esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Emerging Trends in FSO Communication
### Incorporation of Emerging Technologies
#### 5.1.1 Hybrid FSO/RF System  
FSO and RF technologies have complementary strengths that can be harnessed to overcome the limitations of each. While FSO provides high bandwidth and secure communication, RF is less affected by weather conditions and can penetrate obstacles. This integration enhances reliability and resilience by providing alternative communication paths in adverse conditions. Also, in the hybrid system, RF links can serve as a backup for FSO links during adverse weather conditions (e.g., fog, rain) that degrade optical signals. This redundancy ensures continuous and reliable communication. Likewise, advanced algorithms can switch dynamically between RF and FSO links based on real-time environmental conditions and link quality, optimizing the communication system's performance.  
Because of the offered reliability and redundancy, the hybrid FSO/RF system is ideal for providing last-mile connectivity in urban areas where physical obstructions and varying weather conditions pose challenges. Also, integration with RF enables the deployment of FSO links in mobile networks, supporting high-speed data transfer for moving platforms like vehicles and ships. Also, the scheme's high-speed data transmission capabilities make it a suitable solution for backhaul and fronthaul applications, improving the overall performance of the 5G infrastructure [2; 93; 365].
# A Review of Oil Spill Remote Sensing
## 4 Slick Thickness Measurements
### Acoustic Travel Time  
The acoustic travel time of oil is an indication of thickness as the acoustic velocities through most oils is similar [62]. One method of creating the acoustic pulse is to use a CO\({}_{2}\) laser. The rapid heating in the oil caused by the absorption of the infrared signal sets up an acoustic signal in the oil. A portion of the acoustic wave in the oil is reflected by the water/oil interface and travels back to the oil surface. The reflected acoustic wave on the surface can be measured by another laser. The acoustic signal appears in the reflected laser signal by Doppler shift, and the audio can be demodulated from the laser signal. The travel time of the acoustic pulse is directly related to the thickness. A prototype instrument using this principle has been built and tested over various pool thicknesses and was able to measure these thicknesses.
## 2 Compact Model and Physics
### Discussion on Aging Rate (AR)  
The aging rate (AR) is inversely proportional to the device's lifetime, i.e., \(AR\propto\frac{1}{\tau}\), and \(\tau\propto k_{f/r}\). The forward reaction rate as well as the reverse reaction rate are related to the maximum electric field in the channel, so the aging rate can be written as \(AR\propto\exp(-\phi_{\text{fit}}/q\lambda E_{m})\). The maximum lateral electric field in the channel is given by \(E_{m}=(V_{ds}-\eta V_{\text{dast}})/1\), where \(V_{dast}\) is the saturation voltage, calculated as \(V_{\text{dast}}=V_{gs}-V_{\text{TH0}}\); \(l\) is the characteristic length of the three-gate silicon FinFET device; and \(\eta\) is a dimensionless fitting parameter (ranging from 0 to 1) that models the short-channel effect [13].  
In MVE theory, channel carriers are categorized into three modes based on their energy. Under a high horizontal electric field, carriers gain energy from the field. In this mode, Si-H bond dissociation at the interface is solely driven by single-carrier processes. When the horizontal electric field is lower, the dissociation of the bond by multiple carriers raises the vibrational state of the bond, eventually providing enough energy (denoted as \(E_{B}\)) for dissociation, which is shown in Figure 3. When the energy of the carriers does not reach the MVE region, the degradation is dominated by electron-electron scattering. Corresponding to the stress voltage, the low-energy region and the high-energy region of the carriers are in the following two voltage ranges: \(V_{ds}<V_{gs}\) and \(V_{gs}\leq V_{ds}\)[6]. Thus, the lifetime corresponding to the above three modes is inversely proportional to the current between the drain and source, so the aging rate can be written as \(AR\propto(I_{\text{ds}})^{k}\). Replacing \(I_{ds}\) with voltage yields, the AR can be written as  
\[AR=A\big{(}V_{\text{gs}}-V_{T}\big{)}^{n_{1}}\exp\left(-\frac{H_{1}}{V_{ds}- \eta V_{\text{dast}}}\right)\exp\left(-\frac{E_{s}k}{k_{B}T}\right) \tag{9}\]  
\[H_{1}=\frac{\phi_{\text{fit}}}{q\lambda l} \tag{10}\]
# Numerical Study on Tip Vortex Cavitation Inception on a Foil
###### Abstract  
In this paper, the inception of tip vortex cavitation in weak water has been predicted using a numerical simulation, and a new scaling concept with variable exponent has also been suggested for cavitation inception index. The numerical simulations of the cavitating flows over an elliptic planform hydrofoil were performed by using the RANS approach with a Eulerian cavitation model. To ensure the accuracy of the present simulations, the effects of the turbulence model and grid resolution on the tip vortex flows were investigated. The turbulence models behaved differently in the boundary layer of the tip region where the tip vortex is developed, which resulted in different pressure and velocity fields in the vortex region. Furthermore, the Reynolds stress model for the finest grid showed a better agreement with the experimental data. The tip vortex cavitation inception numbers for the foil, predicted by using both wetted and cavitating flow simulation approaches, were compared with the measured cavitation index values, showing a good correlation. The current cavitation scaling study also suggested new empirical relations as a function of the Reynolds number substitutable for the two classic constant scaling exponents. This scaling concept showed how the scaling law changes with the Reynolds number and provided a proper scaling value for any given Reynolds numbers under turbulent flow conditions.  
tip vortex cavitation; RANS; Reynolds stress model; cavitation inception; scaling law; elliptic planform foil +
Footnote †: journal: Applied  
**Applied Science 2021**, _11_, 7332. [[https://doi.org/10.3390/app11167332](https://doi.org/10.3390/app11167332)]([https://doi.org/10.3390/app11167332](https://doi.org/10.3390/app11167332))
## 4 Numerical and Discussion
(**a**) \(\delta_{\text{Lxx}}\) and \(\delta_{\text{Soxv}}\) (**b**) \(\delta_{\text{Ixy}}\) and \(\delta_{\text{Syyv}}\), (**c**) \(\delta_{\text{Lxy}}\) and \(\delta_{\text{Sxyv}}\), (**d**) \(\delta_{\text{Ixyx}}\) and \(\delta_{\text{Syxv}}\). For various transmission distances, Figure 8 illustrates the heterodyne efficiency as a function of detector aperture D. As shown in Figure 8, the heterodyne efficiency of a downlink path and an uplink path decreases with increasing detector size. The heterodyne efficiency of a downlink path is also higher than that of an uplink path. Additionally, the heterodyne efficiency increases with an increasing transmission distance on a downlink path and an uplink path. This is due to the fact that the zenith angle decreases as the transmission distance increases, the corresponding atmospheric turbulence intensities decrease, spatial coherence deteriorates and the impact of atmospheric turbulence on the beam is reduced. Figure 6: Effect of inner scale and outer scale on the heterodyne efficiency and DoP on an uplink path and a downlink path in atmospheric turbulence, (**a**) \(l_{0}\) (**b**) \(L_{0}\). Figure 7: Effect of atmospheric turbulence intensities on the heterodyne efficiency and DoP on an uplink path and a downlink path (**a**) \(C_{n}^{2}=10^{-12}\) m\({}^{-2/3}\) (**b**) \(C_{n}^{2}=10^{-14}\) m\({}^{-2/3}\) (**c**) \(C_{n}^{2}=10^{-16}\) m\({}^{-2/3}\).
## 3 Results
### Differences in Physical Performance Variables Based on Maturity Status and Gender
#### 3.4.1 Interaction Effects of Chronological Age and Maturity Factors
\begin{table}
\begin{tabular}{l l l l l l l l} \hline  & & \multicolumn{2}{c}{**MANOVA Age \(\times\) Maturity**} & \multicolumn{2}{c}{**MANOVA Age**} & \multicolumn{2}{c}{**MANOVA Maturity**} \\ \hline  & & **[F (\(p\))]** & \(\mathbf{n^{2}}\) & **[F (\(p\))]** & \(\mathbf{n^{2}}\) & **[F (\(p\))]** & \(\mathbf{n^{2}}\) \\ \hline \hline \multirow{2}{*}{\(\mathrm{VO}_{\mathrm{2 max}}\) (mL\(\cdot\)min\({}^{-1}\)kg\({}^{-1}\))} & Boys & 1.712 (0.119) & 0.045 & 2.950 (0.001) \({}^{*}\) & 0.130 & 0.442 (0.643) & 0.004 \\  & Girls & 1.242 (0.291) & 0.029 & 1.262 (0.248) & 0.062 & 0.696 (0.500) & 0.007 \\ MAV (km\(\cdot\)h\({}^{-1}\)) & Boys & 1.712 (0.119) & 0.045 & 2.950 (0.001) \({}^{*}\) & 0.130 & 0.442 (0.643) & 0.004 \\  & Girls & 1.262 (0.282) & 0.029 & 1.301 (0.225) & 0.064 & 0.786 (0.457) & 0.007 \\ SJ height (cm) & Boys & 0.494 (0.812) & 0.013 & 2.740 (0.002)\({}^{*}\) & 0.122 & 0.883 (0.415) & 0.008 \\  & Girls & 0.358 (0.877) & 0.008 & 1.571 (0.109) & 0.076 & 1.010 (0.366) & 0.010 \\ CMJ height (cm) & Boys & 0.769 (0.955) & 0.021 & 3.462 (0.001) \({}^{*}\) & 0.149 & 1.130 (0.325) & 0.010 \\  & Girls & 1.363 (0.239) & 0.031 & 1.870 (0.045) \({}^{*}\) & 0.089 & 1.198 (0.304) & 0.011 \\ SJ Power (W\(\cdot\)kg\({}^{-1}\)) & Boys & 0.823 (0.553) & 0.022 & 4.529 (0.001) \({}^{*}\) & 0.187 & 0.961 (0.384) & 0.009 \\  & Girls & 0.228 (0.950) & 0.005 & 0.962 (0.483) & 0.048 & 0.674 (0.511) & 0.006 \\ CMJ Power (W\(\cdot\)kg\({}^{-1}\)) & Boys & 0.636 (0.701) & 0.017 & 5.566 (0.001) \({}^{*}\) & 0.220 & 0.982 (0.376) & 0.009 \\  & Girls & 1.345 (0.246) & 0.031 & 1.638 (0.090) & 0.079 & 1.384 (0.253) & 0.013 \\ SJ height-S (cm) & Boys & 0.673 (0.672) & 0.018 & 2.767 (0.002) \({}^{*}\) & 0.123 & 1.976 (0.141) & 0.018 \\ Girls & 0.703 (0.622) & 0.016 & 1.496 (0.135) & 0.073 & 2.472 (0.087) & 0.023 \\ CMJ height-S (cm) & Boys & 0.808 (0.565) & 0.022 & 2.596 (0.004) \({}^{*}\) & 0.116 & 1.288 (0.278) & 0.012 \\  & Girls & 1.641 (0.150) & 0.038 & 1.342 (0.203) & 0.066 & 3.767 (0.025) \({}^{*}\) & 0.035 \\ SJ Power-S (W\(\cdot\)kg\({}^{-1}\)) & Boys & 0.403 (0.876) & 0.011 & 2.187 (0.016) \({}^{*}\) & 0.100 & 0.917 (0.401) & 0.008 \\  & Girls & 0.334 (0.892) & 0.008 & 1.235 (0.265) & 0.061 & 1.794 (0.169) & 0.017 \\ CMJ Power-S (W\(\cdot\)kg\({}^{-1}\)) & Boys & 0.364 (0.901) & 0.010 & 2.391 (0.008) \({}^{*}\) & 0.108 & 0.930 (0.396) & 0.008 \\  & Girls & 0.884 (0.492) & 0.021 & 1.049 (0.405) & 0.052 & 2.556 (0.080) & 0.024 \\ Five-Jump test (m) & Boys & 1.196 (0.309) & 0.032 & 8.010 (0.001) \({}^{*}\) & 0.289 & 0.148 (0.863) & 0.001 \\  & Girls & 1.478 (0.198) & 0.034 & 1.255 (0.253) & 0.062 & 1.906 (0.151) & 0.018 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of MANOVA analyses examining the difference in physical performances according to age category and/or maturity status per test cluster.
# Development of Potato Mass Estimation System Based on Deep Learning
## 2 Materials and Methods  
### Potato Mass Estimation System  
The system used to estimate the mass of potatoes in the present study is configured as follows. The sorting unit of the potato harvester is separately fabricated for the experiment, as shown in Figure 1, and the system is configured to photograph the images of the sorting unit and transmit them to the connected computer using a camera (KYT-U400-01M, KAYETON) installed on top of the system, and to collect locational information through the installed RTK-GPS (MRP-2000, MBC, the Republic of Korea). To ensure that accurate locational information is collected at this time, a GNSS and an LTE antenna are installed to reduce errors, and the specifications related to this are shown in Table 1.  
The system used in the present study was fabricated by improving the potato yield monitoring system designed in [PERSON] et al. [18] and adding a function for the estimation of the potato mass. The process used to estimate the potato mass is as follows: (1) the image photographed at the top end of the sorting unit of the potato harvester is transmitted to the connected computer. (2) The connected computer performs the distortion correction of the images transmitted by the camera and then calculates the length per pixel. (3) Then, it sorts out potatoes from foreign substances by applying YOLOv5--one of the deep learning algorithms--to the corrected images, and it tracks objects by applying DeepSORT--an object tracking algorithm--to the sorted potato object. (4) The volume of the tracked potato object is estimated by entering the lengths of the major axis and minor axis of the potato into the regression equation, (5) and the mass of the potato is estimated by multiplying the ultimately estimated volume and the density of potato. This process is illustrated in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Specification** \\ \hline Width \(\times\) height \(\times\) depth (mm) & 46.5 \(\times\) 96.4 \(\times\) 24 \\ Power (W) & 5 \\ Weight (g) & 98 \\ Operation temperature (\({}^{\circ}\)C) & \(-\)25–85 \\ RTK accuracy & 0.010 m + 1 ppm CEP \\ RTK fix time (s) & 10–60 \\ GNSS TTFF (s) & Cold start: 25–28, aided start: 2 \\ Input port I/F & GNSS: MCx, DMB: SMA, LTE: MMCX \\ Output I/F & RS232 Serial (D-sub 9 pin) \\ Output type & NMEA-0183 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of RTK-GPS.  
Figure 1: Sorting unit of potato harvester.
## Materials and Methods  
### Synthesis of the Samples  
Nanocrystalline powders of Mn-doped Nb\({}_{2}\)O\({}_{5}\) were prepared by the precursor polymeric method (Pechini method). Niobium chloride (NbCls, 99%, (Sigma-Aldrich, Munich, Germany)) and manganese nitrate (Mn(NO\({}_{3}\))\({}_{2}\).4H\({}_{2}\)O (Merck, Darmstadt, Germany)) were used as the starting materials; citric acid (C\({}_{6}\)H\({}_{8}\)O\({}_{7}\), 99.5%, (PanReac, PanReac AppliChem, Barcelona, Spain)) and ethylene glycol (C\({}_{2}\)H\({}_{6}\)O\({}_{2}\), 99.8%, PanReac) were used as the chelating agent and polymerizing agent, respectively; distilled water was used as a solvent. Completely solubilized manganese nitrate was added into the niobium chloride solution for four different manganese weight contents: 1.0, 2.5, 5.0 and 10.0 wt.%. The solutions were stirred and kept at 90 \({}^{\circ}\)C in a stove until the volume of water was reduced and resins were produced. Afterward, these resins were placed in an oven at 300 \({}^{\circ}\)C for 2 h. Finally, the powders obtained after deagglomeration were placed into an oven to administer four different thermal treatments for 2 h in air: 400, 500, 600 and 700 \({}^{\circ}\)C, respectively.
# Data Knowledge Dual-Driven Rate of Penetration Prediction Method for Horizontal Wells
## 3 Methodology
### Feature Selection  
To ensure the performance of the model and subsequent drilling optimization efforts, it is necessary to optimize the drilling variables that affect the ROP. Horizontal wells, like vertical wells, achieve rock fragmentation by manipulating the weight on bit (WOB), rotational speed (RPM), and pump flow rate. WOB, RPM, and flow rate all need to be considered as characteristic variables. Torque and standpipe pressure are state variables of drilling efficiency that cannot be directly manipulated during drilling and are not considered characteristic variables [30]. According to previous studies, ROP is not only related to drilling parameters but also influenced by rock strength [31]. However, it is relatively difficult to obtain rock strength data directly, and the simulation data in this paper do not involve formation changes, so the effect of rock strength is not considered directly in the characteristic variables. In general, as the measured depth (MD) increases, the compaction effect is obvious and the ROP becomes slower as the rock strength becomes greater, so the well depth needs to be considered. Based on the above analysis, WOB, RPM, flow rate, and MD are selected as input variables to the ROP prediction model for horizontal wells.
# Security Challenges for Light Emitting Systems
## 2 Security Challenges in VLC System  
### Security of MAC in Li-Fi  
The IEEE 802.15.7 specification describes the MAC security mechanisms that higher protocol levels may necessitate [5]. The standard indicates that cryptographic algorithms should provide data transmission confidentiality and integrity, but their implementation should not be overly complicated and, in particular, should not use excessive quantities of computer power, storage space, and power sources [5]. The need stems from VLC applications in personal area networks and body area networks (PANs and BANs), where computing resources are severely limited. Moreover, because VLC networking is also utilized as LAN technology, the standard's current security measures may be insufficient. For MAC-level security, the IEEE 802.15.7 standard employs symmetric-key cryptography. The standard does not include key generation and management procedures. Frame protection is implemented at the MAC level as follows: (1) A \"link key\" that is only utilized by peers is used for peer-to-peer communication; (2) a shared \"group key\" is used for communication between a group of devices. This technique allows for some flexibility and an application-specific trade-off between key storage and key maintenance costs vs. the cryptographic security given. The 128-bit AES algorithm is used for encryption at the MAC layer. A keyframe counter is also used to force key initialization and prevent replay attacks [5]. The data, beacon payload, and control payload frames are encrypted.
## 3 Control Strategy Design
### Valve Control  
In the NIMV system, the movement of the cylinder is independently controlled by two metering valves including meter-in and meter-out. However, controlling two metering valves at the same time causes more throttling loss. To solve this problem, one metering valve is fully opened and one metering valve is controlled. In the case of controlling the meter-in valve, not only the throttling loss from the valve but also loss from the pump due to the pressure difference between the pump and the cylinder chamber occur [15]. Therefore, to reduce energy loss, controlling the meter-out valve is used via the VLFPB method meanwhile the meter-in valve is fully opened.  
The valve control signal \(U_{K}\) (\(K=sa\), \(ab\) or \(bt\)) from the VLFPB method is obtained by the combination of two control signals. In which, the velocity-load feedforward signal \(U_{K}^{VLC}\) is calculated from the velocity requirement, the load force, and the pressure of the system.  
Figure 3: The proposed control method (VLF + FLC) of the NIMV system applies to the boom excavator.  
Meanwhile, a position feedback signal \(U_{K}^{PB}\) obtained from the FLC is added to improve the tracking position of the cylinder and compensates for the energy loss in the valve. The mathematical modeling of the VLFPB method on each mode is described as follows:  
The valve control signal of valve can be calculated by Equation (1).  
\[U_{K}=U_{K}^{VLF}+U_{K}^{PB} \tag{1}\]  
According to the Newton's Law, the dynamics of the boom cylinder can be provided by Equation (2).  
\[p_{a}A_{a}-p_{b}A_{b}-mg-B_{v}\dot{x}=m\dot{x} \tag{2}\]  
where \(p_{a},p_{b}\) are the pressure at the bore and rod chamber, \(A_{a}\), \(A_{b}\) are an area of the bore and rod side of the piston respectively, \(m\) is the load, \(B_{v}\) is the viscous friction coefficient, and \(x\) is the target displacement of the cylinder. Depending on each mode, the pressures at the bore and rod chamber are different.
## 4 Materials and Experimental Results
### Emotion Recognition Binary Classification Experiment  
To verify the efficacy of the feature extraction method proposed in this article, we selected EEG features that lacked frequency feature extraction and spatial transformation and compared them to the method outlined in this paper. For each set of features, we performed two- and four-classification tasks on the dataset. The set of features lacking frequency feature extraction and spatial transformation is referred to as the \"baseline features\" throughout this study, the detailed extraction process is shown in Figure 10.  
To ensure the maximum effectiveness of the proposed method, the same baseline removal, window segmentation, and frame-segmentation processes were applied to the baseline features. The resulting data form of \(\text{X}_{\text{trial,rmov}}\) obtained from 32 channels of the subject is 32 \(\times\) 60 \(\times\) 128, and window segmentation generates the window fragment data \(\text{X}_{\text{S}}^{\text{i}}\). The data form of \(\text{X}_{\text{S}}^{\text{i}}\) is 32 \(\times\) 5 \(\times\) 128. Then, \(\text{X}_{\text{S}}^{\text{i}}\) is divided into five frequency bands, and its data form is expanded to 32 \(\times\) 5 \(\times\) 128 \(\times\) 5. To extract complete sample timing information and then carry out the time-length 0.5 s framing process, the form is transformed to 32 \(\times\) 0.5 \(\times\) 128 \(\times\) 5. The five data bands are superimposed in the dimension to convert them into a three-dimensional matrix sequence of size 5 \(\times\) 32 w  
Figure 10: Baseline feature extraction process.  
64 (0.5 \(\times\) 128) so that each frame enters the convolutional coding module proposed in this paper in the form of 5 \(\times\) 32 \(\times\) 64. The important temporal features of EEG bands in matrix sequence data are extracted by Bi-LSTM, and the results of the binary classification on the dataset are shown in Figure 11 below:  
The average training and testing accuracies of binary classification for the four evaluation metrics were 98.99% and 94.43%, respectively.  
To enhance the spatial representation of the EEG signal, this paper employs a detailed transformation technique that maps the frequency band's sequential signal into a two-dimensional matrix. This approach rectifies the loss of spatial information in the baseline features. Additionally, the more indicative frequency features, namely, DE and PSD, are utilized as the matrix element values to substitute for the sub-band amplitudes in the baseline features. The results of the binary classification on the four DEAP dataset metrics are displayed in Figure 12 below.  
Figure 11: (**a**) Valence dimension recognition results; (**b**) arousal dimension recognition results; (**c**) dominance dimension recognition results; and (**d**) liking dimension recognition results.  
It is evident that the average accuracy of the suggested feature extraction method in this article is 99.21% and 97.84% for training and testing in valence, arousal, dominance, and liking states, respectively. This is approximately a 3% improvement over the test accuracy of baseline features.