar between MCSST and NLSST; NLSST (Figure 6c,d) has a smaller and uniform bias than MCSST (Figure 6a,b) during both the daytime and nighttime.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Time**} & \multicolumn{4}{c}{**Coefficients**} & \multirow{2}{*}{**RMSE (\({}^{\circ}\)C)**} & \multirow{2}{*}{**Bias (\({}^{\circ}\)C)**} \\ \cline{4-4} \cline{6-9}  & & & **a0** & & & & & & \\ \hline \multirow{4}{*}{MCSST} & Split & Day & \(-\)0.4907 & 1.0039 & 1.9956 & 0.7340 & 0.67 & \(-\)0.02 \\  & Split & Night & 0.6351 & 1.0196 & 1.5888 & 0.7250 & 0.79 & \(-\)0.04 \\  & Triple & Night & 2.0183 & 0.9849 & 0.7737 & 0.4149 & 0.56 & \(-\)0.02 \\ \hline \multirow{4}{*}{NLSST} & Split & Day & 2.1785 & 0.9071 & 0.0650 & 0.7499 & 0.58 & \(-\)0.01 \\  & Split & Night & 2.7423 & 0.9272 & 0.0563 & 0.6946 & 0.71 & \(-\)0.08 \\ \cline{1-1}  & Triple & Night & 3.2185 & 0.9381 & 0.0259 & 0.4450 & 0.52 & \(-\)0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Coefficients of MCSST and NLSST algorithms in the Equations (1)–(4) and root mean squared errors (RMSEs) and bias errors.
# Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles
## 4 Discussion  
The purpose of this study was to ensure the stable operation of autonomous vehicles. While it is important for autonomous vehicles to travel on the shortest route, it is also important to drive on a safe and optimal route with less collision risk. Most obstacles surrounding a moving vehicle are dynamic obstacles. Therefore, speed information must be taken into account when detecting free space. By excluding the free space in which driving is impossible in consideration of the speed information, unnecessary calculations can be reduced, and the risk of a collision can be further reduced. This can be confirmed by the difference between the result path creation screen and the collision risk. If this algorithm is applied to an autonomous vehicle, a safe route can be generated in various environments where route creation is impossible, such as an environment where lanes are not recognized, or where it is difficult to build an accurate map. Therefore, safer driving is possible through this algorithm.
## 2 Signal Division Method of Bearing Health State
### Health Indicator Selection  
The health indicator (HI) can characterize the degradation status information of the bearing and is an important indicator to evaluate the health status of the bearing. For health status classification, it is essential to construct HI curves that can accurately characterize the health status of bearings from the monitoring data of mechanical equipment.  
To further reveal the degradation characteristics of the bearing, the time-domain characteristics are extracted from the bearing monitoring signal as HI. Some time-domain characteristics of the bearing vibration signal are shown in Table 1. Kurtosis is particularly sensitive to shock signals, and early bearing failures mainly originate from the action of alternating shock loads, so it is particularly suitable for early fault diagnosis. Furthermore, the advantage of the root mean square (RMS) is better stability. Thus, the kurtosis factor and RMS were initially selected as HI.  
To take into account both the time-domain and frequency-domain characteristics of the bearing signal, the sum of the frequency energy per unit acquisition period of the short-time Fourier transform (STFT SUM) is selected as the health factor in the frequency domain.  
The window function is chosen as the Hemming window, the window length is set to 32,768, and the overlap is set to 0. From the XJTU-SY dataset, it is known that the data volume of the signal collected every 1.28 s is 32,768, and the STFT SUM index of the signal collected every minute can be obtained by summing its amplitude.  
In the table, N is the number of samples and \(\rm x_{i}(t)\) is the value of the data at moment t.  
Based on the above-selected health factors, signal analysis was performed on the XJTU-SY dataset. The signal characteristics reflecting the health factors are shown in Figure 2. The RMS value starts to rise in the degradation stage and reaches the threshold value quickly in the damage stage. The kurtosis factor is suitable for the diagnosis of early failure, and the kurtosis indicator has a significant fluctuation of the trend in the health phase, which can easily misjudge the normal health phase as the fault phase. Compared with the other two health indicators, the kurtosis factor is more influenced by external noise and cannot reflect the degradation trend of bearing performance well.
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 3 Numerical Analysis and Results
### Location Estimation Simulation
#### 3.2.2 Radio Propagation Model  
This section describes the radio propagation model used in the simulation of radio source location estimation using the HAPS. In this simulation, statistical data were not used in the propagation model because a ray tracing simulation was conducted to determine  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Direction** & **Item** & **Value** \\ \hline Horizontal & Number of elements & 12 \\ Element spacing [m] & 0.6\(\lambda\) \\ \hline Vertical & Number of elements & 3 \\ Element spacing [m] & 0.5\(\lambda\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the cylinder antenna.  
Figure 14: Cylinder antenna used for simulation.  
specific radio waves. The free propagation loss was modeled using Friis formula, as follows [16].  
\[PL_{fsp}=10 log_{10}\bigg{(}\frac{4\pi d}{\lambda}\bigg{)}^{2}[dB] \tag{35}\]  
where \(d\) and \(\lambda\) are the distance between the transceiver pairs and the wavelength of the considering radio wave, respectively.  
The overall path loss can be expressed by Equation (36), where the additional attenuation \(L_{ref}\) due to earth and building reflections was calculated using the Fresnel equation. The permittivity and conductivity of the surface materials used in these calculations were taken from the ITU-R study [17]. Assuming that both the ground surface and the building are concrete, the relative permittivity and conductivity were calculated as 5.31 and 0.0548 [S/m], respectively.  
\[PL=PL_{fspl}+PL_{ref}\ \ \ \ \ [dB] \tag{36}\]
# Effect of Montmorillonite on 4-Nonylphenol Enrichment in Zebrafish
###### Abstract  
The aim of this study was to investigate the effect of montmorillonite on nonylphenol (4-nonylphenol, 4-NP) enrichment in a zebrafish model. The AB strain zebrafish were used as the animal subjects, and three concentration gradients were set for both nonylphenol and montmorillonite, according to their actual concentrations in aquaculture water in Huzhou City. A group treated with nonylphenol alone was also set, adding up to 12 experimental groups. Concentrations of nonylphenol enriched in the liver, muscle and gills of zebrafish were detected by solid phase microextraction-high performance liquid chromatography at Days 7, 15 and 30, respectively. Additionally, the relative enzymatic activity of superoxide dismutase (SOD) and the glutathione S-transferase (GST) were also detected, and the data were statistically analyzed. The results showed that the concentrations of nonylphenol in zebrafish peaked at Day 7 and gradually decreased afterwards for all the experimental groups. The montmorillonite reduces short-term accumulation of nonylphenol in gills, and the high concentration of nonylphenol facilitates its enrichment in liver and muscle, while the low concentration of nonylphenol does not. Meanwhile, the low concentration of nonylphenol in liver exerts an influence on the inductive effect of SOD and GST, while the high concentration of nonylphenol shows the inhibiting effect of SOD and GST.  
4-nonylphenol; montmorillonite; zebrafish; enrichment; enzyme activity +
Footnote †: journal: Journal of Environmental Research and Public Health