esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Nitrogen and Water Stress Impact on Chlorophyll and N Content in Leaves  
In order to investigate the effect of different N and water treatments on sugar beet and celery plants, leaf total chlorophyll and leaf N contents were measured. Water stress influences the N availability, movement, and uptake by the crop, which subsequently affects the chlorophyll and nitrogen content in leaves. Water and N interaction has a significant effect on N leaf content in sugar beet (Figure 2a) and celery (Figure 2b) plants. The variation in leaf nitrogen concentration between plants growing under different N fertilization was higher for celery plants than for sugar beet. Higher doses of N fertilization increased leaf N content in LW and OW treatments. Excess water supply (HW) improved the nitrogen accumulation in leaves of sugar beet, whereas the N content in celery leaves decreased, except for the N133 variant. Under the OW condition, the leaf nitrogen content for celery plants in the N33 treatment was as much as 50 % lower than N100, whereas for sugar beet it was only 10% lower. It can be affirmed that the water deficit and the increase in nitrogen levels caused a greater accumulation of nitrogen in the leaves. Previous studies demonstrated that water deficit reduces the activity of nitrate reductase, which, combined with high N content availability in the rhizosphere, favors its absorption by the roots and accumulation in the plant leaves [47].  
It could be noted that the exposed sugar beet (Figure 3a) and celery (Figure 3b) plants to N deficiency (N33 and N 67 variants) caused a significant decrease in chlorophyll content in the case of sugar beet, compared to the N100, for all studied water treatment. In the case of celery, the difference is also visible, although much less pronounced. However, a higher than optimum amount of N fertilizer (N133 variant) causes a slow increase in chlorophyll content in celery plant leaves. The excessive N fertilization had a non-significant influence on chlorophyll content in sugar beet. Water stress (water deficit and excessive water stress) decreases the leaf chlorophyll concentration in both studied plants. As a result of the reduction of the chlorophyll content in plant leaves caused by water stress, photosynthesis decline which leads to stunting of the plants and yield reduction. In the literature, conflicting results are reported on the effects of N availability on plant pigment concentrations. Some authors reported that the water deficit led to a reduction in the leaf chlorophyll, which was in line with our findings [47]. However, according to [48] the increase in the N level does not affect leaf chlorophyll content. The pigment content in plant leaves is known as one of the factors that affects the curve of spectral reflectance. Therefore, to develop stable, widely applicable, and highly robust prediction models, the high variability of chlorophyll content between the variants of the experiments are important and necessary.
# Wheel Odometry with Deep Learning-Based Error Prediction Model for Vehicle Localization
## 5 Results and Discussion
### Tests on the Dataset
#### 5.1.1 Tests in Challenging Driving Conditions
Test results for the hard brake driving condition are displayed in Table 11 and Figure 8. The results show that Transformer-DC and Transformer-NDC exhibited almost identical performance on the two data subsets. The four metrics of the proposed models are reduced by up to 96%, 93%, 97%, and 95%, respectively, compared to LSTM. Compared to WhONet, the two proposed models show up to 84%, 95%, 92%, and 77% improvements in the four metrics. It is thus proven that the accuracy, stability, and reliability of the transformer-based model are significantly improved compared with LSTM and WhONet under this challenging driving condition. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**IO-VNB Dataset**} & \multirow{2}{*}{**Performance Metrics**} & \multicolumn{4}{c}{**Model Error (m)**} \\ \cline{3-6}  & & \multicolumn{4}{c}{**CRSE**} \\ \cline{3-6}  & & **LSTM** & **WhONet** & **Transformer-NDC** & **Transformer-DC** \\ \hline \multirow{4}{*}{V-Vua11} & max & 7.88 & 7.35 & 4.76 & 4.77 \\  & min & 0.24 & 0.11 & 0.04 & 0.03 \\ \cline{1-1}  & \(\mu\) & 2.42 & 1.75 & 0.31 & 0.30 \\ \cline{1-1}  & \(\sigma\) & 2.78 & 2.66 & 0.73 & 0.73 \\ \hline \multirow{4}{*}{V-Vfb02d} & max & 1.55 & 0.49 & 0.36 & 0.48 \\ \cline{1-1}  & min & 0.37 & 0.14 & 0.03 & 0.03 \\ \cline{1-1}  & \(\mu\) & 1.04 & 0.30 & 0.09 & 0.09 \\ \cline{1-1}  & \(\sigma\) & 0.37 & 0.09 & 0.07 & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Test results for the roundabout scenario. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**IO-VNB Dataset**} & \multirow{2}{*}{**Performance Metrics**} & \multicolumn{4}{c}{**Model Error (m)**} \\ \cline{3-6}  & & \multicolumn{4}{c}{**CRSE**} \\ \cline{3-6}  & & **LSTM** & **WhONet** & **Transformer-NDC** & **Transformer-DC** \\ \hline \multirow{4}{*}{V-Vw16b} & max & 2.93 & 0.88 & 0.36 & 0.36 \\  & min & 0.10 & 0.10 & 0.01 & 0.01 \\ \cline{1-1}  & \(\mu\) & 1.49 & 0.36 & 0.06 & 0.06 \\ \cline{1-1}  & \(\sigma\) & 0.95 & 0.22 & 0.05 & 0.05 \\ \hline \multirow{4}{*}{V-Vw17} & max & 3.51 & 0.87 & 0.14 & 0.15 \\ \cline{1-1}  & min & 0.41 & 0.61 & 0.03 & 0.03 \\ \cline{1-1}  & \(\mu\) & 2.13 & 0.78 & 0.06 & 0.06 \\ \cline{1-1}  & \(\sigma\) & 0.01 & 0.01 & 0.03 & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Test results for the hard brake driving condition. Figure 7: Test results for the roundabout scenario. (**a**) V-Vta11; (**b**) V-Vfb02d. In summary, the proposed model has higher accuracy, stability, and reliability under various challenging driving conditions where it is difficult to measure odometry signals accurately compared with LSTM and WhONet. Meanwhile, the Transformer models with and without the driving condition characteristics can obtain approximately the same performance under these challenging driving conditions by adjusting the parameters.
## 3 Hydrogen Production Technologies
### Background Concepts
#### 3.1.6 Water Electrolysis  
This method refers to the oldest method of producing hydrogen, dating back to the 19 th century [49]; it generally refers to a DC electrical power source connected to two electrodes, which are then placed in water. The flow of an electric current splits water molecules into its constituents, making hydrogen appear at the cathode side and oxygen appear at the anode side. The efficiency of electrolysis is increased through the addition of an electrolyte to the solution (such as a salt, an acid, or a base) and the use of electrocatalysts, which raise the reaction's rate too.  
The article now focuses on recent developments of this technology, discussing its efficiency, durability, cost, and overall challenges.
## 7 Case Study
### Simulation Results Considering Slow Charging at Destinations  
Based on the slow charging probability and Equation (24), we employ the MCS to determine whether the user can slow charge at each destination and how long it will take. When the slow charging probability is 0, the result is shown as in Section 7.2. Based on the siting result of the fast charging stations in Section 7.2, under the different slow charging probabilities, we re-analyze the trip feasibility of the above-mentioned traveling data. The travel success ratios under different slow charging probabilities would be the average result of running the MCS 10 times independently, as shown in Figure 21.  
Figure 21: The travel success ratio under different slow charging probabilities.  
Figure 20: The travel success ratio under the three scenarios.  
From Figure 21, we can see that the increase in the slow charging probability at the destination, that is, the increase of the slow charger coverage in the public parking lot, will lead to a proportional growth in the travel success ratio. Figure 22 shows a fitting chart showing the proportion of users who do not need to charge and the proportion of users who need to take a detour to recharge under different slow charging probabilities. As can be seen from the figure, with the increase in the coverage of slow chargers in the public parking lot, there will be more and more users who do not need to charge their vehicles on daily trips and the number of users who take a detour to charge will also decrease, which means that the increase in the slow charger coverage in the public parking lot will effectively reduce the fast charging times of the users and greatly saves their time. Moreover, from the decreasing proportion of users who need to take a detour to charge, we can see that it would be more convenient for the users and greatly improve the user experience. In addition, the growth in the coverage of slow chargers will lead to a sharp increase in the travel success ratio, thus making up for the limited travel range of EVs compared to traditional fuel vehicles and improving public recognition of EVs. At the same time, considering the higher cost of electricity for fast charging, the construction of slow chargers is more economical and effective for users.  
With the large-scale popularization of future EVs, EV ownership will experience a sharp increase, as will the fast charging demand. Figure 23 shows the fast charging demands under different slow charging probabilities and the different growth rates of EVs at 0%, 15%, 30%, and 50%. From Figure 23, we can conclude that the large-scale construction of slow chargers in public parking lots would significantly reduce the fast charging demand brought by the increase of EV ownership, relieving the pressure on the fast charging stations.  
Figure 22: The influence on users under different slow charge probabilities.
###### Abstract  
The control of vortex-induced vibration (VIV) in shear flow with different distributions of Lorentz force is numerically investigated based on the stream function-vorticity equations in the exponential-polar coordinates exerted on moving cylinder for Re = 150. The cylinder motion equation coupled with the fluid, including the mathematical expressions of the lift force coefficient \(C_{l}\), is derived. The initial and boundary conditions as well as the hydrodynamic forces on the surface of cylinder are also formulated. The Lorentz force applied to suppress the VIV has no relationship with the flow field, and involves two categories, i.e., the field Lorentz force and the wall Lorentz force. With the application of symmetrical Lorentz forces, the symmetric field Lorentz force can amplify the drag, suppress the flow separation, decrease the lift fluctuation, and then suppress the VIV while the wall Lorentz force decreases the drag only. With the application of asymmetrical Lorentz forces, besides the above-mentioned effects, the field Lorentz force can increase additional lift induced by shear flow, whereas the wall Lorentz force can counteract the additional lift, which is dominated on the total effect.  
Keywords:flow control; vortex-induced vibration; electro-magnetic control; hydrodynamic force +
Footnote †: journal: Applied Sciences
# AVSS: Airborne Video Surveillance System
## 4 UAV Placement Algorithm  
In this section, the placement algorithm is explained. The metric to determine what is a good placement and how to find the best placement under such condition is discussed. Figure 2(a) shows the map of focusing areas before the placements, and Figure 2(b) is the outcome of placement algorithm which will be explained in this section. Brighter regions mean higher focusing score, and the lines in Figure 2(b) dividing areas are the boundaries of paced UAVs. The algorithm itself works on the frame level resolution. In other words, this algorithm operates whenever there is a change in the score schematics.  
A more important issue is the physical coupling of a target area and the UAV. If an area is completely decoupled with the assigned UAV, the UAV may be commanded to travel a long distance with every frame, which may result in more downtime of surveillance. The design of our algorithm supports a level of physical coupling between a target surveillance area and the UAV assigned to it. Because we use the priority scores of UAVs and the UAV ID, the UAV assigned to a target area will stay assigned to the same target area, even if the target area moves. This ensures the physical coupling of a target area and the UAV unless there is a change in the fleet membership or a change in the order of priority scores. One cycle of our algorithm with 20 UAVs and 15 focusing areas on a laptop, LG 15U780, took an average of 15 ms, which can ensure 66 frames per second, which is fast enough to be considered real time.
## 2 Preliminary Knowledge and a Description of the Issue
### Time Base Generator  
The function known as the time base generator (\(TBG\)) can be continuously differentiated, where certain requirements must be met by both the original and first-order time derivative.  
**Definition 1** ([35]): _The continuous differentiable function \(\xi(t)\) satisfies the following characteristics:_  
* _if_ \(t\in[0,+\infty)\)_,_ \(\xi(t)\) _is a minimum of_ \(\mathbb{C}^{2}\)_._
* _When time first begins,_ \(t_{0}=0\)_, and at the time moment_ \(t_{f}\)_, the value of_ \(\xi(t)\) _satisfies_ \(\xi(0)=0\)_,_ \(\xi(t_{f})=1\)_, and_ \(\xi(t)\) _is a function that does not decrease._
* _The initial derivative of_ \(\xi(t)\)_'s temporal function at the instant_ \(t=0\) _and at_ \(t_{f}\) _satisfies_ \(\lim\limits_{t\to 0^{+}}\frac{d\xi(t)}{dt}=0\) _and_ \(\lim\limits_{t\to t_{f}}\frac{d\xi(t)}{dt}=0\)_._
* _When_ \(t\in[t_{f},+\infty)\)_,_ \(\xi(t)\) _and_ \(\xi(t)\) _maintain the same, where_ \(\xi(t)=1\) _and_ \(\xi(t)=0\)_._ _TBG is corresponding to function_ \(\xi(t)\) _in this case._  
**Lemma 1** ([36]): _Think of a dynamic model where the system status is indicated as \(p(t)\in\mathbb{R}\), which can be said in the following manner:_  
\[\dot{p}(t)=-k(t)\cdot p(t),\quad p(0)=p_{0} \tag{1}\]  
_The TBG gain, denoted by \(k(t)\), aims to_  
\[k(t)=\frac{\xi(t)}{1-\xi(t)+\delta} \tag{2}\]  
_where \(\xi(t)\) is the TBG and the known constant \(\delta\) satisfies the condition \(0<\delta\ll 1\). The result is that \(p(t_{f})=\frac{\delta}{1+\delta}p_{0}\)._  
**Remark 1**: _A time base generator provides an accurate time signal that serves as a reference for the timing of multiple agents within a multi-agent system. By synchronizing the time base generator with the individual agents, it ensures that they maintain a consistent time reference during task execution and interaction. In comparison to traditional fixed-time control approaches, time base generators offer enhanced accuracy, stability, and flexibility. They can adapt to dynamic environments and the specific demands of multi-agent systems, thereby facilitating time synchronization and bolstering system consistency and reliability._
## 5 Significance of the Simulation Findings  
Several scholars have examined the application of physical or empirical models on the impact of rainfall on slope stability (e.g., [41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56]). Most have argued that the effects of meteorological, morphological, and geological characteristics have been mixed. Indeed, different geographical locations have yielded different results concerning rainfall thresholds for a landslide.  
In terms of the results reported in this study, they follow the same pattern as those of past work. For example, [PERSON] et al. [11] were able to demonstrate the impct of extreme rainfall on the recurrence  
Figure 37: Simulated water pore pressure of the soil slope using FLACSlope in rainy conditions with a water table with 2000 m\({}^{3}\) volume of water.  
Figure 38: Simulated water pore pressure of the soil slope using FLACSlope in rainy conditions with a water table with 2500 m\({}^{3}\) volume of water.  
Figure 36: Simulated water pore pressure of the soil slope using FLACSlope in rainy conditions with a water table with 1500 m\({}^{3}\) volume of water.  
and occurrence of landslides. This is especially true in steep and clay soil areas, as has also been interestingly found in the present study. It may therefore be said that rainfall and other yet unknown factors have much influence on the occurrence of slope instability within the Thohoyandou areas.  
The findings reported in this paper provide a broad view of the common mechanisms associated with the recurrence of slope instability in the Thulamela area. In line with the aforesaid, several scholars have conceded that extreme rainfall, active tectonics, and weathering strongly influence rock slope instability [57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68]. This study is no different, with results that compare with previous studies conducted in other areas.  
On another note, numerical simulation has been resorted to in this paper is an attempt to understand the overall behavior of slopes. Nevertheless, it is crucial to indicate that numerical software does not replace critical thinking and visual observation. That is why actual data collected from a controlled environment, such as a laboratory or in the field, are required for validation. It is anticipated that the simulation findings presented in this study may guide the compilation of meaningful data for such a purpose.
# MiFT: A Moment-Based Local Feature Extraction Algorithm
## 4 Experimental Results
### Keypoint Matching Accuracy
#### 4.1.1 Image Deformation Dataset  
We conducted an experiment to matching accuracy for a dataset containing six image deformations [15]: scale, image, viewpoint, blur, JPEG-compression, and illumination. Figure 7 shows some example testing images, where the left and right images represent reference and corresponding deformed images, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stage** & **Alg.** & **SIFT** & **PCA-SIFT** & **MDGHM-SIFT** & **SURF** & **MDGHM-SURF** & **MIFT** \\ \hline
1 & DoG & DoG & DoG & Integral image & Integral image & MDGHM \\  & & & & Hessian matrix & MDGHM matrix &
\begin{tabular}{c} MDGHM \\ pyramid \\ \end{tabular} \\ \hline
2 & Localization & Localization & Localization & & & Localization \\ \hline
3 & Gradient & Gradient & \begin{tabular}{c} Accumulated \\ MDGHM \\ (multi-order derivatives) \\ \end{tabular} & Haar wavelet & \
\begin{tabular}{c} MDGHM based \\ orientation \\ \end{tabular} &
\begin{tabular}{c} MDGHM based \\ (single order \\ derivative) \\ \end{tabular} \\ \hline
4 & Histogram & PCA & Histogram & \begin{tabular}{c} Sum of Haar \\ wavelet \\ \end{tabular} & \
\begin{tabular}{c} MDGHM based \\ descriptor \\ \end{tabular} &
\begin{tabular}{c} MDGHM based \\ histogram \\ \end{tabular} \\ \hline \hline \multicolumn{6}{l}{Notes: Alg. = Algorithm, SIFT = scale invariant feature transform, PCA = principle components analysis, MDGHM = modified discrete Gaussian-Hermite moment, SURF = speeded up robust features, MIFT = MDGHM based invariant feature transform, DoG = difference of Gaussian.} \\ \end{tabular}
\end{table}
Table 1: Proposed MIFT and SIFT based algorithms used for comparison.  
Figure 6: Process to calculate MDGHM descriptors.
## 2 Study Area  
We used measurements recorded at the Bily Kriz ecosystem station in the Czech Republic, which is part of the Czech Carbon Observation System (CzeCOS; [[http://www.czecos.cz/](http://www.czecos.cz/)]([http://www.czecos.cz/](http://www.czecos.cz/))) network. The Bily Kriz site is situated in the northeast Czech Republic (49\({}^{\circ}\)30\({}^{\prime}\)07.474\({}^{\prime\prime}\) N, 18\({}^{\circ}\)32\({}^{\prime}\)12.777\({}^{\prime\prime}\) E) at an altitude of 875 m above sea level on a 13\({}^{\circ}\) southwest-oriented planar slope that is approximately 100 m downslope of a mountain crest extending from west to east [32]. This site is a Class 2 candidate site of the Integrated Carbon Observation System (ICOS) (www.icos-cp.eu) and it regularly contributes to the FLUXNET dataset labeled as CZ-BK1 [33]. The site is predominated by a Norway spruce (_Picea abies_ (L) H. [PERSON]) forest stand that represents the evergreen needle leaf forest vegetation class. The stand had a mean height of 17 m and the age of 35 years at the end of the 2016 vegetation growing season. This site is located in a moderately cold, humid, and precipitation-rich temperate climatic zone [34]. The soil at the Bily Kriz site is an Entic Podsol type with a texture ranging from sandy loam to sandy clay with a 15-35% clay fraction [35]. The site is equipped with an eddy covariance flux tower system that has been measuring the energy and trace gas fluxes between the ecosystem and the atmosphere since 2004. We investigated the retrieval of vegetation properties and the estimation of GPP from remote sensing data for the 2016 vegetation growing season (April to September) when airborne hyperspectral data and ground measurements of vegetation biochemical and structural properties were available for this site.
## 4 Simulation and Analysis
### Simulation Under Injected IMU Gyroscope and Accelerometer Faults  
For the simulations of the error overboundings, the prior probabilities of an IMU gyroscope and accelerometer sensor faults were assumed to be \(10^{-3}\), considering the IMU hardware performance used for a low-cost use and consumer-grade inertial device [25]. In the simulation, the ramp IMU gyroscope and accelerometer faults, with magnitudes of 1.5 m/s\({}^{2}\) in the accelerometer and 0.03 rad/s in the gyroscope, was injected into the IMU accelerometer and gyroscope for three axes after 200 s. The faults injection lasted 10 s.  
As an example, the vertical position errors are presented as the red dotted curve, which is the difference between the EKF estimated vertical position and the true vertical position, in Figure 4. In addition, the vertical error overboundings (\(\mathrm{VPL}_{\mathrm{error.overboundings}}\)) are presented in Figure 4, as blue solid curves for the error-state EKF and green solid curves for the full-state EKF. In order to intuitively present the vertical state error in Figure 4, the state error is represented in Figure 5, as a blue dotted curve for the error-state EKF and a red solid curve for the full-state EKF. In this simulation, the value of \(\mathrm{K}_{\mathrm{md,IMU}}\) was chosen to be 3.29, which overbounds the noise of the state bias estimates with a missed detection probability of \(10^{-3}\) (Appendix B.3). The simulation results show that the overboundings had the same trend as the position error and that the position error can be accurately overbounded during UAV operations under the missed detection probability requirement. Moreover, the error overboundings of the two EKFs were the same when no fault occurred. When faults were injected, the error overboundings for the error-state were higher than the full-state; the reason for this is that the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**IMU Sensor (Consumer-Grade)**} \\ \hline \multicolumn{2}{c}{Accelerometer} & \multicolumn{2}{c}{Gyroscope} \\ Noise & Bias Noise & Noise & Bias Noise \\ \(\left(\left[\mu g/\sqrt{Hz}\right]\right)\) & \(\left(\mu g\right)\) & \(\left(\left[{}^{\circ}/h\right]\right)\) & \(\left(\left[{}^{\circ}/h\right]\right)\) \\
120 & 150 & 50 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: IMU noise simulation parameters.  
Figure 3: Unmanned aerial vehicle (UAV) trajectory in the simulation tests.  
error-state EKF is more sensitive to the faults than the full-state EKF. The east and north state errors and overboundings (i.e., east error overboundings (EPLerror.overboundings), north error overboundings (NPLerror.overboundings)) are also presented in Figures 6 and 7, respectively, which further proves that the position error can be accurately overbounded and that the error overboundings of the two EKFs are the same in all directions.