esa-sceva/satcom-chunk-collection huggingface dataset:
# MRI Compatible Planar Material Acoustic Lenses
## 2 Methodology and Theoretical Analysis
### Numerical Model  
The finite elements method (FEM) has been used to obtain a numerical solution of the physical problem. The finite elements method allows us to study the physical phenomena involved in the interaction of waves with FZPs. Therefore, a mathematical model that replicates the conditions of the problem has been implemented. This method also allows us to determine the pressure distribution of the diffracted fields generated by the FZP when there is a piston emitter, causing interference phenomena. From the mesh generated by FEM, a partial differential equation solution is obtained for each node [18]. In this case, acoustic Helmholtz equation is considered (Equation (6)). To solve the Helmholtz equation, standard values of water such as density of the medium (\(\rho=\)1000 kg/m\({}^{3}\)) and sound propagation velocity (\(c=1500\) m/s) have been considered. The working frequency of the FZPs is 250 kHz and it can be found by its relation with the angular velocity (\(\omega\)). Finally, \(p\) corresponds to the acoustic pressure.  
\[\
abla\cdot\left(-\frac{1}{\rho_{0}}(\
abla p)\right)=\frac{\omega^{2}p}{\rho _{0}c^{2}} \tag{6}\]  
If a 3D model is considered, this will require high computational resources. To simplify the model and reduce this computational cost, as shown in previous works [19; 20], the geometrical properties of the model are used taking advantage of its axisymmetry. Therefore, the model is simplified by implementing a semi-lens only. A complete solution is obtained by rotating it from its symmetry axis. This procedure achieves a reduction of the degrees of freedom necessary to obtain the results of the numerical simulation and thus significantly diminishing the calculation time.  
The boundary conditions defined in the numerical models are explained below as seen in Figure 2. The contours of the model are defined as wave radiation condition boundary to emulate an infinitely large medium and therefore the Sommerfeld condition is satisfied. Acoustic impedance domain definition has been used for all opaque Fresnel regions for each lens. In the case of the SZP lens, the contours are considered infinitely rigid, applying the Neumann condition (the sound velocity in the contour is zero).  
Figure 2: Scheme of the finite element method (FEM) conditions.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 3 Element Design
### Amplitude-Control RRA Element
#### 3.2.1 RRA Element Design  
The split-ring resonator had structures with metallic ground as the RRA element, as illustrated in Figure 6. The split-ring element primarily consists of the metal ground provided by the Printed Circuit Board (PCB) board, a silicon substrate from chip manufacturing, a split-ring from metal interconnect layer M9, and a terahertz switch. The element period \(p\) is valued as 260 \(\upmu\)m. The silicon substrate is equivalent to a low-resistivity silicon with a thickness of 220 \(\upmu\)m, an equivalent permittivity of 11.9, and a conductivity of 13.5 S/m. The outer diameter \(D\) of the split-ring is 138.6 \(\upmu\)m, and a thickness \(w_{1}\) of 3.4 \(\upmu\)m. The NMOS transistor, sized 20 \(\upmu\)m/65 nm in the 65 nm CMOS process library, is paired with the custom-designed inductive loop. The parallel inductive loop has a diameter \(d\) of 21.8 \(\upmu\)m and a width \(w_{2}\) of 2 \(\upmu\)m. The gap length \(g\) between the source and drain is valued as 6 \(\upmu\)m.  
Due to the limitations of the CMOS process, design parameters such as the dielectric constant of the various dielectric layers in the RRA element, as well as the thickness and relative height of the metal layers, are difficult to modify. Among these, only the thickness  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Parameters**} & **Value** \\ \hline \(L\) & 15.95 pH \\ \hline \(L_{eq}\) & 1.37 pH \\ \hline OFF-state & \(R_{\text{off}}\) & 198.7 \(\Omega\) \\ \cline{2-3}  & \(C_{\text{off}}\) & 16.61 fF \\ \hline ON-state & \(R_{\text{on}}\) & 48.6 \(\Omega\) \\ \cline{2-3}  & \(C_{\text{on}}\) & 12.29 fF \\ \hline \end{tabular}
\end{table}
Table 1: Equivalent parameters of the switch.  
Figure 5: Comparison between the measured and fitted insertion loss of the switch with inductive loop.  
of the back silicon can be slightly adjusted through polishing, and the line width and pattern of the metal layer can be designed and optimized. Since the element structure adopts a split-ring form, the metal line width has a relatively minor impact on resonance performance. Therefore, the key design parameters for the RRA element are the diameter of the split-ring and the thickness of the silicon substrate. The diameter of the split-ring directly affects the resonance frequency of the RRA element. A larger diameter results in a lower resonance frequency. Similarly, a thicker back silicon also leads to a slight decrease in resonance frequency.
## 3 Results and Discussion
### Application to Electrodesorption of SO\({}_{4}{}^{2-}\)  
To confirm the applicability of the proposed technique to general anion electrodes-orption, we matched the electrode configuration and predicted the complete desorption potential for the electrodeposition of SO\({}_{4}{}^{2-}\). Based on the electrochemical description of SO\({}_{4}{}^{2-}\), the complete desorption potential was calculated as \(-1.45\) V, lower than that of Cl\({}^{-}\). Therefore, the graphite anode-titanium cathode electrode configuration was an appropriate choice for SO\({}_{4}{}^{2-}\) electrosorption. Figure 4a plots the experimentally obtained desorption efficiency of SO\({}_{4}{}^{2-}\) at different desorption times. The electrodeposition results of SO\({}_{4}{}^{2-}\) were improved from those of Cl\({}^{-}\); and after 3 h, the desorption efficiency reached 100%, indicating that SO\({}_{4}{}^{2-}\) desorption occurs more easily than Cl\({}^{-}\) desorption because the complete desorption potential is lower than \(-1.7\) V. The low cathode potential requirements were attributed to the weak chemical bonds between SO\({}_{4}{}^{2-}\) and GAC [38; 53]. In a previous report on the adsorption of sulfanilamide to activated carbon, the higher effect of Cl\({}^{-}\) compared to SO\({}_{4}{}^{2-}\) on the adsorption capacity was similarly attributed to different bond strengths (strong versus weak chemical bonds) [54]. Therefore, we speculate that the potential of complete desorption depends on the strength of the formative chemical bonds between the anions and electrode materials during the electrosorption process.  
It is worth noting that the prediction of complete desorption potential is applicable only to anions with desorption ability under the same chemical conditions. Unlike Cl\({}^{-}\), anions such as ReO\({}_{4}{}^{-}\) are not desorbed because they strongly adhere to GAC at the chemical condition of Cl\({}^{-}\) desorption, as revealed in our previous work [38]. Therefore,  
Figure 4: The desorption efficiency. (**a**) Study on the desorption efficiency of SO\({}_{4}{}^{2-}\). (**b**) Effect of the ionic strength on the Cl\({}^{-}\) desorption efficiency. (**c**) Effect of circulation velocities on Cl\({}^{-}\) desorption efficiency.  
the application of a prediction method of desorption potential is not appropriate for the adsorption of ReO4\({}^{-}\) on GAC.
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 2 Related Works
### Detection Based on Driver's Facial Features  
The detection method based on the driver's facial features refers to the analysis of the driver's face; it compares the driver's different facial expressions under fatigue conditions and normal conditions and summarizes some typical fatigue characteristics such as the driver's head posture, blink frequency, line of sight direction and yawn detection. According to these features, computer vision technology is used to detect and extract fatigue features from the driver's video image, calculate fatigue parameters and finally determine the driver's fatigue level. This detection method is low in cost, does not require contact and is convenient to detect; it is the popular research direction of driver fatigue detection systems.  
[PERSON] et al. [8] used a Haar-based feature integrated detector combined with the intensity of multiple feature sets to characterize the structure and texture information of eye patches and proposed a new feature descriptor multi-scale principal gradient histogram (MultiHPOG), which can process the eye appearance in complex situations, improve the model's robustness to image noise and is of great significance for driving fatigue detection. With the development of deep learning, convolutional neural networks are widely used in image processing, target detection and other fields. [PERSON] et al. [14] combined ResNet and DHLSTM to construct a driver fatigue detection system that copes with complex environmental disturbances. ResNet is used to extract driver facial features in space and CNN encodes the image sequences into feature vectors for input into a recurrent neural network LSTM for classification of driving status. Since the network structure of ResNet is relatively simple, it can achieve good real-time performance, but the performance of the network is more general for complex situations such as facial occlusion. Focusing on fatigue detection in terms of driver head pose estimation, [PERSON] et al. [15] proposed an improved bidirectional long- and short-term memory depth neural network for the analysis of head acceleration data in three time series. The driver's fatigue state was classified into three states: fatigue, normal and transition, and the overall accuracy of the training was 99.2%. This machine learning-based approach does not consider the detection of driver mouth features and eye features, which are the most obvious areas of driver fatigue signals. [PERSON] et al. [16] proposed a residual channel-based attention network, which uses five-point localization to locate key parts of the driver's face and then uses RCAN to classify the state of the mouth and eyes. The channel attention module is added to RCAN, which improves at the same time. They also added a head pose estimation module using PnP [17] to evaluate whether the head pose is offset and finally achieved high detection accuracy on multiple datasets.
## 4 Experiments and Results
### EEG-TCNTransformer with Different Bandpass Filter Parameters  
The bandpass filter is also added to the preprocessing to validate the performance of EEG-TCNTransformer. Table 22 describes the average accuracy of different orders of the filter \(N=\) (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) with the minimum attenuations required in the stop band \(rs=\) (10, 20, 30). The highest average accuracy when training EEG-TCNTransformer  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **EEG-TCNet** & **EEG-Conformer** & **EEG-TCNTransformer** \\ \hline
**S1** & 82.90 & 83.96 & **87.85** \\
**S2** & 61.51 & 57.95 & **68.18** \\
**S3** & 91.45 & 94.48 & **94.48** \\
**S4** & 61.62 & 78.06 & **81.73** \\
**S5** & 75.05 & 78.60 & **81.80** \\
**S6** & 58.78 & 63.75 & **68.19** \\
**S7** & **91.43** & 88.80 & 89.51 \\
**S8** & 82.08 & **88.84** & 88.53 \\
**S9** & 79.38 & 82.14 & **89.00** \\
**Average** & 76.02 & 79.62 & **83.25** \\ \hline \hline \end{tabular}
\end{table}
Table 21: Comparisons of the F1 scores of baseline EEG-TCNet, baseline EEG-Conformer, and EEG-TCNTransformer.  
Figure 13: Confusion Matrix of EEG-TCNTransformer model for 9 subjects.  
with a bandpass filter is 80.27%, while increasing \(rs\) reduces the performance (Table A6). Therefore, the bandpass filter is inefficient for EEG-TCNTransformer.
## 4 Results and Discussions
### Effect of Dimple/Protrusion Arrangement
Among all cases, the protrusion produced the best enhanced heat transfer performance with the maximum \(Nu\)/\(Nu_{0}\) of 1.16, while the dimple case hds the lowest \(Nu\)/\(Nu_{0}\), whose maximum value was only 1.08, between which was the dimple-protrusion case. Within the \(Re\) range of 10,000-100,000, the \(ff_{0}\) of protrusion case gradually decreased with larger \(Re\), while the \(ff_{0}\) of dimple and dimple-protrusion cases had little change, and whose relative relationship was consistent with the \(Nu\)/\(Nu_{0}\). Figure 13 shows the variations of overall thermal performance with \(Re\) for different dimple/protrusion arrangements. Obviously, the \(TP\) variation law of three cases is similar to \(Nu\)/\(Nu_{0}\) (Figure 12a), that is, increases first and then decreases with larger \(Re\), and the maximum \(TP\) is obtained at the \(Re\) of 30,000. This demonstrates that, the overall thermal performance of the trailing edge investigated in this paper mainly depends on the heat transfer characteristics. Moreover, the \(TP\) of dimple case and dimple-protrusion case was relatively close, while the protrusion case produced a significantly higher \(TP\), whose maximum value reached 1.13 at the \(Re\) of 30,000, indicating a 13% improvement in the overall thermal performance compared with the smooth case. However, the \(TP\) of  
Figure 11: Variations of Nusselt number and friction with \(Re\) for different dimple/protrusion arrangements. Figure 12: Variations of relative Nusselt number and relative friction with \(Re\) for different dimple/ protrusion arrangements. dimple case and dimple-protrusion case was increased by only 8.3% and 8.5% relative to the smooth case, respectively. In general, for the cooling design of a blade trailing edge with lateral slots, the protrusion structures can significantly improve the heat transfer performance. Although additional flow resistance is produced, it still has the better overall thermal performance, compared with the dimple and dimple-protrusion cases. Therefore, the arrangement of protrusions should be given priority in practical applications.
## 3 Fault Diagnosis of the Protection System Based on Gradient Boosting Decision Tree (GBDT)
### Fault Diagnosis Process of the Protection System  
Based on the above content, the fault diagnosis process of the protection system of an intelligent substation based on the gradient boosting decision tree is constructed, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Diagnostic Accuracy** \\ \hline GBDT & 0.9905 \\ [12] & 0.9871 \\ [13] & 0.9609 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of diagnostic accuracy of different models.  
Figure 5: Accuracy of models with different training set samples.  
The specific steps are:  
1. To avoid the false start of the diagnosis process, set the minimum number \(k_{0}\) of alarm messages within 30 s after receiving the first alarm message. When the number of alarm messages received by the secondary monitoring system within the specified time is greater than or equal to \(k_{0}\), the fault diagnosis of the protection system of this intelligent substation is triggered. The intelligence and integration of the secondary system make it produce a lot of alarm messages when a fault occurs. When the maintenance personnel repair the equipment incorrectly or the equipment is disturbed by environmental factors, alarm information is also generated, but the alarm information is single and small in number. In this case, the fault diagnosis of the protection system should not be started. To avoid the false start of diagnosis, according to the actual fault data analysis and field experience of the intelligent substation, within 30 s after the first alarm message appears, whether the number of alarm messages received by the secondary equipment monitoring system is greater than or equal to \(k_{0}\) is the trigger diagnosis condition, and set \(k_{0}\) = 3;
2. If the number of alarm messages is greater than \(k_{0}\), the fault feature information of the protection system of the intelligent substation is extracted to form a set of fault feature information. The feature information in Table 3 is collected, including the integrated alarm information, link information of GOOSE and SV, device self-check information, and sampling value information from the secondary monitoring system. After data processing, the fault feature information set \(X\)= \(\{A\,,I,C,S^{\prime}\}\) in Section 2.3 is generated, which prepares the data for the fault diagnosis of the protection system of this intelligent substation;
3. Input the processed fault feature information set \(X\)= \(\{A\,,I,C,S^{\prime}\}\) into the fault diagnosis system based on GBDT for diagnosis. The specific process is: Input \(X\) into the binary classifiers in the GBDT model, respectively, and calculate the probability \(P_{f_{i}}(i\) = 1, 2,... ) that this fault belongs to each complex fault. The one with the highest probability determines that the fault belongs to this type of complex fault and outputs the diagnostic result set R = \(\{f_{1},f_{2},\ldots\}=\{1,0,\ldots\}\) (Suppose the fault is \(f_{1}\)).
# CONDA-PM--A Systematic Review and Framework for Concept Drift Analysis in Process Mining
## 5 Concept Drift Analysis Approaches in Process Mining  
Despite the number of proposals and contributions made in the context of concept drift in process mining, there is no comprehensive approach addressing concept drift analysis from different aspects and with all of its types. After surveying literature in process mining in general and concept drift specifically, we notice that research efforts on concept drift analysis have a similar purpose but differ in how to achieve it. We conclude that some of the approaches share commonalities regarding inputs and used techniques. In particular, these commonalities affect the studied change form as well as the process perspectives covered by an approach. This is illustrated in the following subsections that shall answer RQ2.  
As a commonality, the presented approaches rely on the presence of an event log. Moreover, they all focus on detecting the drift through the effect it has on the resulting process instances, that is, cases in the event log, rather than detecting it by comparing process models representing different process instances before and after the drift occurs. We present an attempt to summarize these approaches and categorize them according to common characteristics, mainly along the _techniques used_ (cf. Section 4.6). Figure 13 shows the distribution of studies over the categories described through the following subsections.  
Figure 11: Number of publications classified by implementation platform.  
Figure 12: Number of publications classified by evaluation artefact.  
As a primary observation from Figure 13, the biggest share of studies depend either on:  
1. windowing to select the traces that shall be considered for drift analysis and for which a statistical analysis is conducted, or on
2. clustering-based techniques to find groups of traces that share similar characteristics that can be generalised and used to detect drifts.  
Less studies depend on graph-based analysis techniques to detect drifts or on model-to-log alignment. The lack of studies adopting graph-based analysis techniques can be explained with the computational complexity of graph analysis techniques, as graph matching is NP-complete [47] and some balance is needed between this inherent complexity on the one hand and the accuracy of computations on the other. Note that such balance is missing concerning in the results presented in [5] (cf. Section 5.2).
## 3 Time Course and Current Status of Field-Evolved Resistance
Plant-based bioassays with progeny of western corn rootworm from these fields showed elevated survival on Cry34/35 Ab1 maize compared to Bt-susceptible controls, which indicated that the high levels of feeding injury observed in the field were associated with western corn rootworm resistance to Cry34/35 Ab1 [49]. However, for these populations, larval survival was lower on Cry34/35 Ab1 maize than on non-Bt maize, indicating that resistance was incomplete [49]. Similarly, a field population of western corn rootworm from Minnesota, which was sampled in 2013, was found to have incomplete resistance to Cry34/35 Ab1 [50]. More recently, [PERSON] et al. [51] examined field populations sampled in Iowa during 2017, which were collected from two fields with a high level of western corn rootworm feeding injury to maize pyramided with Cry3 and Cry34/35 Ab1. Both populations were found to have resistance to Cry34/35 Ab1 in addition to resistance to all three Cry3 toxins. For one population, no difference in larval survival or development was detected between non-Bt maize and maize with Cry34/35 Ab1, either alone or in a pyramid with Cry3 Bb1, suggesting complete resistance to Cry34/35 Ab1. In addition to these field populations associated with injury to Bt maize, [PERSON] et al. [51] also included three field populations that were not associated with injury to Bt maize, and one of these populations also displayed resistance to Cry34/35 Ab1. Taken together, these data suggest that resistance to Cry34/35 Ab1 has persisted in the agricultural landscape, and appears to be increasing in magnitude. A key factor affecting the future utility of Bt maize for management of western corn rootworm will be how quickly additional cases of resistance to Cry34/35 Ab1 evolve.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 6 UAV Applications Areas
### Precision Agriculture  
UAVs can be used in precision agriculture for collecting data from ground sensors (water quality, soil properties, moisture, etc.), pesticide spraying, disease detection, irrigation scheduling, weed detection and crop monitoring and management. The incorporation of UAVs in precision agriculture is a time-saving and cost-efficient technology that can enhance profitability, productivity and crop yields in farming systems. In addition, UAVs assist pest damage, weed monitoring, chemical spraying and agriculture management, thereby they effectuating a better yield of the crops to meet specific production requirements. UAVs along with remote sensing can be a game-changer for precision farming.  
Figure 10: UAV and remote sensing applications.  
It provides temporal, spatial and spectral resolution, but can also offer multi-angular observation and detailed vegetation height data. UAVs can put a significant impact on agriculture industry by performing smart aerial mapping. UAVs equipped with right sensors and appropriate cameras can monitor health of crops in terms of leaf thickness, foreign contaminants, chlorophyll level and temperature [103]. In ref. [104], [PERSON] et al. briefly discussed the progress on remote sensing with UAVs in growth vigor assessment, nutrient status, pathogens and weed detection and drought stress. In the future, image processing techniques can be used to investigate plants diseases and other traits from UAV captured high resolution images.