esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Active Odorants
Principal component analysis (PCA) was performed with eight active odorants, and the results are shown in Figure 3. The two principal components (PC1 and PC2) accounted for 79% of total variance (57.73% and 21.22%, respectively). The first component, PC1, was composed of monoterenes and aldehydes such as linalool, hexanal, (_E_)-2-hexenal, and (_Z_)-3-hexenal, while, the second component, PC2, was composed primarily of (_E_)-\(\beta\)-damascenone. The grape harvested at 12 WAFB was located on the upper-left quadrant plane, which was positively correlated with (_E_)-\(\beta\)-damascenone. The grapes harvested at 14 and 16 WAFB were located in the lower-left quadrant, which was positively correlated with (_E_)-2-hexen-1-ol; those harvested at 18 WAFB were positively correlated with 1-hexenol; and those harvested at 20 and 22 WAFB were grouped in the upper-right quadrant, which represents a positive correlation with hexanal, (_E_)-2-hexenal, (_Z_)-3-hexenal, and linalool. The flavor of grapes harvested at 12, 14, and 16 WAFB seemed to be characterized by fruity, green, and herbaceous flavor owing to the dominant influence (_E_)-\(\beta\)-damascenone and (_E_)-hexen-1-ol, while the flavor of grapes harvested at 18 WAFB seemed to be primarily affected by 1-hexanol and also linalool, giving rise to a floral flavor. The grapes harvested at 20 and 22 WAFB were clearly distinguished from the others, being largely affected by the odor activity of linalool. Thus, it was clearly observed that the primary aroma compounds of Shine Muscat grapes changed from C13-norisoprenoids, C6-alcohols, C6-aldehydes, and finally to monoterenes, with ripening. Figure 2: Pie charts representing the percentages of free volatile aroma compounds classified into odor types in Shine Muscat grapes harvested at 16, 18, 20, and 22 weeks after full bloom (WAFB): Others include citrus, earthy, fatty, minty, tropical, waxy, and aldehyde odors.
## 2 Materials and Methods
### Physicochemical Methods of Analysis  
The _freeze-drying method_ was used in order to obtain dried MIP and NIP. For it, synthesized copolymer was frozen at around \(-20\)\({}^{\circ}\)C and then dried using a Labconco 7934030 (Kansas City, MO, USA) freeze dryer.  
_Fourier transform infrared (FT-IR) spectroscopy_ was used to identify the chemical composition of the MIPs and NIPs. The FTIR spectra were received from a Nicolet iS10 FT-IR (Waltham, MA, USA) spectrometer in the region of 4000-450 cm\({}^{-1}\). For this, samples of MIP and NIP were crushed into powder form.  
A _scanning electron microscopy (SEM)_ study was performed with a ZEISS Crossbeam 540 (Oberkochen, Germany) apparatus to identify the morphology of the object under study.  
The _adsorption method_ was carried out in order to determine the adsorption capacity of the MIP and NIP. At first, the wavelength of the terbium solutions was determined using a concentration of 0.5 g/100 mL by a \"Shimadzu UV-1900i\" (Kyoto, Japan) UV-VIS spectrophotometer. Then, 5 types of solutions were prepared with different concentrations, like 0.03125, 0.0625, 0.125, 0.25, and 0.5 g/100 mL, and a calibration curve was built for terbium nitrate solutions with wavelengths of 281 nm (Figure 2). After that, 10 mL of the metal solutions were prepared with a concentration of 0.5 g/100 mL, and 0.01 g of MIP and NIP was placed into them in a glass filter. Then, using a \"Shimadzu UV-1900i\" (Kyoto, Japan) UV-VIS spectrophotometer, the absorbance of the solution was measured every 15 min throughout 3 h. After 24 h, a control measurement was carried out. The concentration of absorbed metal was found by subtracting the concentration of solution from the initial concentration.  
The _desorption process_ was carried out to define the release property of the polymer from its matrix. For this purpose, a weak solution of nitric acid was prepared with a concentration of 0.05 M (mol/L), and the samples obtained from the adsorption analysis were placed into beakers with 10 mL of this solution. As in the case of the adsorption analysis, the absorbance of the solution was measured every 15 min throughout 3 h with a wavelength for Tb of 281 nm. After 24 h, a control measurement was carried out. The concentration of desorbed metal was found by subtracting the concentration of solution from the absorbed concentration.  
Figure 2: Calibration curve of terbium(III) nitrate.
### Deep Learning Innovations in Electrical Tomography  
Since 2017, research on image reconstruction using deep learning has emerged. Deep learning provides a novel alternative to traditional algorithms that rely on the sensitivity matrix, such as Linear Back Projection, the Tikhonov regularization method, and the Landweber iterative algorithm. Also, deep learning is data-driven, which allows for learning the nonlinear mapping from detection signals to the actual field distribution. It has demonstrated extremely high accuracy in image reconstruction for specific samples or details. In our previous work, two optimized deep learning network structures, a stack sparse autoencoder with a radial basis function network and an optimized fully connected network, were used to achieve the two-dimensional image reconstruction of EMT in simulations. We designed 30,000 samples, including two types: multiple disconnected small objects and a single connected large object. Even after adding 7% noise, the image reconstruction results were relatively excellent [21]. The Restricted Boltzmann Machine, Deep Belief Network, Stacked Autoencoder, and Denoising Autoencoder have all been utilized for the detection of cerebral hemorrhage. The network's noise resistance performance reached 20 dB [22]. The Restricted Boltzmann Machine has also been applied to the image reconstruction process of EMT, and experimental results have shown that this method has high accuracy. A model-based deep learning network named FISTA-Net was established [23]. After comparing four methods--Laplacian regularization, FISTA-TV, FBP-ConvNet, and ISTA-Net--FISTA-Net exhibited superior performance and demonstrated good generalization capabilities under different noise conditions.  
Following extensive research on deep learning for 2D image reconstruction, some progress was made in the study of deep learning for 3D reconstruction. Convolutional neural networks with a SegNet architecture were utilized for 3D image reconstruction in Electrical Resistance Tomography [24]. The study specifically addressed the influence of various factors on 3D image reconstruction, including the size of the dataset in the training process, data resolution, noise in data feeding, and the characteristics of the training models. Graph Convolutional Networks were applied to 3D Electrical Capacitance Tomography [25]. A unique design of 1012 electric permittivity phantoms was created, with 1000 phantoms for training and 12 for testing. The results showed improvements of 35.5% and 3.74% in the normalized mean square error and Pearson correlation coefficient, respectively. \(U^{2}\)-Net with combined electrodes was used for 3D Electrical Impedance Tomography [26]. Simulations and experiments indicated that \(U^{2}\)-Net exhibited better performance than U-Net. Combined electrodes demonstrated greater robustness than traditional structures and showed superior performance when the signal-to-noise ratio (SNR) was 20 dB or 30 dB.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 2 Analysis of Models and Limitations in VLC Systems  
In a VLC system, the data rates of transmitted signals are mainly limited by the physical restrictions caused by the intrinsic characteristics of optical devices In addition, the channel loss, nonlinearity of devices and noises can lead to inter-symbol interference (ISI) and the distortion of original signals. To achieve a transmission with a higher data rate, eliminating these unwanted effects is significant. Moreover, realistic models of the optical devices and channels are necessary for better elimination of limitations since they can provide systematic analyses with mathematical calculations of how the physical restrictions relate to the interference in the signal.
## 4 Results
### Influence of the Slope Angle on the Stability of an Anti-Dip Interbedded Rock Slope
#### 4.3.1 Cracks Distribution Characteristics  
The results of a slope model with a gentle angle of 60\({}^{\circ}\) have been displayed in Section 4.2. Figure 15 reveals the crack evolution in the slope with a slope angle of 80\({}^{\circ}\). The interbedded anti-dip rock slope bent downward under the influence of the gravitational moment, causing relative shear displacement between the two rock strata and new crack initiation (Figure 15a,b). As the number of cracks increased, the bending deformation continued to develop (Figure 15c). The cracks not only occurred between the bedding plane between the two kinds of rock strata but were also generated inside the rock strata. The cracks within the rock strata were oriented perpendicular to the interlayer structural plane. These cracks connected rapidly and formed an arc-shaped sliding surface. Additionally, rock blocks slid from the upper position and gradually piled up at the slope toe, leading to tension at the slope crest and a downward collapse.  
The failure modes were different for the two kinds of rock slopes with different slope angles. The failure mode of the slope with an 80\({}^{\circ}\) slope angle was a toppling-sliding compound failure, which was similar to the slope with a 70\({}^{\circ}\) dip angle of the rock strata. However, shear-sliding failure occurred in the rock slope with a 60\({}^{\circ}\) slope angle.  
There are multiple deformation zones in an interbedded anti-dip rock slope. Each deformation zone comes from one rock stratum. The rock stratum unit is taken as the research object (Figure 16a shows one rock stratum in the rock slope), and only the gravity of the rock stratum is considered in the analysis. The stress state of the research object is shown in Figure 16a. The gravitational moment can be calculated as:  
\[M=FS=\frac{1}{2}mgl\cos\theta \tag{1}\]  
where _mg_, \(l\), and \(\theta\) are the gravity, length, and dip angle of the rock stratum, respectively. A larger gravitational moment \(M\) will lead to easier folding and toppling failure of the rock slope. The rock strata from a rock slope with different slope angles (60\({}^{\circ}\) or 80\({}^{\circ}\)) at the same height level are shown in Figure 16b,c. The length \(l_{1}\) and mass \(m_{1}\) of the rock strata from the slope with 60\({}^{\circ}\) are all smaller than the length \(l_{2}\) and mass \(m_{2}\) of the rock strata from the slope with 80\({}^{\circ}\). Therefore, the gravitational moment of the rock slope with an 80\({}^{\circ}\)  
Figure 14: Contact force chain of slopes with (**a**) 20\({}^{\circ}\) and (**b**) 70\({}^{\circ}\) dip angles of rock strata when the time step was 30,000.  
slope angle is larger, which can account for the blocky toppling failure in the interbedded anti-dip rock slope with an 80\({}^{\circ}\) slope angle.
## 1 Introduction
In this paper, we propose a two-level clustering (TLC) mechanism for LEACH network that provide significant enhancement in WSN energy consumption such that the network lifetime is extended. At first level, the selection of appropriate CHs jointly balanced the energy consumption of all nodes in a uniformly distributed energy region. An algorithm is developed for first-level clustering that selects a node with a higher level of energy, called the CH, as a routing path to transmit the information. In second level, an angular distance estimation mechanism is introduced to minimize the node's data transmission energy consumption in the wireless system network. The paper has following main contributions as follows:  
* A novel clustering algorithm is introduced in the first level with a grid mechanism followed by CH selection and having a predefined number of CHs. * A CH selection mechanism is developed in the first level based on the minimum average distance between nodes in each cluster while taking into consideration the residual energy level per node. * A distance threshold parameter is introduced in the first level to improve the communication between the nodes to CH and CH to BS. * An angular distance parameter in the second level is introduced CH selection for remaining outside nodes while clustering due to insufficient boundary conditions. * A robust comparison has been demonstrated with three different clustering algorithms (LEACH, I-LEACH, E-LEACH) under different network conditions, and significant performance improvements. The remainder of the paper is organized as follows. In Section 2, related works in the previous literature are discussed. In Section 3, the TLC model is illustrated with the proposed algorithm and analysis. The simulation performance of the proposed algorithm and its comparison with existing protocols are presented in the Section 4. Finally, Section 5 concludes the paper with a brief summary of the proposed work.
## 2 Antenna Design
The WLAN requires a significantly lower frequency than millimeter-wave applications, so it is challenging to integrate this antenna with the two aforementioned antenna arrays. Thus, a loop antenna was designed to suit the design space on the flank. The current distributions are shown in Figure 4b. The currents along the horizontal segment change direction at the midpoint, while the currents along the vertical segments show relatively strong magnitudes, which follows the operating principle of a half-wavelength loop. Additional studies were performed to create a meandered inverted L antenna (ILA) that resonated at 2.45 GHz. However, narrow impedance bandwidths were observed for the meandered ILA (2.43-2.46 GHz in terms of a \(-\)10 dB reflection coefficient). Although meandered ILAs are a common choice for WLAN modules in mobile handsets, the proposed loop antenna is more suitable for an environment that has parallel conductive plates. In particular, thanks to the design on the edge and currents on the ground, this loop antenna is more compact compared to earlier designs for WLAN antennas, as shown in Table 2[28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]. The area occupied by this antenna is only 96.3 mm\({}^{2}\), compared to at least 210 mm\({}^{2}\) for the earlier designs. For modern mobile phones, consumers desire a light weight, reduced volume, and a low cost. In addition, the transceiver modules and the battery occupy large areas in the handset. These characteristics demand that the design space for the antennas be miniaturized. When compared to the state-of-the-art, the proposed three antennas are very compact, and thus, they fulfill this requirement for modern mobile devices. \begin{table}
\begin{tabular}{c c c} \hline
**No. ** & **Dimensions (mm\({}^{2}\))** & **Total Area Occupied by the Antenna (mm\({}^{2}\))** \\ \hline
[28] & 40 \(\times\) 28 & 1120 \\
[29] & 28.2 \(\times\) 36.7 & 1034.97 \\
[30] & 30 \(\times\) 30 & 900 \\
[31] & 27 \(\times\) 25 & 675 \\
[32] & 10 \(\times\) 40 & 400 \\
[33] & 15 \(\times\) 20 & 300 \\
[34] & 15 \(\times\) 14 &
## 4 Results
### Bearing Capacity Based on CPT  
As illustrated in Figure 8, the cone tip resistance, sleeve friction, and friction ratio of CPTs, measured near the test PHC pipe piles, shows that the measured parameters profiles of the different CPTs were in good agreement with each other in the same soil layers. The cone-tip resistance values are mostly in the range of 0\(\sim\)22 MPa in coarse sand (4) and medium sand (6 & 2) below. The sleeve friction values were mostly in the range of 0\(\sim\)220 kPa in coarse sand (4) and medium sand (6 & 2) below. The UBC values of a single pile were calculated for long PHC pipe piles with a length of 21 m and short PHC pipe piles with a length of 12.5 m, respectively, based on the JGJ94-2008 method, as shown in Figure 9. The average UBC value for a single short PHC pipe pile in coarse sand was 2766 kN, while the average UBC value for a single long PHC pipe pile in medium sand was 3824 kN. It can be seen that the calculated UBC values with the CPTs data are close to the average value of the single pile UBC values calculated with the in-situ SPTs.  
Figure 7: (**A**) The ultimate bearing capacity of long PHC pipe pile (L = 21 m, D = 500 mm); (**B**) The ultimate bearing capacity of short PHC pipe pile (L = 12.5 m, D = 500 mm).  
Figure 8: Cone penetration test (CPT) results (**a**) CPT-1#; (**b**) CPT-2#; (**c**) CPT-3#; (**d**) CPT-4#; (**e**) CPT-5#; (**f**) CPT-6#; (**g**) CPT-7#; (**h**) CPT-8#; (**i**) CPT-9#; (**j**) CPT-10#.  
Figure 9: Ultimate bearing capacity based on CPT results.
# Monitoring Vehicles on Highway by Dual-Channel \(\varphi\)-OTDR
Another problem is that the conventional differential method (CDM), which is often used in signal demodulation of the SC-\(\varphi\)-OTDR, is highly dependent on the differentiating step to determine the vibration-induced signal peak. Inappropriate selection of step size would also lead to wrong locations of vibration events. In the paper, an intensity-demodulated dual-channel \(\varphi\)-OTDR (DC-\(\varphi\)-OTDR) is investigated to monitor the traffic situation on a highway. The two sensing channels are extracted from an armored four-core cable. Furthermore, we propose an improved signal processing algorithm to demodulate the vibration signal for the purpose of alleviating the strong dependence of the demodulation on the differential step. With the help of the DC-\(\varphi\)-OTDR system, we hope to decrease the probability of false measurements. We firstly validate the DC-\(\varphi\)-OTDR on a campus road, and then apply it to monitor the running vehicles on the highway along a 9 km-long armored cable.
# Ontology-Based Representation and Reasoning in Building Construction Cost Estimation in China
###### Abstract  
Cost estimation is one of the most critical tasks for building construction project management. The existing building construction cost estimation methods of many countries, including China, require information from several sources, including material, labor, and equipment, and tend to be manual, time-consuming, and error-prone. To solve these problems, a building construction cost estimation model based on ontology representation and reasoning is established, which includes three major components, i.e., concept model ontology, work item ontology, and construction condition ontology. Using this model, the cost estimation information is modeled into OWL axioms and SWRL rules that leverage the semantically rich ontology representation to reason about cost estimation. Based on OWL axioms and SWRL rules, the cost estimation information can be translated into a set of concept models, work items, and construction conditions associated with the specific construction conditions. The proposed method is demonstrated in Protege 3.4.8 through case studies based on the Measurement Specifications of Building Construction and Decoration Engineering taken from GB 50500-2013 (the Chinese national mandatory specifications). Finally, this research discusses the limitations of the proposed method and future research directions. The proposed method can help a building construction cost estimator extract information more easily and quickly.  
ontology; cost estimation; semantic relationships; reasoning; specification for cost estimation +
Footnote †: journal: Future Internet
## 3 Results of Simulations
### SHJ Cell Characterization under Illumination
#### 3.2.2 Energy Band Diagrams under Illumination  
The energy band diagram simulated for cell SHJ\({}_{1}\) and displayed in Figure 5a at non-equilibrium under AM1.5G illumination and short circuit conditions (illumination-SC), and assuming drift diffusion hole transport indicates the presence of a quasi-Fermi energy split \(\delta\), or equivalently a forward bias, amounting to 80 mV on the pn junction, despite the short circuit condition. Such a bias is established in order to compensate the voltage drop on the cell dark series resistance \(R_{s}\) and hence to satisfy the cell closed loop equation with short circuit terminals. Therefore, \(\delta\) represents the minimum junction bias at short circuit condition irrespectively of the transport mechanism and of the a-Si:H doping level. The value of \(R_{s}\) is estimated from [PERSON]'s law  
\[R_{s}=\delta/I_{SC} \tag{3}\]  
to 1.9 \(\Omega\) cm\({}^{2}\), which accounts for the resistance of the bulk and of the back contact. Although such a resistance is relatively high and can be reduced to less than half this value if the back contact is optimized, it will be maintained throughout the present work. On the other hand, a much larger quasi-Fermi energy split, designated by V\({}_{\text{SC}}\) in the band diagram of Figure 5b, has been reported when thermionic emission hole transport is considered, and qualitatively linked to hole reflection at the hetero-interface [35]. A clear interpretation, however, is still needed for the exact process leading to the creation of Vsc and for its dependence on the a-Si:H(p\({}^{+}\)) doping concentration and on the applied bias. In addition, as can be deduced from Figure 5b and as listed in Table 2, the built-in potentials V\({}_{\text{bi,c-Si}}\) in c-Si and V\({}_{\text{bi,a-Si}}\) in a-Si:H at short circuit condition are quite different from their values at equilibrium depicted in Figure 2a and listed Table 1 when thermionic emission transport is assumed, while they remain practically unchanged under drift-diffusion transport, as shown in Figure 5a. Such changes in the built-in potentials may significantly affect the hole profiles, which is relevant to the investigation under study, and hence need to interpreted. Finally, it is useful to reiterate that such important differences between the band diagrams in Figure 5a resulting from simulations assuming drift-diffusion transport and in Figure 5b assuming thermionic emission transport result from the hetero-interface being considered as a boundary in the latter case, as explained in Section 3.2.