esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Unmanned helicopters, with the advantages of high-speed flying, stable hovering and vertical takeoff or landing, are widely used in civil applications and national defense fields [1]. With the increasingly deep research on unmanned aerial vehicles (UAVs) in recent years, unmanned aerial manipulators (UAMs), manipulators installed on UAVs, were designed to replace people who work in the air.  
Regarding the hovering position control of a helicopter under disturbance, a double-loop controller based on a reduced-order observer was designed in [2], and the system had certain robustness in a gusty environment. In [3], a nonlinear robust controller based on a sliding mode method was proposed, which can guarantee a helicopter's hovering under an uncertain disturbance with a perturbation of 10%. A sliding mode controller and a nonlinear controller were combined in [4], and a particle swarm algorithm was used to optimize the control parameters for coping with the helicopter hovering in a general disturbance environment. Robust control of a helicopter in an experiment under wind disturbance was studied in [5] to realize position hovering control for performing aggressive missions.  
In addition, most of the current UAMs use a multi-rotor UAV as their mobile platform. Based on a quadrotor UAV as a mobile platform, a sliding mode controller was proposed in [6] for implementing UAM hovering attitude control in a disturbance environment. A robust controller of the variable inertia parameter model of a UAM was proposed in [7]. A backstepping controller to realize the stable flight of a helicopter was demonstrated in [8]. And a passive nonlinear controller was proposed in [9] to solve the problem of quadrotor UAM hovering stability. Based on a six-rotor UAV as a mobile platform, a robust control strategy based on the interference observer was demonstrated in [10]. An adaptive trajectory linearization controller was demonstrated in [11] to deal with perturbations.  
Although helicopters have the advantages of faster flight speed and longer-distance transportation than multi-rotor UAVs, research on UAMs with a helicopter as a mobile platform is limited due to the more complex structure and control. Therefore, it is of profound significance to study UAM control with an unmanned helicopter as the mobile platform.  
The contributions of this paper are summarized as follows:  
* An improved double-loop control strategy of position and attitude is designed to increase the hovering precision of UAMs with a variable-parameters model.
* A sliding mode controller with high precision and strong robustness is proposed for helicopter control under continuous disturbance, and chattering suppression is also considered.
* When the manipulator is continuously operating, the hovering attitude stability of a UAM is a challenge. This paper gives a solution for the UAM variable center of gravity and moment of inertia. Based on [12] and a general backstepping controller proposed in [13], a backstepping integral sliding mode controller is presented, demonstrating the further promotion of robustness and attaining fine control performances.
## 3 Bayesian Coupled with Markov Chain Monte Carlo Approach  
The fully Bayesian method coupled with MCMC approach was employed to estimate the model parameters and conduct inferences. Bayesian MCMC estimates parameters using full conditional posterior distribution through a sample average approach that is stochastically optimized [21]. In this section, we present the inference with Bayesian for the FSSN and MSNBurr mixture models, and we construct the algorithm.  
Suppose \(N\) denotes the number of pixels of the MRI image and \(K\) denotes the number of the mixture component; then, we assume that each pixel \(i\) belongs to a single cluster that is indexed by label \(\mathbf{z}_{i}\). For each observation, label \(\mathbf{z}_{i}\) is assumed to be independent, and the value is given by:  
\[\mathbf{z}_{i}=(z_{i1},z_{i2},\dots,z_{iK}) \tag{6}\]  
where:  
\[z_{ij}=\left\{\begin{array}{ll}1,\ if\ y_{i}\ \text{belongs to}\ j^{th}\ \text{mixture component},\\ 0,\ \text{otherwise}.\end{array}\right.\]  
For each \(\mathbf{z}_{i}\), there is only one of \(z_{ij}\) that equals to \(1\), and the rest are \(0\). For a certain \(i\), therefore, \(\sum_{j=1}^{K}z_{ij}=1\). Single-trial results for each \(\mathbf{z}_{i}\) in exactly one of \(K\) possible mixture components have the probabilities \(p_{1},p_{2},\dots,p_{K},p_{j}\in(0,1)\), \(j=1,\ 2,\dots,K\). Since \(\mathbf{z}_{i}\)'s are independent, the joint density of the variable is:  
\[f(\mathbf{z}\big{|}\mathbf{p})=\prod_{i=1}^{N}f(\mathbf{z}_{i}\big{|}\mathbf{ p})=\prod_{i=1}^{N}\prod_{j=1}^{K}p_{j}^{z_{ij}}. \tag{7}\]Let \(\mathbf{Y}=Y_{1},Y_{2},\ldots,Y_{N}\) be an i.i.d random sample drawn from neo-normal mixture density. The likelihood of the mixture density is given by Equation (2). By integrating the label \(\mathbf{z}_{i}\) to the likelihood, we can rewrite the likelihood as:  
\[L(\mathbf{y},\mathbf{z}\big{|}\theta,\mathbf{p})=f\big{(}\mathbf{y}|\mathbf{z}, \theta,\mathbf{p}\big{)}f(\mathbf{z}|\mathbf{p})=\prod\
olimits_{i=1}^{N}\prod \
olimits_{j=1}^{K}\big{(}p_{j}f_{j}\big{(}y_{i}\big{|}\theta_{j}\big{)}\big{)} ^{\tilde{z}_{ij}}, \tag{8}\]  
where \(\theta_{j}\) is the set of the neo-normal parameters.
## 3 Results and Discussion
### Experimental Tests  
Whe is considered a complex substrate that is constituted mainly by carbohydrates, proteins, and lipids. At the conditions evaluated in this work, it is observed that total carbohydrates are consumed up to 95% in the first 35 h of operation (Figure 2).  
However, the COD consumption only reaches a consumption between 30 and 50%, which suggests that the consumption of lipids and proteins was less significant. At pH = 7.5 and T = 40 \({}^{\circ}\)C, 50% of the COD consumption is reached, while at pH = 5.5 and T = 35 \({}^{\circ}\)C the minimum consumption is obtained with 30% (Figure 3). These results correspond to those obtained by [PERSON] et al. [49], who achieved COD removal percentages between 14% and 32% using diluted whey.  
Regarding VFA production, the amount and types of VFA produced mainly depend on the initial pH. In Figure 3, the production of VFA is shown, noting that at initial pH = 5.5, butyric, valeric, and caproic acids are favored. In contrast, at initial pH = 7.5, the production of acetic and propionic acids is favored. These results agree with that reported by different authors, who indicate that at pH < 6, butyric acids are favored, and at pH > 6 propionic and acetic acids are favored [50; 51; 52; 53].  
The pH time series exhibit dynamic changes that correspond to the observed changes in key process variables. For the first 35 h, where the carbohydrate consumption rate is high, the pH series shows strong fluctuations, which could indicate the high activity of hydrolytic microorganisms. Subsequently, after 40 h, the experiments at pH 5.5 present an alkalinization stage, observing an increase in pH values of 6.8-7. This behavior can be associated with the production of biogas. Indeed, the presence of CO\({}_{2}\) promotes the generation of bicarbonates which causes an increase in pH. For the experiments at pH = 7.5, the alkalinization stage begins at 80 h, showing a slight increase in pH, reaching a maximum  
Figure 2: The dynamic profile of total carbohydrate consumption.  
value of pH = 8.3. Finally, when the pH stabilizes, the highest VFA production rate is observed, which indicates the high activity of the acidogenic-acetogenic microorganisms.
# All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
## 5 Experimental Results
### Decoding Performance Under Integer Quantization
Additionally, a reasonable increase in the quantization level improves the performance of the quantized MS-SC decoding algorithm while reducing the error value \(\epsilon\).
## 4 Experiments
Though all the three trackers are not very satisfying, the proposed method performs better than the others. In fact, the measurement of state particle usually introduces larger error during a challenging situation, the PS-L1 tracker use efficient state particles with associated weights to estimate the target state which is more reliable and stable to alleviate the problem. That is the reason that the PS-L1 tracker performs better than the other two trackers. Then the tracking results are quantitatively evaluated by two widely used criteria of position error(PosErr) and F-score. The former measures the distance by pixels between the ground truth target center and the tracking result center which is defined as  
\[PosErr=\sqrt{(c_{trk}^{x}-c_{gt}^{x})^{2}+(c_{trk}^{y}-c_{gt}^{y})^{2}} \tag{9}\]  
where \((c_{trk}^{x}-c_{trk}^{y})\) and \((c_{gt}^{x}-c_{gt}^{y})\) denote the central position of tracking and ground truth respectively. The F-score evaluates tracking results by taking both the recall and precision into account which is defined as  
\[F\text{-}score=\frac{TP\times 2}{TP\times 2+FP+FN} \tag{10}\]  
where TP, FP, and FN are the true positive, false positive, and false negative respectively. For an image, F-score can be calculated as  
\[F\text{-}score=\frac{\#(R_{trk}\bigcap R_{gt})\times 2}{\#(R_{trk}\bigcap R_{ gt})+\#(R_{trk}\bigcup R_{gt})} \tag{11}\]  
where \(R_{trk}\) denotes the target region within tracking bounding and \(R_{gt}\) denotes the object region obtained through ground truth. Besides, \(\#\) denotes the number of pixels. The results of three trackers are shown in Figures 5 and 6 and the numerical comparisons are shown in Tables 1 and 2. Figure 2.0 Tracking results of three trackers for sequence LW-18-15. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**SequenceMethods** & **L1** & **APG-L1** & **PS-L1** \\ \hline LW-16-08 & 17.77 & 20.54 & 3.24 \\ LW-17-20 & 8.44 & 8.33 & 5.83 \\ LW-19-NS & 5.85 & 5.50 & 4.45 \\ LW-18-15 & 4.78 & 14.06 & 4.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Numerical Comparison of Average of PosErr. Figure 5: Quantitative comparison of three trackers in terms of position errors (in pixel). (**a**) LW-16-08; (**b**) LW-17-20; (**c**) LW-19-NS; (**d**) LW-18-15. Figure 6: Quantitative comparison of three trackers in terms of F-score. (**a**) LW-16-08; (**b**) LW-17-20; (**c**) LW-19-NS; (**d**) LW-18-15. The computational cost of PS-L1 tracker is compared to that of L1 tracker and APG-L1 tracker on the four sequences, and the speed is measured in frame/s. The comparative result is shown in Table 3. It shows that the advantage of proposed algorithm is obvious. The speed of PS-L1 tracker is about two times than the APG-L1 tracker, which is primarily achieved by the parallel strategy. In fact, the bottleneck of computation efficiency depends on solving \(\ell_{1}\) norm minimization problem. Due to the parallel search strategy, efficient target states are selected simultaneously for each template. Thus only \(n\)\(\ell_{1}\) norm minimization problems should be solved which is far less than the number of state particles \(N\).
## 4 Salinity Origin and Its Impacts on Plants
### Primary Metabolism
#### 4.2.1 Photosynthesis  
Photosynthesis is a major essential physiological process and is potentially influenced by salinity at all stages [66; 67]. However, disentangling the cause-effect nexus between photosynthesis inhibition and decline in sink demand due to salinity is controversial. Many roads lead to photosynthesis inhibition by salinity including increased oxidative stress-related impacts [68], changes in chlorophyll concentration [69; 70], and osmotic stress. The water deficit associated with salinity induces a reduction in leaf cells turgor and the concomitant stomata closure, decreasing stomatal conductivity (gs) and the intrinsic CO\({}_{2}\) concentration, which leads to reduced carboxylation rates by RuBisCo (ribulose-1,5-bisphosphate carboxylase) [69]. Moreover, the response of the sesame (_Sesamum indium indium_ L.) plants to salinity stress was analyzed by estimating the levels of photosynthetic enzymes activity. Photosynthetic rate, activities of RuBisCo, and sucrose phosphate synthase (SPS) decreased with increasing salinity. In addition, the activities of Dichlorophenol Indophenol (DCPIP) reduction were decreased [71].
## 3 Results  
### The Characterization of Nitrogen-Saturated and -Unsaturated Plots Using Physiological Properties  
From the 78 grassland plots investigated between stages 1 and 3, 16 plots fulfilled the physiological saturation criteria and were considered nitrogen-saturated. Specific statistical testing then revealed significant differences (\(\alpha=0.05\)) between saturated and unsaturated plots for both net nitrate (NO\({}_{3}\)\({}^{-}\)-N) concentrations and net pH levels (Figure 3a,b). More specifically, net NO\({}_{3}\)\({}^{-}\)-N concentrations were shown to be substantially higher for nitrogen-saturated plots (65.28 mg/L), as opposed to plots that were not saturated (34.23 mg/L) (Figure 3a), whereas net pH levels were significantly lower for saturated plots (\(-0.175\) KCL) as compared to non-saturated plots (\(-0.0001\) KCL) (Figure 3b). In addition, both net calcium and net magnesium concentrations were noticeably lower for saturated plots (Figure 3c,d). More precisely, net calcium concentrations declined from 260 mg/L for unsaturated plots to 138 mg/L for saturated plots (Figure 3c), while net magnesium concentrations decreased from 35 mg/L for non-saturated plots, to \(-14.06\) mg/L for saturated plots (Figure 3d).  
From the associated site factors investigated, net ammonium (NH\({}_{4}\)\({}^{+}\)-N) levels were shown to decrease from 8.87 mg/L for non-saturated plots to \(-1.2\) mg/L for saturated plots (Figure 4a). Net foliar nitrogen concentrations, however, were found to be higher for saturated grassland plots (0.36%) as compared to non-saturated plots (0.19%) (Figure 4c). Both biomass and soil organic carbon concentrations demonstrated negligible changes between saturated and non-saturated plots (Figure 4b,e). Unsaturated plots demonstrated a mean biomass yield of 0.92 kg/m\({}^{2}\), whilst saturated plots recorded a biomass mean of 0.95 kg/m\({}^{2}\) (Figure 4e). Soil organic carbon showed an insignificant change, with concentrations declining from \(-2.89\%\) for saturated plots to \(-2.90\%\) for non-saturated  
Figure 3: Mean differences in soil properties for saturated vs. not saturated grassland plots across the growing stages, where (**a**) represents net NO\({}_{3}\)\({}^{-}\)\(-\)N concentrations, (**b**) represents changes in acidity (pH), (**c**) represents net calcium concentration (Ca), and (**d**) represents net magnesium concentration (Mg).  
plots. Subsequent testing discovered net total soil nitrogen concentrations to be higher for saturated plots (Figure 4d). Lastly, computation of the nitrogen enrichment factor demonstrated a higher nitrogen enrichment factor of 0.37% for saturated grassland plots as opposed to 0.22% for non-saturated plots (Figure 4f). Moreover, significant positive linear correlations (\(\alpha\) = 0.01) were documented between net calcium, magnesium, and pH levels.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 2 Preliminaries
### Model Problems
The considered model problem, described by the local (strong) formulation (1), completed with the boundary conditions (2), can also be presented in the weak variational form:  
\[B(\mathbf{u},\mathbf{v})\;=\;L(\mathbf{v}) \tag{4}\]  
where the admissible displacement vector is \(\mathbf{v}\in\mathcal{V}\), while the corresponding space reads \(\mathcal{V}=\{\mathbf{v}\in(H^{1}(V))^{3}:\mathbf{v}=\mathbf{0}\text{ on }S_{W}\}\). The solution function for displacements is \(\mathbf{u}\in\mathbf{w}+\mathcal{V}\), with \(\mathbf{w}\) standing for the lift of the Dirichlet data (see [41]). This lift is consistent with the second Equation (2). The bilinear form \(B(\mathbf{v},\mathbf{u})\) and linear form \(L(\mathbf{v})\) from the above functional represent the virtual strain energy and the virtual work of the external body and surface loadings, \(\mathbf{f}\) and \(\mathbf{p}\), respectively, i.e.,  
\[ \begin{array}{rcl}B(\mathbf{u},\mathbf{v})&=&\int_{V}\mathbf{\varepsilon}^{T}(\mathbf{v}) \mathbf{D}\,\mathbf{\varepsilon}(\mathbf{u})\,dV\\ L(\mathbf{v})&=&\int_{V}\mathbf{v}^{T}\mathbf{f}\,dV+\int_{S_{P}}\mathbf{v}^{T}\mathbf{p}\,dS \end{array}  \tag{5}\]  
Above, \(\mathbf{D}\) stands for the matrix representation of the elasticity constant tensor present in the second Equation (1), while \(\mathbf{\varepsilon}\) is the six-component vectorial representation of the strain tensor defined with the third Equation (1). Let us introduce now the finite element approximation of the variational functional (4). The approximation is based on the general rules of \(hp\) approximations [41], applied to 3D-based hierarchical shell models of order \(q\) presented in [38; 45]. The approximated variational functional reads  
\[B(\mathbf{u}^{hpq},\mathbf{v}^{hpq})\;=\;L(\mathbf{u}^{hpq}) \tag{6}\]  
where the approximated admissible displacements \(\mathbf{v}^{hpq}\in\mathcal{V}^{hpq}\) belong to the space \(\mathcal{V}^{hpq}=\{\mathbf{v}^{hpq}\in(H^{1}(V))^{3}:\mathbf{v}^{hpq}=\mathbf{0}\text{ on }S_{W}\}\). Additionally, \(\mathbf{u}^{hpq}\in\mathbf{w}^{hpq}+\mathcal{V}^{hpq}\), with the approximated values \(\mathbf{w}^{hpq}\) of the lift \(\mathbf{w}\). Note that the approximated variational formulation (6) can also be expressed in the language of finite elements  
\[\mathbf{K}\mathbf{q}^{hpq}=\mathbf{F} \tag{7}\]  
where \(\mathbf{K}\) and \(\mathbf{F}\) are the global stiffness matrix and the global forces vector, while \(\mathbf{q}^{hpq}\) stands for the displacement dofs vector corresponding to the solution field \(\mathbf{u}^{hpq}\) of displacements.
## 3 Link Level Parameters and Channel Coefficient Generation
### Small Scale Parameters  
In this step we consider the Small Scale Parameters (SSPs), i.e., cluster parameters such as delay, power, and angular spread, and intra-cluster or ray parameters, such as angle of departure and arrival and cross polarization power ratio.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 2 Study Area and Problems  
The Agbat-Shaar in the region of Asir in Saudi Arabia is one of the gigantic projects established more than 25 years ago and includes 11 tunnels along 32 bridges over 65 m high and about 351 drainage ferries. The Agbat-Shaar linked Sarat Asir to its charge and reduced the journey time for travelers. The biggest concern is disrupting the people passing through the crowded trucks with bends and tunnels, especially on weekends and vacations. The slogan obstacle is the only outlet for trucks that connects Tihama Asir with its Saratha, and Agbat is crowded with small cars in addition to trucks, and then many tragic accidents happen and the area becomes littered with corpses [33]. The affected road rock-fall is 0.6 km out of the 49.8 km road that is partly single lane from Muhaial to Abha city. On this road, the average speed is 86.1 km/h, and the daily average vehicle rate is 6245. There is a peak hour mainly during the morning and a rush hour during the night, the first at 07:00 for a limit of 320 cars and the second at 15:00, when the total number of vehicles reaches 320 vehicles. The partial collapse phenomenon at the Agbat Shaar of Asir Region was studied by the Saudi Geological Survey to find out the causes of this collapse and ensure there are no consequences for other collapses in the same area [34].  
The report issued by the working group assigned to study this phenomenon indicated that one of the most important reasons for this collapse was a crack at the top of the collapsed rock slope. This crack widened with time due to various factors, including ground movements or the action of water pools inside fissures due to rainwater, as the porosity in these rocks is high. In addition, the absence of rainwater drains in the concrete barrier caused pressure inside the rock cracks, which caused a decrease in the strength of cohesion between the blocks and thus led to loosening and collapse. In addition, the various weathering factors, whether mechanical or chemical, that work on weathering the rocks play an essential role in reducing the strength of the rock's resistivity, making it weak, fragmented, and vulnerable to collapse [35], as illustrated in Figure 1.
## 4 Characterization of Experimental Conditions  
During the tests, the configuration of the experimental setup remained unchanged: the volume of fluid in the circuit was constant, the pressure sensors aligned with the atmosphere, there was \(\pm 0.5\)\({}^{\circ}\)C temperature deviation from the room temperature, and the laser beams were converged at the same height.  
At a rotation speed of 6800 rpm, the VAD Sputnik axial pump provided the necessary flow in the circuit. The flow sensors and pressure sensor readings were recorded for each test zone (SVC, IVC, RPA, LPA) at the flow rates of 0.5, 1, 1.5, 2, 2.2, 2.5, and 3 L/min. Observance of the necessary proportions when distributing the flow through the SVC and IVC was carried out using two Hoffmann clamps.  
The total pressure is equal to the sum of the static and dynamic pressures in the measured area. In our case, due to technical limitations, it was not possible to measure the pressure directly at the inlet and outlet of the TCPC model. Pressure sensors were installed at a distance of 35 mm from the measurement location. In order to compensate losses in the additional section, the hydrodynamic losses dependence in the tube \(P_{tube}\) was determined and evaluated. These losses are due to friction within the moving fluid or along the tube walls. In addition, fluid viscosity has also an impact on the system dynamics and efficiency. It may likewise result from the physical elements used in the system such as the valve or the tube fittings. The aforementioned losses are classified as major (head) and minor losses.  
\[P_{tube}=\sum_{1}^{k}P_{Lmajor_{i}}+\sum_{1}^{k}P_{Lminor_{i}} \tag{4}\]  
Major (head) loss is expressed via the Darcy-Weisbach equation:  
\[P_{Lmajor}=f_{D}\cdot\frac{L}{D}\cdot\frac{\langle v\rangle^{2}}{2}\cdot\rho \tag{5}\]  
where \(f_{D}\) is the Darcy friction factor, \(L\) is the pipe length (m), \(D\) is the hydraulic diameter of the pipe (m), \(\langle v\rangle\) is the average water velocity (m/s), and \(\rho\) is the density of the fluid (kg/m\({}^{3}\)).  
For the laminar flow, the following equation is used to calculate the friction factor  
\[f_{D}=\frac{64}{Re} \tag{6}\]  
where \(Re\) is the Reynolds number.  
\(P_{Lminor}\) is the minor losses caused by tube bends, fittings, and valves:  
\[P_{Lminor}=K\cdot\frac{\langle v\rangle^{2}}{2}\cdot\rho \tag{7}\]  
where \(K\) is the resistance coefficient.  
Thus, Equation (3) was transformed. Calculation of power losses was carried out according to the following equation:  
\[\begin{array}{ll}\textit{PowerLoss}=Q_{IVC}&\cdot\left(P_{static}+P_{dynamic }-P_{tube}\right)_{IVC}+Q_{SVC}\\ &\cdot\left(P_{static}+P_{dynamic}-P_{tube}\right)_{SVC}-Q_{LPA}\\ &\cdot\left(P_{static}+P_{dynamic}+P_{tube}\right)_{LPA}-Q_{RPA}\\ &\cdot\left(P_{static}+P_{dynamic}+P_{tube}\right)_{RPA}\,,\end{array} \tag{8}\]  
where \(Q_{IVC}\), \(Q_{SVC}\), \(Q_{LPA}\), \(Q_{RPA}\) (m\({}^{3}\)/s) represent mean flow rates in IVC, SVC, LPA, and RPA, respectively.