esa-sceva/satcom-chunk-collection huggingface dataset:
# Active Electromagnetic Clutch for Crankshaft Decoupling from a Belt Drive System
## 2 System Architecture and Mechanical Assembly  
The pulley-clutch system under study operates as an active mechatronic device able to decouple the BDS and the ICE if desired. Installing this device between the belt drive system and the ICE adds a new degree of actuation to the vehicle. When stopping at a traffic light, for instance, the clutch can open. Then, the compressor of the air conditioning can be electrically powered by the EM with no fuel consumption and no ICE inertia involved. This action reduces the amount of time the engine spends idling, thereby reducing fuel consumption and emissions. Figure 1 illustrates the clutch operation and its interaction with the belt drive system and the ICE.  
To achieve this decoupling feature, an active clutch is needed. This actuator is illustrated in Figure 2. It consists of an electromagnetic device placed between the ICE crankshaft and its pulley, which is coupled to the belt drive system. When the clutch is closed, torque and motion transmission are guaranteed through a front gear. When the clutch is open, the pulley (1) can rotate freely with respect to the shaft by means of ball bearings (5). An electromagnet (2) is fixed to the crankcase of the engine to exert forces on a mover (3). This mover slides shaft splines (4). Its front face presents a toothed profile, which couples with a matching pattern (8) on the back of the pulley (1). This configuration benefits from compactness and null slippage when closed. The clutch is normally closed due to the axial force exerted by a conical spring (7), which forces the coupling between the pulley (1) and the mover (3). However, when the electromagnetic coil is energized, a counteracting axial force is produced, thereby compressing the conical spring (7) and opening the coupling between the pulley (1) and the mover (3).  
From an electromagnetic standpoint, a controlled current is imposed on the electromagnet to generate a magnetic flux and hence, an attractive magnetic force that opens the clutch. When the hub starts moving, the air gap between the electromagnet and the mover changes from 2.5 mm to the minimum air gap (\(\sim\)0). A holding current of approximately 2 A is applied to hold the clutch in the open position. To close the clutch, the coil current is set to zero and, hence, the spring force pushes the clutch back to normally closed mode.  
Figure 1: Decoupler clutch operating modes. (**a**) Clutch closed: ICE on and coupled to the belt drive system. In this condition, the electric motor can be used either for boosting (battery discharging) or recuperation (battery charging). (**b**) Clutch open: ICE shut down and its inertia is decoupled from the belt drive system. In this case, the load of the AC compressor is electrically powered.
# A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
## 2 Literature Review for Wind Power Forecasts Using NWP Data
### Numerical Weather Prediction (NWP)  
NWP plays an important role in wind power forecasting. Thus, the inputs of the wind power forecasting model should involve NWP data. Figure 1 shows a typical probabilistic model for wind power generation based on the input of deterministic NWPs, in which both measured data from wind farms and deterministic NWPs are considered. NWP-based forecasts can be obtained either by prediction error approaches or direct approaches [20].  
Many forecasting approaches with ensemble NWPs have been utilized to provide the probability of wind uncertainties. A typical ensemble NWP model may include a unique conversion model, multi-conversion forecasting model, or dimensional reduction model, as shown in Figure 2.  
Figure 1: Schematic diagram of probabilistic approaches based on deterministic Numerical Weather Prediction (NWP).  
In [5], the authors used a dimensional reduction method to extract the features of ensemble NWP data and constructed a short-term probabilistic forecasting engine using a LUBE-based neural network. However, this work only considered a single NWP model. The study in [13] proposed an approach that improves short-term wind power forecasting by the combination of a K-means algorithm, artificial neural network, and Bayes information criterion that is used for identifying and mining bad data of NWPs. However, the proposed model includes many parameters, which requires a long time for computation and analyses in real applications. In [20], a hybrid approach was developed using K-means cluster analysis and a generalized regression neural network (GRNN) model that is based on NWP data. However, other advanced clustering methods may surpass the K-means algorithm and obtain better forecasting results.  
All the above works considered the improvement of the input data before they are input into wind power forecasting models. This paper continued the previous work in [21] and improved the performance of probabilistic wind power forecasting considering various NWP models. Finally, a comprehensive analysis about the forecasting results was performed.
## 4 Materials and Methods
### Expression Pattern Analysis  
Total RNA isolation kits (Vazyme, Nanjing, China) were used to extract total RNA from the abovementioned plants. The integrity of RNA was detected by agarose gel electrophoresis, and the concentration of RNA was detected by a spectrophotometer. ATGScript Reverse Transcriptase was used to reverse-transcribe total RNA into cDNA (Vazyme, Nanjing, China). The expression pattern of _SmCCD4_ was examined by using RT-qPCR and calculated by using the comparative CT method (\(2^{-\Delta\Delta\text{CT}}\)) [65]. By using _SmACT_ (DQ243702) [66] as an internal reference, the gene expression levels of different samples were detected. The expression analysis of _SmCCD4_ in all samples was performed by using three biological replicates and three technical replicates. Asterisks indicate significance differences from 0 h by using the \(t\)-test. Data are shown as means \(\pm\) SDs, n = 3.
# Modelling, Analysis and Performance of a Low Inertia AC-DC Microgrid
## 3 Stability Analysis
### Impact of DC Loading Variation  
Figure 7 shows the dominant eigenvalues of the system by varying the DC loading from 0.5 to 2 MW. It is obvious that the poles around the origin are shifted towards the  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline AC Grid Rated voltage & 400 V & DC microgrid Resistive Load Rdc & 0.5 MW \\ AC Grid Rated Power & 2 MVA & Current controller bandwidth 1/\(\gamma_{i}\) & 2000 \\ Rated angular frequency & \(2\pi*50\) & DC bus capacitance Cdc & 0.012 F \\ Base impedance & \(0.4^{2}/2\) & HPF corner frequency & \(2\pi*5\) rad/sec \\ DC Link Voltage controller gain: kpc, kick & \(1.75\), \(50\) & HPF gain & \(5*1800*6\) \\ DC Bus voltage & \(1800\) V & LPF corner frequency & \(2\pi*5\) rad/sec \\ \hline \hline \end{tabular}
\end{table}
Table 2: The proposed control system parameters.  
Figure 6: Eigenvalues with varying Capacitance.  
left, increasing the system damping and hence the stability, while the poles representing the DC microgrid system model slightly moved to the right because of the decrease in the resistance and consequently the decrease in the damping of the system. However, the overall effect of the poles is more damped.
## 4 Theoretical Analysis of Roof Influence on Underlying Coal and Rock Strata After Coal Seam Mining
### The Effect of Horizontal Stress on Roadway Stability  
The above analysis primarily focuses on the influence of vertical stress on roadways. However, under the geological conditions of shallow-buried \"three-soft\" coal seams, horizontal stress also affects roadway stability to a certain extent. Restricted by two factors--shallow burial depth (vertical height of 120-150 m) and the mechanical properties of soft coal and rock (coal mass firmness coefficient f = 0.13-0.17, classified as Class III-IV fractured coal mass)--the magnitude of horizontal stress is relatively low, typically less than 20% of vertical stress. Its influence on roadway stability is manifested in the following aspects:  
(1) Dominant role in sidewall deformation  
Horizontal stress primarily induces shear failure and plastic flow in the coal mass of the roadway sidewalls. When horizontal stress exceeds the shear strength of the coal mass, sidewall displacement increases compared to conditions without horizontal stress, prone to sidewall spalling, and failure of the support structure.  
(2) Auxiliary role in roof fracture  
Although horizontal stress does not directly dominate the fracture of the main roof (which is driven by vertical bending moments), the lateral deformation of the coal mass induced by horizontal stress alters the boundary constraints of the main roof.  
(3) Coupling effect with vertical stress  
In local areas where the ratio of horizontal-to-vertical stress is relatively high, the roadway roof is prone to combined tension-shear failure, forming an \"X\"-shaped fracture network that significantly reduces the self-stabilizing capacity of the surrounding rock.  
Overall, in shallow-buried \"three-soft\" coal seams, horizontal stress primarily triggers sidewall instability, but the overall control factor for roadway stability remains the vertical stress-dominated fracture of the main roof. In the design of gob-side entry retaining, reinforcement measures for sidewall deformation induced by horizontal stress should be adopted to complement roof fracture control techniques and ensure the stability of gob-side entry retaining.
## 5 Analysis of Deformation Performance of CFRP-Strengthened Corroded RC Beams under Fatigue Loading
### Effect of Steel Corrosion on Flexural Stiffness of CFRP-Strengthened Corroded RC Beams  
This study assumes that there is no relative slip between CFRP and concrete. The effect of steel corrosion on the stiffness of the strengthened beams is mainly reflected in the reduction of steel bar cross-sectional area, the degradation of steel bar strength and the degradation of the bonding performance between steel bar and concrete. The first two factors are considered to be affected via changing the geometric parameters of the steel bar and the yield strength of the steel bar. When steel bars are uniformly corroded, the mass corrosion degree \(\eta\) and the average cross-section corrosion degree \(\eta_{\mathrm{s}}\) of the steel bars are numerically equal. The bond performance between steel bars and concrete is related to the strain inhomogeneity coefficient of longitudinal tensile steel bars between cracks in the Code of GB50010-2010 [14]. \(k(\eta_{s})\) is introduced to modify the strain inhomogeneity coefficient of steel bars after corrosion and substituting it into Equation (18) to obtain the formula for calculating the short-term flexural stiffness of CFRP-strengthened corroded RC beams under static load. Considering the applicable range of \(\beta\) in Section 4, this formula is applicable for \(\eta_{s}\leq 17\%\), as shown in Equation (19):  
\[B_{\rm s}=\frac{E_{\rm s}A_{\rm s}(\eta_{\rm s})h_{0}^{2}+E_{\rm cf}A_{\rm cf}h ^{2}}{1.15k(\eta_{\rm s})\varphi+6.28(\alpha_{\rm E}\rho_{\rm s}+\alpha_{\rm F }\rho_{\rm cf})+0.27}\ \ \ \eta_{\rm s}\leq 17\% \tag{19}\]  
where \(k(\eta_{\rm s})\) is the strain incongruity coefficient of the steel bar. When \(\eta_{\rm s}=0\), the steel bar is not corroded, and take \(k(\eta_{\rm s})=1\). When \(\eta_{\rm s}>0\), take \(k(\eta_{\rm s})=0.915+0.112\eta_{\rm s}\)[28]. \(A_{\rm s}(\eta_{\rm s})\) is the cross-sectional area of the tensile steel bar after corrosion, which is calculated using \(A_{\rm s}(\eta_{\rm s})=A_{\rm s}(1-\eta_{\rm s})\), and \(A_{\rm s}\) is the cross-sectional area of the steel bar before corrosion.
# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
## 4 Discussion
### Application of Mixup Method  
Our study focuses on the application of the mixup method [18], which stands out for its effectiveness in stabilizing model predictions and improving generalization capabilities. Mixup operates by blending pairs of training samples and their corresponding labels in a linear interpolation manner, thereby generating synthetic data points lying along the line segment connecting the original samples. Applied across diverse maize and soybean datasets, the mixup method demonstrates promising results in enhancing prediction performance. Specifically, our analysis reveals significant improvements, particularly evident in the top-performing lines across all the datasets examined. This underscores the potential of mixup as a valuable augmentation technique for enhancing predictive capabilities within genomic prediction frameworks. It is important to point out that we augmented only a portion of the training data since augmenting the whole training set produced worst results in terms of prediction performance.
## 2 Materials and Methods
### Experimental Setup  
We selected 150-pair images (see Table 1) from normal and abnormal (effusion and tympanostomy tube) categories, which have the highest number of paired images in our database. Even after we enhanced our database with selected video frames of the videos, our dataset contained 150 paired endrum images to train the model. Even though we know that the balanced amount of data for each category would avoid the bias towards the majority classes and minimize the overall error rate, we could not add more normal-abnormal pair images to our dataset because of the limited number of cases.  
Our limited number of pair images was used for both training and testing groups of data. Our system had two training steps: one for the transfer learning training and the other for the random forest classifier training. We used a separate validation dataset for transfer learning training, which contained single 83 'normal' and 54 'abnormal' endrum images  
Figure 3: Creation of a feature vector structure for a pair of endrum images. Rigid and Non-Rigid registration errors were calculated for a pair of eardrums. RGB pair images are converted to the L*a*b* color space, and a* and b* components are used for creating color histograms and calculating statistical measurements. All features are concatenated to form a handcrafted feature vector. RRE: Rigid Registration Error, NRRE: Non-Rigid Registration Error, HR a*: Histogram of Right a*, HR b*: Histogram of Right b*, HL a*: Histogram of Left a*, HL b*: Histogram of Left b*, SMR a*: Statistical Measurements of Right a*, SMR b*: Statistical Measurements of Right b*, SML a*: Statistical Measurements of Left a*, SML b*: Statistical Measurements of Left b*.  
(not pairs) during the retraining of the lookup table generation feature extraction phase. We could allocate more cases for training because we used a separate dataset for validation.  
We used k-fold (k = 3) cross-validation to test the generalizability of our results. Because the number of 'normal-abnormal' and 'abnormal-normal' pair images was low, the fold number (k) was also kept low. The paired images were divided into three random groups for each category: one group was used for testing and the other two groups were used for training. The training group was used to learn the network parameters in transfer learning and fit a model for the tree bigger classifier part. Because the data were divided into groups before the system was run, we put the same pair in the same group, either in training or testing. Accordingly, we made sure that each patient's eardrum image pairs were used for either training or testing, but not both.  
The tree bigger algorithm was also evaluated with a three-fold cross-validation method. To properly model the system, the size of the dataset and number of categories play an important role in the tree bigger classifier. We empirically decided to use five trees to model the classifier because we had 100 pairs for training and 50 pairs for testing and the number of categories was four (N-N, N-A, A-A, A-N).
## 3 Implementation and Results  
The filter bank was fabricated on Rogers' RO4350b substrate and all SMD components, which includes resistors and capacitors, were soldered as shown in Figure 12 and tested using a vector network analyzer (VNA). The SMA connectors that are used have an operating frequency of greater than 1 GHz. The testing setup consists of an Arduino UNO, VNA, and coaxial cables.  
Figure 13 shows the comparison of simulated and measured results for all four filters, which shows that these measured results are in good agreement with the simulated one. In Figure 13a, the outcomes for BPF1 are presented. For BPF1, the maximum measured insertion loss (IL) remains below 1.8 dB, while the minimum return loss (RL) exceeds 20 dB, and the 3 dB bandwidth spans 210 MHz. Moving to Figure 13b, the results for BPF2 indicate a maximum measured IL of 1.2 dB, with a minimum RL exceeding 15 dB. The 3 dB bandwidth for BPF2 is approximately 215 MHz. In Figure 13c, the maximum IL and minimum RL for BPF3 are less than 1.4 dB and greater than 15 dB, respectively. BPF3's 3 dB bandwidth is measured at 240 MHz. Finally, Figure 13d displays the maximum insertion loss and minimum return loss for BPF4, recorded at 2.8 dB and 21 dB, respectively, with a 3 dB bandwidth of 190 MHz. The filter bank is working perfectly and showing good isolation between filters on switching the channels through four different combinations applied using the Arduino controller. We have tested the functionality of the filter bank by sending audio signals at centre frequencies using two USRPs and antennas, one at the input side as a transmitter and other at the output side of the filter bank as a receiver.  
Figure 12: Fabricated RF switched filter bank.  
In the end, we have summarised in Table 5 some recent studies that reported on filter banks and compared them with our own work.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  
Figure 13: Simulated and measured S11 and S21 of filter bank. (**a**) 2.0–2.2 GHz. (**b**) 2.3–2.5 GHz. (**c**) 3.1–3.3 GHz. (**d**) 3.9–4.1 GHz.
## 3 Defect Localization
### Three-Dimensional X-ray Microscopy  
To verify the failure location, the above 3D package with TSV daisy chain was scanned with a high-resolution 3D X-ray microscopy, Zeiss Xradia 510 Versa. The three-dimensional characterization completely displayed the internal structure of the sample, overcame the limitations of the two-dimensional characterization, and combined the high-resolution objective lens to capture the details of the internal structure of the sample to the maximum extent so that the structural characteristics of the TSV array inside the sample could be seen. Two-dimensional X-ray projection image and 3D X-ray reconstruction image of the TSV sample under the overall scan are shown in Figure 11. As shown in this figure, due to the limitation of the resolution, the failure site cannot be observed under the overall scan.  
In order to achieve sub-micron resolution, according to the electrical test results, local scanning should be conducted with channel 6 as the center. Due to the long channel, multiple partition scanning is required, followed by image reconstruction and observation, which requires a longer test time. Under the test voltage of 150 kV and the test current of 15 mA, the three-dimensional stereogram, XY section, YZ section and XZ section morphology of the sample in the local magnification scanning are shown in Figure 12. It can be seen that TSV copper columns are arranged neatly and uniformly in thickness, with no voids or cracks. Defects were observed in the Top_M1 layer connecting the two copper pillars. The fracture morphology of the metal layer observed in the XY section is shown in Figure 13. The defect location identified by magnetic field imaging is consistent with three-dimensional X-ray microscopy.  
Figure 11: X-ray image under the overall scan: (**a**) 2D X-ray projection image; (**b**) 3D X-ray reconstruction image.  
Figure 12: Three-dimensional stereogram, XY section, YZ section and XZ section morphology in the local magnification scanning.  
Figure 14 is the 3D stained image rendered by 3D Viewer software, and the defect structure such as cracks in the sample can be more intuitively observed.
### Analytical Approaches and Instrumentation  
When assessing the contribution of volatiles to the flavor perception, some factors need to be taken into account. Different extraction techniques applied for the evaluation of mate teas have generated different results [3]. Some studies performed analyses of mate tea distillates [1; 2; 7; 8]. Techniques based on headspace sampling and lower temperatures than those used for distillation were successfully applied for mate and other teas with good sensitivity and reproducibility [3; 5; 9; 10]. Other studies about mate tea volatiles employed gas chromatography (GC)--for separation--and its common hyphenated techniques for detection, identification, quantification, and semi-quantification [1; 2; 3; 4; 5; 6; 7]. Semi-quantification has already been performed in other studies involving numerous mate tea volatiles by integrating all the peak areas registered in the chromatogram and calculating their individual relative peak area [1; 2; 5; 8].  
Finally, for the interpretation of analytical results it is necessary to consider that the potential sensory impacts of the different volatiles tend to be not just directly proportional to their concentrations but also inversely proportional to their odor thresholds--among several other factors [11].