esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Test Results
### Analysis of Triaxial Compression Test Results
#### 4.3.2 Strength Properties
When the temperature exceeds 800 \({}^{\circ}\)C, the high temperature effect has a degrading effect on the sample, and the material strength \(Q\) is negatively correlated with the temperature (as shown in Figure 15a). After 400-600 \({}^{\circ}\)C high temperature effect on the material strength \begin{table}
\begin{tabular}{c c c c} \hline \(T/^{\circ}\)C & \(k\) & **Q/MPa** & \(\mathbf{R^{2}}\) \\ \hline
25 & 5.786 & 77.80 & 0.979 \\
400 & 5.233 & 96.61 & 0.989 \\
600 & 6.833 & 94.17 & 0.973 \\
800 & 5.784 & 111.20 & 0.940 \\
1000 & 9.078 & 65.61 & 0.975 \\ \hline \end{tabular}
\end{table} 
Table 1: Strength characters of samples under conventional triaxial compression after temperature. Figure 15: The relationship between the strength of material and temperature and confining pressure. is roughly equivalent, after 800 \({}^{\circ}\)C high temperature effect on the material strength has been greatly improved, after the 1000 \({}^{\circ}\)C high temperature effect, the material strength decreased sharply, while at 800 \({}^{\circ}\)C, material strength showed a sudden change. Compared with the normal temperature of 25 \({}^{\circ}\)C, the average increase of sample material strength after 400, 600, 800, and 1000 \({}^{\circ}\)C high temperature is 18.87%, 20.99%, 42.78%, and \(-\)15.98%. The relationship between material strength and confining pressure under different temperature conditions is shown in Figure 15b. The average material strength increases with the increase of confining pressure at 25 \({}^{\circ}\)C and high temperature. When the confining pressure is lower than 15 MPa, the material strength increases with confining pressure. When the confining pressure is higher than 15 MPa, the strength of the material decreases, and the strength of the material with a confining pressure of 15 MPa is abrupt. Compared with the material strength at room temperature, the strength of the material increased after 400, 600, and 800 \({}^{\circ}\)C high temperature, and the strength of the material decreased after 1000 \({}^{\circ}\)C high temperature. That is to say, under the condition that the temperature is lower than 800 \({}^{\circ}\)C and the confining pressure is lower than 15 MPa, the material strength is positively correlated with temperature and confining pressure. When the temperature exceeds 800 \({}^{\circ}\)C and the confining pressure is higher than 15 MPa, the material strength, temperature, and confining pressure are negatively correlation, indicating that temperature and confining pressure conditions have an impact on the strength of the sample material, 800 \({}^{\circ}\)C is the temperature threshold, and 15 MPa is the confining pressure threshold.
## 2 General Notations and Model Definition
### AUV Kinematic and Dynamic Model  
This section discusses the general kinematic and dynamic model of the vehicle presented in the paper. We consider a class of torpedo-shaped vehicles that can translate, and it has 5 actuated degrees of freedom which are along the surge, sway, heave, pitch, and yaw directions. The notations used in this paper to describe rigid-body dynamics follow the conventions of the Society of Naval Architects and Marine Engineers (SNAME) presented in [48]. In the state space representation, \(\mathbf{\eta}=[\mathbf{\eta}_{1}^{\top}\mathbf{\eta}_{2}^{\top}]^{\top}\in\mathbb{R}^{6 \times 1}\) represents the vehicle position vector \([x,y,z,\phi,\theta,\psi]^{\top}\) in \(\langle w\rangle\) frame. \({}^{b}\mathbf{v}=[{}^{b}\mathbf{v}^{\top}{}^{b}\mathbf{\omega}_{b/w}^{\top}]^{\top}\in \mathbb{R}^{6\times 1}\) represents the generalized velocities expressed in \(\langle b\rangle\), where \({}^{b}\mathbf{v}=[u,v,w]^{\top}\in\mathbb{R}^{3\times 1}\) is the absolute linear velocity and \({}^{b}\mathbf{\omega}_{b/w}=[p,q,r]^{\top}\in\mathbb{R}^{3\times 1}\) is the angular velocity of the \(\langle b\rangle\) w.r.t. \(\langle w\rangle\) expressed in \(\langle b\rangle\).
## 2 Materials and Methods
### Cell Viability Test  
In the study, the effect of H\({}_{2}\)O\({}_{2}\) on the viability of PC-12 cells was assessed. Cells, with a density of 1 \(\times\) 10\({}^{4}\) cells/well in a 96-well plate, were exposed to concentrations of 100, 150, 200, and 300 \(\mathrm{\SIUnitSymbolMicro m}\) H\({}_{2}\)O\({}_{2}\) for intervals of 12, 24, and 48 h. Alkaloid extracts at concentrations of 100, 250, 500, and 1000 \(\mathrm{\SIUnitSymbolMicro g}\)/mL in a 100 \(\mathrm{\SIUnitSymbolMicro m}\) cell culture medium with 0.05% DMSO were also applied to the cells for 12, 18, and 24 h durations. Furthermore, to evaluate the combined effects, PC-12 cells were treated with selected concentrations (100,250, and 500 ug/mL) of alkaloid extracts for 18 h, followed by an application of 200 uM H\({}_{2}\)O\({}_{2}\) for 24 h [39; 40].  
To gauge cell viability against a control group, the 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) assay was employed. Post incubation, each well received 20 uL of MTT solution (Sigma-Aldrich) at a concentration of 5 mg/mL in dH\({}_{2}\)O. After a 4 h incubation at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\), the medium was discarded from each well and replaced with 200 uL of DMSO. Following a further 30 min incubation under similar conditions, the optical density was read at 570 nm using a microplate reader. The absorbance observed in the control group was designated as representing 100% cell viability. Viability percentages for the other groups were computed based on this benchmark using the following equation: Cell viability (%) = (Average sample absorbance/Average control absorbance) \(\times\) 100 [41; 42].
# Construction Project Cost Prediction Method Based on Improved BiLSTM
## 4 Hyperparametric Optimisation Methods
### Hyperparameter Optimisation Process  
The hyperparameter optimisation process is shown in Figure 6.  
Figure 5: Negative selection evolutionary strategy.  
Figure 6: Hyperparameter optimisation process.  
The specific steps are as follows:  
1. The batch size, number of LSTM layer cells, dropout probability, number of dense layer cells, and number of iterations in the HAM-BiLSTM model are selected as hyper-parameters to be optimised, and the respective search ranges are set.
2. We defined the population size \(N\), maximum number of iterations \(T\), inertia weight \(w\) search range, learning factor \(c_{1}\) search range, and particle dimension \(d\) of the IPSO algorithm and initialised the particle velocity \(V\) and particle position \(X\).
3. The MSE of the true and predicted values was used as the fitness function.
4. The performance was evaluated by training the HAM-BiLSTM model, calculating the fitness of each particle, and recording the average fitness \(f_{ave}\) and global optimal fitness \(f_{best}\) of the current particle population.
5. We determined whether \(f_{best}\) was less than the predefined value \(g_{best}\) or whether the current number of iterations \(t\) reached \(T\). If so, then we output the optimal solution.
6. The individual and population extremes were updated based on particle fitness.
7. We updated \(w\), \(c_{1}\), and \(c_{2}\) according to Equations (18)-(21).
8. We determined whether the ratio \(C\) of \(f_{best}\) to \(f_{ave}\) was greater than the critical value \(K\) according to Equations (22) and (23). If so, the negative selection evolution strategy was executed.
9. The \(V\) and \(X\) values of the current particle were updated according to Equations (7) and (8) before returning to step four.
# Simulation of Electronic Quantum Devices: Failure of Semiclassical Models
## 3 Partially Quantum-Mechanical Device Modeling Based on the Wigner-Function Formalism  
The Wigner function is introduced as the Weyl-Wigner transform of the single-particle density matrix. Even though the latter is defined over the very same phase-space \(\mathbf{r},\mathbf{k}\) of the semiclassical distribution, and that very often the notation \(f(\mathbf{r},\mathbf{k})\) is used for the Wigner function as well, this new quantum distribution, while still being a real quantity, is no longer positive-definite. Its time evolution is described by the so-called Wigner transport equation. The latter can still be written analogously to Equation (1); due to this similarity, Monte Carlo simulation techniques for its solution (so-called Wigner Monte Carlo schemes [32; 35]) have been developed. However, despite this formal resemblance, the explicit form of the deterministic as well as of the stochastic Wigner-function terms are expected to differ from their semiclassical counterparts.  
The deterministic contribution is the quantum-mechanical generalization of the diffusion-plus-drift term in Equation (3) and can be conveniently formulated in terms of Moyal brackets [70]. Indeed, denoting with \(V(\mathbf{r})\) the scalar potential corresponding to the electric force \(\vec{F}\), one has  
\[\frac{\partial f(\mathbf{r},\mathbf{k})}{\partial t}\bigg{|}_{\text{det}}=- \vec{v}(\mathbf{k})\cdot\
abla_{\mathbf{r}}f(\mathbf{r},\mathbf{k})-\int d \mathbf{k}^{\prime}\,\mathcal{V}(\mathbf{r},\mathbf{k}-\mathbf{k}^{\prime})f( \mathbf{r},\mathbf{k}^{\prime})\, \tag{12}\]  
where  
\[\mathcal{V}(\mathbf{r},\mathbf{k}^{\prime\prime})=\frac{t}{\hbar}\int d \mathbf{r}^{\prime}\frac{e^{-k\mathbf{k}^{\prime\prime}\cdot\mathbf{r}}}{(2 \pi)^{3}}\left[V\left(\mathbf{r}+\frac{\mathbf{r}^{\prime}}{2}\right)-V\left( \mathbf{r}-\frac{\mathbf{r}^{\prime}}{2}\right)\right] \tag{13}\]  
is the nonlocal Weyl-Wigner superoperator corresponding to the scalar-potential profile [20]. Comparing Equation (12) with its semiclassical counterpart in Equation (3), we notice that the diffusion term remains unchanged while the quantum-mechanical version of the drift term is still local in \(\mathbf{r}\) but non-local in \(\mathbf{k}\).  
The stochastic contribution can still be cast in terms of dissipation as well as thermalization phenomena, as in Equation (2). It is then a common practice to treat these processes within the same local models previously described, namely the RTA model in Equation (7) and the Boltzmann collision operator in Equation (10). However, within the Wigner-function picture, this local approach suffers of intrinsic limitations. The latter is highlighted in the remainder of this section, where selected examples focusing on the analysis of energy dissipation versus decoherence phenomena [61] (Section 3.1) and on the device-reservoir carrier thermalization dynamics [67] (Section 3.2) are briefly reviewed.
## 3 Engineering Example
### Numerical Analysis Based on the Response Displacement Method
#### 3.2.3 Result Analysis  
(1) Interlayer Displacement Angle  
The interlayer displacement angle can be a good measure of the stability and damage degree of the structure under earthquake conditions. Table 3 summarizes the maximum interlayer displacement angles of the left wall, middle partition wall, and right wall under conditions 1 and 3. By analyzing these data, it is concluded that, under working condition 1, the largest angle is the right wall, with a maximum value of 1/7028; under working condition 3, the largest one is the right wall, with a maximum value of 1/7039. Both of these values are lower than the limit required by the \"Standard for Seismic Design of Nuclear Power Plants\".  
(2) Opening value of joints  
For studying the change law in the longitudinal opening value of the immersed tunnel joint, the curves for each joint along the tunnel, depicting their positions under four different working conditions, are obtained. They are shown in Figure 13. It is found that all the parts with large joint openings are located in sections where there are changes in the thickness of the foundation strata and mutations in the soft and hard soil layers, while the parts with small opening values correspond to the positions where the thickness of the foundation strata is uniform.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Peak** \\ **Acceleration** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Type of Water** \\ **Stop** \\ **10\({}^{6}\) (N\(\cdot\)m\({}^{-1}\))** \\ \end{tabular} } & \multicolumn{3}{c}{**Joint Stiffness Coefficient**} & \multirow{3}{*}{
\begin{tabular}{c} **Working** \\ **Condition** \\ \end{tabular} } \\  & & **Tensile** & **Compressive** & **Elevantal** & **Elevantal** \\  & & **Stiffness** & **Stiffness** & **Stiffness** & **Stiffness** \\  & & **10\({}^{6}\) (N\(\cdot\)m\({}^{-1}\))** & **10\({}^{6}\) (N\(\cdot\)m\({}^{-1}\))** & **(N\(\cdot\)m\({}^{-1}\)-rad)** & **(N\(\cdot\)m\({}^{-1}\)-rad)** \\ \hline
0.15 g & 320-370-51 & 150 & 1342 & 252 & 150 & 1 \\  & 320-370-62 & 207 Compared with the change in the joint opening value under different working conditions, as the stiffness of the GINA waterproof strip increases, the longitudinal opening trend under different working conditions shows no significant changes with position, but the joint opening value decreases in all cases. For SL2 seismic peak accelerations of 0.15 g and 0.30 g, it was observed that the increase in seismic loading does not affect the trend of joint opening changes with position but results in an overall increase in joint opening.
# Painting Path Planning for a Painting Robot with a RealSense Depth Sensor
## 4 Wall Extraction Algorithm
### Real-Time Appearance-Based Mapping (RTAB-Map)  
RTAB-Map is an open-source library implementing loop closure detection with a memory management approach [23; 24; 25; 26; 27]. This method limits the size of the map so that loop closure detection is always performed under a fixed time limit, hence satisfying online requirements for long-term and large-scale environment mapping [23]. RTAB-Map includes a complete graph-based simultaneous localization and mapping (SLAM) approach, and it is, therefore, possible to use it in various setups and applications [23]. RTAB-Map has the following options [23; 24; 25; 26; 27]:  
* Online processing;
* Robust and low-drift odometry;
* Robust localization;
* Practical map generation and exploitation;
* Multisession mapping;
* Integration in ROS.  
RTAB-Map can accept a variety of inputs, such as depth color images (RGB-D), stereo images, odometry, 2D laser scans, 3D point clouds, and other user data. All input data can be used, depending on the available sensors. Further, RTAB-Map supports the use of multiple RGB-D cameras, as long as they have the same image size [23]. The loop closure detection method is implemented using the bag-of-words approach described by [PERSON] and [PERSON] [27]. Proximity detection is used to localize nodes close to the current position with laser scans when they are available [24]. Using proximity detection, the localization can be done when the robot traverses back through the same corridor in a different direction, during which the camera cannot be used to find loop closures. After loop closure or proximity detection are performed, or when certain nodes are retrieved or transferred because of memory management, a graph optimization approach is executed to minimize errors in the map [23]. If loop closure occurs, the global map should be reassembled according to all new optimized positions for nodes in the map's graph [23; 24; 25; 26; 27]. This process is necessary so that obstacles that were wrongly cleared before the loop closure process may later be reincluded. The point cloud outputs are composed of all points on the local maps, presented in the standard ROS format. Finally, voxel grid filtering can be applied to merge overlapping surfaces in point clouds. The resulted merged point cloud is then suitable for further processing [23].
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 3 Corrosion and Degradation of Structural Materials in Farm Environment
### Acid Corrosion Mechanism in Cement Structures
#### 3.4.2 Organic Acids and Impact of Dissociation Constants  
Organic acids induce corrosion and degradation of concrete using a similar approach as inorganic acids. Higher concentrations of acetic acid lead to the complexation of calcium in the cement leading to the formation of calcium and aluminum complexes. A summary of the reactions that define organic acid-induced corrosion of structures is presented in Table 7. In brief, it is of note that the formation of the complexes varies depending on the acid dissociation constant of organic acids. Oxalic acid has a lower dissociation constant compared to acetic acid pKa = 1.28 and 4.28 and 4.76, respectively [20]. Therefore, the risk of concrete degradation can be predicted using the acid dissociation constant. In general, weak and monoprotic acids pose a marginal risk to the durability of concrete structures compared to strong polyprotic acids.
# The Impact of Gravity on Different Height Systems: A Case Study on Mt. Medvednica
## 3 Leveling Survey  
Precise leveling is a fundamental geodetic method used to determine height differences between points with high accuracy. In this approach, known elevations of the initial and final benchmarks serve as a reference for calculating the elevations of newly established benchmarks along the route. By measuring the height differences between successive benchmarks, it is possible to systematically determine the elevations of all the intermediate points. For the survey conducted on Medvednica Mountain, the initial and final benchmark heights were used as a basis for establishing a series of new benchmarks along the road Slyeme.  
To achieve this, it was first necessary to establish the benchmarks and measure their coordinates using the GNSS method, as these benchmarks' heights would be determined through precise leveling.
# Improvement of Arsenic Phytoextraction Using Indigenous Bacteria and Mobilizing Agents
## 2 Materials and Methods
### Preparation of Microcosms  
Microcosm tests represent the first stage in treatability studies by phytoremediation, as they aim at selecting the most suitable plants and treatments for the specific contaminated soil and evaluating the potential toxic effects of contaminants. The microcosm tests were set up using 300 g of mixed soil per test; different combinations of potassium hydrogen oxalate (KHC\({}_{2}\)O\({}_{4}\)) and ascorbic acid (C\({}_{6}\)H\({}_{8}\)O\({}_{6}\)) were used as mobilizing agents. The following conditions were investigated:  
* Plant species: _C. sativa_ (10 seeds) and _Z. mays_ (6 seeds); the seeds were germinated on cotton and then subsequently transplanted;
* Four different treatments for each type of plant, i.e., a control (CT) and treatments with 0.1 M KHC\({}_{2}\)O\({}_{4}\) + 0.01 M C\({}_{6}\)H\({}_{8}\)O\({}_{6}\), 0.2 M KHC\({}_{2}\)O\({}_{4}\) + 0.01 M C\({}_{6}\)H\({}_{8}\)O\({}_{6}\) and 0.2 M KHC\({}_{2}\)O\({}_{4}\) + 0.02 M C\({}_{6}\)H\({}_{8}\)O\({}_{6}\);
* Administration of mobilizing agents: 5 mL of solution for 5 days + irrigation according to the needs of the plants;
* PGPR: added to all microcosms (except CTs);
* Fertilization: a few days after transplanting the plants, 12 mL of a 0.06% urea solution was added to the microcosms.  
As was mentioned, the chosen plants had already been successfully tested for arsenic phytoextraction [52; 53]. All tests were performed in triplicate, with a total of 24 microcosms, for a duration of 30 days. The treatment was administered about 10 days after the transplant, with the mobilizing agent added over the course of 5 days in order to dilute the effect of a possible too-rapid absorption of the As. In these tests, nitrogen fertilization with urea was also used to improve the agronomic properties of the soil. At the end of the test, the plants were collected, the roots were separated from the aerial parts and the plant samples thus obtained were carefully washed and prepared for analysis.
###### Abstract
proposed a method to separate compound signals in the hyperplane space with variational modal decomposition (VMD), and used it for extracting single-channel fault characteristics of compound signals for rolling bearings [14]. The BSS method plays an important role in the separation of the vibration source signal, but the amplitude and phase of the separated vibration source signal are uncertain. This uncertainty is detrimental to the long-term tracking and monitoring of mechanical conditions. The above studies in the literature reflect that the separation effect of BSS is limited by the accuracy of the number of independent sources. However, the vibration excitation source is complicated in actual engineering, the prior features of the vibration source signal (or noise) are difficult to know, and the number of independent sources is difficult to determine. The requirements of the denoising separation method are difficult to meet, and the separation effect of the BSS cannot be guaranteed. The difficult-to-obtain prior knowledge is one of the key issues in the separation of vibration source signals. In order to solve this problem, a low-rank constrained vibration source signal separation method is proposed for rotating machinery (e.g., robot bearings, etc.). Low rank is a common feature of rotating machinery vibration source signals. The low-rank feature is used as the target constraint of the source signal separation in this method. The low-rank noise model and the multi-low-rank noise model are described in this study. This method does not need to obtain prior knowledge of the vibration source signal. The effectiveness and superiority of this method are verified by simulations and experimental tests.
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 3 Position Solution and Measurement Exclusion
### NLoS BS Exclusion Mechanism
#### 3.3.1 Computation of Test Statistic
Thus, the detection is based on a hypothesis testing where:  
\(T_{i}\leq\gamma:H_{0}\) is accepted (no biased measurement error),  
\(T_{i}>\gamma:H_{1}\) is accepted (biased measurement error).