esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Damage Diagnosis Approach Based on Hybrid SSA and LS-SVM  
The damage diagnosis approach for NC machine tools proposed in this paper includes two phases. The first phase focuses on model training and obtains sample feature parameters for different tool states. First, reconstructed multi-dimensional signals are transformed by PSR and the stationary and non-stationary parts are extracted using SSA. Subsequently, some dimensionless time-frequency parameters in the non-stationary component are calculated, to be used as sample data for LS-SVM classification. The second phase is damage detection for tools whose state is unknown. The specific procedure is constructed through the main steps shown in Figure 1.  
The process of the proposed SSA + LS-SVM method is described as Algorithm 3.  
Step 1: Model training  
* Measure vibration time signals of NC machine in three damage classes (normal, mild wear and severe wear) \(n\) times each.
* For each signal, find the stationary and non-stationary sources using Algorithm 1.
* Train the LS-SVM classifier model using Algorithm 2 and the Gaussian RBF kernel function.  
Step 2: Damage detection  
* Periodically collect vibration time-domain signals from the NC machine under operating condition while the state of the tool is unknown and to identify its class.
* Find the unknown tool's stationary and non-stationary signal sources using Algorithm 1, using the reconstructed parameters from Step 1.
* Calculate 10 dimensionless time-frequency indexes for the non-stationary signal source and identify the class of the unknown signal using the trained LS-SVM classifier.
## 3 Materials and Methods  
Power plants emit GHGs through a chimney called the flue stack, producing a flue gas plume. Plants that are more efficient or have air pollution control equipment generally have flue gas plumes that are difficult to see. Furthermore, fuel characteristics and power plant equipment vary, impacting the visibility of flue gas plumes. For these reasons, directly inspecting smoke plumes only provides a weak indicator of emissions. A better indicator of emissions is the water vapor plumes from two primary sources:  
* Natural draft cooling towers (NDT): Plants using NDT have a large hyperbolic structure that allows vapor plumes to form during evaporative cooling.
* Wet flue gas desulfurization (FGD): After desulfurization, flue gases become saturated with water, increasing the visibility of plumes from the flue stack.  
In terms of size, NDT plumes are generally larger and wider than FGD plumes, making them easier to see in multi-spectral satellite imagery, as shown in Figure 1. A power plant may have one, both, or neither of these technologies. Due to the differing plume sizes and shapes from their sources, we created separate NDT and FGD models.  
Observing the visible emitted water vapor plumes in multi-spectral satellite imagery, we built an ML modeling pipeline using gradient-boosted decision trees and convolutional  
Figure 1: PlanetScope CNN predictions on the James H. Miller Jr. power plant at low vs. high generation on two observation dates. Separate NDT and FGD models predicted on NDT cooling tower (blue) and FGD stack (red) patches, respectively. These predictions were ingested by subsequent models to estimate generation, then CO\({}_{2}\), for the plant. © 2023 IEEE. Reproduced with permission from [34].  
neural networks (CNNs), and then trained these ML models to infer a power plant's operational status. Specially, we designed models to perform two tasks:  
1. Sounding-level models: consist of (A) a classification model to classify whether a plant was running or not (on/off), and (B) a regression model to predict the capacity factor (i.e., what proportion of the power plant's generation capacity was being used to generate power, generally between 0 and 1), for a given a satellite image of that plant at a certain point in time.
2. Generation models: aggregate the predictions from the sounding-level models into the estimates of a power plant capacity factor over the preceding 30 days.  
Each sounding model was trained on satellite images paired with the reported generation status. After predicting the capacity factor, it was multiplied by capacity (maximum electric power output) to infer generation. Our models are trained on power plants in countries for which we have hourly or sub-hourly generation data, which can be closely matched to the satellite image timestamp. Then, the models were applied globally using country-, fuel-, and prime-mover-specific average carbon intensities to convert the modeled plant-level generation into emission estimates. Figure 2 provides an overview of how these different models and data sources are integrated to estimate emissions.
## 4 Part II: The Impact of Head Tracking on the Localization Performance
### Results
Figure 12: Average elevation error, when localizing virtual sources generated with V11, V21, V23 and HTK in TestDynamic and with V11\({}_{\text{s}}\), V21\({}_{\text{s}}\), V23\({}_{\text{s}}\) and HTK\({}_{\text{s}}\) in TestStatic. (**Bottom**) Absolute error, averaged over 14 subjects for negative (N), zero (Z) and positive (P) target elevations. Error bars indicate 95% confidence intervals. (**Top**) Percentages of response elevations which were perceived as negative (below \(-5^{\circ}\)), zero (between \(-5^{\circ}\) and \(+5^{\circ}\)) or positive (above \(+5^{\circ}\)). The polar diagrams in Figure 13 show that externalization rates were in general lower in TestStatic than in TestDynamic. Figure 14 shows the externalization rates averaged over the target positions. According to the Shapiro-Wilk test of normality, the average externalization rates could not be assumed to be normally distributed. Therefore, the Wilcoxon signed-rank test was applied, which revealed that for all BRIRs, average externalization rates were higher in TestDynamic than in TestStatic (\(p\)-values shown in Figure 14). The results confirm the positive impact of head tracking on the externalization of virtual sources presented through headphones [3; 5; 20]. **Reversal rates:** Table 3 shows the reversal rates in both tests. When localizing virtual sources generated with V11\({}_{\rm s}\), V21\({}_{\rm s}\), V23\({}_{\rm s}\) and HTK\({}_{\rm s}\) in TestStatic, the reversal rates were considerably larger than when localizing virtual sources generated with V11, V21, V23 and HTK in TestDynamic. This result clearly confirmed the positive impact of head movements on the reduction of front-back reversals [3; 4; 25]. Figure 14: Externalization rate, averaged over target positions, when listening to virtual sources generated with V11, V21, V23 and HTK in TestDynamic and with V11\({}_{\rm s}\), V21\({}_{\rm s}\), V23\({}_{\rm s}\) and HTK\({}_{\rm s}\) in TestStatic. Horizontal bars indicate significant differences (according to the Wilcoxon signed-rank test). Error bars indicate 95% confidence intervals. Figure 13: Externalization rate when localizing virtual sources generated with V11, V21, V23 and HTK in TestDynamic (**left**) and with V11\({}_{\rm s}\), V21\({}_{\rm s}\), V23\({}_{\rm s}\) and HTK\({}_{\rm s}\) in TestStatic (**right**).
## 2 A Hypothetical Example Application of the V-GSI Chart in a Tunnel
### Summary of Geological and Geotechnical Conditions Encountered on the Project  
The majority of the mainline tunnels were excavated within the Hawkesbury Sandstone, with the Ashfield Shale and Mittagong Formation being intersected in some areas. The Hawkesbury Sandstone is described in further detail below given its relevance to the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hoek-Brown Parameter** & **Lower Limit** & **Mean** & **Upper Limit** \\ \hline mb & 4.0 & 6.141 & 8.469 \\ \hline s & 0.0256 & 0.097 & 0.264 \\ \hline a & 0.502 & 0.501 & 0.5 \\ \hline Rock mass tensile strength (MPa) & \(-0.115\) & \(-0.284\) & \(-0.56\) \\ \hline Rock mass uniaxial strength (MPa) & 2.86 & 5.597 & 9.238 \\ \hline Rock mass global strength (MPa) & 5.2 & 7.2 & 9.863 \\ \hline Rock mass modulus (GPa) & 2.7 & 3.476 & 3.789 \\ \hline Equivalent cohesion c (MPa) & 0.503 & 0.852 & 1.387 \\ \hline Equivalent friction angle (Phi-degrees) & 51.8 & 53.668 & 54.2 \\ \hline \hline \end{tabular} Notes: 1. mb, s and a are Hoek–Brown material parameters [4] calculated with the estimated V-GSI rating overlays provided in Figure 1. 2. Other parameters calculated using software RSData Version 1.0.0.3 (© 2020 Roscience Inc.). Input includes: Rock type—sandstone; V-GSI = 79 (limits ranging from 67 to 88, see Table 4); intact uniaxial strength (UCS) = 18 MPa; m  
example tunnel heading provided later in the text, i.e., the location in Figure 4 comprises the Hawkesbury Sandstone from the surface to well below the tunnel invert level.  
The Hawkesbury Sandstone is a fine-to-coarse grained quartzose sandstone that is described as a fluvial fan and river deposit. It comprises sequences of sheet (cross-bedded) facies, massive facies and thin shale/lamnitie facies (beds) interpreted to have resulted from overbank and swamp deposits during quiescent periods. The facies are differentiated by their appearance and are the results of changes in the energy of the alluvial environment of deposition. The Hawkesbury Sandstone formation is in excess of 290 m thick and is made up of repetitions of the three facies described above [24; 25].  
Laboratory testing indicates the unconfined compressive strength (UCS) of fresh sandstone ranges from 10 MPa to 40 MPa but can be up to 80 MPa for thin beds (usually 1 m to 2 m thick) which may have higher quartz contents and stronger siliceous or carbonate cement. All sandstone types are generally highly abrasive, with the siliceous and carbonate cemented types having the highest abrasivity. Shale units within the Hawkesbury Sandstone, where unweathered, may have an UCS of about 10 MPa to 40 MPa (normal to bedding). Where present, the laminate beds are generally highly anisotropic with a much lower strength parallel to bedding [25].
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
## 5 Discussion  
The performance of IRFMFD is better than AODV, GeOpps, and GPSR in the context of delivery ratio as shown in Figure 7 with an increasing number of vehicles. As mentioned before, the GeOpps is a DTN vehicular routing protocol, and it is designed to adapt to the environment in which packets are carried by the vehicles to the destination. Thus, this limitation of the GeOpps protocol makes it difficult to cope with the scenario where vehicles are carrying a high number of packets. The performance of GPSR is unsatisfactory because it only considers a single factor which is the geographic distance between the vehicle with the packet and the destination. The AODV outperforms GPSR and GeOpps in common urban environments where the speed of vehicles is not very fast and the topology does not change rapidly. Also, the AODV can adapt to changes more quickly than GeOpps and GPSR. The IRFMFD shows the best performance on delivery ratio because more elements are taken into account to decide for selecting the next-hop vehicle to forward the packet. Furthermore, it exhibits the characteristics of the topology-based routing protocols between two adjacent SNs.  
Figure 8 also reveals that the IRFMFD, GPSR, and GeOpps show almost similar performance on the end-to-end delivery delay. However, the AODV has the highest delay compared with the others because the AODV establishes a route between packet carrier and destination to forward the packet. This strategy makes it possible for the packet-carrying vehicles to keep the packet for a long time in order to increase the delivery delay. However, since the path to SN has been established in advance, the packet-carrying vehicles do not need to wait.  
As shown in Figure 9, IRFMFD has more hop count compared with other routing protocols. This is because the choice of each hop of IRFMFD has more constraints. It also inevitably increases some unnecessary hop that the packets have to be forwarded to every SN. At the same time, more hop count also increases the data delivery ratios.  
In the future, our work will be focused on the routing design supported by 5G and blockchain technology. We will study and analyze which characteristics of vehicles are more suitable for 5G and blockchain technology. It is foreseeable that vehicles will be equipped with a large number of sensors in the future and that data collection and sharing will be greatly supported by 5G and blockchain technology.
# Research on Unsupervised Classification Algorithm Based on SSVEP
###### Abstract  
Filter Bank Canonical Correlation Analysis (FBCCA) is used to classify electroencephalography (EEG) signals to overcome insufficient training data for EEG signal classification. This approach is not constrained by the training data or time and also performs unsupervised Steady-State Visual Evoked Potential (SSVEP) classification in a short time, which is easy to extend and optimize. By examining the data set from the Brain-Computer Interface (BCI) contest and comparing it to Canonical Correlation Analysis (CCA) using various parameter settings, the results show that FBCCA carries better classification performance than CCA. When the number of harmonics is 4 and the number of subbands is 5, the identification rate of 40 targets with the frequency difference of 0.2 Hz achieves 88.9%, and the maximum information transfer rate (ITR) achieves 88.64 bits/min, which shows superior compatibility and practicability.  
BCI; SSVEP; unsupervised classification; FBCCA +
Footnote †: journal: Medical Imaging
## 2 Materials and Methods
### Biological Regeneration and Allocation  
Recovery strategies are essential in enhancing muscle repair and function following intense physical activity. In this study, we compared two recovery methods: vibration therapy (VT) and passive rest (PR). Participants were randomly assigned to begin with either VT or PR using a random number generator to ensure unbiased allocation.  
Vibration Therapy (VT): This involved a 15 min session of vibration massage targeting the upper limb muscles. The therapy was administered using a Vitberg vibration therapy device (Rehabilitation Medical Equipment VITBERG, Nowy Sacz, Poland) set to frequencies ranging from 20 to 50 Hz. The vibration therapy was applied to the following muscle groups:  
* Biceps Brachii: The therapy was applied for 5 min per arm.
* Triceps Brachii: The therapy was applied for 5 min per arm.
* Deltoid Muscles: The therapy was applied for 5 min per arm.  
Each muscle group received targeted vibrations aimed at enhancing blood flow and promoting muscle relaxation. The sessions were divided evenly to ensure comprehensive coverage of the primary muscles involved in boxing and kickboxing, especially the extensors and flexors of the elbow joints, which perform essential work when performing straight punches.  
Passive Rest (PR): Participants in this group were instructed to rest passively in a supine position for 15 min without any additional interventions. This served as a control to compare the effects of VT.  
Crossover Design: After a washout period to eliminate any residual effects of the first treatment, participants switched to the alternate recovery method. This crossover design allowed each participant to serve as their own control, thereby reducing inter-individual variability. A washout period of 7 days was implemented between the two experimental sessions to eliminate any residual effects from the first intervention.  
Outcome Measures: Muscle performance was assessed using isokinetic dynamometry to measure peak torque and endurance. Plasma lactate levels were measured at three time points: after the warm-up, immediately post-exercise, and post-recovery. These measures provided insights into both the physical and metabolic aspects of muscle recovery.  
The 18 participants engaged in the study twice, serving as both the experimental and control groups, with group assignment determined by the type of recovery method applied. Fourteen athletes completed all the required trials.
## 4 Verification of Pipeline's Dynamic Response by Numerical Simulation  
### Numerical Model  
The existing pipeline's dynamic responses induced by the arch cover method and blasting are rarely studied. Buried pipelines are not convenient to monitor and analyze. Thus, we used FLAC3d software to establish a numerical model to study the effects of blasting excavation and the arch cover method on the existing pipeline's dynamic responses. According to Section 3, the equivalent blast pressure of 13.4 MPa was applied as a surface pressure on the normal direction of the excavation face of each arch cover construction sequence. Figure 7 shows the numerical model of the Shi Kui Subway Station.  
The overall size of the numerical model was 110 m \(\times\) 75 m \(\times\) 18 m. The top of the model was set as a free boundary, whereas the other surfaces were quiet boundaries. The quiet boundary was selected to reduce the reflective waves on the boundary because the explosive pressure was applied inside the numerical model. According to the engineering background in Section 2, the ground is respectively set as plain fill, medium-weathered quartzite, and strongly-weathered quartzite. The mesh generation of the Shi Kui Subway Station was established based on the arch cover method construction sequences in Section 2. The Shi Kui Subway Station is excavated parallel beneath some existing pipelines. The existing pipelines include two water pipelines (DN400, DN900), a drainage pipeline (DN800), and a gas pipeline (DN300). The maximum overburden depth is 3.0 m. The vertical clearances between the pipelines and the station are 12.00 m, 12.47 m, 10.66 m, 12.25 m, respectively. Figure 8 shows the relative location between the pipelines and the subway station. The explosive area is from 1.5 m to 3.0 m along the construction direction.  
Figure 6: Equivalent blast pressure. (**a**) Explosion pressures; (**b**) Equivalent pressures.  
The TC-4850N blasting vibration vibrometers were used to measure blasting excavation in the arch cover method. With a sampling rate of 16 kHz and a monitoring range of 0 to 35 cm/s, the devices can record the vibration velocity versus time curve and easily capture the PPVs of the existing pipelines.  
The existing pipelines are not easily accessible for direct monitoring because they are buried in the soil. Therefore, two vibration vibrometers were attached to the ground surface directly above the No. 2 and No. 4 pipelines to obtain the actual vibration velocity. The actual vibration velocity verified our established numerical model.
## 4 Results of the Study
### Numerical Modeling of Uncentered Surfaces
#### 4.2.2 Different Soil Bin Pressures in Unreinforced Soil  
In order to further verify the trend in the influence of different soil compartment pressures on the change in surface settlement during shield tunnel excavation under the unreinforced condition of the shield tunnel, five working conditions, as shown in Table 5, were set up, and the corresponding surface settlement change graphs were plotted, as shown in Figure 8.  
Figure 8: Surface settlement values of shield tunnel under the influence of different soil compartment pressures.  
Figure 7: Surface settlement of shield tunnel at different burial depths.  
By analyzing Figure 8, the pressure of the soil bin during the excavation of the shield machine has a certain influence on the amount of surface settlement of the shield tunnel, when other factors are constant. Since the tunnel excavation direction has an angle of \(-4^{\circ}\) with the horizontal plane, it leads to a certain downward pressure on the soil in front of the cutter plate. Through analysis, the amount of soil settlement above the front of the cutter plate continues to increase as the pressure of the soil bin increases. However, when the distance from the centerline of the shield tunnel is equal to three times the tunnel diameter, the surface settlement at each observation point tends to be almost the same, and at this time, the effect of the size of the soil compartment pressure value on the surface settlement is relatively small. Comparing the surface settlement curves of the soil in front of the cutter plate of the shield machine under five different soil compartment pressures, it can be found that when the pressure of the soil compartment reaches 146 kPa, the change in surface settlement is more stable. It being too small will affect the construction, and it being too large will increase the value of the surface settlement of the soil in front of the cutter plate.
## 4 Creep Model of Surrounding Rock and Parameter Determination
### Inversion of Creep Parameters of Surrounding Rocks
#### 4.2.3 Training and Testing of Inversion Model  
The GA-BP neural network inversion model is developed with MATLAB (R2022b). The 100 settlement curves of the arch or convergence curves of the sidewall shown in Figure 10 are taken as the input data. Each curve contains 19 groups of deformation data. Therefore, the number of input neurons is 19, the number of output neurons corresponding to the creep parameters is four, and the number of hidden layer neurons is 10. The average values of the parameters obtained from the two input methods (i.e., the settlement deformation curve of the arch and the convergence deformation curve of the sidewall) are the final inversion values. Figure 11 shows the predicted results of the test data (here \"+\" indicates that the inversion value is greater than the actual value, and \"-\" indicates that the inversion value is less than the actual value).  
As observed, the inversion accuracies of the shear moduli (\(E_{\text{M}}\) and \(E_{\text{K}}\)) are higher than those of the coefficients of viscosity (\(\eta_{\text{M}}\) and \(\eta_{\text{K}}\)). The error of \(E_{\text{M}}\) in test group 9 is 16.7%  
Figure 11: Inversion results of monitored data.  
Figure 10: Deformation monitoring curves of surrounding rocks: (**a**) arch, (**b**) sidewall.  
and that of \(E_{\text{K}}\) in test groups 5 and 7 is larger than 15%; however, the errors in other test groups are all smaller than 7%. The errors of \(\eta_{\text{M}}\) and \(\eta_{\text{K}}\) in test groups 4, 5, 6, and 7 are all more than 15%. The main reason is that \(\eta_{\text{M}}\) and \(\eta_{\text{K}}\) are small, and despite the small absolute difference between the inversion value and the actual value, the relative error is considerably large. In general, the inversion accuracy of monitored data is high, and relative errors are controlled within 20%, with the majority being no more than 10%.
# Surface Topographical Control of a Liquid Crystal Microlens Array Embedded in a Polymer Network
## 1 Introduction
The fabricated microlens array has two unique properties, (i) surface topographical control and (ii) tunable-focus control, all of which occurred due to the nematic-to-isotropic phase transition of the host LC. The results of this study will be helpful in practical applications in providing a microlens array with thermally tunable focus control in the future.