esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Enhanced Object Detection
#### 2.3.2 Image Enhancement  
The image \(\rm{I^{bil}}\), where noise has been suppressed using the bilateral filter, is then processed using SRCNN to convert the low-resolution image into a high-resolution image, enabling a more accurate and detailed capture of the object features. Consequently, SRCNN helps effectively detect various types of drones based on distance and shape. As shown in Figure 5, SRCNN consists of three layers: patch extraction and representation, nonlinear mapping, and reconstruction [14,15].  
The first layer of SRCNN extracts small regions (patches) from the low-resolution ROI image \(\rm{I_{s}^{bil}}\), which has undergone bilateral filtering, and converts them into high-dimensional vectors to create feature maps. The operation is defined as follows:  
\[\rm{I_{i}^{sr1}}=max\Big{(}0,w_{1}*\rm{I^{bil}}+b_{1}\Big{)}. \tag{6}\]  
Here, \(\rm{w_{1}}\) and \(\rm{b_{1}}\) denote the filter weights and bias of the first layer, respectively, \(*\) denotes the convolution operation, and \(\rm{max(0,\,)}\) denotes the rectified linear unit activation function. The feature map generated by the first layer is the result of a linear transformation based on the low-resolution image, which is unsuitable for expressing complex nonlinear patterns. To address this issue, the second layer performs nonlinear mapping to extract the complex features required for generating the high-resolution image as follows:  
\[\rm{I_{i}^{sr2}}=max\Big{(}0,w_{2}*\rm{I_{i}^{sr1}}+b_{2}\Big{)}. \tag{7}\]  
In the third layer, the high-dimensional vectors extracted from the previous two layers are combined to reconstruct the final high-resolution image as follows:  
\[\rm{I_{i}^{sr}}=w_{3}*\rm{I_{i}^{sr2}}+b_{3}. \tag{8}\]  
Figure 6 compares the results of using SRCNN for the resolution enhancement of small drones. Figure 6a shows the original low-resolution image, and Figure 6b shows the high-resolution image obtained after applying SRCNN. Compared with the original image, the enhanced image restores the detailed information around the object and sharpens the boundaries, offering a higher-quality image.  
Figure 5: Image processing flow of SRCNN.
## 4 Effects of Cannabinoids on Gastrointestinal Motility
### Effects of Phytocannabinoids on Gastrointestinalal Motility
#### 4.2.3 Effects of Other Phytocannabinoids on Gastrointestinal Motility  
The results of a study investigating CBC showed that this phytocannabinoid normalized in vivo inflammatory hypermotility in mice treated with croton oil but did not have any effect on motility in control animals. In this same study, CBC preferentially inhibited electrically stimulated contractions versus ACh-induced contractions of the intestine in vitro. The inhibitory effect of CBC on ACh-induced contractions was diminished by verapamil, suggesting that CBC exerts this effect through mechanisms involving L-type Ca\({}^{2+}\) channels. The inhibitory effect of CBC on electrically induced contractions was found to be attenuated by the N-type Ca\({}^{2+}\) channel blocker omega-conotoxin, suggesting the involvement of these channels in CBC's mechanism of action. Both in in vivo and in vitroconditions, the effect of CBC on contractility was not attenuated by CB1, CB2, or TRPA1 receptor antagonists [61].  
Another phytocannabinoid, CBN, was found to inhibit the contractile response of guinea pig intestinal tissue to electrical stimulation in vitro, but this inhibition was not affected by the CB1 antagonist rimonabant [76], p. 199. In contrast, an earlier study did not detect any effect of CBN on guinea pig in vitro intestinal twitch response to ACh, histamine, or 5-HT [74]. The results of a later study, however, showed inhibition of in vivo colonic propulsion in mice via CB1, but not CB2, receptor activation [83].
## 4 Discussion  
### Effect of Saturation and Age on FBB Modulus  
The increase in FBB modulus with curing time was significant for both the cured (Figure 4) and saturated (Figure 5) conditions. The average FBB cured modulus increased by 33% after 7 days curing, compared to after three days curing. Similarly, by 14 days curing, the cured modulus was 72% higher than after three days curing. The effect of curing was greater for the saturated modulus results, with 47% and 98% increases, after 7 days and 14 days curing, respectively.  
Comparison of the cured and saturated modulus values in Figures 4 and 5 also indicates the effect of saturation on the FBB modulus. This is highlighted in Figure 6, which shows that the saturated modulus was, on average across the three curing durations, 56% (Ayreshire) to 68% (Weipa I) of the equivalent cured modulus. That is, even though the reference material (Ayreshire) had the highest cured modulus, it was most affected by saturation. These general trends are consistent with results reported by others [8; 11].
## 3 Numerical Calculation and Discussion of Results
### Effect of the Preload State and Axial Boundary Conditions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Internal Preload**} & \multicolumn{3}{c}{**External Preload**} \\ \cline{2-7}
**Coefficient** & **0.8\%** & **1.6\%** & **2.5\%** & **0.8\%** & **1.6\%** & **2.5\%** \\ \hline \(a_{1}\) & 0.2593 & 0.2867 & 0.3413 & 0.2558 & 0.2883 & 0.3370 \\ \(a_{2}\) & \(-\)0.0092 & \(-\)0.0105 & 0.0275 & 0.0140 & 0.0394 & 0.0425 \\ \(a_{3}\) & \(-\)0.0675 & 0.0221 & \(-\)0.1313 & 0.1344 & \(-\)0.0369 & 0.0272 \\ \(a_{4}\) & \(-\)0.4254 & \(-\)0.5379 & \(-\)0.9656 & 0.2838 & \(-\)0.1799 & \(-\)0.1885 \\ \(a_{5}\) & 0.4385 & 2.7711 & 2.9038 & \(-\)1.4229 & 1.3868 & 0.3617 \\ \(a_{6}\) & 1.4708 & 5.3331 & \(-\)0.3238 & \(-\)0.8294 & \(-\)2.8985 & \(-\)3.6424 \\ \(a_{7}\) & 4.2736 & \(-\)0.3495 & 1.2461 & \(-\)2.6517 & 0.8550 & 0.4057 \\ \(a_{8}\) & 1.3357 & 0.9991 & 11.6435 & \(-\)2.1848 & \(-\)5.2641 & 1.5481 \\ \(a_{9}\) & \(-\)10.9707 & \(-\)0.4705 & \(-\)10.2659 & 58.3857 & 2.6007 & 5.7922 \\ \(a_{10}\) & 0.2158 & 2.4878 & \(-\)39.4096 & \(-\)9.5983 & 7.3398 & 1.4243 \\ \(a_{11}\) & 0.7160 & \(-\)87.9092 & \(-\)45.8087 & 33.6653 & \(-\)19.3324 & 3.4407 \\ \(a_{12}\) & \(-\)30.8714 & \(-\)85.8314 & 1.2025 & 95.1276 & 8.7059 & \(-\)0.3547 \\ \(a_{13}\) & \(-\)164.4423 & \(-\)5.9095 & \(-\)7.2920 & \(-\)5.0656 & \(-\)32.2820 & \(-\)32.8691 \\ \(a_{14}\) & \(-\)0.7037 & 2.1150 & \(-\)40.9135 & \(-\)88.8018 & \(-\)68.1837 & \(-\)17.7638 \\ \(a_{15}\) & 0.2467 & \(-\)3.4719 & \(-\)4.3433 & \(-\)2820.6377 & 0.9387 & 1.2630 \\ \(a_{16}\) & 2.1301 & 0.9619 & 1.4475 & 5.4057 & \(-\)1.7299 & \(-\)123.6476 \\ \(a_{17}\) & 10.3865 & 1.9055 & 3.2611 & 2.0014 & 15.4294 & \(-\)2396.1895 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fitting coefficients for the volume fraction of the MR under different preload conditions. Figure 4: Volume fraction distribution of the MR without pre\(-\)tensioning. that the radial preload force applied does not impact the natural frequency and modal loss factor of the MRCS.
## 4 Conclusions  
The software platform GisaxStudio, which is aimed at the simulation, analysis, and fitting of GISAXS maps from different types of 2D or 3D lattices of NPs or QDs, is demonstrated. It is specifically developed for GISAXS measured on thin films consisting of 3D lattices of NPs grown by the self-assembly processes, and for the NPs formed within ion-tracks during ion beam irradiation. Therefore, it contains two main groups of models: 3 dLattice for application in analysis of GISAXS from NP lattices formed by self-assembled growth, and iBeam aimed at the NPs formed during the ion-beam irradiation of materials. Each group contains several models of the NP arrangement that depend on the material production conditions, i.e., the type of disorder along each of the basis vectors of the formed 3D NP lattice. The main NP shapes supported by GisaxStudio are ellipsoid, spheroid, and sphere with full or core-shell structure. The core-shell structure includes the option of single or double shells, as well as the possibly displaced center of the core with respect to the center of the shell. The models of the NP ordering and their main features available in the software, along with the NP shapes, are discussed. The options for the fitting are provided and supported by examples. The platform is suitable for use by broad audience and non-experts in the GISAXS field.  
Figure 10: (**a**) Tab related to _Batch fit_ option. (**b**) Start or delete options of _Batch fit_ in the main window.  
**Author Contributions:** Conceptualization M.M.; methodology, I.M. and M.M.; software, I.M.; formal analysis, G.S., S.I. and M.M.; data curation, I.M. and M.M.; writing--review and editing, all authors.; visualization, I.M. and M.M.; project administration, M.M.; funding acquisition, I.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Croatian Science Foundation (no. IP-2018-01-3633) and the European Regional Development Fund under the grant KK.01.1.1.01.0009 (DATACROSS). The authors acknowledge the CERIC-ERIC Consortium for the access to experimental facilities and financial support.  
Not applicable.  
The authors are thankful to [PERSON] for assistance in the sample preparation and [PERSON] for GISAXS measurements. The authors acknowledge the CERIC-ERIC Consortium for the access to the experimental SAXS facilities and financial support.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
## 4 Proposed Approaches
### Threshold for Gap of Normalized Eigenvalues  
T-GANE is our proposed threshold based source enumeration approach by employing the machine learning algorithm using gaps of normalized eigenvalues. In this approach, a large number of NN gaps and NS gaps are observed, and the probability density functions (PDFs) for NN gaps and NS gaps are derived to compute the optimal threshold that minimizes source enumeration error probability. Finally, the source enumeration is performed with the optimal threshold computed by the procedures above.  
T-GANE can be divided into three steps: datasets generation, learning and computing optimal threshold, and source enumeration using the optimal threshold. The detailed procedures for T-GANE are described as follows.
# Enhancing the Flavor Profile of Summer Green Tea via Fermentation with _Aspergillus niger_ RAF106
## 3 Results
### Changes of Tea Volatiles during Fermentation
#### 3.1.2 Volatile Compounds Identified by HS-SPME-GC-MS
During fermentation, the content of nornytytane, 2,6,10-trimethylpentadecane, and 4-methyltetradacane exhibited a decreasing trend. Specifically, after 6 days, compared to the control group (CK), their content was significantly reduced by 70.64%, 68.60%, and 38.85%, respectively (\(p<0.05\)). 3-methylundecane, 9-methylmonadecane, 7-methylheptadecane, and 10-methylmonadecane were newly formed
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 4 Experiments  
### Dataset  
#### 4.1.1 For Separation  
The used separation model's functioning and accuracy are tested on samples of the downloaded dataset, the WSJ0-2 mix dataset. However, to check its reliability, correctness, and the ability to integrate the real-time voice recordings with the separation module, we prepared a dataset ourselves. It was a voice recording of multiple speakers. Some of the speakers were chosen to be relatives such as family members who shared nearly similar genetics and features which make them harder to separate. The rest are random speakers such as friends. The combination between relative and non-relative is done to avoid data bias results.  
The system is tested on the two-speaker speech separation issue with a custom dataset which has been prepared by the authors. This dataset consisted of four speakers, two males and two females. The dataset includes about four hours of speech divided into 1800 samples for each speaker, with 53.9 h of training data and 200 samples for validation and 200 samples for testing data. The mixtures are created by randomly mixing the two speakers' utterances. The original waveforms generated with 44.1 kHz. Then, to reduce the computational cost, these waveforms are downsampled to 8 kHz. We further expand our dataset to three and four speakers with 55.2 h and 58.8 h of training, respectively.
## 3 Results and Discussions
### Effect of Seal Clearance
In order to further analyze the static pressure distribution of blade surface considering the leakage flow, the static pressure distributions of four configurations under the design condition are depicted in Figure 8. For the 1% blade height of the rotor, the pressure on the suction side from the leading edge to the 70% chord length increases gradually as the seal clearance increases. This results in the reduction of the pressure difference between the pressure side and suction side, further causing the reduction of the power capacity of the rotor blade. For the 99% blade height of stator2, the increase of seal clearance leads to the raising of pressure on the suction side from the leading edge to 90% chord length. Especially when \(\overline{C}\) is 0.016, the cross of pressure distribution curves at the leading edge of stator2 indicates the existence of a negative attack angle, serious streamline deflection and further terrib  
Figure 8: The static pressure distributions under different seal clearance: (left: 1% blade height of rotor; right: 99% blade height of stator2). Figure 7: The Mach number contours: (up: 1% blade height of rotor; down: 99% blade height of stator2). For the sake of more detailed analysis on the turbine performance, the following performance parameters are defined. The isentropic efficiency is given by  
\[\eta=\frac{W}{\Delta h_{\text{is}}\cdot\dot{m}}, \tag{5}\]  
Relative leakage of LS on stator1 is defined by  
Figure 4:0  
Relative leakage of LS on rotor is defined by  
Figure 4:1  
where \(\Delta h_{\text{is}}\) represents the isentropic enthalpy drop from the inlet of stator1 to the outlet of stator2. \(\dot{m}\) represents the mass flow rate. \(\dot{m}_{\text{sout}}\) and \(\dot{m}_{\text{rout}}\) denote the mass flow rate at the outlet of the leakage passage on stator1 and rotor respectively. The variations of relative leakage, power and efficiency with pressure ratio are presented in Figure 4:2 Under any working condition, the relative leakage of stator1 and rotor increase while the power and efficiency decrease with an increase in seal clearance. Below the design condition point, the relative leakage increases sharply with an increase in seal clearance. However, the leakage is almost kept constant after the pressure ratio exceeds the design pressure ratio. The efficiency increases firstly and then decreases. This is because there is the optimal speed ratio near the design point. When the speed ratio deviates from the design point, the residual speed loss increases, causing a reduction of efficiency. The power increases gradually as the pressure ratio increases, which is mainly due to the raising of the mass flow rate and enthalpy drop. Furthermore, the relative leakage of the rotor is always higher than that of stator1. This can be explained by the fact that pressure drop from the inlet to the outlet of the rotor is the largest because the working fluid pushes the rotor blade to generate power.
## 5 Performance Evaluation
### Impact of Selfish Nodes on All Performance Metrics
This occurs as a result of EMS rewarding nodes for their cooperation, encouraging cooperative nodes to exchange much more resources. In terms of dropped packages, as seen in Figure 5d, the EMS helps to reduce dropped packages for all the other examined selfish node percentages. Particularly, in comparison to a similar strategy without any selfish node detection, the EMS drops 200, 200, 250, 250, 260, 260, 265, and 265 bundles for the 20% strategy, as shown in Figure 1d. The EMS can discard fewer than 500, 512, 500, 560, 566, 567, 567, 570, and 570 packages in the worst-case situation. By monitoring the existence of selfish nodes and minimizing their interaction with cooperative nodes, the EMS can reduce network resources (such as buffer storage space). This saves resources for cooperative nodes that only use them to forward packages to certain other cooperative nodes. It is also due to the reason that many packets are transferred among the nodes; whenever the number of selfish nodes inside the network is significant, more energy is required to identify selfish nodes. The suggested method can identify selfish nodes in shorter durations, resulting in fewer packet drops and less energy consumed when re-sending packets via the network.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Operation Related Problems in WSNs
#### 3.2.2 Coverage Efficiency  
When the sensor nodes are deployed randomly, the number of sensors is usually more than necessary [84]. Therefore, it is not essential to operate all the nodes in active mode simultaneously. A proper sensor scheduling scheme is required to keep some nodes in sleep state and others in the active mode to help ensure coverage efficiency and energy conservation. A multi-objective optimization formulation has been suggested in [85] to optimize the conflicting objectives of coverage efficiency, life time and connectivity. The authors argued that the proposed algorithm could provide better coverage with the same level of energy conservation as compared to the others. Maintaining efficient coverage and prolonging the lifetime of wireless sensor networks is one of the important issues in WSNs. In [86], a multi-objective optimization algorithm has been proposed to get optimal coverage efficiency and prolonged network lifetime even in the presence of sensing errors. Pareto optimal solutions have been achieved in [87, 88] for finding the balance between coverage efficiency and the capacity of the network.
# Application of Virtual Reality in Learning Quantum Mechanics
## 4 Discussion
However, the performance of the experimental group is better than that of the control group, according to the analysis results. It is inferred that the virtual teaching modules provide a scaffold for independent learning by making complex and abstract concepts observable and more impressive so they can improve learning effectiveness. This study used virtual reality technology to concretize the abstract concepts of quantum mechanics and make them easy to observe. The teaching modules can provide an interactive and immersive operational environment, allowing learners to understand quantum theory through the photoelectric effect experiment. The cathode ray tube experiment shows that all atoms contain negatively charged subatomic electrons. The oil drop experiment can be used to calculate the mass and fundamental charge of electrons. The gold foil experiment displays that the atom contains mostly empty space and a tiny nucleus with a positive charge. The electron diffraction experiment shows that electrons exhibit wave behavior because they can be diffracted by a crystal structure. According to the above experimental results, [PERSON]'s atomic model can be used to explain the discontinuous spectral lines of hydrogen atoms, and [PERSON]'s uncertainty principle shows that the momentum and position of a particle cannot be determined at the same time, which is of great help for understanding quantum mechanics. Learners can observe from complementary and multiple perspectives to deepen their learning impression by creating more intuitive and realistic experiences and attain a clearer understanding of the particles and wave duality of microscopic substances.
## 2 Study Area and Data Preparation
### Data Preparation
#### 2.2.2 Impact Factor  
The formation of landslides is influenced by multiple factors and involves complex mechanisms. Based on the development characteristics of landslide disasters within the study area, 14 influencing factors were selected to construct the susceptibility model, including elevation, slope, aspect, roughness, profile curvature, plane curvature, lithology, distance to fault, land cover type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), distance to rivers, distance to roads, and annual precipitation (Figure S1 in Supplementary Materials). Annual precipitation is derived from individual district and county statistics. Detailed data sources are listed in Table 1. To ensure the accuracy of the data and the model, a grid cell size of 30 m by 30 m was chosen for the landslide susceptibility assessment model. The continuous data were divided into eight classes using the ArcGIS natural breaks method, while the discrete data were classified based on their inherent attributes. Refer to Figure S1 in Supplementary Materials and Table 2 for more details. \begin{table}
\begin{tabular}{c c c c} \hline
**Data Type** & **Source** & **File Type** & **Resolution/Scale** \\ \hline Terrain data & China GSCloud ASTER GDEM 30 m ([[https://www.gscloud.cn](https://www.gscloud.cn)]([https://www.gscloud.cn](https://www.gscloud.cn)) & Raster & 30 m \\  & (accessed on 1 April 2023)) & & \\ Geological data & China National Geological Archives ([[http://www.ngac.org.cn](http://www.ngac.org.cn)]([http://www.ngac.org.cn](http://www.ngac.org.cn)) & \\  & (accessed on 4 April 2023)) & Image & 1:500,000 \\ \hline Basic geographic information data & (open street map) OSM ([[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org)) (accessed on 10 April 2023)) & Polyline & \\ \hline Environmental features & China GSCloud Landset 8 Image ([[https://www.gscloud.cn](https://www.gscloud.cn)]([https://www.gscloud.cn](https://www.gscloud.cn)) (accessed on 23 April 2023)) & Raster & 30 m \\  & & Globe Land 30 & \\  & ([[http://www.globallandcover.com/home.html?type=data](http://www.globallandcover.com/home.html?type=data)]([http://www.globallandcover.com/home.html?type=data](http://www.globallandcover.com/home.html?type=data)) & Raster & 30 m \\  & (accessed on 3 May 2023)) & & \\ \hline \end{tabular}
\end{table} 
Table 1: Metadata of the employed datasets. Figure 2: Optical remote sensing interpretation results of landslides in the city of Lishui using Google Earth imagery.