esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Establishment of CFD Model
### Governing Equation and Turbulence Model  
The average speed of a FSAE racing car is about 50 km/h, and 15 m/s velocity for free flow is taken as a calculation condition, which is less than Mach 0.3. Therefore, a 3D, isothermal, and incompressible viscous flow model under low Reynolds number is  
Figure 8: Impact of shutter-like fairing structure on 3D flow field. (**a**) High vorticity of wingtip vortex is reduced significantly by passing through the shutters; (**b**) low-pressure condition in the vortex is improved because of the reduction in vorticity.  
adopted. The governing equation used in the calculation is the homogenized turbulent Navier-Stokes equation.  
\[\left\{\
abla\cdot(\rho\overline{\mathbf{v}})=0\right. \tag{2}\]  
where \(\rho\) is the air density, \(\overline{\mathbf{v}}\) denotes the time average speed, \(f_{b}\) is the volume force per unit mass, \(\overline{p}\) is the time averaged pressure, \(I\) represents a unit tensor, and \(T_{t}\) is the Reynolds stress term, which is unknown; thus, \(T_{t}\) should be further modeled.  
If merely consider the agreement between CFD simulation and actual wind tunnel test results, the standard k-\(\omega\) turbulence model (SKO) is the best choice [11]. However, a realizable k-\(\varepsilon\) two-layer turbulence model (RKE 2L) is selected to both ensure the accuracy of calculation and to save computing resources, since RKE 2L has better convergence speed and relatively high accuracy even compared with SKO.  
The model of \(T_{t}\) is called turbulence model, and in RKE 2L, the transport equation of turbulent kinetic energy \(k\) in steady state is  
\[\
abla\cdot(\rho k\overline{\mathbf{v}})=\
abla\cdot\left[\left(\mu+\frac{\mu_ {t}}{\sigma_{k}}\right)\
abla k\right]+P_{k}-\rho(\varepsilon-\varepsilon_{0} )+S_{k}, \tag{3}\]  
and the transport equation of Dissipation rate \(\varepsilon\) is  
\[\
abla\cdot(\rho\varepsilon\overline{\mathbf{v}})=\
abla\cdot\left[\left(\mu +\frac{\mu_{t}}{\sigma_{\varepsilon}}\right)\
abla\varepsilon\right]+\frac{ 1}{T_{\varepsilon}}C_{\varepsilon 1}P_{\varepsilon}-C_{\varepsilon 2}f_{2}\rho \left(\frac{\varepsilon}{T_{\varepsilon}}-\frac{\varepsilon_{0}}{T_{0}} \right)+S_{\varepsilon}, \tag{4}\]  
where \(\mu\) is the dynamic viscosity; \(\mu_{T}\) is the turbulent viscosity; \(\sigma_{k}\), \(\sigma_{t}\), \(C_{\varepsilon 1}\), and \(C_{\varepsilon 2}\) are coefficients that could be modified according to different fluid media; \(P_{k}\) and \(P_{\varepsilon}\) are generative items; \(f_{2}\) is the damping function; \(s_{k}\) and \(S_{\varepsilon}\) are custom source items; \(\varepsilon_{0}\) represents ambient turbulence value in the custom source items; \(T_{\varepsilon}\) is the large eddy time scale; and \(t_{0}\) is specific time scale correspond to \(\varepsilon_{0}\).
## 3 Results and Discussion
### Study Area and Background
#### 3.1.1 Change Detection Based on the Images from 2011, 2015, and 2016
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **FA (\%)** & **TE (\%)** & **OA (\%)** & **KAPPA** \\ \hline HH and improved K\&I & 6.55 & 7.03 & 92.96 & 0.4413 \\ HV and improved K\&I & 5.06 & 5.80 & 94.20 & 0.4774 \\ VV and improved K\&I & 5.19 & 5.67 & 94.33 & 0.5041 \\ CVA and improved K\&I & 1.03 & 4.73 & 95.27 & 0.0415 \\ Test statistic with 5\% significance level & 6.17 & 6.42 & 93.58 & 0.4857 \\ Test statistic with 1\% significance level & 4.59 & 5.04 & 94.96 & 0.5398 \\ Test statistic and K\&I & 3.96 & 4.51 & 95.49 & 0.5629 \\ Test statistic and CFAR & 6.53 & 6.84 & 93.16 & 0.4630 \\ Test statistic and improved K\&I (the proposed method) & 2.69 & 3.51 & 96.49 & 0.6098 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation of the change detection on East Lake between 2015 and 2016. Figure 10: Change detection results in Region 1 between 25 June 2015 and 6 July 2016 based on the following: (**a**) log-ratio (HH) and improved K\&I; (**b**) log-ratio (HV) and improved K\&I; (**c**) log-ratio (VV) and improved K\&I; (**d**) CVA and improved K\&I; (**e**) test statistic with 5\% significance level; (**f**) test statistic with 1\% significance level; (**g**) test statistic and K\&I; (**h**) test statistic and CFAR; (**i**) test statistic and improved K\&I (the proposed method); (**j**) Ground reference (white denotes the change and black denotes the non-change).
## 5 Computational Experiments
### Case Setting  
We consider a safe transportation route selection problem for trucks, aiming to minimize transportation time of trucks and safety risks of drivers, as investigated by [PERSON] et al. [32].  
Following the notation of [PERSON] et al. [32], an urban traffic network consists of a set \(V\) of nodes indexed by \(i\) or \(j\), and a set \(E\) of arcs indexed by \((i,j)\). To be specific, \(V=\{0,1,2,\ldots,n\}\), where 0 and \(n\) represent the starting point and the ending point, respectively. \(E\subseteq\{(i,j)|i,j\in N\}\) denotes arcs connecting nodes in the urban traffic network. Let \(t_{ij}\) and \(T\) denote the travel time between node \(i\) and node \(j\), and the maximum travel time, respectively. Let \(w_{ij}\) and \(C\) represent the safety risk of the trucks traveling on the arc \((i,j)\), and the maximum acceptable risk, respectively. \(x_{ij}\) denotes the binary decision variable, which equals 1 iff arc \((i,j)\) is selected, and 0 otherwise. Based on the notation given above, a bi-objective programming model can be formulated.  
\[\min\begin{pmatrix}\sum_{(i,j)\in E}t_{ij}x_{ij}\\ \sum_{(i,j)\in E}w_{ij}x_{ij}\end{pmatrix} \tag{8}\]  
\[\text{s.t.}\quad\sum_{(i,j)\in E}t_{ij}x_{ij}\leq T \tag{9}\]  
\[\sum_{(i,j)\in E}w_{ij}x_{ij}\leq C \tag{10}\]  
\[\sum_{(0,j)\in E}x_{0j}=1 \tag{11}\]  
\[\sum_{(i,n)\in E}x_{in}=1 \tag{12}\]  
\[\sum_{(i,j)\in E}x_{ij}=\sum_{(j,i)\in E}x_{ji}\quad\quad\forall j\in V \setminus\{0,n\} \tag{13}\]  
\[x_{ij}\in\{0,1\}\quad\quad\quad\quad\quad\forall(i,j)\in E \tag{14}\]In a randomly generated urban traffic network, the travel time and safety risk of the trucks traveling on arcs are set to random integers uniformly distributed within the range of 1 to 20, and within the range of 1 to 50, respectively. Both the maximum travel time and maximum acceptable risk are set to 200.
## 4 The Application Model of Machine Learning in Wireless Sensor Networks
### A Machine Learning Model of a Wireless Sensor Network  
The machine-learning-based wireless sensor cascaded automatic encoder (SAE) can effectively extract low dimensional features of data, which is an important part of the feature extraction classification model SAEM.  
The AE loss function includes the input and output mean square error constraints, weight attenuation constraints, and sparsity constraints:\[\min L(W,b)=\left[\frac{1}{m}\sum_{i=1}^{m}I\left(W,b;x^{(i)},y^{(i)} \right)\right]+\frac{\lambda}{2}\sum_{i=1}^{n_{l}-1}\sum_{i=1}^{s_{l}}\sum_{j=1 }^{s_{l+1}}\left(W_{ji}^{(k,l)}\right)^{2}+\beta\sum_{j=1}^{s_{2}}KL(\rho/ \rho j). \tag{20}\]  
The wireless sensor automatic encoder trains the network parameters through the gradient descent algorithm to minimize the loss function. The main steps are as follows:  
* For all \(l\), let the matrix \(\triangle W^{(k,l)}=0\) and the vector \(\triangle b^{(k,l)}=0\).
* For \(i=1\) to \(m\), calculate: \[\Delta W^{(k,l)} :=\Delta W^{(k,l)}+\
abla_{W^{(k,l)}}J\left(W,b;x^{(i)},y^{(i)} \right)\cdot\]
* Update the parameters: \[W^{(k,l)}=W^{(k,l)}-\alpha\Big{[}\Big{(}\tfrac{1}{m}\Delta W^{( k,l)}\Big{)}+\lambda W^{(k,l)}\Big{]},\] \[b^{(k,l)}=b^{(k,l)}-\alpha\Big{[}\tfrac{1}{m}\Delta b^{(k,l)} \Big{]}\]
* Repeat Step 2 until it converges or reaches the maximum number of iterations, and output \((W^{(k,1)},b^{(k,1)},W^{(k,2)},b^{(k,2)})\).  
A few remote sensors arrange AEs to be cascaded to construct a multilayer neural arrangement [25]: the cascaded programmed encoder (SAE). The yield of the SAE can be seen as the included representation of the input data after different dimensionality decreases. The parameters of each layer of the SAE can be obtained through greedy training layer by layer (while training, the yield of the past hidden layer of the AE is utilized as the input for the following layer of the AE). The particular strategy was as follows:  
We set the number of SAE hidden layers Nk of the sensor, utilized test x to train the primary AE to obtain its parameters, and the hidden layer yielded (1,2); we utilized (1,2) as the input, trained the next AE to obtain its parameters, and the hidden layer yielded (2,2), and so on. After the greedy training of the Nk AES layer by layer, we obtained the parameter to gather \(\{(W^{(k,l)},\,b^{(k,l)})\) lk = 1,..., Nk\(\}\), and we took \((W^{(k,l)},\,b^{(k,l)})\) as the association weight between the SAE layers, as shown in Figure 4.  
Figure 4: The feature extraction sensor classification model, SAEM, structure.
# An Estimation Method of River Dry Runoff Alteration after Upper New Reservoirs Storage
## 2 Background and Methodology
### Hydrological Characteristics Alteration  
To analyze the impact of dam storage on the downstream river hydrological characteristics, long-term historical datasets of the Jinsha River (Pingshan hydrological station, 1956-2011 and Xiangjiaba hydrological station, 2012-2020) and Min River (Gaochang hydrological station, 1980-2020) were collected from the Hydrologic Data Year Book of the People's Republic of China (P.R.C.). The hydrological data officially released has undergone error analysis and corrective measures during the compilation process, resulting in high data accuracy, thus making it directly suitable for analyzing the hydrological characteristics of rivers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**River** & **Reservoir** & **Storage Time** &
\begin{tabular}{c} **Adjustable Volume** \\ **(Billion m3)** \\ \end{tabular} & **Adjustable Capacity** \\ \hline Min River & Zipingpu & 2006 & 0.8 & Non-annual regulating \\ \hline \multirow{3}{*}{Dadu River} & Pubugou & 2010 & 3.9 & Non-annual regulating \\  & Houziyan & 2018 & 0.4 & Non-annual regulating \\  & Changheba & 2018 & 0.4 & Non-annual regulating \\ \hline \multirow{3}{*}{Jinsha River} & Xiangjiaba & 2014 & 0.9 & Annual regulation of the combine \\  & Xiluodu & 2014 & 6.5 & Annual regulating \\ \cline{2-5}  & Baihetan & 2021 & 10.4 & Multi-annual regulating \\  & Wudongde & 2021 & 3.0 & Annual regulating \\ \hline \multirow{2}{*}{Yalong River} & Ertan & 2000 & 3.4 & Annual regulating \\  & Jinping 1 st & 2014 & 4.9 & Multi-annual regulating \\ \hline \hline \end{tabular}
\end{table}
Table 1: Designed information of the studied reservoirs.  
Figure 1: Major reservoirs in the basins of the Jinsha River and Min River, the upper watershed of the Yangtze River.
## 2 Theoretical Background
### Visual Cognition  
Visual cognition refers to the process by which objective information from the outside world serves as a source of stimulation, transmitted to the brain through the optic nerve, and then processed by the brain [28]. When observing interface information, the eye is the primary organ for acquiring visual data. The physiological behavior of the human eye's gaze during the processing of interactive interface information is closely related to brain cognition [29]. However, since human visual attention is limited, presentingvirtual museum interface information in an unorganized manner may cause cognitive confusion for users. Eye-tracking technology enables the measurement of visual gaze focus, reflecting individual interest preferences and providing insights into cognitive performance during human-computer interactions. Consequently, scholars measure human visual cognitive characteristics through eye-tracking experiments. [PERSON] et al. [30] conducted eye movement experiments to analyze the impact of different information presentation methods on interface usability, noting particularly that novice users tend to favor simpler graphical interfaces. [PERSON] et al. [31] examined various train-driving interface layouts to study human visual cognitive load, search efficiency, and attention allocation through eye movement experiments. [PERSON] et al. [32] conducted experimental measurements to evaluate health-care information system interfaces and proposed an optimization methodology for interface design that can serve as a valuable reference. Furthermore, [PERSON] et al. [33] analyzed the gaze trajectory, gaze point distribution, and gaze frequency of pilots using a cockpit instrument panel interface. Their goal was to reduce pilot operational errors by incorporating insights from eye movement experiments.  
In summary, scholars have evaluated interface usability based on either interface layout aesthetics or visual cognitive features, but they have not attempted to combine the two. Relying solely on one evaluation dimension cannot fully capture user needs. Additionally, without distinguishing interface types, the evaluation method may not be applicable to infrequently contacted public service interfaces. To address this shortcoming, this study evaluates interface usability from the dimensions of interface layout aesthetics and users' visual cognitive characteristics, quantitatively describing the mapping relationship between the two. This approach aims to propose a new methodology for the design and usability evaluation of public service interfaces, providing a guiding principle for designers in future interface design.
# Monitoring and Data Analysis in Small-Scale Landslide Physical Model
## 3 Review of Landslide Monitoring Equipment
### Geotechnical Monitoring System
#### 3.2.1 Water-Related Sensors
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sensor Name**} & **Volumetric Water** & **Water Potential** & **Temperature** & **Electrical** & **Volume of** & **Dimensions** \\  & **Content Range** & **Range** & **Range** & **Conductivity** & **Influence** & **(L \(\times\) W \(\times\) H)** \\ \hline TEROS 10 & 0.00–0.64 m\({}^{3}\)/m\({}^{3}\) & - & - & - & 430 mL & 5.1 cm \(\times\) 2.4 cm \(\times\) 7.5 cm \\ \hline TEROS 12 & 0.00–0.70 m\({}^{3}\)/m\({}^{3}\) & & \(-\)40\({}^{\circ}\) to +60\({}^{\circ}\) & 0–20 dS/m & 1010 mL & 9.4 cm \(\times\) 2.4 cm \(\times\) 7.5 cm \\ \hline TEROS 21 & - & \(-\)9 kPa to \(-\)100,000 kPa & \(-\)40\({}^{\circ}\) to +60\({}^{\circ}\) & - & - &
\begin{tabular}{c} 9.6 cm \(\times\) 3.5 cm \(\times\) 1.5 cm, \\ sensor diameter 3.2 cm \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Characteristics of the TEROS soil moisture and potential sensors. Figure 5: Soil moisture and soil water potential sensors: TEROS 10 with two needles and TEROS 12 with three needles. In the figure is also soil water potential sensor TEROS 21 with a ceramic disc. potential can be measured. TEROS 21 measures dielectric permittivity of a solid matrix (porous ceramic discs) to determine its water potential. Using the moisture characteristic curve of the ceramic, the water potential can be derived from the readings of the water content. For accurate measurements, a good hydraulic contact with the surrounding soil must be achieved during the sensor installation. While TEROS 21 does not have the accuracy of tensiometers and the ability to measure positive pressure, its extended range for negative pressure, i.e., matric suction, that can be seen in Table 6, makes it an ideal sensor for dry soils and high matric suction (e.g., clays) where tensiometers have a cavitation problem [63]. Although there are many studies which used similar types of sensors to monitor volumetric water content and soil potential from different manufacturers, the most important are studies from [PERSON] and [PERSON] [23] and [PERSON] et al. [44], as their sensors are predecessors of the TEROS sensors. The reliability and accuracy of the sensors is defined by the manufacturer and can be checked with an accuracy verification standard. For a simple material like sandy soil, generic calibration curve for all soil types was used, which ensures accuracy of \(\pm\)3% of sensor readings.
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 4 Submarine Amplifiers
### Experimental Demonstrations of Submarine SDM-Based Amplifiers at Transoceanic Distances
Their results showed that the transmission throughput achieved by 2 cores using PDM and QPSK modulation for the distance of 8007 km was 18.6 Tb/s and 14.5 Tb/s, respectively. Another recent circulating loop experiment using innovative SDM-based amplifiers was demonstrated and experimentally studied in [66]. This study demonstrated a 130.8 Tb/s SDM transmission in full C-band over transoceanic distance of 12,700 km using 12-core MCFs (110 \(\upmu\)m\({}^{2}\)) (Figure 25). Regarding power efficiency, both a \(\sim\)70 mW of pump power per path EDFA and a new 8D-QPSK modulation format with SE of 2.2 b/s/Hz were implemented. However, the fundamental network elements of this structure were hybrid micro-assembly-based EDFAs, which are being developed for applications requiring a small form factor such as plug-in modules (showed in Figure 25). These micro-assembly EDFAs seem to be an important candidate for supporting the future era of SDM amplifiers, considering the limitations in repeater housings. The experimental results showed that the use of compact amplifiers and the improvements in power efficiency can effectively provide a significant increase in the number of transmission paths, which is a crucial factor to achieve higher capacities in the future. [PERSON] et al. in [23] studied various SDM designs for different target performances (OSNR, GOSNR, capacity, etc.) at transpacific and transatlantic lengths. They pointed out that long-haul systems with classical repeaters without pump farming have no chance to increase submarine systems' capacity, and so, the introduction of the pump farming concept seems mandatory. In [28], [PERSON], [PERSON] et al. made a circulating loop experiment demonstrating a 105.1-Tb/s submarine transmission in a 12-core fiber over \(\sim\)14.350 km, using a power-efficient 8 DAPSK modulation format and optical EDFA amplifiers. To simulate a submarine system, they limited the total pump power used by all 12 EDFAs (Figure 26) that may reside in one repeater to that of a single, standard LD with an \(\sim\)800 mW rating. Their results confirmed another record capacity-distance product of 1.51 Pb/s \(\times\) km compared to the previously reported world record distance product of 1.03 Eb/s \(\times\) km [38]. Figure 27 demonstrates the most recent long-haul transmission (at transoceanic distances) experiments implementing different types of EDFA amplifiers [38, 41, 42, 43, 44, 45, 54, 65, 67]. Figure 26: Schematic structure of circulating loop experiment composed of 12 EDFAs [28]. Figure 25: Schematic structure of a transmission test bed based on 12-core fiber and hybrid microassembly-based amplifiers with fan in–out devices, wavelength selective switch (WSS), and a loop synchronous polarization controller (LSPC) [66].
# Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics
## 3 Results
### In-Row Plant Uniformity Estimators in the Context of Plant Detection
In fact, the GT spacing of plants was used as a standard to measure correlations. In some cases, however, a stronger correlation was seen when comparing the output data from Mask R-CNN detection. Research demonstrated that vibrations had a beneficial impact on the rise in the proportion of failures in sowing (Mi), aligning with the findings of [PERSON] et al. [51]. The impact of vibrations on the occurrence of failures in sowing is determined to be 50% based on GT data. Undoubtedly, the abrupt rise in the speed of the seeding row units raises the likelihood of sudden seed discharge from the sowing plate, particularly when managing larger seeds, resulting in a failure. Based on the provided information and the GT data, the vibration amplitudes were more significant for missed spacing occurrence than the vibration energy. In contrast, the presence of multiple plants was inversely correlated with vibration, which can be explained by the fact that vibration reduces the likelihood of retaining a greater number of seeds on the seeding plate. The correlation between the percentage of misses and multiples with vibrations leads to a significant impact on QFi, which is an inverse derivative of the same analysis. QFi has a high association with the amplitude and power of vibration, reaching up to 77% in the case of GT data. Comparing two distinct DNN algorithms using a bench-based seeding quality trial configuration, [PERSON] et al. [52] discovered comparable results regarding QFi. Significant correlations were established between the average variation in plant spacing and vibrations, with explanations reaching as high as 65%. This suggests that even with the testing of a modern seeding system that integrates advanced techniques for seed manipulation and singulation, we have not completely eliminated extrinsic factors like vibration. Future research should focus on mitigating the adverse effects of vibrations during sowing. One potential solution is to employ a specialized suspension system to connect the sowing apparatus and the seeder's main frame. This suspension system, acting as a buffer, can effectively dampen vibrations and prevent their amplification due to resonance frequencies. Figure 10: Spectral densities of the seeding row unit vibrations excited by the working speed.
# A Cloud-Based Distributed Architecture to Accelerate Video Encoders
###### Abstract  
Nowadays, video coding and transcoding have a great interest and important impact in areas such as high-definition video and entertainment, healthcare and elderly care, high-resolution video surveillance, self-driving cars, or e-learning. This growing demand for high-resolution video boosts the proposal of new codecs and the development of their encoders that require high computational requirements. Therefore, new strategies are needed to accelerate them. Cloud infrastructures offer interesting features for video coding, such as on-demand resource allocation, multitenancy, elasticity, and resiliency. This paper proposes a cloud-based distributed architecture, where the network and the storage layers have been tuned, to accelerate video encoders over an elastic number of worker encoder nodes. Moreover, an application is developed and executed in the proposed architecture to allow the creation of encoding jobs, their dynamic assignment, their execution in the worker encoder nodes, and the reprogramming of the failed ones. To validate the proposed architecture, the parallel execution of existing video encoders, x265 for H.265/HEVC and libvpx-vp9 for VP9, has been evaluated in terms of scalability, workload, and job distribution, varying the number of encoder nodes. The quality of the encoded videos has been analyzed for different bit rates and number of frames per job using the Peak Signal-to-Noise Ratio (PSNR). Results show that our proposal maintains video quality compared with the sequential encoding while improving encoding time, which can decrease near 90%, depending on the codec and the number of encoder nodes.  
cloud computing; video coding; hyperconvergence; video quality; jobs distribution 2020 10 5070; doi:10.3390/app10155070