esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Modular Multilevel Converters for Stationary Applications
## 5 Power Losses and WBG Technology
The simulation results are given in Figure 17:1 It is observed that power losses are reduced considerably by employing SiC semiconductors compared to Si modules in Figure 19a. In particular, the power losses of Si power modules rise rapidly as the switching frequency increases, while those of the SiC devices increase much slower. This leads to a difference in terms of MMC efficiency, as shown in Figure 19b. It is noted that the MMC efficiency is capable of maintaining above 97% by using SiC switches at the switching frequency span of 20-100 kHz in this case, whereas the efficiency of Si-based MMC decreases to around 92% at the switching frequency of 100 kHz. The comparison of performance indicates that the differences in the material properties of switching devices lead to the fact that the WBG components have superior performance regarding reduced power losses and higher power efficiency, especially at the high switching frequency. The advantages of WBG power transistors can further substantially reduce the size of whole power conversion systems with strong robustness. It is expectable that WBG power switches will play a prominent role together with the MMC in the power conversion applications in the near future. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline No. of submodules per leg & 5 \\ Nominal SM voltage & 300 V \\ DC-link voltage & 1500 V \\ RMS grid voltage & 380 V \\ Grid frequency & 50 Hz \\ Nominal power level & 100 kW \\ Modulation technique & PSC-PWM, PD-PWM, SAM-PWM \\ Switching frequency rang & 20 kHz; 20–100 kHz \\ \hline \hline \end{tabular}
\end{table} 
Table 3: MMC parameters for performance testing. Figure 18: Comparative performance of using Si and SiC semiconductors with different modulation methods and load (100 kW) and input voltage per MMC HBSM (300 V): (**a**) Semiconductor power losses; (**b**) MMC efficiency; (**c**) Output voltage total harmonic distortion (THD).
## 4 Fixed-Time NITSM Controller Formulation  
### Controller Design  
This study aims to develop a spin recovery controller leveraging the fixed-time NITSM control methodology. Once \(\phi\), \(\theta\), and \(\dot{\psi}\) reach their desired values, then \(p\), \(q\), \(r\) will converge to zero, resulting in a successful recovery from the spin state. When \(\dot{H}\) converges zero, the aircraft will attain level flight trim condition. This is equivalent to the control objective of steering \(\mathbf{y}_{1d}\) towards the desired values \(\mathbf{y}_{1d}=\left[\phi_{d},\theta_{d},\dot{\psi}_{d},\dot{h}_{d}\right]^{T}\) within a fixed time.  
Instead of using a linear sliding function in [17], the nonsingular integral terminal sliding manifold (NITSM) based on \(\mathbf{e}=\mathbf{y}_{1}-\mathbf{y}_{1d}\) is designed.  
\[\mathbf{s}=\mathbf{e}+C_{1}\int sig^{m}(\mathbf{e})dt+C_{2}\int sig^{n}(\mathbf{e})dt \tag{13}\]where \(\mathbf{e}=[e_{\phi},e_{\theta},e_{\dot{\psi}^{\prime}},e_{\dot{h}}]^{T}\in\mathbb{R}^{ \mathbf{4}}\), \(\mathbf{s}=\left[s_{\phi},s_{\theta},s_{\dot{\psi}^{\prime}},s_{\dot{h}}\right]^{T} \in\mathbb{R}^{\mathbf{4}}\), \(\mathbf{C}_{1}=diag(c_{11},c_{12},c_{13},c_{14})\) and \(\mathbf{C}_{2}=diag(c_{21},c_{22},c_{23},c_{24})\) are two positive definite diagonal matrixes, and \(0<m<1\), \(n>1\) are defined positive constants.  
Taking the time derivative of \(\mathbf{s}\), we have the following:  
\[\dot{\mathbf{s}}=\dot{\mathbf{e}}+\mathbf{C}_{1}sig^{m}(\mathbf{e})+\mathbf{C}_{2}sig^{n}(\mathbf{e})= F(\mathbf{x})+G(\mathbf{x})\mathbf{u}-\dot{\mathbf{y}}_{1d}+\mathbf{C}_{1}sig^{m}(\mathbf{e})+\mathbf{C}_{2} sig^{n}(\mathbf{e}) \tag{14}\]  
To guarantee that the states of the spin recovery system (10) approach the sliding mode manifold within a fixed time, regardless of initial conditions. A fixed-time type of reaching law is chosen as follows:  
\[\dot{\mathbf{s}}=-\mathbf{k}_{1}sig^{p}(\mathbf{s})-\mathbf{k}_{2}sig^{q}(\mathbf{s}) \tag{15}\]  
where \(\mathbf{k}_{1}=diag(k_{11},k_{12},k_{13},k_{14})\) and \(\mathbf{k}_{2}=diag(k_{21},k_{22},k_{23},k_{24})\) are diagonal positive definite matrices, \(0<p<1\) and \(q>1\).  
Based on fixed time and NDI theory, the spin recovery algorithm is designed as follows:  
\[\mathbf{u}=-G^{-1}(\mathbf{x})\big{[}F(\mathbf{x})+\mathbf{C}_{1}sig^{m}(\mathbf{e})+\mathbf{C}_{2} sig^{n}(\mathbf{e})+\mathbf{k}_{1}sig^{p}(\mathbf{s})+\mathbf{k}_{2}sig^{q}(\mathbf{s})-\dot{y}_{1d}\big{]} \tag{16}\]  
where \(G(\mathbf{x})\) is invertible.
# Model Updating of Bridges Using Measured Influence Lines
## 2 Materials and Methods
### Measurements  
The Tomacevo bridge is a B-WIM and SHM living-lab facility designed to develop and test new technologies, sensors, and methods. The measurements setup on the bridge varied over time, but for this study, the following sensors, shown in Figure 6, were considered:  
* Fourteen Dewesoft-type IOLITEi 3 xMEMS-ACC tri-axial MEMS accelerometers (Dewesoft, Trbovlje, Slovenia) measuring the ambient and traffic-induced vibrations and to determine the modal parameters of the bridge;
* Two inductive strain transducers (denoted in the paper as B-WIM sensors) measuring traffic-induced strains on both girders, and to obtain input for the B-WIM measurements;
* Two linear variable differential transformers (denoted as LVDTs or. displacement sensors) (HBK, Darmstadt, Germany) measuring traffic-induced vertical displacements on both main girders;
* Two temperature sensors, T_OUT (Dewesoft, Trbovlje, Slovenia)measuring environmental temperature and T_IN, measuring the temperature approximately 5 cm inside the main girder MG1.  
DewesoftX 2023.5 data acquisition software [17] was used for data recording with a 500 Hz sampling frequency.  
Figure 5: Plan view, side view, and cross-section view of the bridge. Typical elements are labelled with abbreviations, described more in detail in Table 3.  
Besides the sensors that measure the mid-span strains on main elements such as girders and slabs, B-WIM systems generally require additional sensors to detect the axles and calculate the vehicle's speed. In the lateral direction, they are installed on the elements and locations close to the vehicle's wheel track to capture clear spikes, representing individual axles. One pair of such strain sensors was installed on the SLAB1 and one on the SLAB2 element.
# Fault Diagnosis Approach of Main Drive Chain in Wind Turbine Based on Data Fusion
## 2 Fault Features Extraction of Wind Turbine Main Drive Chain Based on Data Fusion
### Process of Fault Features Extraction of the Main Drive Chain in Wind Turbine
The effective removal of background noises is beneficial to the extracting of the high-frequency vibration features of the main drive chain itself. When using the SCADA system to extract low-frequency vibration signals related to drive chain faults, it is also necessary to use two types of signals, the speed of main shaft and generators of the wind turbine and the operation control mode of the wind turbine, to classify its background noise. Between different speed ranges of the main shaft and generators and between the power-up and power-down operation intervals of the wind turbine, background noises are classified and removed to extract the low-frequency vibration characteristics of the main drive chain itself.
# _microorganisms_
## 3 Results and Discussion
### Regression Analysis of Gene Copy Numbers, Odorant Concentrations, and Cyanobacterial Cell Abundance  
The relationships among gene copy number, 2-MIB/geosmin concentration, and cyanobacterial cell abundance were evaluated. Figure 5A shows the results of the log-log model and regression analysis of _mic_ copy number and 2-MIB concentration. A positive linear correlation (R\({}^{2}\) = 0.8478, \(p\) < 0.0001) was determined from the equation y = 0.576x \(-\) 0.73 (Figure 5A) and indicated that one _mic_ copy corresponds to 4-50 fg of 2-MIB. A previous study [32] examined the correlation between _mic_ copy number and 2-MIB concentration and found a log-linearity coefficient of 0.604 2-MIB (ng L\({}^{-1}\)) per _mic_ copy in field samples, with 2-MIB production ranging from 11 to 57 fg per _mic_ copy. Our microscopic analysis of 2-MIB concentration and cyanobacterial cell abundance (Figure 5B) showed a positive linear correlation between the two. However, as reflected by the correlation coefficient, R\({}^{2}\) = 0.6031, the correlation was weaker than that between 2-MIB concentration and _mic_ copy number.  
Figure 5C shows the positive relationship between _geo_ copy number and geosmin concentration (R\({}^{2}\) = 0.601, \(p\) < 0.001), determined with the equation y = 0.199x + 0.19. According to the log-log regression analysis, the estimated geosmin production was 1-186 fg per _geo_ copy. [PERSON] et al. [39] reported a geosmin production potential of 1-40 fg of geosmin per _geo_ copy, whereas another study reported an average geosmin production of \(\sim\)100 fg cell\({}^{-1}\)[40]. Regression analysis of _geo_ copy number and geosmin  
Figure 5: Relationships between _mic_ copy number and 2-MIB concentration (**A**), cyanobacterial cell abundance and 2-MIB concentration (**B**), _geo_ copy number and geosmin concentration (**C**), and cyanobacterial cell abundance and geosmin concentration (**D**), with 95% confidence intervals (dotted lines).  
concentration also showed that the distribution was wider than that of _mic_. A previous study reported a significant correlation between geosmin synthase gene copy number and geosmin concentration (R\({}^{2}\) = 0.694, \(p<0.001\)), with field samples showing a broad distribution pattern [39].  
A comparison of cyanobacterial cell abundance and geosmin concentration (Figure 5D) showed a weak correlation (R\({}^{2}\) = 0.2013, \(p\) = 0.05). Similarly, another study found no correlation between cell density and _geo_ expression levels, as determined with qPCR [41]. The geosmin-producing cyanobacterial genus _Anabaena_ was reported to be responsible for 46% of geosmin events [42], and the geosmin concentration was strongly correlated with _Anabaena_ cell density (R\({}^{2}\) = 0.97) [8]. In this study, the total cyanobacterial cell abundance and thus geosmin-producing and non-producing species were evaluated, explaining the weaker correlation between cyanobacterial cell abundance and geosmin concentration determined in this analysis.
## 3 Q2: What Are the Challenges of the Current UIoT System
### Channel Characteristics of UIoT  
[PERSON] et al. pointed out that most of the characteristics of IoT systems are suitable for the UIoT environment since UIoT is the subclause of IoT [26]. Most of the available IoT protocols are designed and developed for stable nodes. Additionally, the performance of IoT networks can be reduced with the addition of new nodes and variations in terrestrial environment techniques. This statement highlights why the existing protocols and security models of terrestrial IoT should not be directly applied to UIoT.
## 3 Controller Design  
The structure of the improved PID control method proposed in this paper is shown in Figure 6. The BP neural network-Smith predictor-PID (BPNN-SP-PID) controller was divided into two parts. One part was the BPNN-PID controller. ANN1 could realize self-optimization of the PID parameters and improve the control performance. The other part was the Smith predictor. The Smith predictor was used for reducing the influence of the pure time delay [27]. ANN2 was used for identifying the open-loop model of the controlled object Go(s), eliminating the pure time delay. Higher identification accuracy brought by ANN2 could make the Smith predictor compensate the time delay more effectively. The two parts are described in detail below.  
Figure 5: Comparison of the open-loop step response between the mathematical and actual models.  
Figure 6: Backpropagation neural network-Smith predictor-proportional–integral–derivative (BPNN-SP-PID) controller structure diagram.
# The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
## 6 Experiment Results and Analysis
### Evaluation Index  
In order to assess the detection performance of the model, this paper employed precision (precision), recall (recall), average precision (AP), and mean average precision (mAP) as evaluation metrics [28].
## 3 Results
### Multivariate Prediction Models
#### 3.2.2 Classification Trees  
Classification trees are recursive partition models that minimize the impurity of the classes defined by the partition. They provide a simple classification system that is easy to implement, but they often lack high discrimination ability. In this study, we used the Gini index as the loss function and set the minimum number of observations required for a split to 20 in a node. Additionally, we set the minimum number of observations in any terminal node to seven and limited the maximum depth of any node in the final tree to 30. Figure 6 displays the classification tree.  
The slope is the variable that best discriminates in the first node, followed by the duration of the deceleration on the second level, and the duration for the left branch or the reperfusion time for the right branch in the third level. To assess the impact of predictor variables on acidemia prediction, we present the variable importance (VIMP) plot in Figure 7. The VIMP quantifies the difference in prediction error when a predictor is perturbed by applying a permutation that assigns the variable to a terminal node different from its original assignment. These calculations are performed for each tree in the model, resulting in the Breiman-Cutler Variable VIMP [43]. The three variables that exerted the most influence on acidemia prediction were the slope, the drop, and the duration of the last deceleration window.  
Figure 5: Nomogram for the multivariate logistic regression model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Odds Ratio (95\% CI)** & \(p\)**-Value** \\ \hline
30 min window & & \\ \hline Deceleration area & 1.121 (1.064, 1.189) & \(<\)0.001 \\ \hline Final deceleration window & & \\ \hline Duration (s) & 1.157 (1.117, 1.209) & \(<\)0.001 \\ \hline Drop (s) & 0.809 (0.743, 0.871) & \(<\)0.001 \\ \hline Slope (bpm/sg) & 2.814 (1.541, 5.523) & 0.001 \\ \hline Difference between Final and Initial deceleration window & & \\ \hline Duration (s) & 0.950 (0.925, 0.974) & \(<\)0.001 \\ \hline (s) & 1.133 (1.088, 1.188) & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multivariate logistic regression model.
# An Approach for Selecting the Most Explanatory Features for Facial Expression Recognition
## 4 Experiments
### Protocol  
In this work, eight feature extraction methods--GW, LBP, HOG, LPQ, RAW, GEO, VGG, and VGGF--were used, according to the methodology proposed. These features have been used widely in the literature for FER [17; 18; 19; 27; 30; 39; 41; 57; 58]. We generated 247 possible scenarios, which were all combinations of the selected feature extraction method. We denote the classification schemes as follows: GW+SR with W, LBP+SR with B, H\(\underline{\text{o}}\)G+SR with H, LPQ+SR with P, RAW+SR with R, GEO+SR with G, VGG+SR with V, and VGGF+SR with F.  
Figure 3: Classification results. The two top rows show different users, and the two bottom rows show each user with two different expressions. We denote the expression marked by the classifier system in red. Original images extracted from BU-3 DFE [42; 43].  
Next, for example, a possible combination scenario might be denoted as W/B/H, which corresponds to G**W**/**LBP**/**H**OG+SR. We tested 14 combination rules for each scenario: five soft-level rules--product rule (RP), sum rule (RS), max rule (RMX), min rule (RMI), and median rule (RMD) [20]; three hard-level rules--weighted majority vote rule (WMV), recall rule (REC), and naive Bayes rule (NB) [59]; and six trainable methods--fuzzy integral (FI) [51], linear opinion pools (LOP) [52], Bayes model (MB), SVM linear kernel model (ML), and SVM polynomial kernel model (MP). The results of these combinations were also compared to those of the individual methods.  
The parameters for each of the methods were selected according to those obtained using state-of-the-art methods. The GW representation was obtained using five scales and eight orientations, in order to construct a set of Gabor filter banks for \(25\times 25\) and \(k=7\) neighborhoods [60]; the image resolution was \(256\times 256\) pixels. For the extraction of LBP features, as used by [PERSON] et al. [38] and [PERSON] et al. [17], we adopted a uniform LBP operation, with parameters of \(P=8\) and \(R=2\) in images sized \(256\times 256\) pixels. The histogram was extracted for each patch sized \(25\times 25\). For HOG, the bin number was set to 9, the cell size was \(16\times 16\) pixels, and a block size of \(2\times 2\) was adopted for each selected landmark point [19]. For the extraction of the LPQ [28], we set \(M=5\) and \(a=1/5\). The histograms were extracted from each selected landmark point for those patches sized \(25\times 25\). In the case of VGGF, as a feature, we selected the output of the ReLU layer 33; while, in the case of VGG, we selected the output of the ReLU layer 34.
# Influence of the Radial Gap on the External Gear Pump Performance
## 3 Pump Performance at Different Values of the Radial Gap
### Methodology for Pump Efficiency Determination at Different Radial Gap Sizes  
If the experimentally determined characteristics \(\eta(n)\) and \(\eta_{v}(n)\) at nominal gap sizes are available, using the CFD results, one can predict the characteristics \(\eta(n)\) and \(\eta_{v}(n)\) at different values of the radial gap, which cannot be verified experimentally unless a pump with the same radial gap happens to be available. For this purpose, the following methodology was developed:  
Using the CFD results, the average flow rates \(q\) for the desired values of the rotation frequency \(n\) and outlet pressure \(p_{out}\) at nominal radial gap sizes are calculated.  
The theoretical flow rate of the pump \(q_{p,th}\) is determined by (13). In our case, \(V_{p,th}\) is equal to 19 cm\({}^{3}\).  
The volumetric efficiency of the pump obtained with the CFD model \(\eta_{v,CFD}\) is calculated by (16). Typically, the volumetric efficiency obtained with the 2D CFD model is larger than the actual one, due to the highly simplified and idealized environment in which the fluid simulation is performed.  
The actual volumetric efficiency of the pump \(\eta_{v}\) is determined. This can be done either by experiment or by technical data from the manufacturer. In the present study, data from the technical documentation of the pump are used.  
In order to account for the actual volumetric losses of the real 3D geometry, an additional volumetric efficiency \(\eta_{v,ad}\) is introduced:  
\[\eta_{v,ad}=\frac{\eta_{v}}{\eta_{v,CFD}}100,\%, \tag{27}\]  
The actual pump overall efficiency \(\eta\) is determined. This again is done either by experiment or by manufacturer's data. In the present study, \(\eta\) is calculated by (27), where \(P_{h}\) is calculated by (22), in which \(q_{p}\) is determined by an experiment performed by the authors, and experimental information from the manufacturer is used for \(P_{m}\).  
The hydromechanical efficiency of the pump \(\eta_{hm}\) is determined. It includes all losses that are not considered in volumetric efficiency \(\eta_{\
u}\):  
\[\eta_{hm}=\frac{\eta_{l}}{\eta_{\
u}}100,\%. \tag{28}\]  
Using the CFD model, the average values of the flow rate \(q\) for the desired rotation frequency \(n\) and pressure drop \(\Delta p\) at different values of radial gap are calculated.  
\(\eta_{\
u,CFD}\), \(\eta_{\
u}\), and \(\eta\) for a new radial gap value are successively calculated using the Formulas (16), (29) and (30) again, where the values of \(\eta_{\
u,nd}\) and \(\eta_{hm}\) are assumed to be equal to those at the nominal value of the radial gap.
## 2 Materials and Methods
### AE Signal Analysis  
The sequence used in this study for the signal processing of AE was as follows: the AE signal was received from both sensors fixed to the test specimen. The signal was fully recorded with a sampling frequency of 8 MHz for each cut of the test. Then, a hit detector called the post-processing hit detector was created, which was then applied to the recorded signal for the entire test (offline, after the end of the actual test). We took each 20 ms of the signal and applied Fast Fourier Transform (FFT) on it to transform the signal from the time domain to the frequency domain. It was found that every change in cutting conditions generates distinct AE signal shapes (within a single revolution). This makes it possible, for example, to track the number of actively engaged cutting edges when machining with a particular tool. In turn, this allows for evaluating the \"quality\" of the cut and the degree of tool wear as a function of the cutting conditions. Then we identified the dominant frequency of each part of the signal. We applied a similar sequence of actions for the analysis of the AE signal that was received from both sensors fixed to the wood-based material. Many AE parameters were also extracted, but we chose to present just the RMS, the envelope of the signal (MAX_sig) in correlation with different cutting conditions, since MAX_sig is the most-used AE parameter according to the literature.  
Figure 4 shows an example of a 20-millisecond signal sample from both sensors, taken midway through the milling period. From this, the number of temporally separated \"sprinkles\" of AE hits can be observed. These hits are regularly repeated and exhibit very good repeatability. It is clear from the record that this is not a typical continuous signal (i.e., a slowly changing noise-like signal), but rather distinct, short-term, time-separated bursts of signal--so-called AE hits--corresponding to individual events in the AE source. These segments of the signal were extracted during evaluation and assessed separately. From this, the number of temporally discrete bursts of AE hits can be identified (indicated by red ellipses, which correspond to the number of cutting edges actively engaged during the milling process).  
For clarity, the signals from sensors S1 (channel A) and S2 (channel B) are color-coded within a fixed amplitude range (\(\pm\)30 mV) and evaluated in three time phases for detailed signal analysis: Phase I (beginning and entering the cut), Phase II (halfway through the  
Figure 4: Sample waveform during three rotations of the instrument, showing its individually separated signal packages (AE hits).  
cutting time, when the tool is centered between the sensors), and Phase III (ending and exiting the cut), as shown in Figure 5.