esa-sceva/satcom-chunk-collection huggingface dataset:
# Concept Drift Adaptation with Incremental-Decremental SVM
## 3 Background: The Adaptive Window Model for Drift Detection and the Incremental-Decremental SVM
### Kuhn-Tucker Conditions and Vector Migration in Incremental-Decremental SVMs
\tag{10}\]Based on these conditions, a vector \(x_{i}\) could belong to one of the following sets: (i) _support vectors_, where \(h(x_{i})=0\) and \(0<\lambda_{i}<C\), defining the hyperplane, (ii) _error vectors_, where \(h(x_{i})<0\) and \(\lambda_{i}=C\), vectors situated on the wrong side of the separation hyperplane (or in the separation region), and (iii) _rest vectors_, where \(h(x_{i})>0\) and \(\lambda_{i}=0\), vectors situated on the correct side of the separation hyperplane. We map the input to a multi-dimensional space characterized by a kernel \(K(\cdot,\cdot)\) and use the notation:  
\[Q_{ij}=y_{i}y_{j}K(x_{i},x_{j}) \tag{11}\]  
We generalize the penalty function to \(h(x_{i})=\sum_{j}\lambda_{j}Q_{ij}+by_{i}-1\). Incremental-decremental training comes down to varying the \(\lambda_{i}\) parameters so that the KT conditions are always fulfilled. These variations determine vector migrations between the previously mentioned sets of vectors. The variations are defined by:  
\[\left[ \begin{array}{c}\Delta b\\ \Delta\lambda_{s}\end{array} \right]=\underbrace{-\left[ \begin{array}{cc}0&y _{s}\\ y_{s}&Q_{ss}\end{array} \right]^{-1}\left[ \begin{array}{c}y_{c}\\ Q_{sc}\end{array} \right]}_{\beta_{s}}\Delta\lambda_{c} \tag{12}\]  
\[\left[ \begin{array}{c}\Delta h_{r}\\ \Delta h_{c}\end{array} \right]=\underbrace{\left\{\left[ \begin{array}{cc}y_{ r}&Q_{rs}\\ y_{c}&Q_{cs}\end{array} \right]\beta_{s}+\left[ \begin{array}{c}Q_{rc}\\ Q_{cc}\end{array} \right]\right\}}_{\gamma_{s}}\Delta\lambda_{c} \tag{13}\]  
where the's' index stands for _support_ and the 'r' is used for both _error_ or _rest_ vector sets. By computing the exact increments of \(\Delta\lambda\)s, we carefully trace vectors' migrations among the sets, thus performing the learning/unlearning (which are symmetrical procedures). Considering the first relation, \(\Delta\lambda_{s}=\beta_{s}\Delta\lambda_{c}\), where \(\beta_{s}\) is the s-th component of vector \(\beta\), we find that \(-\lambda_{s}\leq\Delta\lambda_{s}\leq C-\lambda_{s}\), and further that \(-\lambda_{s}\leq\beta_{s}\Delta\lambda_{c}\leq C-\lambda_{s}\); this means, for the incremental case, that:  
\[\Delta\lambda_{c}=\min\left\{\frac{C-\lambda_{s}}{\beta_{s}},-\frac{\lambda_ {s}}{\beta_{s}}\right\} \tag{14}\]  
Equation (14) is for support vectors only; a similar equation can be written for the rest vectors. The entire discussion has already been provided in detail in [39].
## 4 Results
### Performance Analysis of PPP-RTK  
According to the above analysis, the interpolation accuracy of DSM was the best among the interpolation methods that were tested, followed by DIM. Therefore, DIM was selected for comparison with DSM in PPP-RTK positioning performance for typical medium- and large-scale reference networks. During the experimental observation period from UTC 1:00 to 23:30, the PPP solution was reset every 5 min for the GPS and Galileo dual-system observation data. Each solution lasted for 5 min, resulting in a total of 270 groups of data. The atmospheric delays for the service end were modeled for each epoch, and the sampling interval was 5 s. The widely used ratio test was adopted as the quality control standard with IAR. The time to first fix (TTFF) was defined as the time that was required for the ratio value to be greater than 2, for the horizontal direction to be less than 5 cm, and for the vertical direction to be less than 10 cm in 20 consecutive epochs. When the TTFF was less than 5 min, the ambiguity was considered to be fixed successfully. The percentage of convergence time is defined as the ratio of the number of convergence periods to the number of successfully fixed periods. The initial variance of atmospheric delay is the key to the rapid convergence of PPP-RTK. The mean squared errors of the undifferenced ionospheric delays were 2 cm and 4 cm in scenarios A and B, respectively, with the empirical linear function [24], and the mean squared errors of the tropospheric delays were 1.5 cm and 2.5 cm [25] in scenario A and B, respectively. The tropospheric delay model used the LSM with first-order horizontal and vertical coordinates.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
###### Abstract  
Excessive pilot workload is one of the significant causes of flight accidents. The detection of flight workload can help optimize aircraft crew operation procedures, improve cockpit human-machine interface (HMIs) design, and ultimately reduce the risk of flight accidents. However, traditional detection methods often employ invasive or patch-based devices that can interfere with the pilot's control. In addition, they generally lack real-time capabilities, while the workload of pilots actually varies continuously. Moreover, most models do not take individual physiological differences into account, leading to the poor performance of new pilots. To address these issues, this study developed a real-time pilot workload detection model based on low-interference devices, including telemetry eye trackers and a pressure-sensing seat cushion. Specifically, the Adaptive KNN-Ensemble Pilot Workload Detection (AKE-PWD) model is proposed, combining KNN in the outer layer for identifying the physiological feature cluster with the ensemble classifier corresponding to this cluster in the inner layer. The ensemble model employs random forest, gradient boosting trees, and FCN-Transformer as base learners. It utilizes soft voting for predictions, integrating the strengths of various networks and effectively extracting the sequential features from complex data. Results show that the model achieves a detection accuracy of 82.6% on the cross-pilot testing set, with a runtime of 0.1 s, surpassing most studies that use invasive or patch-based detection devices. Additionally, the model demonstrates high accuracy across different individuals, indicating good generalization. The results are expected to improve flight safety.  
pilot workload; low-interference detection; ensemble learning; soft voting +
Footnote †: journal: Applied sciences
# A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System
## 3 Experiment and Discussion
### Experimental Simulation
#### 3.3.2 Data Preprocessing  
Different sensors have different physical meanings and numerical ranges. In order to eliminate the influence of ranges of value on the degree of contribution, a standardized method is used to adjust the range of each sensor. The standardized rules are learned in the training set. Then, the sensors are selected based on the trends of the raw data.  
To the system working under the single operating condition, sensors can be divided into three categories, which are ascending, descending and unchanged, according to the trend of the data, as shown in Figure 5a. Data without obvious changes throughout the life time of the system do not contribute to predicting the remaining useful life of system. Thus, 13 kinds of sensors are selected as the raw data of the aging status, including sensor data with ascending trends (2, 3, 4, 8, 9, 11, 13, 15, 17) and descending trends (7, 12, 20, 21) for FD001, and 11 kinds of sensors (the 3, 4, 8, 13, 15, 17 have an ascending trend and the 7, 11, 12, 20, 21 have a descending trend) are selected for FD003.  
For the system working under multiple operating conditions, the trend of sensor data depends mainly on the switching of operating modes, so the degradation trend does not show a noticeable monotonic trend. As shown in Figure 5b, the same sensors 11, 16, 20 and 21 show irregular trends in FD002 (working in multi-modal). Therefore, principal component analysis method is used to reconstruct features based on the principle of maximum difference. The threshold value of total contribution is set to 95% to filter cumulative dynamic difference features of irregular sensors. More details about principal component analysis may be found in [41]. The working mode of the sample in the dataset is represented by three operational settings. To determine the mode switching sequence, k-means clustering method is used to label six working modes. The results are as shown in Figure 6.  
Figure 5: The trends of four sensors in different working conditions. (**a**) System working under a single mode in FD001. (**b**) System working under multi-modes in FD002.
# PTMD Control on a Benchmark TV Tower under Earthquake and Wind Load Excitations
## 7 Conclusions  
Making use of the energy dissipation during impact, an application of a PTMD on controlling the responses of high-rise structures under wind load and earthquake inputs are presented in this paper. An improved bounding force model was proposed. After optimizing the parameters, the comparison results between the simulated and experimental data demonstrates that the proposed model appears to be more precise over the traditional Hertz-based bounding model in describing the bounding force between the mass and the viscoelastic layer. By taking the benchmark Canton Tower with a height of 600 m as the host structure, TMD- and PTMD-controlled cases were simulated and compared under wind and earthquake excitations. Particularly, the recorded typhoon data collected from the structural health monitoring system on the tower was adopted to excite the structure. The proposed PTMD will perform as a traditional TMD under wind load excitations. The generated impact will not be strong enough to dissipate energy even when trying to adjust the gap to force bounding. Under earthquake excitations, a TMD can only be effective under certain excitations and the control effectiveness is very sensitive to the mass ratio. On the contrary, the PTMD appears to have better performance over the traditional TMD. In addition, the robustness of the device is demonstrated by the stability of the control results under different earthquake inputs. The superiority of the device over traditional TMD is also verified by the fact that it is able to achieve similar, or even better, control effectiveness with less weight, and the improvement is even better when the intensity of the earthquake input increases. Simulation results indicate that the performance of the PTMD has a lot to do with the selected gap value and, thus, it should be carefully chosen with respect to the optimal value.  
The authors declare no conflict of interest.
## 2 Finite Element Analysis of the Original Structure
### Finite Element Thermal Analysis of the Original Structure
In the in-orbit operation of the parabolic reflector antenna, the antenna surface undergoes deformation under various loads, resulting in displacements at each node on the reflector surface. This leads to deviation of the reflector surface from the smooth curvature envisioned during theoretical design, as illustrated in Figure 14. Root mean square (RMS), as a parameter describing the actual extent of reflector deformation, is one of the crucial indicators influencing the antenna's electrical performance. Its definition is as follows:  
\[RMS=\sqrt{\frac{1}{N}{\sum\
olimits_{n=1}^{N}{{{\left|{r_{n}}\right|}^{2}}}}} \tag{9}\]  
where \(r_{n}\) represents the displacement value of the respective nodes under the corresponding conditions. \[r_{n}=\sqrt{{{\left({x_{n}^{\prime}-x_{n}}\right)}^{2}}+{{\left({y_{n}^{\prime }-y_{n}}\right)}^{2}}+{{\left({z_{n}^{\prime}-z_{n}}\right)}^{2}}} \tag{10}\]  
Extracting node information from the models of Figures 12 and 13, the RMS of the antenna reflector surface was calculated using Equation (9). The RMS under the original reinforced rib layout was determined to be 0.6025 mm, while the RMS in the absence of reinforced ribs was 0.6774 mm. The optimization ratio of the RMS of the antenna surface for the original reinforced layout compared to the antenna without reinforced ribs was 11.1%. Figure 13: Thermal deformation contour plot of the antenna without reinforced ribs.
# Structure and Optical Properties of Transparent Cobalt-Doped ZnO Thin Layers
## 3 Results and Discussion
### Optical Measurements  
#### 3.2.1 Transmission Spectra  
The transmission curves for thin layers of cobalt-doped ZnO at different annealed temperatures are shown in Figure 2a.  
The non-annealed layer shows low optical transmission, with very low transmission in the region below 600 nm due to the presence of cobalt ions [PERSON] et al. (2019). The increase in transmission after annealing at 300 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, compared to the non-annealed layer, is due to a reduction in the amount of oxygen vacancies in the layer [PERSON] et al. (2019). The excellent surface quality and uniformity of the layers is confirmed by the appearance of interference bands in the transmission spectra (Figure 2b), which occurs when the surface of the layer reflects light without much scattering/absorption over most of the layer area. The number of interference fringes observed on the curve is limited to eight maxima only because of the relatively small thickness of the sample (1000 nm). As the thickness of the layers increases, the number of interference fringes increases. It is worth mentioning that the ZnO:Co samples prepared and annealed in this way are thin and homogeneous. This was  
Figure 2: (**a**) Optical transmission spectra of ZnO:Co layers at different annealing temperatures in air (in semi-logarithmic scale). The arrows show the transmission threshold shift to shorter wavelengths. (**b**) Transmission spectra of the ZnO:Co layer annealed at a temperature of 400 \({}^{\circ}\)C in air with interference patterns.  
confirmed by the appearance of regular interference peaks in the measured transmission spectra of the layers. In the area of shorter wavelengths (\(\lambda<500\) nm), transmission increases sharply with increasing wavelength, most likely due to the influence of the absorption edge. The increase in the annealing temperature leads to a transmission threshold shift to shorter wavelengths (in Figure 2a). This shift of the band-gap optical width toward higher energy may be associated with better structural order, residual stress removal, and quantum constraint effects.  
To increase the crystalline character of the layers, they were annealed, which led to an increase in the size of the crystallites. Changing the annealing temperature weakly affects the degree of crystallinity. On the other hand, the transmission of the sample without annealing has the lowest values (black line in Figure 2a), which means that this sample has the highest refractive index. The transmission of the sample annealed at 300 \({}^{\circ}\)C has the highest values, which corresponds to the lowest refractive index among the samples.
# Investigation on the Measurement Method for Output Torque of a Spherical Motor
## 2 Electromagnetic Theory and Simulation of the Output Torque of a Spherical Motor
### Torque Simulation  
In order to calculate the magnetic field and electromagnetic torque by the FEM, a three-dimensional finite element simplified model of the PMSM is established according to its structural characteristics and is shown in Figure 2. In the simulation model, the right hand reference coordinate system OXYZ is established, and the position of the centre of the spherical motor stator is set as the origin O of the coordinate system. The Z axis passing through the origin O is perpendicular to the XOY plane and pointing in the direction of the vertical upward direction.  
The direction of the cross-section current of the stator coil is set according to the direction indicated by the red arrow in Figure 2, so that the S pole of the coil faces the surface of the rotor. To compare the simulation results with the experimental ones, the simulation current parameters are set as same as the measured current, as shown in Table 1. The total current applied to the coil starts from 1.45 A, increasing by about 0.3 A at every interval, and finally reaches 5.42 A. The meshes of stator and rotor for the PMSM simulation model are shown in Figure 3.  
The Neumann boundary conditions are applied to the simulation model. As shown in Figure 4, the magnetic field line does not cross the boundary under this setting. Figure 5 shows the simulation results of the output torque of the PMSM. It can be seen that the magnitude of the simulated torque increases slowly and is approximately linear with the increase of current. The total current of the coils increases by every 1 A, the torque increases by about 115 mN\(\cdot\)m. This indicates that the output torque is proportional to the current of the stator coil under this power-on strategy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Current of Each Coils (A)**} & \multicolumn{2}{c}{**Total Current(A)**} \\ \hline
**No.14** & **No.2** & **No.20** & **No.8** & \\ \hline
0 & 0.4 & 0.049 & 1 & 1.45 \\
0 & 0.9 & 0.052 & 0.95 & 1.90 \\
0 & 1 & 0.024 & 1.01 & 2.03 \\
0 & 1.15 & 0.052 & 1.17 & 2.37 \\
1.12 & 0.45 & 0.042 & 1.06 & 2.67 \\
1.03 & 0.96 & 0.023 & 1.01 & 3.02 \\
1.12 & 1.12 & 0.030 & 1.12 & 3.39 \\
1.22 & 1.22 & 0.039 & 1.21 & 3.69 \\
1.31 & 1.31 & 0.054 & 1.3 & 3.97 \\
1.42 & 1.42 & 0.054 & 1.4 & 4.29 \\
1.5 & 1.5 & 0.026 & 1.5 & 4.53 \\
1.63 & 1.62 & 0.054 & 1.6 & 4.90 \\
1.74 & 1.69 & 0.054 & 1.77 & 5.25 \\
1.8 & 1.78 & 0.037 & 1.8 & 5.42 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Current value of stator coil excitation source.  
Figure 3: Meshes of stator and rotor.  
Figure 2: Simulation Model.
## 3 Test Results and Analyses
### Influence of the Interface Form on the Load-Bearing Performance of Rehabilitated Pipes
#### 3.3.2 Comparison of the Ultimate Load and Load Capacity Improvement  
The ultimate load of each pipe section with different interface forms is shown in Table 10, and the ultimate load of pipe section S-0 in the initial state was 6.32 kN. The improvement of the bearing capacity of three interface forms of the initial pipe section  
Figure 12: Damage form of a pipe section with a plastic film: (**a**) Cracking; (**b**) sabotage.  
Figure 13: Schematic diagram of the crack width of pipe sections with a plastic film: (**a**) Stripping of the left side of the interface; (**b**) stripping of the right side of the interface; (**c**) crack on the left side of the pipe; (**d**) crack on the right side of the pipe.  
after repair was 69.46%, 85.13%, and 247.47%, respectively; these interface forms were a gluing plastic film, a naturally bonded interface, and an interfacial nail interface. The results show that in the case of the same corrosion and repair thicknesses, the bearing capacity of the repaired pipe section was greatly improved by using the interfacial nail treatment method, which was approximately 3.56 and 2.91 times that of the bonded plastic film and the naturally bonded interface, respectively. This is due to the adhesion of the plastic film reducing the interfacial bonding force between the lining layer and the existing pipeline. The pipeline was in an approximate composite structure state, and the interface between the two layers peeled off during the loading process, thereby hindering any further improvement in the overall structure's bearing capacity. The interfacial nail implantation strengthened the interfacial bonding between the pipeline and the lining, so that the pipeline was in a composite structure state under the action of an external load. The coordinated deformation relationship between the lining layer and the existing pipeline was enhanced, so that the bearing capacity of the lining layer was fully utilized and the bearing capacity of the overall structure was greatly improved. This also shows that when repairing existing reinforced concrete pipes, the bearing capacity of the pipe section can be improved by planting nails on the interface before repairing it to enhance the interfacial bonding effect. The appropriate design of the lining structure and wall thickness can effectively reduce the loss of the flow section [21; 22].
## 2 Experimental: Setups and Samples
### Modeling  
Simulations were conducted using Finite Difference Time Domain (FDTD) solver, XFdtd Bio-Pro, (v.7.6.0.5.r48456, Remcom, State College, PA, USA). The simulations were analysed with point sensors acting as detectors 0.015 mm outside the diamond prism (Figure 2).  
The model sample used was rendered pig lard. It has experimentally established dielectric parameters in the 0.9 to 1.0 THz spectral window with the refractive index of \(n_{2}=1.48\) and the absorption coefficient \(\alpha_{abs}=8\) cm\({}^{-1}\); \(k_{2}=\alpha_{abs}\lambda/4\pi\)[24]. The simulations were conducted with the same parameters. The spacing over the ATR crystal was achieved with prefabricated non-reflective polypropylene spacers with 0.05 mm thickness. The diamond ATR crystal had a refractive index of \(n_{1}=2.42\) at the THz wavelengths used.  
The ATR reflection coefficients were modeled using Equations (2) and (3) in Section 1. The fraction of reflected radiation at the surface is dependent on the real and imaginary part of the sample and is plotted for s- and p-polarisations (Figure 3). The reflectance drops with increasing absorption coefficient (Figure 3a) and increases with frequency (Figure 3b), hence, the portion of light coupled into evanescent wave is smaller. The evanescent waves are more important at lower frequencies, and the ATR reflection technique will be more sensitive to changes in the absorption coefficient smaller than 15 cm\({}^{-1}\).  
Figure 2: The computational FDTD model. A gold mirror was set up to model varying thickness \(d\) of the sample (a pig lard). The diamond ATR crystal is encased in a metal envelope (protective shield) to prevent extraneous signals reaching the detector. The evanescent wave generated at the interface of the ATR crystal and the sample. It can travel through the sample (transmitted), back-reflected by top Au-mirror as a frustrated traveling wave, or reflected to the detector. Changes in thickness of the sample alters the intensity of the evanescent wave in reflection (FDTD detector).
## 2 Materials and Methods
### Methodology
#### 2.5.2 Input Variable Combinations  
Vegetation photosynthesis is regulated by the soil, the atmosphere, and plant physiology. At the vegetation canopy scale, vegetation photosynthesis is closely related to solar radiation, air temperature, and nitrogen validity [82]. In the ecosystem level, vegetation photosynthesis is affected by climate, making the plant form different phenological stages. VIs can greatly reflect the canopy characteristics of vegetation and thus are widely used in models to estimate GPP. Based on previous research [27; 74; 83; 84], NDVI, Tmean, Tmin, Tmax, SSR, SLN, and NMP were selected as input variables. Different input combinations were considered to evaluate the importance of input variables (Table 2). SLN and NMP characterizing the physiology of vegetation were introduced in ML models (A1, A2, A3) with the aim of enhancing model performance in contrast to existing studies. In this study, A1 and A2 aimed to test if individually considering SLN or NMP positively affected maize GPP prediction. A3, considering both SLN and NMP together, was utilized to compare with A1 and A2. A3 aimed to explore how including NMP when SLN was already an input (A1) or considering SLN when NMP was already an input (A2) impacts model accuracy. It also assessed whether combining both inputs enhances or reduces the accuracy of the original model (A0).