esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Examples
### Two-Layer Model  
When SGFD methodology is used for elastic wave simulation, the grid step is determined. If the interface is located at an integral multiple of the step, the grid can accurately describe the interface; otherwise, it will cause an inaccuracy in the travel time of the reflected wave. The GFDM can directly set the nodes at the interface, therefore it can accurately describe the changes in the interface and can obtain more accurate travel time simulation results. A two-layer horizontal model (2000 m \(\times\) 2000 m) was designed to show the advantages of the GFDM over the SGFD model. The P-velocity, S-velocity, and density of the upper layer of the model were 4000 m/s, 2300 m/s, and 2.4 g/cm\({}^{3}\), respectively, and those of the lower layer were 6000 m/s, 3500 m/s, and 2.6 g/cm\({}^{3}\), respectively. The simulation time step was set at 0.5 ms, and the total computation time at 1.0 s. The source was located at (1000 m, 0 m), and the dominant frequency of the Ricker wavelet was 20 Hz. When the model is discretized, the SGFD model adopted a fixed grid step of 10 m. Therefore, only when the interface was located at an integral multiple of 10 (for example, when the interface was located at 1000 m), can the grid points accurately describe the interface. However, when the interface is located between 1000 m and 1010 m, the results obtained by SGFD are the same, and the travel time of the reflected wave will not change. When the 4 th-order 21 nodes GFDM was used, the proposed node generation algorithm was applied to discretize the model, using a node radius of 10 m for the first layer, and 12 m for the second layer. The coordinates of the nodes change along with the real interface position, so more accurate travel time information of the reflected wave can be obtained. When the interfaces were located at 1002 m, 1005 m, and 1008 m, respectively, the GFDM and SGFD results obtained are shown in Figure 9. The black and blue dotted lines represent the seismic records obtained by SGFD when the interface depth was set as 1000 m and 1010 m, respectively. The green, red, and blue solid lines correspond to seismic records obtained by GFDM when the interfaces were located at 1002 m, 1005 m, and 1008 m, respectively. Comparing the reflected wave information for the two algorithms, the SGFD can only obtain the exact record time of interfaces located at 1000 m and 1010 m--when the interface depths are 1002 m, 1005 m, and 1008 m, the record time will never change. In contrast, for the GFDM results, the record time shifts with the interface depth, which is more consistent with reality compared to the SGFD results. This shows that the GFDM can accurately describe the changes in the interface, and can obtain more accurate travel time simulation results.  
Figure 8: Comparison between the 80 th trace of shot records: (**a**) X component; (**b**) Z component.
## 3 Results and Discussion
### MEG/EEG-Based Hyperscanning Research  
Table 2 shows a summary of 60 MEG/EEG-based hyperscanning papers. Of 60 MEG/EEG-based hyperscanning papers, the cognition category was the most common application (28, 47%), followed by decision-making (16, 27%), motor synchronization (14, 23%) and education (2, 3%). Out of the hyperscanning studies performed with MEG/EEG, 83% (50/60) of the studies were performed in a laboratory setting. As far as participant orientation and organization, 57% (34/60) of the studies were performed with participants in a face-to-face orientation, and 83% (50/60) were performed with participants in pairs.
## 4 Experimental Details
### Feature Extraction  
The combined features totaling 5760 were extracted by the four baseline ConvNets. Before data augmentation, the entire datasets were divided into train and test subsets in the ratio of 7:3 (5536: 2373), respectively. Table 5 presents the details of the feature set generated by each baseline model and the combination of the four for the ensemble model. The feature input for the proposed ensemble model is a concatenation of the features generated from four baseline models (ResNet50, DenseNet201, ResNet18, and EfficientNetB0).  
Figure 14 presents the one-vs-one coding matrix designed for the eight classes of breast cancer that yield twenty-eight binary classifiers. Each column of the coding matrix is one hot-encoding corresponding to a classifier, and each row corresponds to one of the eight breast cancer classes. For example, the first column of Figure 14 is [\(1\); \(-1\); \(0\); \(0\); \(0\); \(0\); \(0\); \(0\); \(0\)] indicating that the ensemble model trains the first SVM binary classifier using features classifier as Adenosis and Ductal carcinoma because Adenosis corresponds to a positive class; ductal carcinoma corresponds to \(-1\), so it is a negative class. A class output of an unseen breakHis image sample x is determined by the ensemble model using majority voting count as in Equation (16):  
\[\hat{y}=\underset{k\in 1,..,8}{\operatorname{argmax}}f_{k}(x) \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subtype** & **Original No. of Instances** & **No. of Training Instances** & **No. of Test Instances** & **No. of Training Instances** \\  & & & & **after Oversampling** \\ \hline Adenosis & 444 & 311 & 133 & 2416 \\ Fib broadenoma & 1014 & 710 & 304 & 2416 \\ Phyllodes tumor & 453 & 317 & 136 & 2416 \\ Tubular adenoma & 569 & 398 & 171 & 2416 \\ Ductal carcinoma & 3451 & 2416 & 1035 & 2416 \\ Lobular carcinoma & 626 & 438 & 188 & 2416 \\ Mucinous carcinoma & 792 & 554 & 238 & 2416 \\ Papillary carcinoma & 560 & 392 & 168 & 2416 \\ \hline Total & 7909 & 5536 & 2373 & 19,328 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The distribution of benign and malignant images over various subtypes after applying augmentation techniques.  
where \(\hat{g}\) is the class with majority count, \(k\) is the class label, and \(f_{k}(x)\) is the predicting model for the label \(k\) for which the corresponding classifier reports the highest confidence score.
## 3 Results
### Biogas and Methane  
SISER1V1 produced 1.39 \(\pm\) 0.08 dm\({}^{3}\) biogas/day (Figure 5a), which contained 54.8 \(\pm\) 3.9% CH\({}_{4}\) on average. By comparison, S2 SER1V1 yielded 1.42 \(\pm\) 0.09 dm\({}^{3}\) biogas/day (Figure 5a), with a CH\({}_{4}\) fraction of 64.9 \(\pm\) 3.0%. S1 SER2 (55 \({}^{\circ}\)C) showed increased biogas output at 1.55 \(\pm\) 0.16 dm\({}^{3}\)/day, (62.6 \(\pm\) 3.2% CH\({}_{4}\)), whereas S2 SER2 produced 1.47 \(\pm\) 0.14 dm\({}^{3}\)/day (67.0 \(\pm\) 3.0% CH\({}_{4}\)) (Figure 5a). In the OLR = 2.0 gCOD/dm\({}^{3}\)-d variant (V2), the specific biogas production was at 0.34 \(\pm\) 0.03 dm\({}^{3}\)/gCOD\({}_{\rm rem}\). (Figure 5b). The CH\({}_{4}\) fractions were 66.2 \(\pm\) 4.2% in S1 SER1 and 68.3 \(\pm\) 2.6% in S1 SER2, with nominal yields of 2.57 \(\pm\) 0.24 dm\({}^{3}\)/day and 2.65 \(\pm\) 0.25 dm\({}^{3}\)/day (Figure 6a). The CH\({}_{4}\) fractions in S2 SER1 (35 \({}^{\circ}\)C) and S2 SER2 were 67.4 \(\pm\) 6.1%. and 68.5 \(\pm\) 3.8%, respectively (Figure 6a). Daily biogas production was around 2.81 \(\pm\) 0.26 dm\({}^{3}\)/day in SER1 and 2.86 \(\pm\) 0.28 dm\({}^{3}\)/day in SER2 (Figure 5a). The specific methane production was about 0.26 \(\pm\) 0.02 dm\({}^{3}\)/gCOD\({}_{\rm in}\) in E2 SER1 and E2 SER2 (Figure 6b). The methane fraction in the biogas peaked in V3. The MW-heated series (S2) produced 66.7 \(\pm\) 2.9% CH\({}_{4}\) (SER1/35 \({}^{\circ}\)C) and 70.4 \(\pm\) 2.7% (SER2/55 \({}^{\circ}\)C) (Figure 6a). Biogas yields significantly exceeded those in V2, reaching 3.30 \(\pm\) 0.31 dm\({}^{3}\)/day in SER1 and 3.82 \(\pm\) 0.18 dm\({}^{3}\)/day in SER2 (Figure 5a). The corresponding production rates were 0.33 \(\pm\) 0.03 dm\({}^{3}\)/gCOD\({}_{\rm rem}\). and 0.35 \(\pm\) 0.02 dm\({}^{3}\)/gCOD\({}_{\rm rem}\). respectively (Figure 5b). S1 boosted similar output. CH\({}_{4}\) levels in the biogas were significantly lower in V4: 42.3 \(\pm\) 2.9% in S1 SER1 and 53.3 \(\pm\) 5.5% in S1 SER2, with production rates of 4.56 \(\pm\) 0.57 dm\({}^{3}\)/day and 5.01 \(\pm\) 0.54 dm\({}^{3}\)/day, respectively. Daily yields, however, were the highest among the tested OLR variants. Specific biogas output was around 0.34 \(\pm\) 0.02 dm\({}^{3}\)/gCOD\({}_{\rm rem}\). in SER1 (35 \({}^{\circ}\)C) and 0.30 \(\pm\) 0.04 dm\({}^{3}\)/gCOD in SER2 (55 \({}^{\circ}\)C) (Figure 5b). The biogas produced in the MW-heated reactors (S2) contained 49.4 \(\pm\) 3.4% CH\({}_{4}\) in SER1 and 56.7 \(\pm\) 3.7% CH\({}_{4}\) in SER2, with yields of 4.74 \(\pm\) 0.30 dm\({}^{3}\)/day and 4.58 \(\pm\) 0.36 dm\({}^{3}\)/day, respectively (Figure 6a).
# Deep Learning Enhances Radiologists' Detection of Potential Spinal Alignancies in CT Scans
## 2 Materials and Methods
### Experimental Setup  
In this study, our primary goal was to investigate the effectiveness of a deep-learning algorithm (DLA) in assisting radiologists in identifying previously overlooked potentially malignant cases in the spine through abdomen and thoracic CT imaging. A total of 32 studies were selected from our data pool which contained both positive and negative cases in equal numbers. Details of the selection process are given in Figure 1.  
The study consisted of two phases. In the first phase, six radiologists independently reviewed the 32 studies without DLA assistance, following their routine reading process. In this case, a routine reading process is defined as the reading of the radiological images by  
Figure 1: Flowchart of data collection and distribution. All CT studies were collected retrospectively from the clinical databases of multiple institutions.  
the radiologist based on the patient's complaint. This phase aimed to establish a baseline for their diagnostic performance.  
The six radiologists (participants) we recruited have varying experience levels, ranging from 1 to 11 years. Each participant received the same set of 32 studies but presented in random order. The participants were blinded to the gold standard and any patient-specific information to ensure unbiased assessments, except for a brief description of the patient's complaint. These complaints were not focused on spine-related issues but were general complaints of the patients who visited the clinics. The details of this information can be found in Supplementary Table S1.  
After a break of 10 days, the second phase was conducted. In this phase, the participants reevaluated the same set of studies, but this time, they had access to the DLA predictions to assist them in their evaluations. The DLA provided predictions about the presence or absence of abnormalities in the spinal vertebrae in the CT scans.  
To ensure accurate documentation of their assessments, the participants were asked to use screen-recording software [17] to capture their computer screens. They were also instructed to describe their findings verbally while indicating them with their mouse. A DICOM visualization tool [18] was made available to aid in interpreting the scans and the predictions of the DLA.  
To evaluate the effectiveness of the AI intervention, the results obtained during the reading sessions were manually compared to the gold standard. This comparison enabled the determination of the accuracy of the participants' diagnoses with and without the DLA's assistance.
# Fatigue Performance Test and Numerical Analysis of Composite Girders with CSW-CFST Truss Chords
###### Abstract  
Composite girders with corrugated steel webs and concrete-filled steel tubular (CSW-CFST) truss chords are a new type of structure and have been used in bridges. To study the fatigue performance of these composite girders, model tests and numerical analysis were conducted, and the results compared with those for composite girders with corrugated steel webs and steel tubular (CSW-ST) truss chords. The results indicated that concrete-filling the chords constrained chord radial deformation and relieved hot-spot stress in the intersecting weld zones of composite girders with CSW-ST truss chords by 18.5-60.1%. The crack growth rates along both depth and length directions in composite girders with CSW-CFST truss chords were lower than those of composite girders with CSW-ST truss chords during the crack propagation and failure stages. Under the same fatigue load, concrete-filling the chords increased the fatigue life of composite girders with CSW-ST truss chords by 61.5%. Under the condition that there is no fatigue design S-N curve, the fatigue design S-N curve given in API can be used to evaluate the fatigue life of composite girders with CSW-CFST truss chords with a deviation of less than 17.4%.  
composite girders; corrugated steel webs; concrete-filled steel tubular truss chords; fatigue performance test; numerical analysis; fatigue performance; evaluation method
## 3 Sensors Configuration Analysis
### Flat Plate Sensor  
The simplest model taken under investigation was the model of the flat plate sensor. Its shape is presented in Figure 4. The main dimensions of the model were sensor diameter \(d\) = 1 m and depth of the hole \(h\) = 0.3 m.  
The sensor was located at two positions: equal to ground level and at height \(A\) = 1 m above them. Figure 5 presents the exemplary electric field strength distribution in three dimensions \(E\)(\(x\),\(y\),\(z\)) for sensor positioning equally to ground level and at height \(A\) = 1 m above them. This distribution is reduced to a 2D cross-section defined as a Y-Z surface for better readability.  
Figure 4: The flat plate sensor model is taken into consideration.  
As might be expected, electric field strength vectors close to the conducting sensor deviate from vertical directions and lead to its surface. This aspect is described in the next point of the paper. The electric charge accumulated on the sensor equal to ground level had a value of \(Q_{1\_0}=-7.31\)\(\times\)\(10^{-10}\)[C]. However, if the sensor was positioned at height A = 1 m above ground level, this value was nearly four times higher \(Q_{1\_1}=-2.42\)\(\times\)\(10^{-9}\)[C].
# PAPR Reduction in UFMC for 5G Cellular Systems
## 5 Conclusions  
In this paper, to meet the needs of the 5G communications and beyond, a comparative probabilistic technique, called Dcomp-SLM, was proposed in order to mitigate the PAPR distortion in UFMC systems. The proposed method is able to cope with the drawback of high PAPR and OOB radiation, simultaneously, which occur when transmitting a multi-carrier signals. The Dcomp-SLM achieves about 1 dB over the conventional SLM to decrease the PAPR of the transmitted UFMC waveform. Additionally, it shows about 5 dBW/Hz enhancement in the PSD performance over the conventional UFMC system. Moreover, the proposed method shows a negligible deterioration in the BER and EVM with respect to the theoretical results in the presence of both AWGN and Rayleigh fading channels. Hence, these results prove that the UFMC system is feasible and it could be implemented in the case of the multiband transmission that is used by 5G technologies. Therefore, UFMC can be one of the most potential candidate waveforms for 5G, which are able to replace OFDM, used previously in LTE 4G communications. Additionally, this work can be exploited to achieve higher data rates by the realization of the UFMC system based on the Dcomp-SLM approach.  
Conceptualization, M.I.A.-R., H.E.S. and A.M.R.; methodology, M.I.A.-R., H.E.S. and A.M.R.; software, M.I.A.-R. and H.E.S.; validation, M.I.A.-R. and H.E.S.; investigation, M.I.A.-R., H.E.S. and S.A.A.; writing--original draft preparation, M.I.A.-R., H.E.S. and A.M.R.; writing--review and editing, M.I.A.-R., H.E.S., A.M.R. and S.A.A.; supervision A.M.R. and S.A.A.; funding acquisition, S.A.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group no. (RG-1438-092).  
The authors would like to acknowledge the Deanship of Scientific Research at King Saud University.  
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.
###### Abstract  
A series of fluorinated 4,5-dihydro-1\(H\)-pyrazole derivatives were synthesized in the reaction of corresponding acetophenone and different aldehydes followed by the second step synthesis of desired compounds from synthesized chalcone, hydrazine hydrate, and formic acid. Structures of all compounds were confirmed by both \({}^{1}\)H and \({}^{13}\)C NMR and mass spectrometry. Antibacterial properties of compounds were tested on four bacterial strains, _Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis_, and _Staphylococcus aureus_. Among synthesized compounds, the strongest inhibitor of monophoclase activity of mushroom tyrosinase (\(32.07\pm 3.39\%\)) was found to be _5-(2-chlorophenyl)-3-(4-fluorophenyl)-4,5-dilydro-1H-pyrazole-1-carbaldehyde_. The PASS program has predicted the highest probable activity for the phosphodiesteraserase inhibition. To shed light on molecular interactions between the synthesized compounds and phosphodiesterase, all compounds were docked into the active binding site. The obtained results showed that the compound with the dimethoxyphenyl ring could be potent as an inhibitor of phosphodiesterase, which interacts in PDE5 catalytic domain of the enzyme. Key interactions are bidentate hydrogen bond (H-bond) with the side-chain of Gln817 and van der Waals interactions of the dimethoxyphenyl ring and pyrazole ring with hydrophobic clamp, which contains residuals, Val782, Phe820, and Tyr612. Interactions are similar to the binding mode of the inhibitor sildenafil, the first oral medicine for the treatment of male erectile dysfunction.  
fluorinated pyrazole aldehydes; tyrosinase inhibition; phosphodiesterase inhibition; antibacterial activity; molecular docking
# Research of a Multi-Frequency Waveform Control Method on Double-Wire MIG Arc Welding
## 4 Experimental Setup and Program
### Experimental Program  
To validate the effect of the proposed multi-frequency waveform control method for double-wire MIG arc welding on the stability of the welding process and quality of the welding seam, the following procedures were conducted:  
_Step 1:_ By varying the number of high frequency modulation pulses \(N_{fTH}\), duty cycle of trailing wire high frequency modulation pulse \(DC_{TH}\), duration of leading wire current in base value \(T_{bL}\), and peak current of trailing wire \(I_{pT}\) while other conditions unchanged, the ranges of these four parameters by means of ultimate experiments can be obtained, and then their individual levels can also be confirmed. Finally, the detailed experimental program can be designed. The discipline of the effects of the different variables on the welding quality can be obtained.  
_Step 2:_ According to the orthogonal analyses, an array of variables which had optimal levels can be chosen to do actual validation, then the corresponding orthogonal results can be employed to validate the proposed method. In addition, other mathematical analysis tools can be employed to analyze the experiments and provide support for the orthogonal results.  
_Step 3:_ Photos of actual welding seam are provided to validate the results.
# Design of a Laparoscopic Robot System Based on Spherical Magnetic Field
## 4. **Mathematic Modeling**
### **The Magnetic Flux Intensity \(B\)** Produced by **Stator**
#### 4.1.2 Superposition of Magnetic Flux Intensity B Produced by Multiple Electromagnets  
In order to superimpose the magnetic flux intensity \(B\) produced by multiple electromagnets \(B_{i}\), different coordinates need to be established, including five coil coordinates \(CS_{i}\), (\(i\) = 1, 2... 5), a global coordinate \(CS_{0}\), as well as a rotor coordinate \(CS_{r}\). These coil coordinates are used to represent magnetic fields \(B_{i}\) created by every single coil. These rotor coordinates are used to represent the magnetic moment of every IPM \(M_{j}\) in the laparoscopic robot. The global coordinate is used to unify \(M\) of every coil and \(B\) of every IPM into the same coordinate.  
To solve the magnetic field created by every single coil, the position in global coordinate \(CS_{0}\) needs to be transferred into coil coordinate \(CS_{i}\), the transformation relation can be expressed as:  
\[P_{i}=R_{i}P+T_{i};\ (i=1,\ 2,\ 3,\ 4\ or\ 5) \tag{24}\]  
where \(P=(x,y,z)\) represents the position \(P\) in global coordinate \(CS_{0}\). \(P_{i}=(x_{i},y_{i},z_{i})\) is the same position \(P\) in coil coordinate \(CS_{i}\). \(R_{i}\) and \(T_{i}\) are rotation matrix and translation matrix from coil coordinate \(CS_{i}\) to global coordinate \(CS_{0}\).  
After obtaining the magnetic flux intensity produced by all the electromagnets, the total magnetic flux intensity can be obtained by superposing them:  
\[B(x,y,z)=\sum_{i=1}^{5}{R_{i}}^{T}B_{i}(x_{i},y_{i},z_{i}) \tag{25}\]  
According to [16], if the intensity of magnetic field changes within a certain range, the magnetic field intensity created by soft magnetic material is proportional to the intensity of the external magnetic field. Therefore, Equation (25) can be expressed as:  
\[B(x,y,z)=\sum_{i=1}^{5}{R_{i}}^{T}B_{i}{}^{u}(x_{i},y_{i},z_{i})I_{i} \tag{26}\]  
where \(I_{i}\) is the electric current through coil \(i\), and \(B_{i}{}^{u}\) is the magnetic flux intensity created by electromagnet \(i\) by unit electric current.  
In an actual situation, the magnetization of the iron core of a coil will be affected by other electromagnets. Thus, the comprehensive magnetic flux intensity of all five electromagnets is not the linear superposition of that of every coil. Since the relative position among five coils remains unchanged, the actual magnetic flux intensity of all five electromagnets can be expressed as:  
\[B(x,y,z)=\sum_{i=1}^{5}{K_{i}}{R_{i}}^{T}B_{i}(x_{i},y_{i},z_{i})I_{i} \tag{27}\]  
In above formula, \(K_{i}\) is the coefficient of correction of magnetic flux intensity of every coil, which can be fitted by the result of finite-element simulation.
## 1 Introduction
Therefore, we proposed to: (1) apply the atmospheric correction processing (Sen2 Cor; C2 RCC) of Sentinel-2A/B multispectral images for an inland water body, (2) investigate the seasonal variation in water hyacinth biomass and cover using NDVI, and (3) estimate Chl-a and TSM concentrations as proxies of water quality and examine their relation with the water hyacinth extension in Lake Tana during 2020.