esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application
### Failure Probability Analysis Using MCS
#### 3.2.2 Limit State Function
When overflowing, a zero-compression region can occur at the bottom of the cofferdam, especially in vicinity of the heel. So, the uplift pressure should be calculated both for the non-overflow case and the overflow case as shown in Figure 5.  
The uplift pressures in Figure 5a,b have the following formulas, respectively  
\[H_{\text{x}}=H_{2}+\frac{(H_{1}-H_{2})}{L}\]  
where  
\(H_{1}\) = 30.6 m (water depth at heel);  
Figure 5: Uplift pressure (**a**) without a zero-compression region and (**b**) with a zero-compression region. \(H_{2}\) = 0 m (water depth at toe). Figure 3:0  
where  
\(H_{1}\) = larger than 31.5 m (water depth at heel);  
\(H_{2}\) = 0 m (water depth at toe);  
\(T\) = zero compression length. (4) Silt. Silt pressures are considered in the design if suspended sediment measurements indicate that such pressures are expected. (a) Horizontal. Figure 3:1  
where  
\(H_{s}\): horizontal silt pressure at depth 'd' (kN/m\({}^{2}\));  
\(C_{e}\): horizontal silt pressure coefficient;  
\(\gamma_{sh}\): unit weight of submerged horizontal silt (kN/m\({}^{3}\));  
\(d\): depth of silt (m). (b) Vertical. Figure 3:2  
where  
\(H_{s}\): vertical silt pressure at depth '\(d\)' (kN/m\({}^{2}\));  
\(\gamma_{sh}\): unit weight of submerged horizontal silt (kN/m\({}^{3}\));  
\(d\): depth of silt (m). (5) Earthquake forces. The earthquake loading used in the design is based on design earthquakes and site-specific motions determined with a seismological evaluation. The seismic coefficient method of analysis should be used in determining the resultant location and sliding stability of dams. The seismic coefficient method is commonly known as the pseudo-static analysis. Earthquake loading is treated as an inertial force that is applied statically to the structure. The loadings are of two types (\(\lx@sectionsign\)1) inertial force due to the horizontal acceleration of the dam and (\(\lx@sectionsign\)2) hydrodynamic forces resulting from the reaction of the reservoir against the dam. \(\lx@sectionsign\)1 Inertial of concrete for horizontal earthquake acceleration. The force required to accelerate the concrete mass of the dam is determined using the equation:  
Figure 3:3  
Figure 3:4  
where  
\(Pe_{x}\): horizontal earthquake force (kN);  
\(M\): mass of dam (kg);  
\(a_{x}\): horizontal earthquake acceleration (m/s\({}^{2}\));  
\(W\): weight of dam (kN);  
\(g\): acceleration of gravity (m/s\({}^{2}\));  
\(\alpha\): seismic coefficient. 2 The inertial of the reservoir water induces an increased or decreased pressure on the dam concurrently with concrete inertia forces. This force may be computed using the equation:  
Figure 3:5  
where  
\(Pw_{x}\): additional total water load down to depth '\(h\)' (kN);  
\(K_{h}\): horizontal seismic coefficient;  
\(\gamma_{tw}\): unit weight of water (9.8 kN/m\({}^{3}\));  
\(h\): water depth.
## 2 Materials and Methods
### Data Pre Processing  
Each sensor modality could have a different sample rate. Table 2 specifies sensors logging frequencies. The highest frequency is adopted for the definition of a common absolute timestamp: a frequency of 100 Hz corresponding to one observation each of 0.01 s. Signals with lower sample rates are linearly interpolated to obtain signals at 100 Hz.  
In order to have the same time-space association logic for all datasets, and since DGPS is not available on Morocco tests, an odometry-based approach is adopted to relate time-stamp with rover traversed distance. The distance travelled by one of the four rover wheels, \(d_{V}\), between two successive time-stamps is defined in Equation (1):  
\[d_{V}=\omega R\Delta t \tag{1}\]  
where \(\Delta t\) is the sampling interval, \(\omega\) is the wheel angular speed measured by the encoder and \(R\) is the wheel undeformed radius. Sensor data have been subdivided into sub-logs that are associated with each virtual patch. It should be noted that two patches of the same length, in general, may correspond to different actual travel distances due to the extent of wheel slippage. It is also worth mentioning that the use of \"distance\" windows is used under the underlying assumption of continuous motion of the robot without any stop-and-go manoeuvre.
# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 5 Conclusions  
For the regression-based illuminant estimation methods, it is uncertain which features are more efficient to regress illuminant color. In this research, a flexible framework is developed to integrate KFCM, NLSR, and fuzzy weighting, which can result in outstanding performances of illuminant estimation. By extensive experiments with a series of artificial features weaved into this framework, to some extent it is clarified which features are more correlated to illuminant estimation. Moreover, we exploited an optimal feature combination based on the given feature set, which obtains a significant performance improvement for illuminant estimation.  
The proposed method and its framework can be used to test and compare the influences of image features on illuminant estimation. From this, we can optimally deploy the appropriate image features for illuminant estimation in a more objective way, rather than in an empirical, arbitrary manner. If the feature sets contain more feature candidates, the performance of illuminant estimation is expected to be further enhanced.  
This study focuses on determining which features are more correlated to illuminant estimation in a limited feature set. Therefore, more related image features, including high-level semantic features, can be further taken into account. Besides, considering there are different intrinsic and implicit cues for different camera sensors, pre-selecting images in terms of camera sensor characteristics and then constructing multiple models may be another pathway to improve the proposed method.  
Conceptualization, Y.L. and X.W.; methodology, Y.L.; software, Y.L.; validation, Y.L., X.W. and Q.W.; formal analysis, Y.L.; investigation, Y.L.; resources, Y.L.; data curation, Y.L. and X.W.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and X.W.; visualization, X.W.; supervision, Y.L.; project administration, X.W.; funding acquisition, Q.W. and Y.L. All authors have read and agreed to the published version of the manuscript.  
The Project Supported by the Foundation (No. KF201603 and ZR202001) of Key Laboratory of Paper Science and Technology of Ministry of Education of China.  
Not applicable.  
The authors declare no conflict of interest.
## 4 New Classification of Methods for the Detection of Water Network Operating Conditions
### Hydraulic Modeling and Hybrid Algorithms  
Solutions based on hydraulic modeling, in the procedures of which clustering algorithms are implemented, are aimed at minimizing erroneous indications from measuring devices and supplementing missing data forming time series. The results are simulations of a faithfully calibrated model of the water supply network, which reflects the real conditions of its operation. Modern computer software allows for analyses to be performed under steady-state (SS), dynamic extended period simulation (EPS), or short-term transient analysis (TA) [11].  
[PERSON] [65] and [PERSON] [11] implemented TA and ITA methodologies in their studies analyzing hydraulic models calibrated from pressure measurements. They interpreted the relationship between the operating pressure maintained in the zone and the amount of uncontrolled discharge. The simulations were conducted with WaterCAD software. The conclusions made by the authors indicate that the detection of an anomalous state of water network operations by means of analyses of a calibrated hydraulic model is possible.  
A system based on fuzzy clustering (FC) analysis [66] is also a support for water network managers, minimizing the time of failure detection. [PERSON] et al. [67] developed a solution for the detection, prediction, and warning of the occurrence of an emergency condition. It was based on GIS and the measurements of acoustic phenomena occurring inside the ducts. At the stage of creating prognostic tools, the naive Bayes classifier (NBC), statistical regression, artificial neural networks (ANNs), and the genetic algorithm (GA) were used.  
The usefulness of using hydraulic modeling in detecting and reducing the failure rate of a water supply network was also demonstrated by [PERSON] et al. [68]. Based on real data on reservoir water levels, flow rates, and pressures, they developed procedures to reduce uncontrolled leaks. Simulations conducted in EPANET led to the identification of DMA zones where the ILI was too high and the optimal (lower than the current) operating pressure was selected for each area.
# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 5 Conclusions  
To improve the robustness and real-time performance of the tracking algorithm in intelligent video monitoring, this work proposes the FFTD algorithm, which is based on the tracking-by-detection framework. First, the KCF algorithm is adopted in FFTD, with an updating strategy that incorporates the tracking result modified by the detector into the filter model. In addition, the MTCNN face detector enhances the overall robustness performance of the algorithm. The experimental results show that the FFTD algorithm has good precision and accuracy, even under challenging conditions. In conclusion, the FFTD algorithm satisfies the requirements of fast and long-term secure monitoring.  
In future work, we will improve the performance at tracking challenges involving motion blur, scale variation and rotation. Moreover, this work will be extended to track other objects, requiring us to design more-robust neural networks.  
Conceptualization, J.S. and L.G.; Methodology, J.S. and L.G.; Software, L.G. and W.L.; Validation, L.G. and Y.X.; Formal analysis, J.S.; Investigation, W.L. and Y.X.; Resources, J.S.; Data curation, J.S.; Writing--original draft preparation, J.S., L.G. and W.L.; Writing--review and editing, L.G., S.G., and N.C.; Supervision, J.S., N.C. and R.W.; Project administration, J.S., N.C. and R.W.; Funding acquisition, J.S.  
This work was supported by key laboratory of equipment pre-research (No. 6142A010301), science and technology support plan of Hebei Province (No. 18210803D).  
We would like to acknowledge the collaboration of the Fujian Province University Key Lab for Industry Big Data Analysis and Application.  
The authors declare no conflict of interest.
## 4 Verification  
### Ground Experiment  
Due to air resistance and the inability to simulate free boundary conditions, it is difficult to design a ground experiment to simulate the process of satellite attitude rotation, and the flywheel rotation speed change process will be quite different from the simulation result. Therefore, the flywheel control system has made the experimental flywheel rotation speed run according to the simulation curve value. A ground experiment platform built is shown in Figure 11. The experimental equipment includes a satellite prototype, collimator, integrating sphere, flywheel control system and imaging equipment.  
The integrating sphere is used as a light source, and a collimator is used to image the slit target to simulate the incidence of infinite objects. According to the focal length of the collimator and optical subsystem, the system aligns a line target into a magnified image, which is calculated according to the line target image scale of 5 pixels, and the slit width is 13.75 \(\upmu\)m. When the width of the slit image is 5 pixels, the slit diffraction intensity is small, and the internal brightness is uniform to ensure the experimental accuracy (0.1 pixel). The  
Figure 11: Micro-vibration ground experiment.  
Figure 10: Frequency-domain response of the simulation results.  
focal plane uses push-broom CCD, and the line transfer time is 100 microseconds, so the sampling frequency is 10,000 Hz. Figure 12 is the schematic diagram of the light path.
# A Flower-Shaped Miniaturized UWB-MIMO Antenna with High Isolation
## 2 The Proposed Antenna System
### Parameter Analysis  
The lengths of the modified T-like branches Lg and Lf have a dramatic impact on the UWB-MIMO system's impedance-matching and isolation performance. Only the impact of these particular factors on system performance is examined, while other parameters are kept constant. The S-parameters for tuning Lg from 12 mm to 14 mm are illustrated in Figure 6. The impedance bandwidth is further improved when Lg changes from 12 mm to 14 mm, as shown in Figure 6a. Neither UWB features can be achieved when Lg has a value of 12 mm or 13 mm. Different values of Lg also exhibit various isolation effects in terms of their influence on isolation. Although the isolation effect is optimal overall when Lg is at 14 mm, as shown in Figure 6b, the transmission coefficient must be enhanced in the 6-8 GHz band region when Lg is at 14 mm. These results demonstrate that the S-parameters of the system are significantly influenced by the vertical length of the modified L-shaped ground branch (Lg), and it is best to select a value of 14 mm for Lg when taking into account the size of the system.  
The simulated variation in characteristics of Lf from 9 mm to 11 mm are presented in Figure 7. As can be seen from Figure 7a, the effect of Lf on return loss is not very significant at these values, and all can achieve acceptable ultra-wideband properties. However, the  
Figure 6: Simulated S-parameters for tuning Lg: (**a**) reflection coefficient, (**b**) transmission coefficient.  
effect of L\({}_{\text{f}}\) on isolation is even more pronounced. As can be observed in Figure 7b, the optimal isolation effect is obtained when L\({}_{\text{f}}\) is 10 mm, that is, when the I-shaped ground stub and modified L-shaped ground branch are exactly combined to form a T-like branch, which also confirms the authenticity of the proposed decoupling structure.
# An Optimized Hierarchical Cluster Formation Approach for Management of Smart Cities
## 3 Proposed Approach
### Research Problems  
Although the cluster formation step of LEACH includes the largest number of nodes that strongly influence energy conservation, very few efforts have been exerted to improve it. Additionally, the LEACH protocol constructs the clusters based on the shortest distance factor. Some works have tried to enhance this step by adding a residual energy factor. However, the impact of only these two factors is still not sufficient, especially when the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & **Aim** & **Optimization Technique** & **Load Balance** & **Used Factors** \\ \hline LEACH & WSN routing protocol & ✗ & ✗ & Distance-based \\ \hline
[15] & Optimal CH election & Custom & ✗ & Hybrid distance–energy-based \\ \hline
[12] & Optimal CH election & GA & ✓ & Hybrid distance–energy-based \\ \hline
[29] & Optimal CH election & Custom & ✓ & Hybrid distance–energy-based + CG \\ \hline
[38] & CH election & GA & ✓ & Hybrid distance–energy-based \\ \hline
[34] & Optimal route & GA & ✗ & Energy-based \\ \hline
[36] & Optimal route & PSO & ✗ & Hybrid distance–energy-based \\ \hline
[33] & Cluster formation & Fuzzy logic & ✓ & Hybrid distance–energy-based \\ \hline ICSI & Cluster formation & Custom & ✓ & RE, RqE, den, D \\ \hline \hline \end{tabular}  
* D: distance; CG: center of gravity; den: density; RE: residual energy; RqE: required energy; ✗: not included; ✓: included.  
\end{table}
Table 1: Summary of optimization methods used to enhance LEACH.  
network traffic is loaded. Even if the residual energy of the node is high, the node may die during the round if the required energy for communication with the CH increases due to factors other than distance. These factors are listed by the authors of [17; 39], as explained previously. Consequently, the present research is aimed at enhancing the clustering formation step by addressing the following problems of the step that are held to be responsible for energy exhaustion:  
1. The lack of attention to the factors that influence power depletion in each round, as SNs select the closest CH with the shortest distance regardless of the traffic load or the density of the monitoring area.
2. The lack of attention to the eligibility of the remaining energy of nodes to join a certain cluster during CH selection by nodes.
3. The lack of constraints on the cluster size to avoid the fast expiration of CHs and SNs, since LEACH allows the production of clusters with a large size, which rapidly exhausts the energy.
# Prediction of Intraday Electricity Supply Curves
## 3 Methodology
### Models and Predictions
#### 3.4.10. CNN+LSTM Model  
This model consists of CNN layers, followed by LSTM layers, and finally, fully connected layers. The considered hyperparameters were the following:  
* n_CNN. The number of convolutional and pooling layers (we always introduce a pooling layer after each convolutional one). We considered one and two layers;
* n_LSTM. The number of LSTM layers. The considered values are one and two;
* n_dense. The Number of fully connected layers besides the output layer. We tried one, two, three, and four layers;
* filter. The number of filters in each convolutional layer. We considered eight and 16 filters;
* kernel. The dimensions of the kernel matrix in each convolutional layer. The assessed values are \(3\times 3\), \(5\times 5\), and \(7\times 7\);
* unit. Number of cells per LSTM layer. We tried 10, 20, 50, and 100 units;
* dropout. The random deactivation of some input units to reduce overfitting. The studied values are 0, 0.1, and 0.2 (ranging from 0 to 1);
* neurons. The number of neurons in each fully connected layer: 5, 10, 20, 50, and 100 neurons were considered.  
The selected hyperparameters for the CNN+LSTM model are available in Table A5 in Appendix A.
## 4 Discussion
Methyl-salicylate is the most abundant one in green pepper fruit, and is a compound with a strong mint odor that abounds in pepper fruit [78], and is a distinctive flavor-active volatile [79]. Among furans and aromatic hydrocarbons, 2-pentylfuran, and 2,4,6-trimethylanisole are found in larger amounts in green fruit. The first furanic compound is abundant in bell pepper fruits. Finally, in miscellaneous compounds 2-isobutyl-3-methoxypyrazine, a potent bell pepper odorant [77], was abundant in the green fruit with no differences among landraces. A multivariate statistical analysis was performed to make an in-depth evaluation of the differences attributable to grafting and/or landraces. For the green fruit, the CDA can be explained by two canonical variables that explained 98% of the total variability in the dataset obtained from the analysis. The loading plots from the green fruit were composed of the axes of canonical_1 with canonical_2. The grafted \"Cuerno\" was evidently separate from other samples by canonical_1 on the score plot, and the compound that accounted for discrimination was cyclosativene, while the discrimination of the grafted \"Valencia\" was related to allo-ocimene. Cyclosativene is a tetracyclic sesquiterpene whose antioxidant capacity mitigates oxidative injuries in the neurodegenerative disorders field [80]. Allo-ocimene is a monoterpen that occurs in citrus and many other essential oils, and is characterized by a harsh terpene-like and somewhat citrusy character that is also typical in bell peppers [81]. The ungrafted landraces were indistinguishable and spared by the grafted plants according to canonical_2. As shown in Table 4, the graft involved a higher content of 4-methyl-2,3-dihydrofuran, N-methylpyrrole, 2,4,6-trimethylanisole, methyl salicate, octanoic acid, ethyl hexadecanoate, cyclosativene and allo-ocimene. For the red fruit, only the first canonical_1 variable explained 96% of variability (Table 4). According to this discrimination, three groups were observed, characterized by groups of peppers with a short, but significant, distance among them: \"Valencia\" (grafted and ungrafted) and \"Sueca\" ungrafted obtained the highest canonical scores; the ungrafted \"Cuerno\" obtained negative scores; \"Cuerno\" and \"Sueca\" had intermediate score values. Unlike the green fruit, a different response of the three landraces to grafting was observed as regards the profile of VOCs. As Figure 6 depicts, \"Sueca\" and \"Valencia\" were very close when ungrafted, while the graft, \"Sueca\" showed a similar profile to \"Cuerno\". The VOCs most associated with \"Valencia\" (grafted and ungrafted) and \"Sueca\" were 2-propyldiene-1-cyclobutene, 2-heptanone, beta-linalool, and methyl salicate, while ethanol characterized mainly \"Cuerno\" ungrafted.
# Research on Airspace Conflict Detection Method Based on Spherical Discrete Grid Representation
## 6 Conclusions  
Based on the characteristics of air demand, this paper designs the subdivision scheme and the spatial coordinate coding structure based on the regular icosahedral spherical rhombus discrete grid model, realizes the subdivision level most suitable for the AR, overcomes the grid deformation problem in different latitudes, and ensures the accuracy of the AR. The method of transforming longitude and latitude coordinates into global positioning codes and the process of AG representation are designed. Compared with the traditional AR method, the representation method of airspace data matrix based on the regular icosahedral spherical rhombus discrete grid is more convenient. Each grid corresponds to a unique code, which converts the spatial location into the occupied grid, and finally boils down to the operation between the airspace data matrices.  
This method is a \"black-box algorithm\", that is, the operator will input the longitude and latitude coordinates, using a diamond grid algorithm to calculate and derive CD  
Figure 16: Simulation program run time.  
results, the process is not displayed in the operating interface, but directly for the user output results. It effectively shortens the program's running time and improves detection efficiency. In the case of large-scale use of space, it can achieve fast, efficient, and accurate detection of conflicts.  
Conceptualization, G.Z.; Formal analysis, K.Q.; Investigation, L.T. and Y.W.; Methodology, G.Z.; Resources, Y.W. and L.T.; Software, K.Q.; Supervision, G.Z. and Y.W.; Visualization, K.Q.; Writing--original draft, K.Q.; Writing--review & editing, G.Z.; funding acquisition, K.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.