esa-sceva/satcom-chunk-collection huggingface dataset:
# Lateral Oscillation Characteristics of Vehicle Trajectories on the Straight Sections of Freeways
## 4 Data Analysis and Result
### Trajectory Oscillation of Different Vehicle Types  
Most freeway traffic accidents are closely related to the morphology and disturbance of the traveling trajectory. For studying the motion characteristics of the trajectory on straight sections of freeways, the LA and DTOC of various vehicle types were divided into intervals of 5 cm and 10 m, respectively. Figures 7-9 show the relative frequency distribution and cumulative percentile curves of the LA and DTOC for different vehicle types. The results showed that the LA and DTOC of all vehicles obey the distribution of LogNormal. The functional expression for the LogNormal is as follows:  
\[y=y_{0}+\frac{A}{\sqrt{2\pi}wx}\exp\left(-\frac{\left(\ln x-x_{c}\right)^{2} }{2w^{2}}\right) \tag{7}\]  
where \(x_{c}\) represents the expected value of the corresponding indicator. The parameters of the log-normal distribution were obtained by maximum likelihood estimation, and the calculated results and indicator eigenvalues were statistically summarized in Table 5.  
The results showed subtle differences in the overall trajectory oscillation characteristics of the various vehicle types. The LA for extra-large trucks was significantly lower than that for passenger cars and medium-large cars. In contrast, the DTOC for passenger cars was considerably higher than for the other two vehicle types. It implies that the extra-large trucks' longer and broader bodies tend to minimize the driver's psychological margin, improving the driver's awareness of keeping lanes. However, passenger cars usually travel faster and have more lateral space, thus causing the LA and DTOC to appear to be \"double high\".  
Figure 8: (**a**) Distribution of the LA for medium-large cars; (**b**) Distribution of the DTOC for medium-large cars.  
Figure 7: (**a**) Distribution of the LA for passenger cars; (**b**) Distribution of the DTOC for passenger cars.  
The cumulative frequency curves of the LA for all vehicles were S-shaped, with inflection points at the 15 th and 85 th, indicating that most drivers have a relatively concentrated distribution of lateral lane-keeping ability control range of 0.3-0.75 m. Unlike the LA, the cumulative frequency curves of the DTOC were similar to a linear ascending process, showing that drivers are not sensitive to the frequency of trajectory oscillation, and that the distribution is more discrete. Moreover, the fitted parameters of the log-normal model showed that the driver's expectation of the LA kept around 0.5 m, and the expectation of the DTOC stayed at about 200 m. The expected value is closer to the mean value so that the mean can be subsequently utilized as one of the typical indicators.
## 3 Results and Discussion
### Process Model Results
#### 3.1.3 GTM Process Simulation Results  
At 9 \({}^{\circ}\)C temperature and 90 kPa pressure, methanol with 99.5 percent purity was produced from the feed gas volume (26,493 MMscf) and fed into the GTM process for Field Y, with an output volume of approximately 0.7 million tonnes per annum (Mtpa), whereas methanol (99.5% purity) with an output volume of approximately 0.4 Mtpa was produced for Field X (with feed gas volume of 17,247 MMscf) at 24 \({}^{\circ}\)C temperature and 90 kPa. Synthesis gas of output volumes of 94,749 MMscf and 61,062 MMscf was generated in Fields Y and X, respectively, at 17 \({}^{\circ}\)C temperature and 2995 kPa pressure. At 9 \({}^{\circ}\)C temperature and 90 kPa pressure, methanol with 99.5 percent purity was produced from  
\begin{table}
\begin{tabular}{c c c} \hline
**Power Summary** & **Model Simulation Result for Field Y** & **Model Simulation Result for Field X** \\ \hline Gas Turbine Power (MWe) & 303 & 193 \\ Steam Turbine Power (MWe) & 167 & 108 \\ Total Power (MWe) & 471 & 301 \\ Total Auxiliaries (kWe) & 3966 & 2592 \\ Net Power (MWe) & 467 & 299 \\ Net Plant Efficiency (HHV) & 52\% & 49.6\% \\ Net Plant Efficiency (LHV) & 57\% & 54.5\% \\ Net Plant Heat Rate (HHV) (k/kWh) & 6982 & 7282 \\ Net Plant Heat Rate (LHV) (k/kWh) & 6343 & 7254 \\ CONSUMABLES & & \\ Natural Gas Feed Flow (kg/h) & 61,760 & 40,790 \\ Thermal Input (HHV) (kWh) & 905,018 & 601,510 \\ Thermal Input (LLV) (kWth) & 822,136 & 546,423 \\ \hline \end{tabular}
\end{table}
Table 7: Overall NGCC plant performance of Fields Y and X.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Model Simulation Output for Field Y** & **Model Simulation Output for Field X** \\ \hline LNG Output & & \\ LNG Output Feed Rate (tpa) & 580,000 & 380,000 \\ \hline \end{tabular}
\end{table}
Table 8: LNG simulation output for Fields Y and X.  
the feed gas volume (26,493 MMscf) fed into the GTM process for Field Y, with an output volume of approximately 0.7 Mtpa. Synthesis gas of output volumes of 94,749 MMscf was generated in Field Y at 17 \({}^{\circ}\)C temperature and 2995 kPa pressure. Table 9 shows the properties of gas produced and GTM simulation outputs for Field Y.
# Visible Light Communication: A System Perspective--Overview and Challenges
## 2 Visible Light Communication System
### Standardisation Efforts  
In 2003, a VLC consortium (VLCC) was formed to speed up the research and commercialisation of VLC. The VLCC proposed two standards by 2007 [23]; JEITA CP-1221 (VLC system) and JEITA CP-1222 (VL ID system) that was later accepted by Japan electronics and information technology industries association (JEITA). CP-1223 was introduced as a VL beacon system in 2013. Both these standards have meagre data rates of up to 4.8 Kbps.
# Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels
## EDM Process Details
### EDM Process Parameters
In the case of a sinker type EDM, hydrocarbon- and silicon-based dielectric oil and kerosene are used after raising the flash point. Meanwhile, de-ionized water and oil are used in wire EDM. Moreover, some sinker EDMs also use de-ionized water in high-precision machining, such as in fine hole drilling. Many studies were conducted recently to explore oil-based synthetics to avoid harmful effects on workers and the environment [73, 74, 75]. Importantly, the dielectric type and flushing method influence MRR, TWR, and surface roughness (SR) [76, 77, 78]. The dielectric flushing conditions can be improved with workpiece and electrode rotation [79, 80]. This improvement in flushing caused by electrode rotation achieves better SR and higher MRR [81, 82, 83] and minimizes the density of cracks on the surface and recast layer [76]. The effect of the EDM process parameterson the response parameters are difficult to explain because of the stochastic nature of the discharge mechanism [84]. Several researchers performed studies related to EDM processes and explored the influence of process parameters on the performance measures [21; 54; 58; 85; 86].
# Method for Detecting Underwater Microbubbles Using Dual-Mode Fusion of Laser Polarization
## 2 Dual-Mode Fusion Detection Mechanism  
Figure 1 characterizes the specific components of this bimodal fusion mechanism, which can be divided into the following three steps:  
1. Ideal bubble state simulation. This paper establishes a dynamic time-varying simulation model of underwater microbubbles based on the vector Monte Carlo method, which can be used to simulate the polarization characteristics of bubbles of different sizes and numbers according to different water quality parameters fed back from the detector, and outputs the corresponding data as the historical optimal values to provide the corresponding parameters for the subsequent algorithms.
2. Image fusion and feature extraction. This paper proposes an image polarization-weighted fusion method based on polarization fluctuation changes, using polarized image and non-polarized image coefficient weighting, image enhancement of the target of the bubble to be detected, and segmentation of adherent bubbles by the fast radial symmetric transform method (FRS), then combined with ellipse fitting to complete the contour estimation and data expansion, and the relevant feature parameters are output for the subsequent bubble feature recognition statistics.
3. Dual-mode fusion discrimination. This paper proposes a dual-mode fusion bubble feature recognition method based on the inter-correlation between image scattering and bubbles, through the feedback of the fluctuation of the gray value of the image in the two cases of the existence or non-existence of bubbles, and the use of laser polarization and the image of the data complementary to the image from the perspective of statistical probability of the image grayscale and bubble polarization features for the fusion of confidence and the establishment of the bubble size, number, and other discriminative features in an a posteriori distribution probability model. In the experimental test phase, according to the target confidence adaptive fusion dual-mode detection results, the impurities and bubble characteristics are accurately distinguished, and the grayscale fluctuation value (GF) is used to correlate bubble size, number, and other characteristics, so as to realize the target identification and feature extraction of bubbles under complex water quality.  
Figure 1: Block diagram of bubble detection by dual-mode fusion method.
## 5 Simulation Results and Analysis
### Algorithm Comparison and Analysis  
#### 5.2.1 Parameter Calibration  
In the rule-based energy management algorithm designed in Section 4.1, it is necessary to calibrate the threshold for the discharge power of lithium-ion batteries in HESS. Firstly, four fixed system capacity configurations in Table 5 are selected.  
Due to the maximum daily load power of system not exceeding 5 kW, the range of threshold is set to 0-5 kW. By changing the threshold of the rule-based algorithm, several sets of PV-ESS system components sizing in Table 5 are simulated to obtain the system operating costs. The system operating costs for each threshold under the different components sizing obtained are shown in Figure 11.  
It can be seen that when the threshold is too small, the trend of operating costs varies for systems with different components sizing. However, when the threshold increases to a certain level, except for configuration four, the system cost no longer changes with the increase in the threshold. There are two main reasons for this.  
Firstly, during the operation of the system, the cost of power consumption is higher than the cost of battery degradation, and excessive use of batteries can bring more benefits to the system. Secondly, there is a mismatch in the capacity of system components. Excessive SC use will reduce the operating range of the battery, and the coupling between system component capacities limits the operating cost of the system. Based on the simulation results and taking into account the reduction in battery degradation, it is necessary to predetermine the threshold before running the proposed algorithm.  
Figure 11: The influence of threshold on system capacity configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**System Components** & **PV Size (kW)** & **Battery Size (kWh)** & **SC Size (kW)** \\ \hline Configuration 1 & 5 & 5 & 1 \\ Configuration 2 & 5 & 5 & 2 \\ Configuration 3 & 10 & 5 & 2 \\ Configuration 4 & 12 & 20 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Four system capacity configurations.
## 3 Materials and Methods
### Size Measurement Method
#### 3.2.1 Region of Interest Acquisition  
In Figure 6a, an image measuring 2592 \(\times\) 1944 pixels is presented, demonstrating an uneven light reflection. The middle of the image appears brighter, while the two sides are darker, which can be attributed to the structural properties of the lock cylinder. However, since the bead hole of the lock cylinder serves as the measurement target, it is crucial to extract the region of interest (ROI) corresponding to the bead hole. To locate the bead hole, this paper employs the RANSAC (Random Sample Consensus) method to detect the circular shape of the lock cylinder's bead hole. By eliminating the complex background, the effectiveness of image processing can be enhanced. A rectangular area the size of 270 \(\times\) 270 pixels is utilized to extract the ROI following the circle detection. Figure 6b  
Figure 5: Edge detection process.  
Figure 6: Process of edge detection: (**a**) Lock cylinder image; (**b**) obtaining ROI; (**c**) filtering and denoising; (**d**) edge detection.  
illustrates the resulting image of the lock cylinder's bead hole after the ROI processing. It can be observed that the extracted bead hole area has effectively eliminated unnecessary background information.
# Protein Dynamics in F-like Bacterial Conjugation
## 3 Structures Involved in Pilin Processing, Pilus Extension, and Retraction
### Proteins Responsible for Propilin Maturation
#### 3.1.3 TraX  
Propilin modification by N-terminal acetylation is achieved by the expression of the _traX_ gene. The _traX_ gene encodes a mainly hydrophobic IM protein of 248 aa that has also been detected to produce two small products in all observed F-like plasmids; TraX1 and TraX2 [8; 14]. TraX1 and TraX2 are 22 and 24 kDa, respectively, and end with the same C-terminal sequence; however, their function is unknown. Though TraX is required for the acetylation of propilin, TraX is not required for pilus biogenesis [67]. The structure of TraX has yet to be solved and is difficult to predict, likely due to the instability of N-acetylases following purification and the rarity of bacterial N-acetylases [67; 68]. Structural analysis by Phyre2 predict that TraX is a polytopic protein with eight transmembrane (TM) domains; however, earlier predictions estimated TraX has four TM domains [20; 37; 69].  
Figure 3: The F-like pilus and T4 SS. Figures **A**–**C** are in situ structures of the F-like pED208 T4 SS by cryo-ET while (**D**) depicts the overall architecture of the pED208 pili by cryo-EM. (**A**) A tomographic slice of an _E. coli_ minicell showing an extended F pilus attached to the OM. (**B**) A three-dimensional depiction of the pED208 core T4 SS from ATPase to pilus. At the OM, the pilus narrows and connects to the disc at the junction where the pilus meets the OM complex; a luminal space approximately the same diameter of the pilus is present at this junction, which is sealed by a plug domain. (**C**) A central slice of the averaged structure of the core T4 SS with cross-sectional views of the separate domains. The IM complex (the VirB4-like ATPase, TraC) forms a 6-fold rotational domain with a central lumen of \(\sim\)60 Å with the entire domain spanning 300 Å. The OM complex (the VirB7, -B9, and -B10 homologues TraV, TraK, and TraB) has a 13-fold symmetry and spans 250 Å with a central lumen of 19 Å. (**D**) A side view of the surface representation of the pED208 pilus (PDB ID: 5 LEG). It is composed a of a 5-start helical assembly with each helical strand depicted in a separate color. This cryo-EM structure aligns with the architecture of the cryo-ET pilus structure in that it spans \(\sim\)87 Å and has a central lumen of 28 Å. Figure 3**A**–**C** was adapted from [PERSON] et al. Structural bases for F plasmid conjugation and F pilus biogenesis in _Escherichia coli. Proc. Natl. Acad. Sci. USA_**2019**, 201904428 [15].
## 2 Materials and Methods
### Optical Model of Rainy Conditions
Furthermore, in media consisting of particles comparable in size to the wavelength of natural light, the CP light can survive more multi-scattering events than linearly polarized light [20; 21; 22; 23], which is called the polarization memory effect. Since circularly polarized light is point-symmetric, it does not depend on the scattering angle. For example, right circularly polarized light that undergoes scattering of any angle maintains its exact Stokes vector, while the partially linearly polarized light that undergoes the same scattering angle will have its polarization state changed necessarily. The analyses above make it a sound foundation to employ the CP information of scattered light to images in rainy conditions. Figure 3: Paths of light rays scattered by the water droplet according to geometrical optics. Figure 2: **(Top)** Light consideration during the circular polarization (CP) detection process in rainy-day conditions. (**Bottom**) Schematic of natural light’s interaction with rain droplets.
## 2 Materials and Methods
### Study Area
The calculated BMP metrics were placed into one of two groups, BMP summary and BMP type. The BMP summary group consisted of five metrics that were calculated as a summation of all BMPs (_i.e._, MS, LAR, and EC) present in the catchment, excluding riparian area. In the BMP type group there was a total of 28 metrics describing the abundances and locations of the individual BMP types (_i.e._, MS, LAR and EC) within the catchments. The extent of RBAs was also included in the BMP \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Acronym** & **Description** & **Expected Benefits** \\ \hline \multirow{3}{*}{Manure Storage} & \multirow{3}{*}{**MS**} & Impermeable structures & Reduction in non-point \\  & & designed to store & organic matter and \\  & & livestock manure & nutrient pollution \\ \hline \multirow{3}{*}{Livestock Access Restriction} & \multirow{3}{*}{**LAR**} & Fences constructed & \\  & & adjacent to waterways & Reduction in organic \\ \cline{1-1}  & & preventing livestock & matter, nutrient, and \\ \cline{1-1}  & & from disturbing & sediment inputs \\ \cline{1-1}  & & stream channels & \\ \hline \multirow{3}{*}{Erosion Control} & \multirow{3}{*}{**EC**} & Structures preventing & Reduction in sediment \\ \cline{1-1}  & & soil erosion by wind & and particulate \\ \cline{1-1}  & & and/or water & nutrient inputs \\ \hline \multirow{3}{*}{Riparian Buffer Area} & \multirow{3}{*}{**RBA**} & Area of vegetation either & Reduction in non-point \\ \cline{1-1}  & & planted or left intact & organic matter, nutrient, \\ \cline{1-1}  & & within a 30 m buffer on & sediment, and other \\ \cline{1-1}  & & either side of a & contaminant inputs \\ \cline{1-1}  & & watercourse & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptions and expected benefits of four structural best management practices included in this study. type group as a metric describing the vegetated proportion of the total riparian area. Location based metrics were not calculated for RBAs due to difficulties in differentiating the stream channel from the riparian zone given the small stream channel widths (<2 m) and level of resolution available for land cover and DEM layers. EC metrics were not individually examined because of low abundance (\(n=2\)); however, EC BMPs were included in the BMP summary group metrics.
## 4 Electronic Nose, Electronic Tongue, and Electronic Eye in Sensory Food Sciences
### Electronic-Nose
Their E-nose, equipped with six gas sensors sensitive to alcohol, carbon monoxide, ammonia, and hydrogen sulfide, analyzed aroma, while consumer sensory analysis assessed acceptability. [PERSON] et al. [77] demonstrated the ability of the E-nose to identify basil, cardamom, pepper, and turmeric using 12 conductive polymers with a time-delayed neural network. The complexity of coffee aroma has led to several applications of E-nose technology. Traditionally, the roasting degree is assessed visually, requiring highly skilled operators. To address this, a portable E-nose with 10 temperature-controlled metal oxide sensors was tested to automate the roasting process [78]. This reproducible method evaluates coffee bean quality and complements industry standard parameters.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 4 Discussion  
The results of this analysis show that substantial or complete disruption of modern satellite-mediated QKD can be achieved with simple equipment, that is, a kW-class laser, small telescope, and a mount able to track satellites. Continuous wave lasers or laser amplifiers capable of powers on the order of a few kW to tens of kW can be purchased commercial off-the-shelf for circa $100,000 USD. Optics on the order of 10 cm diameter that are able carry the resulting power densities are also commercially available. Telescope mounts able to accurately track satellites can be readily purchased for circa $20,000 USD. Combined, a malicious party aiming to disrupt a crucial satellite QKD link based on the technology demonstrated on board _Micius_ would be able to do so with under $200,000 USD of equipment.  
While the complete additional QBER calculation for the scenario where a satellite-borne QKD receiver is directly targeted by the laser terminal is not carried out here due to the lack of a reliable scattering model for this scenario, from the scattering experiments described in Section 2.2 which showed on the order of one in \(10^{7}\) photons incident on the QKD optic couple into the QKD receiver, we expect that complete disruption of the QKD link in this scenario will be achieved with significantly lower laser powers than for the case where the laser power must scatter off the satellite and into the receiver at the ground station.  
The ease with which such a laser terminal could be built and targeted at a satellite means that developers and users of future satellite QKD systems should take this threat into account. Based on our analysis, we discuss a number of countermeasures future QKD satellites can enact against this form of disruption.
# Monitoring and Calibrating Building Materials Drying Kinetics with Capacitance Sensors
## 3 Results and Discussion
### Correlation of Moisture to Capacitance Values  
In this work, the primary objective is to calibrate the capacitance sensors. In Figures 7-9, the results of the linear correlation model between moisture content and the differences in voltage are presented with compact lines, while with dotted lines are shown the levels of confidence of 5%. The diagrams describe the entire drying process spectrum reflecting the materials' moisture content, ranging from fully saturated with water to its dry state.  
For the _ML_ and the _MC_ mortars, the correlation (Figure 9) was performed for a longer time span after the experiment's initiation, 31 h for _ML_ and 41 h for _MC_. In Table 4, the linear regression equations for each material are presented, along with the standard deviation (Sd) and the relative standard deviation Rsd (%). In this work, a good residual sum of squares was considered a value greater than 0.92 R-squared, with very small Sd values. All materials under investigation resulted in low Rsd values (<10%), corresponding to a narrow spread within the estimated values.  
Figure 6: Moisture content \(X\) (kg/kg dry base), depicted with blue colorand Voltage (mV/mV dry base, depicted with red) difference variations versus drying time \(t\) in hours (hrs) for all materials under investigation.  
Figure 7: Correlation of bricks’ moisture content \(X\) (kg/kg dry base) to the voltage difference \(V\) (mV/mV dry base) for the bricks.  
Figure 8: Correlation of stones’ moisture content \(X\) (kg/kg dry base) to the voltage difference \(V\) (mV/mV dry base) for the stones.  
Figure 9: _Cont._  
As shown in Table 3, a linear regression model is found to be suitable for the calibration of the sensors' measurements to moisture content for all materials. Based on these linear relationships, the moisture content of the materials under investigation can be estimated easily for different water content levels. The equations' constants depend on the material's microstructural characteristics and its chemical-mineralogical composition.  
Figure 9: Correlation of mortars’ moisture content \(X\) (kg/kg dry base) to the voltage difference \(V\) (mV/mV dry base) for the mortars.  
In practice, these models can easily be applied to obtain accurate and reliable moisture values from voltage values for a range of moisture content.