esa-sceva/satcom-chunk-collection huggingface dataset:
# A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra
## 3 Results and Discussion  
### Optimization of Exposure Time and Laser Power  
Appropriate parameter values were essential in the detection. The main parameters included exposure time and laser power. For samples with high pesticide concentration, the signal of characteristic peaks was easy to obtain. However, when the concentration of samples was low, the signal of the characteristic peaks would be weak or disappear if the exposure time was short or the laser power was small. While too long exposure time and too great laser power would lead to signal saturation or samples being burned by laser. Longer exposure time would also lead to time wasting for sample detection and may cause damage to charge-coupled device (CCD) camera. Thus it was necessary to optimize exposure time and laser power before the detection. Different exposure time and power were applied for detecting samples with same concentration. It can be seen that when the laser power was certain, the longer the exposure time was, the stronger the characteristic peak signal was. And when the exposure time or the laser power was short, the signal of the characteristic peaks could hardly be observed. Finally when the exposure time was 3s and the laser power was 450 mW, the signal of characteristic peak was the strongest. The maximum power of the laser was 450 mW. Laser of 450 mW would not damage the apple samples. Hence the laser was set at 450 mW. If the exposure time exceeds 3 s, the signal may saturate and the CCD camera may be damaged.
# Review of Studies on User Research Based on EEG and Eye Tracking
## 5 Discussion  
### The Strength and Contradictions of Reviewed Findings  
This review summarized the current research indicators, paradigms, and applications of EEG and eye-tracking technology in user research. The advantages of EEG and eye-tracking technology lie in their ability to provide objective, accurate, real-time biometric data. By properly connecting devices and processing relevant data, the research indicators obtained can help researchers gain insight into users' true thoughts. The review shows that these technologies are widely used in user research in industrial products, digital interfaces, and spatial environments to better understand users' cognition, emotions, and behavior. However, in the process of conducting this study, it was found that a user research system has not been systematically established; therefore, only a small part of the relevant literature pertains to user research, which is somewhat biased against the current development trend. Through analysis, it was discovered that EEG and eye-tracking are two commonly used biometric measurement technologies in user research. Although they can provide valuable information to researchers, they still have limitations in terms of device accuracy and experiment feasibility. However, overall, biometric technologies such as EEG and eye-tracking are in line with development trends, and in the future, these technologies will be combined with advanced technologies, such as artificial intelligence and machine learning, to overcome existing shortcomings and further develop the field of user research.
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Conductive Non-Metals
#### 2.3.1 Carbon Nanotube (CNTs)  
CNTs are unique nanostructures theoretically considered one-dimensional (1D) quantum wires. The basic building block of the CNTs is very long cylindrical single-walled carbon nanotubes (SWCNTs) that are one atom in wall thickness and ten atoms in circumference. CNTs are the seamless cylindrical shape of graphene sheets. [PERSON] (a Japanese physicist and inventor) first observed the multi-walled carbon nanotubes (MWCNTs), which were remarkable for their unique structure and electrical properties [66]. After two years, [PERSON] and his team discovered SWCNTs, which were supposed to be an important material for their theoretical experiment [67]. It was predicted in 1992 that a historical breakthrough would happen in 1996 after successfully synthesizing bundles of aligned single-wall carbon nanotubes with very small diameters [68].  
CNTs have many exceptional physical properties. CNTs are 100 times more potent than the highest grades of carbon steel, and their higher tensile modulus can almost be stretched five times the original length [69; 70]; moreover, CNTs show extraordinary electronic properties such as \(10^{2}\)-\(10^{6}\) Scm\({}^{-1}\) for SWCNTs and \(10^{3}\)-\(10^{5}\) Scm\({}^{-1}\) for MWCNTs [71], which perform better than copper when transporting electrons long distances without interruption [72; 73]. Consequently, it is considered the ideal nanotube because of its mechanical and electrical characteristics. Given this, many potential applications have been proposed, such as H\({}_{2}\) storage media [74; 75], batteries [76], solar cells [77], transistors [78; 79], diodes [80; 81], and sensors [82; 83].  
CNTs have a high aspect ratio that makes them suitable for reinforced materials. CNTs can make pure CNT fiber or reinforce polymeric, ceramic, and metallic fibers. CNT fibers have been processed from aqueous dispersions and dispersions in strong acids, drawn from CNT forests, or pulled from a CNT CVD reactor in the form of an aerogel fiber.  
CNTs have shown a great range of variety in applications in the last couple of decades of research. However, with the potential of CNTs, it has gained limited success in the marketplace, especially due to issues in processing and scaleup. There are also intrinsic challenges at the nanoscale (such as assembly, the role of interfaces, and contacts), which also affect the applications' progress. Instead of these challenges, a few applications of nanotubes are available commercially.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 4 Q-Learning-Based Position-Aware Routing Protocols in FANETs
### Multi-Objective QL-Based Routing Protocol (QMR)  
[PERSON] et al. [22] proposed a multi-objective QL-based routing (QMR) protocol for FANETs that jointly optimizes the delay and energy consumption in FANETs while making routing decisions. They proposed a new exploration and exploitation mechanism considering the network condition that includes the PTS, link quality, and relative mobility factor, which aids in avoiding local optima. To make the learning process more efficient and stable, they updated the Q-value by adaptively updating the learning rate and discount factor, utilizing the exponential of the normalized one-hop delay and variation in the neighbor set at two different times, respectively. During exploitation, a better decision is made by the QL module as Q-values are weighted by the link-quality metric. QMR estimates link quality using the expected transmission count (ETX) method [52].  
_Advantages_: QMR considers a minimum reward policy when the routing loop, local minimum, and node failure-state occur during relay node selection. It jointly considers delay and energy optimization in the reward function.  
Limitations: QMR does not consider mobility control and the control of the hello interval. The Q-value is updated without considering the SINR level of the links. Proper mobility control is necessary for QMR, as they provide actual velocity constraints for neighboring UAVs by calculating the PTS to satisfy the deadline PTT during data transmission. A mobility control method can control the relative velocity with neighboring UAVs and maximize the link duration between neighboring UAVs for successful data transmission within PTT.
###### Abstract  
Optical camera communication (OCC), enabled by light-emitting diodes (LEDs) and embedded cameras on smartphones, has drawn considerable attention thanks to the pervasive adoption of LED lighting and mobile devices. However, most existing studies do not consider the performance bottleneck of Region of Interest (RoI) extraction during decoding, making it challenging to improve communication capacity further. To this end, we propose a fast grid virtual division scheme based on pixel grayscale values, which extracts RoI quickly without sacrificing computational complexity, thereby reducing the decoding delay and improving the communication capacity of OCC. Essentially, the proposed scheme uses a grid division strategy to divide the received image into blocks and randomly sample several pixels within different blocks to quickly locate the RoI with high grayscale values in the original image. By implementing the lightweight RoI extraction algorithm, we experimentally verify its effectiveness in reducing decoding latency, demonstrating its superior performance in terms of communication capacity. The experimental results clearly show that the decoding delay of the proposed scheme is 70% lower than that provided by the Gaussian blur scheme for the iPhone receiver at a transmission frequency of 5 kHz.  
Keywords:optical camera communication (OCC); visible light communication (VLC); region of interest (RoI); multi-pixel search (MPS); grid virtual division (GVD) +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Raw Material
#### 2.2.2 Reducing Sugars and Antioxidant Characteristics  
For the determination of bioactivity, an extract was obtained by combining 1 g of the sample with 10 mL of ethanol. The mixtures were subjected to continuous agitation at ambient temperature using a rotary shaker (MX-RD PRO, Chemland, Stargard, Poland) for 2 h. Following extraction, samples were centrifuged (MPW-350, MPW MED. INSTRUMENTS, Warsaw, Poland) at 3500\(\times\)\(g\) for 15 min at 4 \({}^{\circ}\)C. The obtained supernatants were stored at 8 \({}^{\circ}\)C until further analysis.  
The content of reducing sugars was determined by a modified dithirosalicylic acid (DNS) assay following the method of [PERSON] (2024) [16]. A volume of 1 mL of DNS reagent was combined with 1 mL of each extract; the mixtures were heated in a boiling water bath for 5 min. After cooling to room temperature, absorbance was recorded at 535 nm. A glucose calibration curve (100-800 \(\upmu\)g/mL) was used to quantify results, expressed as mg glucose equivalents per gram of dry sample (gDW).  
The antiradical potential was assessed using the ABTS\(\bullet\)+ method. Each sample (0.0204 mL) was mixed with 1.0 mL of a diluted ABTS\(\bullet\)+ radical solution, prepared according to [PERSON] and [PERSON] (2019) [20]. Absorbance was measured at 734 nm, precisely 10 s after mixing. A Trolox standard curve (100-800 \(\upmu\)mol/L) was used; results were presented as mg of Trolox equivalents (TE) per gram of dry sample.  
To evaluate free radical scavenging capacity, the DPPH assay was used [20]. Briefly, 0.0345 mL of extract was mixed with 1 mL of a 0.1 mM methanolic DPPH solution. The mixtures were incubated for 20 min at room temperature; absorbance was measured at 517 nm. Trolox served as a reference standard (100-800 \(\upmu\)mol/L); the antioxidant capacity was expressed as mg TE/gDW.  
The ferric reducing antioxidant power (FRAP) assay was also employed to assess the electron-donating potential of the samples. Each extract (0.0035 mL) was added to 1 mL of freshly prepared FRAP reagent, as described by [PERSON] et al. (2023) [21]. Following a 15 min incubation at 36 \({}^{\circ}\)C, absorbance was measured at 593 nm. Calibration was performed using ferrous sulfate (100-800 \(\upmu\)mol/L); the results are expressed as mg of FeSO\({}_{4}\) equivalent per gram of dry matter.  
Additionally, the molecular characteristics of the samples were investigated using Fourier transform infrared spectroscopy (FTIR). Spectra were acquired using a Nicolet 6700 FT-IR spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), equipped with an attenuated total reflectance (ATR) diamond crystal. Each measurement was based on 64 scans collected across the spectral range of 4000-400 cm\({}^{-1}\) with a resolution of 4 cm\({}^{-1}\). Background subtraction was performed against air to ensure signal accuracy.
## 2 Methodology
### Experimental Arrangement  
The key point of the experiment is to find out how the drivers press the drive and brake pedals when they want to reach their desired speed/target speed. To achieve this purpose, a display screen is assembled on the right side of the steering wheel. The target speed for each step is displayed on the screen (see Figure 1, left). The function of this display screen is to emulate the speed limit on real-world roads. If the driver is going too fast, they should decelerate, and if a higher speed is allowed, they should try to match  
Figure 1: (a,b) Internal and external layout of the simulator.  
it. Thus, the collected driving behavior from the driving simulator is consistent with the driving habits of most drivers in real world. In this experiment, the subjects were asked to reach the desired speed/target speed while keeping the ego vehicle in lane without collision with other traffic users. When the actual speed of the ego vehicle stays in a range of \(\pm 2\) km/h of the desired speed for 5 s, the desired speed/target speed of the next step appears on the display along with a \"beep\" sound as a reminder to the driver. In total, 86 steps were carried out for each participant. The total time it took for each participant varied individually, ranging from 18.5 to 64.3 min. Drivers with more driving experience and more familiarity with the simulator or other virtual technologies showed a tendency to finish the driving test faster. During the driving experiment, the driving behavior of the participants (drive and brake pedal position, actual speed, target speed, etc.) are recorded. The data were transmitted 100 times per second.
## 3 Results and Discussion  
### Effect of Single Factors on Four Flavonoids Yields  
#### 3.1.1 Effect of Methanol Concentration on the Flavonoids Yield  
The concentration of solvent plays a key role on the selectivity and solubility of the four flavonoids. The effect of methanol concentration (from 50% to 100%, \(V/V\)) on the yields of the four flavonoids was investigated at the same time (20 min), ultrasonic power (200 W) and liquid-solid ratio (30 mL/g). Figure 4a showed that the yields of four flavonoids increased greatly with the increase of methanol concentration from 50% to 80%. However, with further increases in the methanol concentration from 80% to 100%, slight decreases of the four flavonoids yields were observed. Therefore, 80% methanol was chosen as the suitable solvent for the subsequent experiments.
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 2 Materials and Methods
### Methodology
#### 2.3.2 Preprocessing  
Sentinel-1 data were preprocessed using the ESA SNAP graph processing tool (gpt). With this command line interface, SNAP image processing functions can be accessed and automatically executed [45]. Each Sentinel-1 image was individually processed to generate a geometrically and radiometrically corrected sigma0 output. The images were transformed using Range Doppler Terrain Correction to UTM 10 \(\times\) 10 m images that can be combined with the other data sets. After the preprocessing phase, an additional step was performed to reduce the effect of the incidence angle on the backscatter values, by normalizing every pixel in the preprocessed image using the local incidence angle.  
Depending on the age of the Sentinel-2 data, the preprocessing workflow was slightly different because older data were only available in level 1C format. Level 1C data have to be transformed into level 2A using ESA's Sen2 Cor algorithm. Once the data were in level 2A format, selected spectral bands (Table 1) were resampled and cloud masked using a custom ESA SNAP graph. Cloud and cloud shadow masks were generated from the level 2A scene classification layer. Sentinel-2 tiles slightly overlapped each other, which would cause overestimation of the IEW pixels if each tile would be processed separately. Therefore, single tiles were mosaiced into the original swath at each acquisition day. For the validation of the presented methodology, Sentinel-2 data in level 1C format were downloaded in 2016, while in 2018 the data were in level 2A format.
## 2 Governing Equations of Motion
### Modeling of Waves and Currents
In Equation (6), the first term on the right-hand side denotes the drag force; the second term denotes the inertia force; and the third term denotes the added mass effect. The drag force is much smaller than the inertia [12], so the drag force term is linearized by removing the structural velocity term for numerical simplification. Substituting Equations (2)-(5) into Equation (6), the horizontal and vertical components of hydrodynamic forces in the \(Y\)-direction and the \(Z\)-direction can be obtained as follows:\[ \begin{array}{l}F_{YD}=\frac{1}{2}C_{D}\rho_{w}D\bigg{|}\dot{u}\pm\dot{U}\bigg{|} \Big{(}\dot{u}\pm\dot{U}\Big{)}\\ F_{YI}=C_{M}\rho_{w}\frac{4\pi D^{2}}{4}\ddot{u}\\ F_{ZD}=\frac{1}{2}C_{D}\rho_{w}D\bigg{|}\dot{v}\pm\dot{V}\bigg{|}\Big{(}\dot{v} \pm\dot{V}\Big{)}\\ F_{ZI}=C_{M}\rho_{w}\frac{\pi D^{2}}{4}\ddot{v}\end{array}  \tag{7}\]  
where the subscripts \(YD\) and \(YI\) represent the fluid drag and inertia forces in the \(Y\)-direction respectively; the subscripts \(ZD\) and \(ZI\) represent the fluid drag and inertia forces in the \(Z\)-direction, respectively.
# Phenology and Seasonal Ecosystem Productivity in an Amazonian Floodplain Forest
## Results
### Inter-Annual Variation of Seasonal Drivers and EVI-Multi-Angle Implementation Correction (MAIAC)  
Our inter-annual EVI analysis showed that during extreme wet (2009) or dry (2016) years, phenology patterns have changed due to both excess and lack of water (Figure 7). During 2009, soil moisture was higher compared to other years (Figure 7e). Lower cumulative water deficit (Figure 7a) and lower temperatures (Figure 7b) was also recorded. On the other hand, during 2016, the river level was well below the mean, which decreased soil moisture (Figure 7e) in association with high temperatures and high VPD (Figure 7c). These changes on water availability have produced an anomalous EVI seasonal trajectory between these two years (Figure 7d).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Plots** & \begin{tabular}{c} **Number of** \\ **Individuals** \\ \end{tabular} & \begin{tabular}{c} **Tree Mean** \\ **Height (m)** \\ \end{tabular} & \begin{tabular}{c} **5\% Percentile** \\ **(m)** \\ \end{tabular} & \
\begin{tabular}{c} **95\% Percentile** \\ **(m)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **(m)** \\ \end{tabular} \\ \hline BAN1 & 86 & 11.79 & 6.84 & 18.86 & 28.36 \\ BAN2 & 84 & 12.48 & 4.5 & 19.77 & 38.89 \\ LiDAR & - & 10.2 & 4.8 & 14.9 & 38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental plots and LiDAR statistics regarding tree height measurements.  
Figure 7: Monthly variation of CWD (**a**), temperature (**b**), VPD (**c**), river level (**d**), soil moisture (**e**) and EVI (**f**). The dashed line represents the average value (2004–2016), the standard deviation of the monthly variables is indicated by the grey shading, the blue line represents the wettest year (2009), the red line the driest year (2016) and grey lines represent remaining years (2004–2015).  
The period of leaf exchange in 2009 occurred from July to December, while in 2016, there were two peaks of EVI, a small peak from April to June and another from October to November. Thus, we did not observe the bimodal pattern in 2009, and in 2016 leaf renewal and photosynthetic capacity period have changed due to the decrease of flooding and increases in atmospheric demands, observed through VPD, CWD and temperature.
# Analysis of AMI Communication Methods in Various Field Environments
## 2 Power Line Communication for AMI
### Power Line Communication  
HS PLC is a broadband PLC technique developed in 2009 [25]. HS PLC uses a discrete multi-tone (DMT) for a transmission technique, which is essentially the same as the orthogonal frequency division multiplexing (OFDM). The number of the inverse Fourier transform points for OFDM is 512. There are three transmission modes: Normal, Extended Diversity (EDV), and Diversity (DV) Modes. The transmission mode can be selected according to the channel condition, information type, and the process to be performed [25]. When transmitting user data, such as metering data, Normal Mode is usually used because it has higher data rates than those of the other modes. The number of subcarriers used at Normal Mode is 152 and the subcarrier spacing is 97.66 kHz. The range of the employed frequency band is from 2.15 MHz to 23.15 MHz. For the subcarrier modulation, the differential binary phase shift keying (DBPSK), differential quadrature PSK (DQPSK), or differential 8-ary PSK (D8 PSK) is used according to the tone map (TM) that is obtained from channel estimations. The rate at Normal Mode depends on the TM values and the maximum rate is 25.684 Mbps. Figure 3 shows an external type HS PLC modem connected to a smart meter (CNU Global Co. Ltd., Seongnam-si, Korea).
## 3 Exploration and Production  
### Exploration Method  
Advanced geological exploration techniques are essential for identifying and locating oil shale deposits [1; 29]. Traditional oil shale exploration techniques involve drilling wells and analyzing core samples to determine the organic content and quality of the rock.  
A program must be developed to measure various oil shale characteristics closely related to its ability to produce oil. The program needs to be designed to establish a correlation between the quality of the oil that can be extracted and the properties that can be determined from the logs. This allows for directly determining the oil shale's yield potential using logging data alone [30].  
This method is time-consuming and expensive, and it can be difficult to obtain representative samples due to the heterogeneity of oil shale deposits [30].  
In recent years, seismic technology has been increasingly used in oil shale exploration [31]. Seismic surveys can provide detailed information on the subsurface structure and properties of oil shale deposits, allowing for more accurate mapping and evaluation. Seismic data can also be combined with well log data to create 3D models of the subsurface, which can aid in identifying potential drilling locations [32; 33; 34].  
In the Songliao basin, [PERSON] et al. (2012) used a technique called log-seismic multi-attribute construction to create a set of quantitative attributes that could be used to identify and evaluate oil shale deposits. These attributes included natural gamma, resistivity, acoustic travel time, neutron porosity, and density [35].  
By combining these attributes with seismic data, the authors were able to create 3D models of the subsurface that accurately identified and characterized oil shale deposits. They also used logging-constrained seismic inversion to further improve the accuracy of their interpretation [35].