esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of a Multi-Type PMSM Based on Pyramid Neural Network
## 3 Prediction Model and HPMSM Optimization
### PNN Prediction Model
#### 3.1.2 PNN Model Verification  
In order to test the accuracy of the activation function, the determination coefficient \(R^{2}\), which is defined in Equation (7), was introduced. When the value of \(R^{2}\) was between [0, 1], the activation function was feasible, and the closer the value was to 1, the higher the prediction accuracy was. The value of \(R^{2}\) in this paper was 0.993. This shows that the activation function in this paper had a high prediction accuracy and strong generalization ability, which provided a good basis for the following modeling and optimization.  
\[\left\{\begin{array}{l}R^{2}=1-\frac{E_{\text{ss}}}{E_{\text{ss}}}\\ E_{\text{ss}}=\sum\limits_{i=1}^{n}{(\widetilde{y}_{i-}y_{i})}^{2}\\ E_{\text{sst}}=\sum\limits_{i=1}^{n}{(y_{i-}\overline{y}_{i})}^{2}\\ \end{array}\right. \tag{7}\]  
\[MSE=\frac{1}{N}{\sum\limits_{i=1}^{N}{(f_{t}-y_{t})}^{2}} \tag{8}\]  
\[MAE=\frac{1}{N}{\sum\limits_{t=1}^{N}}|f_{t}-y_{t}| \tag{9}\]  
To detect the accuracy of the PNN network, the mean squared error and mean absolute error, which are defined in Equations (8) and (9),were introduced. The closer the values were to 0, the higher the accuracy was.  
PNN was configured with 16 time block features and 8 space block features. The Adam optimizer [30] was used for model training, with a learning rate of 0.0001 and a batch size of 400. Each method was run five times and the average result was reported.  
In Figure 5, the broken blue line represents the error curve of the total loss, and the broken red line represents the error curve of the output torque. With the increase of the number of iterations, the MAE values of both models processing the total loss and the output torque showed downward trends, and the change trend was basically the same. After the number of training runs reached 400, the MAE values tended to become stable and converge to a smaller value. Thus, in this paper the number of training data runs considered appropriate was 400.  
Figure 5: The diagram of the relationship between iteration numbers and MAE.  
In this paper, 200 test samples were used to predict the PNN fast calculation model, and the accuracy of the model was verified. The accuracy verification of the PNN model is shown in Figure 6. Figure 6a,b represent MAE error graphs of test samples of PNN calculation models established in this paper for two objective functions.  
From Figure 6a,b it could be seen that the PNN model had a high fitting accuracy for loss and torque of the HPMSM. According to the above analysis results, PNN had a strong adaptive ability and generalization ability. As an accurate and fast calculation model, the PNN model could provide support for subsequent optimization and effectively improved the efficiency of optimization.
## 3 Methods  
SBAS-InSAR is a technique that harnesses continuous multiple SAR images to perform time series analysis processing. By setting particular thresholds with respect to the time baseline and space baseline [34], it divides time series SAR images into several independent interference subsets, which ensures the coherence of interference pairs and further capture monitoring results of surface subsidence. Compared to D-InSAR, SBAS-InSAR involves more complex and time-consuming data processing. However, it is better suited for large-scale deformation monitoring and delivers superior time series resolution. Unlike PS-InSAR, SBAS-InSAR does not require highly stable monitoring targets, making it particularly effective for monitoring surface subsidence in mining areas on the eroded Loess Plateau, where few stable structures exist. Nevertheless, the SBAS-InSAR technique faces potential challenges, such as decorrelation in areas of rapid deformation or dense vegetation cover. In this study, the impact of vegetation cover on results is minimal, because the study area has sparse surface vegetation and is characterized by an arid to semi-arid climate. Still, abrupt subsidence in the central subsidence area leads to decorrelation in SBAS-InSAR results. Critically, SBAS-InSAR remains highly effective in detecting subtle deformations along subsidence margins, with validated measurement accuracy. This capability enables precise delineation of subsidence boundaries, providing mining operators with actionable data to strategically deploy conventional ground-based monitoring, which can reduce survey costs while maintaining observational rigor.
# Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model
## 2 A Dynamic Path Planning Model for Tower Crane Operations  
During the lifting and lowering phases of tower crane operations, operators often engage in tasks that reduce hazards for surrounding workers. The risk of dropped loads is greatest during horizontal operations in the air, where the area of potential impact is the widest. Therefore, this study focuses on the horizontal operational process of the tower crane to develop path planning strategies. In tower crane operations, dangers arise when lifted objects pass over the heads of ground workers due to disengagement. To prevent such accidents, lifted loads can be projected onto the ground to determine whether there is a risk of collision in the horizontal direction with the workers.  
The dynamic path planning model framework for tower crane operations proposed in this study is illustrated in Figure 1. First, a camera mounted above the hook monitors the tower crane operations, collecting real-time path information from close range. The path information collection module preprocesses the video data to extract relevant operational path information and generate a dataset of construction path images. Secondly, a safety risk evaluation module for tower crane operations is developed based on the YOLOv8 instance segmentation framework, which defines potential risk factors along the operational path, such as drop zones and the positions of nearby workers. The YOLOv8-seg model is employed to segment potential drop zones and identify protected objects in the video. The pixel coordinate information obtained is then converted into real-world coordinates to determine the actual state of each object, including its size and position as scene information, thereby enabling dynamic risk evaluation during crane operations. Finally, the path planning module applies an improved TC-DWA algorithm to avoid risky areas and optimize the operational path for enhanced safety.
## 3 Materials and Fabrication Techniques
### Fabrication Techniques
#### 3.3.5 Soaking  
If the transistor substrate is represented by natural cotton fibers, deposition of the active layer can be simply achieved by soaking the yarns into a suspension containing the polymer to be deposited. [PERSON] et al. [121] followed this method to fabricate OECTs-based adrenaline biosensors. They used a commercial PEDOT:PSS suspension, modified with ethylene glycol (20%) and dodecyl benzene sulfonic acid (12%) to increase electrical conductivity and reduce solubility in water, in which cotton yarns where soaked for 5 min, followed by baking at 150 \({}^{\circ}\)C for 3 h on a hot plate. This process resulted in the formation of a homogeneous PEDOT:PSS layer (approximately 50 nm thick) all around the fiber. Such yarn, acting as the transistor channel, was then woven into a fabric with a thin Pt wire, acting as gate electrode, also woven in its proximity. The electrolytic bridging between the two yarns was performed by gently pipetting a few drops of electrolytic solution (0.1 M NaCl with 1 mM adrenaline) in the space between the two yarns. Very recently, [PERSON] et al. [122] used the same method for the fabrication of biosensors able to detect L-tyrosine. Cotton yarns were treated exactly as described in the previous reference but, before weaving, they were immersed for 1 h in a solution of 0.1 M sodium citrate buffer containing 30 U mL\({}^{-1}\) of recombinant POXA1b (rPOXA1b) increase at isoelectric point (pH 6.7). The fibers were subsequently rinsed with the same buffer solution and stored at 4 \({}^{\circ}\)C before use. The sensing system was then fabricated by weaving a functionalized cotton yarn and a thin Pt wire next to it into a substrate fabric. Sensing measurements were then performed by joining the  
Figure 11: Schematic diagram of glucose sensors based on fiber organic electrochemical transistors with active layer of PPy nanowires and rGO. Reprinted from [119]. Copyright 2017, with permission from Elsevier.  
two wires with a small drop of aqueous solutions (0.1 M sodium citrate buffer, pH 3.0) at different concentrations of L-tyrosine (between 10\({}^{-8}\) and 10\({}^{-3}\) M).  
Below (Table 2) are summarized some parameters of PEDOT:PSS inks (commercial or homemade) discussed in this section.
## 2 Materials and Methods
### Quantitation of Aroma Compounds
#### 2.4.3 HS-SPME Combined with GC-MS  
The aroma compounds, not quantified by DI-GC-MS and LLE-GC-MS, were detected employing HS-SPME combined with GC-MS. Before detection, spirit samples were diluted to 10% ethanol by volume, then 100 \(\upmu\)L of the mixed internal standards (IS1, pentyl acetate, 200 mg/L; IS2, 4-heptanol, 200 mg/L; IS3, 2-ethylbutanoic acid, 200 mg/L) was also added. Other operating steps and parameters of HS-SPME and GC-MS were the same as that described in Sections 2.3.2 and 2.3.4. A synthetic matrix was prepared by mixing the standard stock solution in a 10% (\(v/v\)) ethanol/water solution at pH 4.0, and diluted to ten different concentrations. The internal standards, the same with those in the samples, were also added to the standard solutions. Detailed information on the calibration curves is listed in Table S2.
## 2 Materials and Methods
### Data Preprocessing
The features with _p_-values < 0.05 were classified using the WEKA machine learning library using 10-fold cross-validation at the subject level. Overall average metrics are reported. Since our dataset included data from 15 unique subjects, we partitioned the data into 10 mutually exclusive folds, each containing either 1 or 2 subjects. In each iteration, 9 folds (representing approximately 13-14 subjects) were used for training and validation, while the remaining fold (1-2 subjects) was held out for testing. This ensured that data from any individual subject appeared in only one fold per iteration, preventing data leakage and maintaining subject independence across train-test splits. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Feature Name**} & \multicolumn{4}{c}{**Before Normalization**} & \multicolumn{4}{c}{**After Normalization**} \\ \cline{3-8}  & & \multirow{2}{*}{**Coef**} & \multirow{2}{*}{_p_-Value**} & \multicolumn{2}{c}{**Predictable**} & \multirow{2}{*}{**Coef**} & \multirow{2}{*}{_p_-Value**} & \multicolumn{2}{c}{**Predictable**} & \multirow{2}{*}{**Coef Diff**} \\  & & & & & **(_p_ **\textless{} 0.05)** & & & & **(_p_ **\textless{} 0.05)** & \\ \hline
1 & Standard Deviation (\(\sigma\)) & \(-\)0.1068 & 0.0657 & 0 & \(-\)0.3947 & 0.0000 & 1 & 0.2880 \\
2 & Root Mean Square (RMS) & \(-\)0.1067 & 0.0660 & 0 & \(-\)0.3943 & 0.0000 & 1 & 0.2877 \\
3 & minMaxDiff & \(-\)0.1268 & 0.0286 & 1 & \(-\)0.3842 & 0.0000 & 1 & 0.2574 \\
4 & Skewness (S) & \(-\)0.2649 & 0.0000 & 1 & \(-\)0.2715 & 0.0000 & 1 & 0.0066 \\
5 & Kurtosis (K) & \(-\)0.1509 & 0.0091 & 1 & \(-\)0.2610 & 0.0000 & 1 & 0.1101 \\
6 & gaitVelocity & \(-\)0.1131 & 0.0511 & 0 & \(-\)0.2523 & 0.0000 & 1 & 0.1392 \\
7 & AvgCadence & 0.1108 & 0.0561 & 0 & \(-\)0.2490 & 0.0000 & 1 & 0.1383 \\
8 & numSteps & \(-\)0.1309 & 0.0238 & 1 & \(-\)0.2102 & 0.0003 & 1 & 0.0793 \\
9 & AvgStepLength & 0.1108 & 0.0561 & 0 & \(-\)0.1988 & 0.0006 & 1 & 0.0880 \\
10 & Entropy Rate (\(H\)) & \(-\)0.0773 & 0.1831 & 0 & \(-\)0.1813 & 0.0017 & 1 & 0.1040 \\
11 & Harmonic Ratio (HR) & 0.1505 & 0.0093 & 1 & 0.1708 & 0.0031 & 1 & 0.0203 \\
12 & Coeff. of Variation of Step Time (\(CV_{\text{step}}\)) & 0.1128 & 0.0518 & 0 & \(-\)0.1346 & 0.0202 & 1 & 0.0218 \\ \hline \multirow{2}{*}{13} & \multicolumn{2}{c}{**Average Useful**} & \multicolumn{2}{c}{0.1302} & \multicolumn{2}{c}{0.2586} & \multicolumn{2}{c}{0.1284} \\ \cline{2-6}
13 & AvgStepTime & 0.0831 & 0.1525 & 0 & 0.0975 & 0.0928 & 0 & 0.0000 \\ \cline{2-6} \multicolumn{6}{c}{**Average All**} & \multicolumn{2}{c}{0.1251} & \multicolumn{2}{c}{0.2312} & \multicolumn{2}{c}{0.1061} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Time-domain features ranked by correlation coefficient.
## 3 Dynamic Response Analysis of RPC Columns Under Coupled Fire and Explosion
### Cross-Section Size  
The effect of cross-section size on the blast resistance of RPC columns was analyzed by selecting cross-section sizes of 400 mm\({}^{2}\), 600 mm\({}^{2}\), and 800 mm\({}^{2}\). The mid-span displacement curves of the columns with different cross-section sizes are shown in Figure 8, from which it can be seen that, as the cross-section size increases, the ultimate deflection and deformation rate of the RPC columns in the mid-span decreases, thus proving that its blast resistance improves.  
The maximum deflection and residual deformation in the span of the RPC column are shown in Figure 9, from which it can be seen that the maximum deflection and residual deformation in the span of the RPC column decrease with the increase in section size. Under the explosion load, the maximum deformation of the RPC column with a cross-section size of 800 mm\({}^{2}\) was 2.76 mm and the residual deformation was 1.46 mm, representing decreases of 29.6% and 20.2% compared to that of the RPC column with a cross-section size of 600 mm\({}^{2}\), a maximum deformation of 3.92 mm, a residual deformation of 1.83 mm, and decreases of 54.3% and 61.2% compared to that of the RPC column with a cross-section size of 400 mm\({}^{2}\), a maximum deformation of 6.05 mm, and a residual deformation of 3.78 mm, respectively. The above results show that when the cross-section size of the RPC column is small, increasing the cross-section size can significantly increase the bending stiffness and shear stiffness of the column, thus reducing the horizontal maximum displacement in the column, and, simultaneously, due to the increase in cross-section size, the concrete explosion  
Figure 8: Time-distance curves of horizontal displacements in RPC column spans of different cross-section sizes.  
Figure 7: Plot of horizontal displacement in RPC column span with fire time.  
load that has not yet entered the plastic region increases, thus effectively reducing the residual displacement. When the cross-section has reached 600 mm\({}^{2}\) in size and the cross-section size continues to increase, the column span displacement reduction is no longer significant, indicating that when the cross-section size reaches a certain value, the increase in the cross-section size for the enhancement of blast resistance is no longer significant.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 2 Parallel Concatenated Convolutional Codes
### Selected Turbo Encoder Scheme  
A coding scheme with a parallel concatenation is used by the selected turbo codes to encode a binary input. Specifically, this scheme employs an interleaver and two convolutional codes. A vector with tail bits on its end is generated by each encoder; the flows of systematic bits, together with the parity bit flows of the first and second encoder, form the output of these tail bits, while a block links these three flows and multiplexes their tail bits.  
In turn, the encoder receives an input signal corresponding to a column vector of the \(L\ x\ 1\) order and generates an output signal corresponding to the column vector of the \(M\ x\ 1\) order. For a given trellis, the \(M\) and \(L\) lengths are given by Equations (5) and (6).  
\[M=L\cdot(2\cdot n-1)+2\cdot NumTails \tag{5}\]  
\[L=\frac{M-4\cdot v}{3} \tag{6}\]  
where  
\(L\) is the encoder input length;  
Figure 1: Scheme for parallel concatenated turbo code.  
\(M\) is the encoder output length;  
\(n=\log_{2}(T_{\text{nos}})\);  
\(T_{\text{nos}}\) is the number of output symbols for the trellis;  
\(NumTails=log2(T_{\text{ns}})\):\(n\)  
\(T_{\text{ns}}\) is the trellis status number;  
\(v\) is the memory of the convolutional component codes.  
The trellis diagram corresponds to the mapping in the sense that all valid mapper configurations correspond to a path in the trellis diagram. The number of states is the number of binary combinations considered as inputs in the trellis diagram. The number of output symbols for the trellis is the number of elements obtained as a result of the trellis path. The post interleaver trellis termination is used, and the termination bits for the second component code are transmitted.  
The configuration of the encoder for a trellis is presented in Figure 2. The trellis structure has the following parameters: a length constraint with a value of 4, a component code with the generator matrix \(G=[1,\ 15/13]\) in octal form. Three \(L\)-bit interlaced length flows compose the output, namely the systematic bits streams (\(u\)) and the parity bits (\(p^{1}\)) and (\(p^{2}\)) of the first encoder and second encoder, respectively. The last three parity bits in the streams \(p^{1}\) and \(p^{2}\) correspond to the tail bits of the two encoders, obtained at the outputs of the discontinuous lines when the switches are in the lower position.
###### Abstract  
We document and show a state-of-the-art methodology that could allow geoheitage sites (geosites) to become accessible to scientific and non-scientific audiences through immersive and non-immersive virtual reality applications. This is achieved through a dedicated WebGIS platform, particularly handy in communicating geoscience during the COVID-19 era. For this application, we selected nine volcanic outcrops in Santorini, Greece. The latter are mainly associated with several geological processes (e.g., dyking, explosive, and effusive eruptions). In particular, they have been associated with the famous Late Bronze Age (LBA) eruption, which made them ideal for geoheitage popularization objectives since they combine scientific and educational purposes with geotourism applications. Initially, we transformed these stunning volcanological outcrops into geospatial models--the so called virtual outcrops (VOs) here defined as virtual geosites (VGs)--through UAV-based photogrammetry and 3D modeling. In the next step, we uploaded them on an online platform that is fully accessible for Earth science teaching and communication. The nine VGs are currently accessible on a PC, a smartphone, or a tablet. Each one includes a detailed description and plenty of annotations available for the viewers during 3D exploration. We hope this work will be regarded as a forward model application for Earth sciences' popularization and make geoheritage open to the scientific community and the lay public.  
virtual outdoor; geosite; Santorini; photogrammetry; GIS; WebGIS +
Footnote †: journal: Applied Sciences  
0021  
Virtual geosite Communication through a WebGIS Platform: A Case Study from Santorini Island (Greece)
## 4 Results and Discussion
### Time Evaluation  
To demonstrate the effect of the running time, the ratio between the total running times of the gradient-weighted method with wide and thin thresholds to the original Hough transform is given in Figure 20. It can be seen that in the case of most of the images, the wide threshold decreases the running time of the Hough transform algorithms by about 50%, and the thin threshold decreases the runtime by another roughly 50%, but in some cases, it can go down to lower than 10% of the original transform's running time. For database CVC-Colon, the results are a bit better than for the other databases. From the 2 nd subplot of Figure 20, we can conclude that these results are almost size independent, though for a smaller number of edge pixels, the results are usually a little bit better.  
Figure 19: The number of circles found in the artificial images containing ellipses of large axes with size 30, 60, and 90 pixels. In the horizontal axis, the fuzziness parameter \(\sigma\) is the main ordering parameter, and within the cluster of each \(\sigma\), the peak percentages \(P_{p}\) increase from 50% to 90%. In the vertical direction the three different greater axis sizes are denoted. Within each group, the topmost row belongs to the circle (axis ratio 1:1), the next row to the axis ratio 5:6, then 4:6, 3:6, 2:6. The last row, separated by a horizontal line, represents the non-physical ghost circles.  
It is important to note that the number of the pixels to be transformed has an almost linear effect on the transformation time, so using Prewitt edge detection is a really important step, as it can be seen from Figure 6. As the continuity of the edges is not a key point in Hough transform, and by giving it up, we can get rid of a lot of weaker edges in the background. This step has a double advantage.
## 4 Experimental Results
### Characterization of CuO on \(\gamma\)-Alumina and Macro-porous Alumina  
BET analysis with N\({}_{2}\) adsorption was performed to characterize the CuO on the \(\gamma\)-alumina and macro-porous alumina. The experiments confirmed that meso-sized pores had been formed in the \(\gamma\)-alumina and macro-porous alumina. As shown in Figure 7, although the amounts of CuO were different for all six samples, the hysteresis curves were observed in the adsorption and desorption isotherms. The nitrogen adsorption over the \(\gamma\)-alumina support was higher than that of the macro-porous alumina, and the nitrogen adsorption amount decreased with increasing amount of CuO supported. In particular, the nitrogen adsorption amount of the sample with a CuO content of 15 wt % was lower than that loaded with 5 and 10 wt % CuO. These adsorption and desorption isotherms correspond to type IV isotherms proposed by IUPAC. These can be considered as having micropores and typical cylindrical or ink bottle-shaped pores showing hysteresis curves due to capillary condensation. In the nitrogen adsorption process, the amount of nitrogen adsorbed by multi-molecular layer adsorption increased with increasing relative pressure (P/P\({}_{0}\)). On the other hand, in the process of decreasing the relative pressure (P/P\({}_{0}\)), the condensed nitrogen was desorbed gradually until the nitrogen condensed in the pore inlet had been completely desorbed. Therefore, the observed hysteresis stems from the condensation of nitrogen adsorbed in the capillary type of pores. The area of the hysteresis curve shown in the adsorption and desorption isotherms of \(\gamma\)-alumina was wider than that of macro-porous alumina, which was attributed to the larger pore volume of \(\gamma\)-alumina. The pore size of the macro-porous alumina was smaller than that of \(\gamma\)-alumina. Table 1 lists the pore volumes and surface area calculated using the BJH equation. The results show that the pore volume and size of \(\gamma\)-alumina were higher than that of macro-porous  
Figure 6: XRD patterns of Cu-based desulfurization sorbents: (**a**) 5, 10, 15 wt % CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\); (**b**) 5, 10, 15 wt % CuO/macro-porous Al\({}_{2}\)O\({}_{3}\).  
alumina. On the other hand, the surface area and pore volume decreased with increasing amount of CuO on the support, but the pore size did not change significantly.
### Organization  
The rest of the paper is organized as follows. The system and channel models of the proposed cooperative relaying scheme are described in Section 2. In Section 3 the performance evaluation of the proposed cooperative protocol is formulated for different users. The simulation and analytical results are presented in Section 4. The final section concludes our work. To look at a glance, Figure 1 presents the organization of this article.