esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Overview of Short-Range and Long-Range Communication  
V2X systems allow vehicles to communicate with each other and with roadside infrastructure, providing real-time information about traffic conditions, potential hazards, and more. There are two main types of V2X communication: short-range (direct) and long-range cellular. V2X technology classification [16] is shown in Figure 2. Both short-range direct and long-range V2X communication both have their own characteristics, benefits, and challenges. Both are important for enabling Advanced Driver Assistance Systems (ADAS), C-ITS, autonomous driving, and hybrid V2X communication. In this section, the characteristics, benefits, and challenges of each type of communication are explored.
## 4 Discussion  
### River Contribution and Sediment Diffusion  
The DFYR was less than 0.5 in southern, middle and western Bohai Bay, which indicates that materials from the Yellow River dominated almost the entire bay. The DFLR in northeast Bohai Bay was less than 0.5, which indicates that material from the Luanhe River dominated only in northeast Bohai Bay. The DFLR in the tidal sand ridge outside Caofeidian was also small, serving as the evidence that its material came from the Luanhe River. The sediment in south and west Bohai Bay had a small DFYR with Yellow River provenance, which may have been related to the accumulation history of the Yellow River Delta. In the past, the Yellow River frequently changed its course to the sea. The area from the north bank of Bohai Bay to Tianjin, Huanghua, Binzhou, Kenli and Lijin was once an estuary of the Yellow River [26,27]. Currently, the west bank and south bank of Bohai Bay are deltas formed by materials from the Yellow River.  
The DFYR distribution showed that the influence of the Yellow River materials gradually weakened from southwest to northeast, an indicator for the dispersion process of the material from the Yellow River as it was transported from the Yellow River Delta to its surrounding areas. The DFLR distribution showed that the influence of Luanhe River material gradually weakened from northeast to southwest, an indicator for the dispersion process of Luanhe River material as it was transported from the Luanhe River Delta to the southwest. Thus, the results of DF and GSTA are unified. Areas C and D in the western part of Bohai Bay (Figure 2) are influenced by mixed material sources from different rivers.  
The sediment transport trends derived from GSTA and DF analyses in this study are consistent with the results obtained from geochemical analyses [5], grain size [28] and remote sensing inversion of suspended sediment [29,30].  
Approximate influence range of different river sources is shown in Figure 3 and is generally consistent with the previous research [3, 4, 5]. [PERSON] et al. [5] gave the location of the influence boundary between the Yellow River and Luanhe River (38 deg42' N). However, this study indicates that the influence of the Yellow River source is still underestimated, and the influence boundary between the Yellow River and Luanhe River is estimated to be at 38 deg50' N.  
The sediment diffusion details of the local sea area were finely described in this study. For example, although we neglected the source of the Haihe River in the discriminant function, the effect boundary between the Haihe River and the Yellow River can still be  
Figure 3: Contour map of DF in Bohai Bay based on Ca/Al. (**A**) DFYR: DF of surface sediments with Yellow River source. (**B**) DFLR: DF of surface sediments with Luanhe River source. Red dotted lines indicate the approximate range of influence of different river sources. Black arrows indicate the direction of sediment diffusion.  
found from the map. Figure 3 shows that the influence of the Haihe River source is limited to the west of 118\({}^{\circ}\) E.
_Applied_  
_sciences_  
_Article_  
**The Effect of Unsupported Sleepers/Bearers on**  
**Dynamic Phenomena of a Railway Turnout System**  
**under Impact Loads**  
[PERSON] 1, [PERSON] 2, [PERSON] 3 and [PERSON] 1,*(c)1  
1 Department of Civil Engineering, School of Engineering, The University of Birmingham, Birmingham B152 TT, UK; [EMAIL_ADDRESS]  
2 School of Metallurgy and Materials, The University of Birmingham, Birmingham B152 TT, UK; [EMAIL_ADDRESS]  
3 Loram Finland Oy, Yliopistonkatu 58 D, 33100 Tampere, Finland; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
Received: 26 February 2020; Accepted: 24 March 2020; Published: 28 March 2020  
**Featured Application: The new findings highlight for the first time the nonlinear dynamic phenomena of turnout systems considering unsupported composite and concrete sleepers and bearers. The in-depth insights stemmed from multi-body simulations of dynamic train--turnout interaction demonstrates the influences of unsupported sleepers on dynamic responses, load redistributions, dynamic impact factors and wheel/rail contact forces. The novel insights can be used (not limited to) as a reference for the design, inspection and maintenance of railway switches and crossings.**  
**Abstract: Track settlement is a common problem observed in ballasted railway tracks. The ballast bed and the material layers underneath it, deform under repeated trainloads and create uneven support conditions along the track. In some cases, the ballast settlement could be detrimental and the sleepers lose contact with the ballast bed partially or completely, resulting in higher contact forces and load distributions over the supported sleepers. Numerous studies have been conducted to investigate the phenomenon for normal tracks. Nevertheless, railway turnouts are somehow neglected. As a consequence, this study focuses on the relation between unsupported sleepers/bearers (particular name for turnouts) and a railway turnout system to develop the understanding of the response of turnout system under dynamic loadings. A 3D Finite Element Method (FEM) model is inherited from previous study and adopted to reflect the cases with unsupported bearer configurations. It is noteworthy that inherited model is capable of reflecting the impact forces, which is an inherent and fundamental characteristic of a railway turnout. Model verification is done with the parent model that was verified by field measurements. Three different support conditions (i.e., one, two, three unsupported bearers), five different velocities and six different positions of unsupported bearers are simulated. The results show that the performance of 'fibre-reinforced foamed urethane' (FFU) bearers are promising and more, unsupported bearers carry significant loads at particular locations, which is contrary to the sleepers on normal track that are subjected to insignificant loads.**  
**Keywords: railway; turnout; switches and crossings; impact forces; unsupported sleepers; unsupported bearers**
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 3 Methods
### Sensitivity Analysis of BC Spectra  
Landsat imagery has limited spatial resolution and contains a significant number of mixed pixels. These mixed pixels comprise multiple land cover types, with the proportion of each type determining the unique spectral characteristics of the pixel. Understanding the sensitivity of each band to the bare coal fraction is essential for establishing a bare coal identification index. Figure 5 illustrates the sensitivity of Landsat OLI reflectance in each band and band combinations to different BC fractions. The results indicate that the reflectance of each band is linearly correlated with the BC fraction. Notably, the NIR, SWIR1, NIR + SWIR1, and NIR-Red bands demonstrate relatively high sensitivity, with regression slopes of 0.312, 0.176, 0.509, and 0.205, respectively. As shown in Figure 4a, the SWIR2-SWIR1 values of the corresponding pixels only show positive values when the bare coal fraction reaches or exceeds 80%. Additionally, the sensitivity analysis results in Figure 5 indicate a negative correlation between the bare coal fraction and all bands or band combinations, except for the SWIR2-SWIR1 band combination. These observations suggest that the SWIR2-SWIR1 band combination has the potential to be used as a feature for the construction of a bare coal index, which can effectively distinguish bare coal from other feature types by its positive and negative values. Therefore, the SWIR2-SWIR1 band combination was selected as one of the key features for bare coal index construction in this study. Given these findings, the feasibility of constructing a BC index using these bands will be further analyzed to enhance the accuracy of bare coal identification.  
Figure 4: (**a**) Spectral curves of different land cover types. (**b**) Correlation between different land cover types.
## 4 Discussion
### Phenology Extraction Comparison between Backscattering Coefficients and Decomposition Parameters
The second case corresponded to #24 sample site on 2 July. The ground campaign indicated that the rice plants were in stage 44 (vegetative  
Figure 11: Phenology decision tree. Thresholds of anisotropy, entropy, and dominant alpha angle divide SAR data into four phenological phases. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Ground Measured Phase**} \\ \cline{3-6}  & & **Transplanting** & **Vegetative** & **Reproductive** & **Maturity** & **UA** \\ \hline \multirow{4}{*}{Extracted} & Transplanting & 4 & 0 & 0 & 0 & 100\% \\  & Vegetative & 0 & 13 & 1 & 0 & 92.8 \% \\ \cline{1-1}  & Reproductive & 0 & 1 & 5 & 0 & 83.3\% \\ \cline{1-1}  & Maturity & 0 & 0 & 0 & 5 & 100\% \\ \cline{1-1}  & PA & 100\% & 92.8\% & 83.3\% & 100\% & OA = 93.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion table derived by validation data for the phenology retrieval algorithm using entropy, anisotropy, and dominant alpha. UA stands for user’s accuracy, PA producer’s accuracy, and OA overall accuracy. Kappa denotes kappa coefficient. phase), whereas the backscattering coefficients method misclassified their phase as the reproductive phase. One possible cause for the misclassification was that rice at the #24 sample site had a denser canopy than other sites on 2 July. The attenuation by the denser canopy resulted in lower HH backscattering coefficients. Hence rice plants at the #24 sample site had similar HH/VH values with the plants in reproductive phase. However, the low entropy (about 0.5) of the #24 sample site can be used to identify the vegetative phase. Since the ears had not appeared and the stems were mostly vertical, double-bounce scattering still contributed significantly to the radar backscattering mechanism. Another two wrong estimates of the backscattering coefficients method also appeared in the decomposition parameters method. Corresponding rice plants were in the transition period from the vegetative to the reproductive phase (stage 47 and 55). The gradual change of rice plants and the similar backscatter properties of fields during this period caused the misclassification. In general, the phenology retrieval method using backscattering coefficients is less effective than the method using eigenvalue/vector decomposition parameters. Since additional data (phase data) are considered in the decomposition, eigenvalue/vector decomposition parameters can provide more information on the physical scattering mechanism. However, the result of the backscattering coefficients method is still acceptable because only amplitude information of backscattering coefficients is used.
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 1 Introduction
Then a 5-dimensional motion feature vector is built for each track point, and the feature vectors of track data within a time interval are selected as the input of the Bi-LSTM network to detect various abnormal behaviors of an aircraft.
# Effect of Washing Temperature on Adsorption of Cationic Dyes by Raw Lignocellulosic Biomass
## 2 Materials and Methods
### Experimental Adsorption Protocol  
The adsorption of the dyes, methylene blue (MB) and crystal violet (CV), on the different studied biosorbents was carried out in a batch process. A mass of 25 mg ofeach adsorbent sample was suspended in 25 mL solutions of MB or CV at a well-defined initial concentration (25, 50, 100, 150, and 200 mg L\({}^{-1}\)). The series of closed Erlenmeyer flasks (Jenway, Camlab, Cambridge, UK) was placed on a multi-stage stirring plate at room temperature at a speed of 250 rpm. The influence of initial pH on the adsorption of both dyes was studied by varying the pH between 2 and 11, with all other process parameters kept constant. The pH adjustments were made by adding small amounts of NaOH or HCl (0.1 M). The effect of the adsorbent dosage was investigated by varying the adsorbent-to-sorbate ratio between 0.25 and 2 g L\({}^{-1}\). Additionally, the impact of ionic strength on dye adsorption was assessed using NaCl solutions with concentrations ranging from 0 to 0.50 M. For the kinetic experiments, adsorption measurements were taken at specified time intervals (5, 10, 15, 20 min, etc.). In all other cases, a contact time of 24 h was applied to ensure that equilibrium was reached. After each adsorption experiment, the adsorbent was separated by centrifugation.  
The initial and residual dye concentrations were analyzed using spectrophotometry at 665 nm for MB and 590 nm for CV. The quantity adsorbed was calculated using the following formula:  
\[Q_{t}=\frac{V(C_{0}-C_{t})}{m} \tag{1}\]  
\(Q_{t}\) is the quantity adsorbed at time t (mg/g), \(C_{0}\) is the initial concentration of the dye solution (mg/L), \(C_{t}\) is the concentration of the dye solution at time t (mg/L), V is the volume of the solution (L), and m is the mass of the adsorbent (g)  
The quantity adsorbed can also be quantified by the removal yield (R). The dye removal yield is defined by  
\[R\%=\frac{(C_{0}-C_{t})}{C_{0}}\times 100 \tag{2}\]
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 5 Performance Evaluation
### Simulation
#### 5.1.4 Missing Neighbor Rate  
This section evaluates the impact of the missing node rate. In this simulation, the missing neighbor rate varies from 0% to 0.3%. The report interval, the moving speed and the elevator waiting time are set to 10 s, 1.1 m/s and 0 s, respectively. The missing neighbor rate is used to calculate the threshold, \(T_{nc}\), which distinguishes between an inside node and an outside node. The average value of the missing neighbor rates observed at all mobile nodes is 0.11%, and the range of the missing neighbor rates is 0%\(\sim\)3.33%. Figures 5c and 6c show the results. As shown clearly in the figures, the success rate decreases when the rate is greater than or equal to 0.05%. These results indicate that some left nodes are classified incorrectly as failed nodes. The reduced threshold, \(T_{nc}\), is the main cause of these incorrect decisions. The classification condition related to the rate is \(Q_{w}<T_{nc}\), and the threshold is calculated as \(T_{nc}=T_{nc}\times(1-missing\ neighbor\ rate)\). As \(T_{nc}\) decreases and \(Q_{w}\) does not change, the number of failed nodes that do not satisfy the condition \(Q_{w}<T_{nc}\) increases. The proposed method has a highest success rate when the missing neighbor rate equals zero. As collisions among data transmissions can affect nodes in a non-uniform way, the most suitable value for the missing neighbor rate is zero, which can be applied to all nodes in common without incorrect decisions.
# Assessment of Transformer Fault Severity from Online Dissolved Gas Analysis Using Positive CUSUM
## 4 Statistical Process Control (SPC)  
A longstanding technique widely applied in industrial manufacturing for controlling production processes is Statistical Process Control (SPC). [PERSON] developed the control chart in 1924 [18; 19; 20]. It is one of the seven essential SPC tools for monitoring production process changes.  
Because Shewhart control charts are insensitive to small process shifts, the CUSUM chart was developed for quality control. First proposed in 1958 by [PERSON] [21; 22; 23], this method has seen continuous application and study across various fields. These include energy savings in industrial processes [24], electrical system parameter monitoring for modeling [25], and analysis of PM2.5 dust levels following changes to natural gas usage [26].  
CUSUM charts suit online transformer measurement because of continuous data alignment with CUSUM principles. For example, they can detect abnormalities in transformer windings and identify faults in transformer coils. The Positive CUSUM presented in this article is the part of a cumulative sum (CUSUM) chart that considers only the positive deviations from a baseline value. It is designed to quickly and accurately detect increases in the NEI value, which reflects the severity of faults in transformers. Monitoring with this value is important for industrial plants that have high transformer load usage, such as steel mills.  
The faults within the transformer generate various types of gases, primarily flammable gases. This research utilizes the correlation of Total Dissolved Combustible Gas (TDCGs), found in the DGA online measurement devices' data, with NEI values in order to derive a linear regression equation and R\({}^{2}\). If we use the TDCG values directly, there will be a  
\begin{table}
\begin{tabular}{c c c c} \hline
**Group** & L\({}_{1}\) & L\({}_{2}\) & L\({}_{3}\) \\ \hline High-O\({}_{2}\) & 0.39 & 0.72 & 1.98 \\ Low-O\({}_{2}\) & 1.02 & 1.87 & 4.00 \\ \hline \end{tabular}
\end{table}
Table 1: NEI limits based on 90 th, 95 th, 98 th percentiles of data.  
significant difference between the minimum and maximum values. Using the logarithm of TDCG helps to mitigate the impact of data skewness, making the linear regression model more suitable.
## 2 Video Camera Imaging Based Method
### Basic Framework
#### 2.1.1 Data Acquisition  
First, data, i.e., the video of skin area of human body, is collected using an imaging sensor such as a digital camera, webcam, smartphone, Microsoft Kinect or unmanned aerial vehicle (UAV), as depicted in Figure 3. A dedicated light or ambient light can be used as the light source. The frame rate varies from as low as 10 to 60 fps.
# Antimicrobial Air Filter Coating with Plant Extracts Against Airborne Microbes
## 4 Conclusions  
Three plant extracts (i.e., tea-tree oil, rosemary, and garlic) were investigated their antimicrobial activities and phytochemicals. All plant extracts tested showed an inactivation of _M. luteus_ and _E. coli_. The tea-tree oil proved to be the most efficient in inhibiting both strains, even at low concentrations. The plant extracts were coated onto PET air filters with a silicate polymer by spray-coating. The characteristics of the plant extract-coated air filters were evaluated using XPS, SEM, and EDS. No significant pressure drop was found for plant extract coating when compared to the control air filters. Tea-tree oil coating on air filters resulted in some inactivation of _M. luteus_ right after exposure to the bioaerosol, while rosemary and garlic extract-coated air filters did not show any inactivating effect. However, all three plant extract-coated air filters achieved a significant inactivation of _M. luteus_ 48 h after the initial bioaerosol exposure.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/24/9120/s1](http://www.mdpi.com/2076-3417/10/24/9120/s1)]([http://www.mdpi.com/2076-3417/10/24/9120/s1](http://www.mdpi.com/2076-3417/10/24/9120/s1)), Figure S1 Inactivation rate of _M. luteus_ vs. time for (a) tea-tree oil, (b) rosemary, and (c) garlic extracts for various extract solution concentrations, Figure S2 Inactivation rate of _E. coli_ vs. time for (a) tea-tree oil, (b) rosemary, and (c) garlic extracts for various extract solution concentrations, Figure S3 XPS spectra of the silicate polymer coating layer: (a) survey spectra, (b) O1s, and (c) Si2p, Figure S4 Scanning electron micrographs of the (a) pristine (control) and (b) tea tree oil-coated filters at a concentration of 9 mg/ml, and (c) results of Si mapping. Bars indicate (a) 33.3\(\mu\)m and (b,c) 100\(\mu\)m, Figure S5 Colony-forming units of _M. luteus_, immediately after bioaerosol exposure, vs. weight of plant extract per unit area: (a) tea-tree oil, (b) rosemary, and (c) garlic.  
Figure 4: Images of the anti-pathogen activity of air filter samples for _S. aureus_ and _K. pneumonia_.  
**Author Contributions:** Conceptualization and design, J.H.L.; Writing, H.R.B.; experiments, H.R.B., S.Y.P., E.T.H.; data acquisition, B.I.S.; data interpretation, E.T.H., J.M., and B.I.S.; data analysis, D.S., W.I.C., S.K. and J.H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Research Foundation of Korea, South Korea (NRF), grant number 2020R1F1A1069515.  
**Acknowledgments:** We acknowledge and appreciate the technical support provided by [PERSON] for the pressure drop experiment and [PERSON] for the ANOVA analysis  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Statistical Analysis  
The Shapiro-Wilk test was used to check the normality of the basic parameters. Height presented a normal distribution, while age, weight, BMI, years of training experience, training hours daily, training days weekly, training hours weekly, and training hours in one year did not present a normal distribution. Participants were categorized by age into two groups: \(\leq\)18 years and \(>\)18 years. The basic parameters of the groups (\(\leq\)18 years; \(>\)18 years) were statistically analyzed using the independent-samples \(t\)-test and Mann-Whitney U-test. Comparisons of injury incidence between groups were analyzed using the \(\chi\)2 test, and injury rates per 1000 training hours and per 1000 training sessions were calculated using a Poisson distribution with a 95% confidence interval (CI). An hour of training was defined as an athlete participating in training for one hour, and a training session was defined as a day in which an athlete participated in training. Injury rates during rowing training were calculated as follows:  
Injury rate per 1000 h = [\(\sum\)(total number of injuries)/\(\sum\)[(training hours per participant per day \(\times\) training days per participant per week \(\times\) weeks per participant one year)]] \(\times\) 1000;  
Injury rate per 1000 training sessions = [\(\sum\)(total number of injuries)/\(\sum\)[(training days per participant per week \(\times\) weeks per participant one year)]] \(\times\) 1000.  
The denominator for injury rate calculations represented the total training hours or sessions aggregated across all the participants. Significant differences in values between groups for injury rate per 1000 training hours and per 1000 training sessions were assumed if the 95% CIs did not overlap. The denominator for injury rate calculations represented the total training hours or sessions aggregated across all athletes.  
For further data analysis, logistic regression models were used to examine the association between the occurrence of rowing-related injuries and variables. Model 1-adjusted variables included gender and age; Model 2-adjusted variables included gender, age, height, weight, and BMI; and Model 3-adjusted variables included gender, age, height, weight, BMI, training years of experience, training hours daily, training days weekly, and training hours weekly. The variance inflation factor (VIF) value was calculated to diagnose suspected collinearity between two variables. If the VIF was 10 or above, values were treated as collinear, and only one variable was entered into a given model. The goodness-of-fit of the three models was evaluated based on the Akaike information criterion (AIC) and Bayesian information criterion (BIC). For statistical analysis, the difference was considered significant at a \(p\)-value \(<\) 0.05.