esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Flow Characteristics and Effects of Turbulence Models for the Butterfly Valve
## 4 Results and Discussion  
### Flow Coefficient  
A numerical approach for the flow behavior through the valve was validated by comparison with the experimentally measured flow coefficient, and the effect of the turbulence models with nominal value of turbulence constants was estimated by comparison of the flow coefficients between numerical and experimental results. The discrepancies of flow coefficient results of the different-sized valves with different turbulence models are shown in Figure 3. Regarding the discrepancies between numerical and experimental results for flow coefficients for the different valve sizes, it is shown that the discrepancy increased  
Figure 1: Illustrations of calculation region for the cross section of a single butterfly valve.  
Figure 2: Result of mesh size dependence study for flow coefficient.  
with increasing valve size, and different degrees of discrepancy were observed with percentage of disc opening for each valve size. For DN 80, numerical results were within 5% discrepancy with turbulence models, and the discrepancy increased with the percentage of disc opening. For DN 262, the discrepancy was within 8%, especially with disc opening over 80%. For DN 400, within 10% discrepancy was observed with disc opening over 70%. The results in Figure 3 show that discrepancies varied with valve size, indicating that specific turbulence model did not satisfy numerical predictions for all valve sizes. For the case of DN 80 and DN 262, the \(k-\omega\) and SST models were suitable except for the 40-70% disc opening. For the case of DN 400, the \(k-\omega\) and SST models were suitable for all disc openings except for the 60-90% disc opening, for which the \(k-\varepsilon\) model was suitable.
# Synchronous Grouting Analysis of Shield Tunneling through High Water Pressure Fault Fracture Zone
## 5 On Site Monitoring of Soil and Water Pressure  
### Monitoring Layout  
As shown in Figure 31, this on-site monitoring test is located in the excavation section of B3 section from Well 7 to Well 6. During the shield tunneling process of the Shiziyang  
Figure 30: Pore pressure variation curve of monitoring points in the fractured zone.  
Figure 29: Pore pressure variation curve of monitoring points in crack zone.  
Water Transfer Tunnel, the entire process of on-site monitoring was carried out at the F113 fault (GS14 + 650-GS14 + 700) location. The main content includes monitoring of the horizontal diameter convergence and internal surface strain of the pipe segment, monitoring of the stress and strain of the steel bars inside the pipe segment, monitoring of the horizontal displacement of deep rock and soil, and monitoring of pore water pressure and soil pressure. The focus of this article is on the stress field and seepage field changes of the rock and soil in the fault fracture zone caused by synchronous grouting, so there is no specific analysis of the segment data.  
The fault fracture zone is located below the river embankment and is relatively close to Shiziyang. There is a coarse sand layer above it, which has strong water permeability. As shown in Figure 32, the bedrock is drilled vertically from the surface in the monitoring test area. The designed burial depth of the tunnel top is about 43.8 m, with an average drilling depth of about 56 m below 22 m for the bedrock. Before shield tunneling, sensors are buried and calibrated before the monitoring process. During monitoring, reference readings are selected and continuously accumulated. The total amount and increment are calculated using the reference pressure for later testing results.  
Based on the monitoring holes arranged, comparative analysis can be conducted on three monitoring sections, namely: the monitoring surface composed of J1-J2-J3-J4-J5 perpendicular to the direction of shield tunneling, the north monitoring surface parallel to the direction of shield tunneling in the J3-J6-J9 group layer, and the south monitoring surface parallel to the direction of shield tunneling composed of J4-J8-J10.
## 4 Evaluation and Performance Results  
The proposed algorithm for vehicle detection and tracking used the BDD100K dataset [58] and auxiliary PASCAL VOC dataset [24] for validation and testing. The performance of this proposed work was evaluated based on the metrics used in the PASCAL VOC dataset [24]. The performance metrics of the parameters are listed in Table 2.  
The true positives, false positives, and false negatives were firstly evaluated for the evaluation of the performance metrics. TP correctly represents detected objects in the image. FP represents the object detected in the image which does not exist in the image. FN represents the non-detected object but exists in the image. The performance metrics parameters can be derived after calculating the true positives, false positives, and false negatives. To compare the performance evaluation of accuracy on the BDD100K validation sets for the three methods, the results are shown in Table 3. Moreover, the comparisons between accuracy performances on the PASCAL VOC datasets are shown in Table 4.  
\begin{table}
\begin{tabular}{c} \hline \hline \multicolumn{1}{c}{**Performance Metrics**} \\ \hline Intersection Over Union \(\left(\text{IOU}\right)=\frac{\text{area of overlap}}{\text{area of union}}\) \\ Recall \(\left(\text{R}\right)=\frac{\text{TP}}{\text{TP}+\text{FN}}\) \\ False Negative Rate \(\left(\text{FNR}\right)=1.00-\text{R}\) \\ Precision \(\left(\text{P}\right)=\frac{\text{TP}}{\text{TP}+\text{FP}}\) \\ False Positive Rate \(\left(\text{FPR}\right)=1.00-\text{P}\) \\ F1 score \(=\frac{2\text{PR}}{\text{P}+\text{R}}\) \\ Mean Average Precision \(\left(\text{mAP}\right)=\frac{1}{\text{N}}\Sigma_{\text{N}}^{\text{i}-1} \text{AP}_{\text{i}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Metrics of performance evaluation parameters.
# Mitigating Global Methane Emissions Using Metal-Organic Framework Adsorbents
## 1 Introduction
It remains as a standing question of how to design these adsorbents to decrease methane global emissions. MOFs may be used to separate and adsorb methane to remove it from the atmosphere. They may also aid to decrease emissions from plants with methane leaks. In the case of adsorptive separation of methane from a stream rich in nitrogen, separation is difficult because the kinetic diameters of the two molecules are similar (3.8 A vs. 3.6 A) and the design of the MOF becomes more important for the separation [35]. Because of the large number of MOF structures, more information regarding the functionality of the MOF and its pore size need to be determined. Furthermore, what is important to design is the interactions of methane and the MOF, which are measured in thermodynamics. Here, we demonstrate how thermodynamics applies to meet the needs of the future energy economy and atmospheric regulations with a focus on methane gas. Statistical mechanics is applied to representative MOF structures with the aim of providing insight into the  
Figure 1: (**a**) M\({}_{2}\)(dhtp) (dhtp = 2,5-dihydroxypropylhalate) crystal unit cell with adsorbed methane molecules on open metal site I. The location of sites was determined by neutron diffraction. (**b**) Metal oxide pyramids, organic linkers, and methane molecules. Adapted with permission from [31]. Copyright 2009 American Chemical Society. design of these materials for methane emission abatement. The adsorption entropy is determined on well-defined MOFs using global predictive equation for porous materials. A correlation is used to describe the effect of translation and rotation of methane in the porous material for methane emission abatement. This information and the entropy of adsorption of methane on MOFs has not been reported before. New correlations for methane using the Bhatia and Myers correlation are also developed and applied in this work [36]. Finally, how fast methane can be desorbed from these MOFs is determined for this application.
# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 2 Establishment of the Electric-Power-Traffic Network Model
### Network Area Division  
The urban area can be divided into residential areas, work areas, and commercial areas based on factors such as load characteristics and user travel characteristics, as shown in Figure 4. Each area includes several transportation nodes and connecting road segments. Residential areas primarily serve as destinations for users with daily life travel needs. Work areas primarily serve as destinations for users with work-related travel needs. Commercial areas primarily serve as destinations for a small portion of users with entertainment travel needs.
## 5 Analysis of Model Performance
### Comparison of Algorithms  
The IABC algorithm, WOA algorithm, ABC algorithm and PSO algorithmare set up to evaluate under the same conditions, i.e., the number of iterations = 500, population number = 32, using different strategies. The objective function values usingfour strategies are concluded in Table 6. Based on the log value of the objective function, thecorresponding convergence curves are shown in Figure 10.  
Figure 10: Convergence curves vs. various algorithms: (**a**) Convergence curves under FEL; (**b**) Convergence curves under FHL; (**c**) Convergence curves under IFEL; (**d**) Convergence curves under IFHL.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**x** & **PV** & **MT** & **Grid** & **Battery** & **TST** & **GB** & **EC** & **AC** & **WHRD** & _ATC_ & _TEC_ & _CDE_ & _F(X)_ \\ \hline FEL & 312 & 765 & 6006 & — & — & 1730 & 247 & 1803 & 1396 & 77,400 & 24,301  
As can be seen from Table 6, under the FEL strategy, the objective function value of IABC is 2.73% lower than that of WOA, 9.52% lower than that of ABC, and 5.67% lower than that of PSO.Under the FTL strategy, the objective function value of IABC decreased by 7.61% compared with WOA, 0.28% lower than the objective function value of the ABC algorithm, and 9.37% lower than the objective function value of PSO.Under the IFEL strategy, the objective function value of IABC is 1.54% lower than that of WOA, 14.98% lower than the objective function value of the ABC algorithm, and 0.73% lower than the objective function value of PSO.Under the IFHL strategy, the objective function value of IABC was 9.27% lower than that of WOA, 2.27% lower than that of the ABC algorithm, and 8.74% lower than that of PSO.In summary, compared with the three algorithms, the objective function value of IABC is the lowest under the four strategies, which can reflect the advantage of IABC in the optimization ability.  
In Figure 10a, both the optimization ability and convergence speed of IABC are better than the other three algorithms. In Figure 10b, the convergence results of WOA and PSO are obviously worse than the other two algorithms. On the other hand, the convergence speed of IABC and ABC are comparable. However, the optimization results of IABC are slightly better than ABC. It can be seen from Figure 10c that IABC has the fastest convergence speed and the best optimization outcome. In Figure 10d, the convergence of WOA and PSO is fast, but they get into a local solution. The optimization result of IABC is the best, and its convergence speed is faster than ABC. In conclusion, IABC presents better performance than the other three algorithms in either convergence accuracy or speed.
# Two Low-Complexity Efficient Beamformers for an IRS- and UAV-Aided Directional Modulation Network
## 3 The Proposed Beamforming Methods: Max-SR-SLNR and MRT-NSP-PA  
In this section, two beamforming methods, called the Max-SR-SLNR and MRT-NSP-PA, are proposed in the IRS- and UAV-assisted DM network. To further enhance the SR of the MRT-NSP-PA, the relationship between the direction of the CM beamforming and \(N_{s}\) is explored.
# Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI Techniques
## 3 Results
### Option 3: ML Applied to CNN-Based Wavelet Spectrogram Features  
The SVM model achieved the highest cross-validated accuracy score of 0.9707 with optimized hyperparameters (i.e., c = 1, gamma = 0.001, and kernel = rbf). The MLP model attained an accuracy score of 0.9649, while the RF model recorded an accuracy of 0.9407.  
Based on these accuracy scores, the SVM model was selected as the best-performing model for this approach. Its final accuracy on the test set was 0.9707, with a confusion matrix indicating 439 true negatives, 11 false positives, 13 false negatives, and 355 true positives. The F1-score was 0.9707, and the Area Under the Curve (AUC) was 0.9872.  
Furthermore, the stacking model, which combined the predictions of the SVM, RF, and MLP models, achieved an accuracy score of 0.9768, an F1-score of 0.9768, and an AUC of 0.9968, indicating a significant improvement over the individual models.  
For computational efficiency, feature extraction took an average of 0.0306 s per sample. However, training times for SVM, RF, and MLP were significantly longer, requiring 3200.16 s, 13,222.45 s, and 1183.51 s, respectively. Inference times for the best-performing model (SVM) were 0.6581 s in total, or 0.000805 s per sample, while the stacking model required 173.42 s, or 0.212 s per sample.
# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 2 Materials and Methods
### Proposed Segmentation System
#### 2.2.3 Speech/Singing Classification  
In our database, the separation of speech and singing voice segments is clear, i.e., there are no adjacent boundaries between the two classes. To create a singing/speech segmentation system, we would have to artificially create new recordings that fulfil this condition and we considered that this would create undesired artefacts. This is why we address the problem as a binary classification of the voice segments detected by the VAD. The pitch parameters we propose for the classification of each segment are: proportion of voiced segments (\(PV\)) and percentage of pitch labelled as a musical note (\(PN\)). The pitch curve has been calculated using PRAAT autocorrelation method [32] with a frame period of 10 ms.  
Voiced/unvoiced segments are obtained directly in the pitch curve where the relative value of maximum autocorrelation is used to take this decision. Stable musical note segments are found using our algorithm explained in Section 2.2.2. The features for classification are calculated according to expressions (5) and (6):  
\[PV=\frac{N_{VF}}{N_{T}}, \tag{5}\]  
\[PN=\frac{N_{NF}}{N_{VF}}, \tag{6}\]  
where \(N_{VF}\) is the total number of voiced frames, \(N_{NF}\) is the total number of frames labelled as a musical note and \(N_{T}\) is the total number of frames, all of them calculated within the segment to be classified.  
Figure 5a,b show the distribution of the proposed classifying features \(PV\) and \(PN\) in Bertsolaritza and NUS databases. In both cases, speech presents a more scattered distribution than a singing voice. However, good discrimination can be achieved when considering both parameters at the same time.  
As a final step of the proposed algorithm, a classifier has to be trained with the vector containing these two parameters to obtain the final speech/singing classification.
## 4 Methodology  
### Data Collection and Preprocessing  
Vibration signals were collected from bearings under different operational conditions using high-precision sensors, and the data were collected from Zhejiang Nulun Bearing Co., Ltd., Xinchang, China and University of Shanghai for Science and Technology in July 2024.  
Figure 2: The overview of research framework.  
The setup includes accelerometers installed on the bearing housing to record the vibration signals. The time-series raw data \(x(t)\) were preprocessed to remove noise and extract relevant features. In order to minimize the influence of ambient noise and interference, a band-pass filter was applied to the raw vibration signal [31]. The band-pass filter can be represented as follows:  
\[y(t)=x(t)*h(t). \tag{1}\]  
where \(y(t)\) is the filtered signal, \(x(t)\) is the raw signal, and \(h(t)\) is the impulse response of the band-pass filter. The filter parameters were chosen based on the typical frequency range of bearing faults to retain only the most informative frequency components.  
The filtered vibration signal \(y(t)\) can be partitioned into smaller windows to capture local temporal features. Each segment is defined as \(y_{i}(t)\) over a fixed time window \(T_{w}\) with an overlap of \(\Delta T\) to improve continuity and smooth transitions between segments:  
\[y_{i}(t)=y(t+i\times\Delta T),\ i=0,1,2,\ldots,N. \tag{2}\]  
where \(N\) is the total number of segments; \(t\) ranges from 0 to \(T_{w}\). From each segmented window \(y_{i}(t)\), essential features are extracted to characterize the condition of bearing in transformer-attention model.
## 4 Experiments and Discussion
### Ablation Experiments
#### 4.5.1 Performance Analysis from Different Modules

Table 3: Ablation results from different modules on the Vaihingen dataset. demonstrate that both modules are essential for remote sensing image segmentation and exhibit strong functional complementarity.
# mGEODAR--A Mobile Radar System for Detection and Monitoring of Gravitational Mass-Movements
## 2 Materials and Methods
### Baseband Filter  
A great advantage of FMCW radars is that range gain control can be achieved with active filtering at baseband frequencies [15]. The baseband filter on the deramp signal has three main functions. Firstly to suppress aliasing from higher frequencies. Secondly to remove any very low frequency content from direct coupling between transmit and receive antenna. Thirdly to ensure a range compensation of the signal strength as it typically declines with \(r^{-3}\) of the target range \(r\).  
Note that with the above mentioned acquisition system, the first purpose of alias suppression is not well achieved with this filter. However, this issue is circumvented by a reasonable geometric arrangement of the radar and addition of a simple low-pass filter before the ADC. Usually, a mass-movement radar is installed on the valley base and the transmit/receive antennas point up towards the release zone at the mountain crest. Beyond the crest is usually free space and no targetsof interest. The line-of-sight distance between the radar and the crest must therefore be smaller than the maximal unambiguous range.  
The baseband filter is implemented as four stage active bandpass filter. The peak gain frequency is at approximately 430 kHz or 1600 m range. The variable gain settings for each stage permit digital selection of an active gain from 13 dB up to 57 dB. Each stage has a different frequency response and the cascading achieves a strong dB per decade roll-off. The simulated response of the baseband filter in mGEODAR is given in Figure 3 with the low gain response in the left panel (A) and the high gain response in the right panel (B).  
The zero dB crossing of the filter is at 5380 Hz. The frequency of the deramp signal \(f\) for a target at range \(r\) is given by Equation (1), and in the current system with \(\tau=0.01\) s equals to a frequency rate of 267 Hz/m. Thus, the zero dB crossing lies at a range of 20 m. By adjusting the chirp duration \(\tau\), the frequency of the deramp signal \(f\) can be shifted into another region of the baseband filter. For example, a road in front of the flow path at 100 m can be practically blanked out at 0 dB of the hardware filter with the judicious choice of \(\tau=0.003\,75\) s. Additionally, another custom filter stage may be added in between the receiver board and the ADC that blanks out certain unwanted ranges.
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 3 Simulation Analysis of Factors Affecting Detection Effect
### Establishment of Simulation Model  
The simulation model is established using ABAQUS as shown in Figure 4[31]. The size of the model is 40 mm \(\times\) 10 mm \(\times\) 5 mm, and the model includes a vertical surface crack with a length of 6 mm, a width of L and a depth of 1 mm. In order to improve the accuracy of calculation and reduce the calculation time, more dense mesh is only set at the crack and near the crack. A point laser is scanning and exciting along the horizontal centerline \(L_{C}\) of the model surface, and the heat flux density of the point laser satisfies a Gaussian distribution [24,32]:  
\[q(r)=\frac{3P}{\pi a^{2}}\exp\left(-\frac{3r^{2}}{a^{2}}\right) \tag{6}\]  
where \(P\) is the effective power of the laser, which indicates the thermal effect caused by the heat flux to the model surface, without considering the heat dissipation, \(a\) is the radius of the laser (at \(1/e^{2}\) of the intensity), and \(r\) is the distance from the center of the laser heat source.  
The scanning speed of laser spot is set as 10 mm/s, the total scanning time is 4 s, the acquisition frequency of thermal images is 50 Hz, the total number of thermal images is 200, the radius of the heat source is 1 mm, and the effective power of the laser is 1200 mW. Taking nickel-based alloy K438 as the model material, and the thermodynamic parameters of the material are shown in Table 1. When the laser spot scans in the direction perpendicular to the crack and approaches the crack, as shown in Figure 5, the conduction of heat wave will be hindered by the crack boundary, so there is an obvious temperature rise at the crack edge. The surface temperature gradient can reflect the shape and geometric distribution of the crack. When the laser gets closer to the crack, the range of the thermal barrier influence expands and the temperature rise increases, and the temperature rise is the largest when the center of the point laser overlaps with the crack.  
The temperature field under laser excitation contains two parts of temperature information: the background temperature without thermal excitation \(T_{0}\) and the temperature change \(\Delta T\) under laser excitation. Taking the temperature image of the initial moment as the background temperature field, and the background is removed for the temperature field of other times to obtain the temperature change field \(\Delta T\). Based on this, the post-processing analyses are carried out.
## 3 Results and Discussion
### Synthesis and Structural Characterization
Figure 4 shows the FE-SEM and TEM images of pure Ni\({}_{3}\)Se\({}_{4}\) and Ni\({}_{3}\)Se\({}_{4}\)-MoSe\({}_{2}\) composites with a ratio of 1:2. The pure Ni\({}_{3}\)Se\({}_{4}\) nanosheets were observed to be aggregated and formed clusters with large surface areas, as shown in Figure 4a. Figure 4b shows the high-resolution transmission electron microscopy (HRTEM) image of Ni\({}_{3}\)Se\({}_{4}\) nanosheets with a lattice fringe of 0.27 nm, which was assigned to the (112) plane [48]. By in situ synthesis, uniform flower-like MoSe\({}_{2}\) nanosheets could be grown on Ni\({}_{3}\)Se\({}_{4}\) nanosheets (Figure 4c). The growth of vertical MoSe\({}_{2}\) nanoflowers was further confirmed by HRTEM (Figure 4d). Moreover, the EDX elemental mapping results shown (Supporting Information, Figure S1) revealed that MoSe\({}_{2}\) nanoflowers were dispersed uniformly on the surface of Ni\({}_{3}\)Se\({}_{4}\). Further increasing the amount of Ni\({}_{3}\)Se\({}_{4}\) in the Ni\({}_{3}\)Se\({}_{4}\)-MoSe\({}_{2}\) composites resulted in agglomeration of the composites (Supporting Information, Figure S2) and could hinder the exposure of active edges of MoSe\({}_{2}\).