esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sensitivity of the Simulation Results to Forest Age  
#### 3.3.1 Sensitivity Analysis of the 3-PG Model at the SP Level  
The simulation results from different adjusted forest ages at sample plot level show that DBH, H, and ACS exhibit different degrees of variation (Table 3). The largest variation occurs in DBH with the RMSE increasing by 33.45% when the forest age increases by 12 months. The H and ACS have consistent change trends. The changes in RMSE are small when the forest age increases, but become larger when the forest age decreases from 3 months to 12 months. The RMSE of H and ACS increase by 42.92% and 82.91% as the forest age decreases 12 months. It should be noted that the sensitivity analysis shows the highest R\({}^{2}\) and the lowest RMSE are not consistent for DBH, H, and ACS in these adjusted forest age designs. For example, the lowest RMSE of simulated DBH occurs when the forest age decreases 6 months, while for H and ACS this occurs in increased by 6 months design. In addition, the highest R\({}^{2}\) and lowest RMSE occur in forest age adjusted designs that are inconsistent.  
Figure 8: Validation of simulated height (**a**) and aboveground carbon stocks (**b**) based on forest age data from Landsat.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Variables** & \multicolumn{2}{c}{**DBH**} & \multicolumn{4}{c}{**H**} & \multicolumn{4}{c}{**ACS**} \\ \hline  & **R\({}^{2}\)** & **RMSE (cm)** & **Change Degree of RMSE** & **R\({}^{2}\)** & **RMSE (m)** & **Change Degree of RMSE** & **R\({}^{2}\)** & **RMSE (Mg/ha)** & **Change Degree of RMSE** \\ \hline \(-3\) months & 0.94 & 1.74 & 5.95\% & 0.86 & 2.50 & 7.30\% & 0.94 & 8.07 & 13.03\% \\ \(-6\) months & 0.93 & 1.68 & 9.19\% & 0.85 & 2.77 & 18.88\% & 0.93 & 9.56 & 33.89\% \\ \(-12\) months & 0.90 & 1.72 & 7.03\% & 0.79 & 3.33 & 42.92\% & 0.90 & 13.06 & 82.91\% \\ No change & 0.93 & 1.85 & 0 & 0.87 & 2.33 & 0 & 0.93 & 7.14 & 0 \\ +3 months & 0.94 & 2.09 & 12.97\% & 0.87 & 2.26 & 3\% & 0.94 & 6.15 & 13.86\% \\ +6 months & 0.94 & 2.29 & 23.78\% & 0.87 & 2.25 & 3.43\% & 0.95 & 5.92 & 17.09\% \\ +12 months & 0.94 & 2.78 & 33.45\% & 0.88 & 2.42 & 3.86\% & 0.95 & 7.41 & 3.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of model predictions of the diameter at breast height (DBH), height (H), and aboveground carbon stocks (ACS) with observations of DBH, H, and ACS under different adjusted forest ages based at SP level.
# Trace Elements and Mineralogy of Upper Permian (Zechstein)
###### Abstract  
Mineral composition and content analysis of selected trace elements (Ag, As, Ba, Be, Br, Cd, Ce, Co, Cr, Cs, Cu, Ga, I, La, Li, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sn, Sr, Ti, Tl, U, V, and Zn; 308 rock samples) were studied in the Upper Permian (Zechstein) potash-bearing deposits in Poland. They represented K-Mg chlorides of PZ2 and PZ3 cyclothens from four salt domes and statiform K-Mg sulphates of PZ1 cyclothem. The dominant mineral components of K-Mg sulphates (polyhalite) are anhydrite and polyhalite. The most common minerals of the K-Mg salts of PZ2 cyclothem are halide, sylivite, kieserite, and anhydrite, and the most common of PZ3 cyclothem are halite, carallite, kieserite, and anhydrite. Most analysed trace elements in the Zechstein potash-bearing deposits show a low content (up to 26 mg/kg) that eliminates them as potential profitable source rocks of such required elements as Ce, Cs, La, Li, or Rb. Common elements, such as Br, Fe, and Sr, are more easily exploited from natural brines, sulphate, and ore deposits.  
trace elements; minerals; potash-bearing deposits; Upper Permian (Zechstein); Poland
## 3 Results and Discussion
### Tensile Test Results  
The elongation-to-failure exceeded 250% in the studied temperatures and constant strain rates of 460-500 \({}^{\circ}\)C and of 10\({}^{-3}\)-10\({}^{-2}\) s\({}^{-1}\) (Figure 3a). The elongation above 500% was observed at the studied temperatures for a strain rate of about 1 \(\times\) 10\({}^{-2}\) s\({}^{-1}\) with a maximum mean elongation value of 560 \(\pm\) 30% at 460 \({}^{\circ}\)C and 0.8 \(\times\) 10\({}^{-2}\) s\({}^{-1}\) and 540 \({}^{\circ}\)C and 2 \(\times\) 10\({}^{-3}\) s\({}^{-1}\). The strain-rate-sensitivity \(m\)-values calculated for a strain of 200% for each testing conditions are presented in Figure 3b. The temperature-strain-rate conditions provided the maximum \(m\)-value were corresponded with the testing regimes for the maximum elongations.  
The superplastic behavior is characterized by elongations above 200% and \(m\)-values above 0.3 [64; 65; 66]. The experimental stress-strain data for strains below 200% (Figure 4) were used to develop mathematical models of the superplastic flow for the testing conditions providing required high elongation and \(m\). An increase in the deformation temperature or a decrease in the strain rate expectedly decreased the peak stress values. Strain softening, attributed to dynamic recrystallization, was observed for most of the testing conditions that in agreement with [16].  
Figure 3: (**a**) Elongation to failure and (**b**) the strain-rate-sensitivity m coefficient as a function of temperature and a constant strain rate of the investigated Al-Mg-Fe-Ni-Zr-Sc alloy.
## 4 Modified Steel slag as Catalysts
### Electrocatalysis
At the same time, the electrode particles have more active sites and reaction space for the degradation of RhB by active substances. Catalytic performance will be greatly improved. The modification flowchart is shown in Figure 6. Results showed that the degradation rate in the catalytic system reached 95.0%. It showed that the catalytic performance of the Sn/Mn-supported steel slag zeolite particle electrode was relatively efficient, and the preparation of the electrode was simple and cheap. Steel slag had great application potential in electrocatalytic systems, and provided an opportunity for the using of solid waste to treat refractory organic matter. Kaolin had the advantages of a large surface area, high mechanical strength, and low costs. It can improve the stability of the catalytic oxidation reaction. Combined with the high catalytic performance of steel slag, a kaolin/steel slag particle electrode can be developed. [PERSON] et al. [31] used kaolin and steel slag as raw materials, calcined to prepare kaolin/steel slag particle electrodes (KSPEs), and the prepared particle electrode was degraded in a three-dimensional electrolytic cell for wastewater containing nonfloxacin (NOR). First, raw materials and NaHCO\({}_{3}\) were mixed in a certain proportion to form spherical particles with a diameter of 1-3 mm, and then calcined in a muffle furnace at high temperature for a period of time to obtain KSPEs. The modification process is shown in Figure 7. KSPEs are polarized by electrostatic induction to form microelectrodes. KSPEs and Fe\({}^{2+}\) react with oxygen to produce hydrogen peroxide (Equation (1)) at the cathode production -OH (Equation (2)). NOR, under the attack of -OH, were degraded as intermediates, and KSPEs eventually degraded small molecules, such as carbon dioxide and water (Equation (3)). Fe\({}^{3+}\) by microelectrode and cathode electron transfer of Fe\({}^{2+}\) (Equation (4)) repeated degradation. The results show that when the voltage is 4V, the initial pH value is 3.0, and the initial NOR concentration is 20 mg/L, the degradation rate of NOR by KSPEs can reach 96.02% within 30 min, and the removal rate of total organic matter (COD) in wastewater can reach 93.45%. In addition, the yield of -OH of KSPEs was much higher than that of the blank kaolin particle electrode. After 10 degradation cycle tests, the degradation rate of NOR decreased by 8.76%, and the removal rate of COD decreased by 18.52%. Therefore, it can be seen that KSPEs have high catalytic activity and stability, indicating that KSPEs have great potential in the electrochemical treatment of wastewater. \[\text{O}_{2}+2\text{H}^{+}+2\text{e}^{-}\rightarrow\text{H}_{2}\text{O}_{2} \tag{1}\]  
\[\text{Fe}^{2+}+\text{H}_{2}\text{O}_{2}\rightarrow\text{Fe}^{3+}+\text{-OH}+ \text{OH}^{-} \tag{2}\]  
\[\text{NOR}+\text{-OH}\rightarrow\text{Intermediates}+\text{CO}_{2}+\text{H}_{2} \text{O} \tag{3}\]  
\[\text{Fe}^{3+}+\text{e}^{-}\rightarrow\text{Fe}^{2+} \tag{4}\]  
Figure 6: Preparation method of Sn/Mn loaded steel slag zeolite particle electrode.
## 3 Selection of Spatial Predictive Methods
### Selecting Spatial Predictive Methods  
Selection of appropriate spatial predictive methods for a response variable (or dependent variable, or primary variable in geostatistics) is critical. For data without predictive variables, geostatistical methods are the only methods that can be used. Method selection was discussed and guidelines were developed for using geostatistical methods in various studies [16; 56; 57; 58; 59; 60; 61]. A decision tree was developed for selecting the most appropriate method from a pool of 25 spatial predictive methods according to the availability and nature of data and the expected predictions, together with the features of each method [10]. However, it was argued that there was no simple answer regarding the choice of appropriate geostatistical methods, because the hallmark of a good geostatistical modeling work is customization of the approach to the dataset at hand [56]. This suggests that \"no free lunch theorems\" [15] are also applicable for spatial predictive modeling using geostatistical methods. Joint application of two spatial predictive methods might produce additional benefits such as the combined procedures in previous studies [10; 62; 63; 64].  
For data with predictive variables, there are many options available. It is often difficult to select an appropriate method because the performance of spatial predictive methods depends on many factors, including the assumptions and properties of each method, the nature and spatial structure of data for the response variable, sample size and distribution, the availability of predictive variables, availability of software, computational demands, and many other factors [10; 11]. All of these factors need to be considered when making an appropriate selection.  
Moreover, if more than one method can be applied, model comparison techniques such as cross-validation in combination with the measures of predictive error or accuracy can be used to select a method. This selection technique not only selects the most appropriate method but also the most accurate predictive model that can maximize the predictive accuracy [12; 65; 66]. This selection technique can be applied to methods irrespective of whether they use predictive variables.
# A New Hybrid Positioning Method by Fusion of BDS and 5G Signal Using the Particle Swarm Method
## 4 Performance Analysis
### Algorithm Simulation Performance Test
The experiment shows that the average convergence time of the MOPSO-TOA/TDOA algorithm is 0.124 s, and the average positioning error is 2.65 m. The average convergence time of the positioning algorithm based on the extended Kalman filter is 0.165 s, and the average positioning error is 3.27 m. The BDS-5G signal fusion positioning algorithm based on MOPSO-TOA/TDOA improves the convergence time by 24.8% compared with the fusion positioning algorithm based on the Kalman filter. The positioning accuracy is improved by 18.9%, and the system's robustness and real-time positioning ability are improved. Figure 4: \(Cont\). Figure 3: Iterative calculation of position error. Figure 2: Iterative calculation of objective function.
## 4 Discussion
### Effect of Smartphone Use on Static Posture
#### 4.1.2 Effect of Smartphone Use on Thoracic Spine Posture  
Four studies have assessed the impact of smartphone use on thoracic spine posture [10; 15; 30; 31]. The results indicate that thoracic flexion angles increase by approximately 5\({}^{\circ}\) with every additional 5 min of smartphone use, particularly during durations of 5, 10, and 15 min. These findings have been consistently supported across multiple studies examining thoracic posture during smartphone use [10; 15; 30; 31].
## 4 Numerical Applications
### Sensitive Analysis
#### 4.2.7 Comparison of Centralized and Decentralized Allocated Energy Storage Devices  
The above sensitive analysis is given when the energy storage devices are considered centralized. However, the energy storage devices can also be allocated as decentralized. To compare the effect of centralized and decentralized allocated energy storage devices on system reliability, several allocation schemes are given. Moreover, the installed \(P_{dclimax}\) of energy storage when the energy storage devices are considered centralized and decentralized is presented in Table 1. Considering centralized and decentralized installation of energy storage, reliability improvement will increase with an increasing installation capacity of energy storage. Compared with the centralized installation, the decentralized installation of energy storage cannot effectively improve the reliability. The effect of the decentralized allocation scheme on system reliability is related to the grid structure.
## 2 End-of-Life of CRC
### Separation of Impregnated Fibers
#### 2.2.1 Mechanical Recycling  
An advantage of mechanical processing is that it addresses the growing amounts of carbon fiber waste [22]. The fiber composite material is shredded and milled through a multi-stage process [23]. Multi-shaft shredders and granulators are used as equipment, followed by a screening of the fibers to obtain a homogeneous particle size distribution [24; 25]. The fibers cannot be completely separated from the matrix (both EP and SBR) [23; 26]. However, recycling can also occur without separating the two components [27]. The shredded carbon fibers can be downcycled as filler in composites, concrete, asphalt, and coatings [24; 26; 27; 28; 29]. Moreover, inserting filler consisting of recycled carbon fibers can increase the mechanical (fatigue and fracture) and tribological (friction and wear) properties of new pure plastics [24; 30]. In summary, fast processing and easy scalability are major advantages for this recycling method. Nevertheless, the length of carbon fibers is greatly reduced and contains resin residues, which affects their recyclability into new products [22].
## 5 Implementation and Results
### Experiment 2
In the ROC curves of the GWO-based wrapper models the SVM curve covers more areas, followed by DT and then NB and KNN (99.69%, 98.27%, 97.57%, and 96.88%, respectively). In the ROC curves of the FPA-based wrapper models, the SVM curve covered more areas, followed by DT and then KNN and NB (99.66%, 96.16%, 95.54%, and 94.87%, respectively). In the ROC curves of BA-based wrapper models the SVM curve covers more areas, followed by the NB and then the DT and KNN (99.0%, 97.57%, 96.11%, and 93.09%, respectively). In the ROC curves of ABC-based wrapper models the SVM curve covers more areas, followed by the KNN and then the DT and NB (98.66%, 98.28%, 97.92%, and 95.42%, respectively). Figure 8 shows the AUC results for all four classifiers in each wrapper selection method in the GE dataset. In the ROC curves of the GWO-based wrapper models, the SVM curve covers more areas, followed by DT, and then KNN and NB (99.33%, 85.0%, 62.5%, and 60.0%, respectively). In the ROC curves of FPA-based wrapper models, the SVM curve  
Figure 6: Comparison of F1 score results between proposed models on the GE dataset. Figure 7: ROC curves of the proposed models on the GE dataset. covered more areas, followed by DT, and then NB and KNN (96.67%, 72.5%, 61.0%, and 60.0%, respectively). In the ROC curves of the BA-based wrapper models, the SVM curve covered more areas, followed by DT, and then NB and KNN (97.34%, 92.0%, 57.5%, and 55.0%, respectively) In the ROC curves of the ABC-based wrapper models the SVM curve covers more areas, followed by KNN, and then DT and NB (96.67%, 96.67%, 52.5%, and 52.5%, respectively). The precision results in the PBC dataset were the best in GWO-SVM and FPA-SVM, while GWO-SVM and ABC-SVM returned the best results in the GE dataset. Moreover, GWO-SVM and BA-SVM gave the best recall results in the PBC dataset, while GWO-KNN, FPA-KNN, FPA-SVM, BA-KNN, and ABC-KNN returned the best results in the GE dataset with 100%. Furthermore, Table 7 shows the number of selected features in each model for two datasets. For the PBC dataset, the BA-based wrapper model obtained the minimum number of features compared to the other models. For the GE dataset, the GWO-based wrapper model obtained the minimum number of genes compared to other models. Therefore, this was reflected in the ability of the GWO-SVM, FPA-SVM, BA-SVM, and ABC-SVM models to gain the highest accuracy results. In general, all algorithms succeeded in reducing the high dimensionality of our datasets.
## 2 Materials and Methods
### Fabrication of an SSC Device  
For the fabrication of solid-state symmetric (SSC) devices, a material-coated electrode sample on a NF substrate with a deposition area of (2 \(\times\) 3) cm\({}^{2}\) was used. Three grams of PVA and three grams of potassium hydroxide were dissolved in thirty milliliters of deionized water to form a semi-solid electrolyte gel. Using 0.45-micron filter paper (Millipore) (procured from Merck Millipore) as a separator, the electrodes were sandwiched together and held at room temperature for two minutes after being soaked in electrolyte gel for two minutes. A protective coating of paraffin film is applied to the fabricated SSC device to prevent leaking and short-circuiting, and it is allowed to solidify at room temperature overnight to ensure optimal operation.
## 2 System Model
### The Transmitted Signal  
At the transmitter end, the data information that needs to be sent is modulated first, and then it is placed in the resource grid of the DD domain. Here, \(x_{\text{dd}}[n,m]\) represents the transmission signal at the \(m,n\) position in the DD domain, and \(x_{\text{tf}}[k,l]\) represents the transmission signal at the \(k,l\) position in the time-frequency domain, where \(k=0,1,\cdots,N-1\), \(l=0,1,\cdots,M-1\). Inverse symmetric finite Fourier transform (ISFFT) transforms the signal from the DD domain to the time-frequency domain with the expression:  
\[x_{\text{tf}}[l,k]=\frac{1}{\sqrt{MN}}\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}x_{ \text{dd}}[m,n]\text{e}^{2\pi(\frac{\text{fft}}{N}-\frac{\text{fft}}{M})} \tag{1}\]Subsequently, signal \(x_{tt}[l,k]\) is converted to the time domain using Heisenberg transformation of the time-frequency domain, as follows:  
\[s(t)=\sum_{k=0}^{N-1}\sum_{l=0}^{M-1}x_{tt}[l,k]g_{t}(t-kT)\mathrm{e}^{j2\pi l \Delta f(t-kT)} \tag{2}\]  
where \(g_{t}(t)\) denotes the shape filter function in the transmit end. When \(g_{t}(t)\) is a non-rectangular pulse, it will cause non-orthogonality between subcarriers, which will reduce the bit error rate (BER) performance [21]. Therefore, in this paper, we assume that \(g_{t}(t)\) is a rectangular pulse, and Equation (2) also degenerates into inverse discrete Fourier transform (IDFT), which is equivalent to traditional OFDM modulation.
# Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing
## 4 Experimental Results
### TSP and SOP Benchmarks  
The following combinatorial optimization problems are used during the analysis:  
* _Symmetric and Asymmetric Travelling Salesman Problem_ (TSP and ASTP) aim to find the Hamiltonian cycle of minimum length given a graph with \(n\) cities. In case the distances between the cities are independent of the direction of traversing the edges \(d_{i,j}=d_{j,i}\), \(\forall(i,j)\), the problem is known as symmetric TSP; otherwise--as asymmetric TSP.
* _Sequential Ordering Problem_ (SOP) consists of finding a Hamiltonian path of the minimal length from node 1 to node \(n\) taking precedence constraints into account. The precedence constraints impose that some nodes have to be visited before some other nodes of the graph \(G\). The characteristics of TSP and SOP instances can be expressed by a distance matrix, where each element \(d_{i,j}\) states the distance from node \(i\) to node \(j\), or in SOP instances it may be equal to \(-1\), indicating that node \(j\) must precede node \(i\). For instance, as SOP imposesthat all tours start at node 1 and end at node \(n\), \(d_{n,j}=-1\), \(\forall j\). Additionally, in symmetric TSP instances, the matrix is symmetric as \(d_{i,j}=d_{j,i}\), \(\forall(i,j)\).  
Table 1 lists the relevant information about the instances used for the analysis: their name, the type of optimization problem, the fitness of the optimal solution and the number of nodes (dimension of the problem instance). These instances belong to the TSPLIB benchmark library [38], which is accessible at [[http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/)]([http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/)) (accessed on 24 October 2022).
## 3 Results  
A total of 60 CLSM operative fields (3-dimensional stacks) were evaluated, 15 from each group. The mean, median, minimum, maximum and standard deviation of log\({}_{10}\) total biovolume (\(\upmu\)m\({}^{3}\)) are presented in Table 1. The mean and range (box plots) of the percent of red cells (dead cells) are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Irrigant**} & \multicolumn{4}{c}{**Log\({}_{10}\) Total Biovolume (\(\upmu\)m\({}^{3}\))**} \\ \cline{2-6}  & **Mean** & **SD** & **Min.** & **Max.** & **Median** \\ \hline Saline & 7.35 & 0.22 & 7.03 & 7.40 & 7.68 \\ \hline NaOCl pH 12 & 7.28 & 0.23 & 6.94 & 7.36 & 7.74 \\ \hline NaOCl pH 6.5 & 7.16 & 0.29 & 6.73 & 7.14 & 7.61 \\ \hline NaOCl pH 12 + pH 6.5 & 7.32 & 0.22 & 7.03 & 7.36 & 7.62 \\ \hline \end{tabular}
\end{table}
Table 1: Biovolume of the biofilm after exposure to the irrigants.  
For the total biovolume, there was no significant difference between the groups (\(p\) = 0.2786). For percentage of dead cells, the biofilms exposed to 0.9% saline solution had a significantly lower percent (13% \(\pm\) 4%) than the other groups (\(p\) < 0.0001). There was no significant difference in the percent of dead cells between 4% NaOCl at pH 12 (63% \(\pm\) 9%) compared to 4% NaOCl at pH 6.5 (70% \(\pm\) 9%) (\(p\) = 0.0802). Sequential exposure of the biofilms to 4% NaOCl pH 12 followed by 4% NaOCl pH 6.5 significantly increased the percent of dead cells (79% \(\pm\) 9%) compared to both the samples exposed to either 4% NaOCl pH 12 or 4% NaOCl pH 6.5 (\(p\) < 0.0008 and \(p\) < 0.0276, respectively). Representative CLSM images of the treated biofilms can be found in Figure 2.  
Figure 1: Mean and range (box plots) of the percent of red cells (dead cells) after 4 min of contact with the solutions. The bactericidal effect of the irrigants on _E. faecalis_ biofilms is expressed as the ratio of red cells to green and red cells, indicating the proportion of dead cells in the biofilm. The percent of dead cells in the 0.9% saline solution group was significantly lower than those in the other groups (\(p\) < 0.0001). There was no significant difference in the percent of dead cells between 4% NaOCl pH 12 compared to acidified 4% NaOCl pH 6.5 (\(p\) = 0.0802). The sequential use of 4% NaOCl pH 12 followed by 4% NaOCl pH 6.5 significantly increased the percent of dead cells compared to both the samples exposed to either 4% NaOCl pH 12 or 4% NaOCl pH 6.5 (\(p\) < 0.0008 and \(p\) < 0.0276, respectively). Similar low case letters indicate no difference; different letters indicate significant difference between the groups. The box represents the interquartile range (IQR) and extends from Q1 to Q3. The rhombus indicates the mean and the horizontal bar indicates the median. Upper and lower “wiskers” indicate the maximum and minimum values.