esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Damage Detection Framework
### Proposed Framework
** Collect new time series data for each unique series, ensuring that the sampling rate matches the training samples. Figure 7: Damage detection framework. **IP Task 2: Feature Construction. ** Create a feature vector representing the current health status using the new time series data in the same order as during training. **IP Task 3: Compute k-Dimensional Error Vector. ** The autoencoder reconstructs the feature vector, generating a k-dimensional error vector by excluding nullified output values from the training data. **IP Task 4: Compute Mahalanobis Distance. ** Calculate the Mahalanobis distance using the k-dimensional error vector, sample mean, and covariance matrix from the training phase. **IP Task 5: Evaluate Structural Integrity. ** Assess the structure's health by comparing the Mahalanobis distance to a user-defined threshold from training; if within the threshold, the structure is healthy; if not, it is deemed damaged. The IP tasks are depicted in Figure 7, where f represents the feature vector and f hat represents the reconstructed feature vector.
## 4 Results and Discussion
### Axial Dynamic Mechanical Characteristics of the Air Spring
#### 4.1.2 Effect of Amplitude Excitation on Axial Load Characteristics of Air Spring  
As depicted in Figure 8, the results of the study on the effect of amplitude excitation on the axial load of the air spring under an initial pressure of 0.3 MPa revealed that the axial load behavior of the air spring changes with varying frequency and amplitude excitations.  
As evident in Figure 8, under the same initial pressure of 0.3 MPa, and for different amplitude excitations, the axial load of the air spring increased with an increase in vibration frequency. At a frequency of 0.25 Hz  
The aforementioned research indicates that the stability of the axial load in an air spring can be enhanced by raising the frequency of excitation, and that the axial load capacity can be augmented by increasing the magnitude of excitation. This has been demonstrated through a thorough analysis and experimentation.
# Experimental Research into the Ultrasonic P-Wave Velocity of Coal Slime Based Backfill Material
## 5 Influences of Mix Proportions of Materials on the Ultrasonic P-Wave Velocity  
Based on orthogonal test results, influences of multiple factors on the ultrasonic p-wave velocity are displayed in Figure 18. Various factors are found to differ in their influences on the ultrasonic p-wave velocity. According to analysis of test results, the content of fly ash was listed in the error row and significance testing was conducted at each level. The results of analysis of variance (ANOVA) are listed in Table 11.  
Figure 17: Heat map for the correlation coefficient between the ultrasonic p-wave velocity and shear strength.  
Figure 18: Relationship between the ultrasonic p-wave velocity and multiple factors. Different color lines in the figure represent different materials A, B, C and D.  
In the ANOVA, due to the small variance of parameter D, by treating D as an error term, its influence on the dependent variable is reduced. This also reduces the variability of the experimental results, thereby enhancing the accuracy and reliability of the experiment.  
ANOVA shows that, within a given range, factor A significantly influences the ultrasonic p-wave velocity, while factors B and C exert slight influences. The values of variance suggest that factor B exerts a greater influence than factor C, and the mass concentration of coal slime is the main factor affecting the ultrasonic p-wave velocity of specimens.
## 2 Materials and Methods
### Ex Vivo Culture of Leukocytes and Measurement of Cytokine Production  
Leukocytes were isolated from venous blood collected in BD Vacutainer(r) K2 EDTA tubes (Becton, Dickinson and Company (BD), Franklin Lakes, NJ, USA) from 3 healthy volunteers (2 men and 1 woman, age range: 30-49 years). Standard hematological parameters were analyzed and confirmed to be in the reference range. The blood samples were centrifuged at 1000\(\times\)\(g\) for 15 min, and then the plasma was discarded. The erythrocytes were lysed with a 0.84% NH4 Cl buffer for 8 to 10 min, and the samples were washed twice with DPBS. The isolated leukocytes were pooled and centrifuged at 1000 rpm for 10 min. The resulting cell pellet was resuspended in complete DMEM. The cell suspension was divided into three aliquots for a triplicate experiment. The cells were seeded on a 12-well culture plate (TPP, Trasadingen, Switzerland) at 1 \(\times\) 10\({}^{6}\) cells/mL and treated for 24 h with 100 \(\upmu\)g/mL nanoparticles or BMNP-MTX conjugates. A triplicate control culturedin complete DMEM without a test sample was also set. At the end of the experiment, 100 uL culture media were collected from all samples. The concentrations of interleukin-2 (IL-2) and interferon-\(\gamma\) (IFN-\(\gamma\)) were determined in the collected samples with an enzyme-linked immunosorbent assay (ELISA). For this aim, human IL-2 and IFN-\(\gamma\) ELISA kits (eBi BiosciencesTM, Thermo Fisher Scientific Inc., Waltham, MA, USA) were used, and the assays were performed according to the manufacturer's instructions.
## 4 Buffeting Response Analysis  
Buffeting loads and mean wind loads were applied to the main girder, pylons, and stay cables, while only mean wind loads were considered for the auxiliary piers. The aerodynamic forces at all the structural components were computed in real-time through a command-stream-driven approach, which iteratively updated based on the structural response from the previous step to rigorously account for the nonlinear effects of effective wind attack angles. To mitigate numerical instabilities caused by step excitation, the critical damping ratio method was employed during the initial 100 s of the simulation. Subsequently, a Rayleigh damping model was adopted, with damping coefficients calibrated using the first vertical bending mode and first torsional mode frequencies of the main girder--both of which are critically linked to the buffeting response characteristics of the CFRP cable-stayed bridge system.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 4 The Experiments
### Comparison with the Popular Trackers
#### 4.2.3 LaSOT  
LaSOT [43] comprises 1400 video sequences, with a total frame count exceeding 3.5 million and an average sequence length of 2512 frames. Utilizing precision and success rate as evaluation metrics, LaSOT's longer video sequences better reflect the algorithm's robustness.  
As shown in Figure 6, for the LaSOT dataset, our algorithm achieves a precision AUC of 68.4% and a success AUC of 68.9%. In comparison to other Transformer tracking algorithms, it holds a leading position.  
Figure 7 shows the precision and success rates of SSTrack and five other popular trackers across three different video sequence attributes such as Fast Motion, Illumination Variation, and Scale Variation. SSTrack achieves the top ranking in success rate across these three attributes. Compared to the second-ranked Swin Track, SSTrack shows improvements in tracking precision by 3.5%, 2.7%, and 2.9%, and an increase in tracking success rate by 3.6%, 2.3%, and 2.2%. Compared to the Transformer algorithm, TrSiam, SSTrack demonstrates improvements in tracking precision by 7.4%, 8.2%, and 8.5%, and an increase in tracking success rate by 6.3%, 5.9%, and 6.4%. This indicates that incorporating a spatial scale attention module into the feature extraction network can effectively mitigate the issue of feature degradation caused by complex environmental conditions, outperforming a conventional Swin Transformer structure.  
Figure 6: (a) SSTrack’s precision curve on LaSOT; (b) SSTrack’s success curve on LaSOT.  
Figure 7: Experiments and comparisons on Fast Motion, Illumination Variation, and Scale Variation.
## 4 Semi-Active Magnetorheological Damper Device for Chatter Mitigation
### Modified FRF of the Cantilever Beam  
For this experimental setup, to validate the effectiveness of the MR damper device, a new cantilever beam made of aluminum alloy 7075T6 was designated with dimensions of \(103\times 127\times 12.7\) mm to induce specific modal parameters and study the stability boundaries of thin walls with a very low critical depth value (below 0.5 mm). In order to evaluate the effects of the MR damper over the stability boundaries, three levels of MR damper performance were studied: highest, 3.4 A; medium, 1.7 A; and lowest, off. The magnetic flux density produced by the electromagnet was measured experimentally with an F.W. [PERSON] gaussmeter, model 5170. Figure 9 shows the magnetic flux density measured from the center of the cylindrical electromagnetic outwards along the radial direction. As expected, the magnitude of the magnetic flux density is almost constant in the radial direction, and the magnitude is proportional to the supplied electrical current. Measurements after 40 mm were not considered, since the cantilever beam was not in contact with the MR fluid.  
The yield stress of the MR fluid changes depending on the magnetic flux density at which it is subjected. It is expected that the dynamic properties of the system imposed will be modified because of the effects of the MR damper device. The FRF was experimentally measured for 4 scenarios and plotted in Figure 10. The scenarios are (1) no damper, (2) damper off, (3) damper on with 1.7 A, and (4) damper on with 3.4 A. The technique used to acquire the FRF and the parameter fitting procedure are similar to those of Section 3.1. Table 4 summarizes the modal parameters computed for each scenario.  
Notice from Table 4 that the natural frequency of the beam, without a damper, is 190 Hz and the damping ratio is 0.007. When MR fluid is in contact with the thin floor and off, the natural frequency shifts to 180 Hz, and the damping ratio increased by about 50%. Note that when the MR damper is off, the electromagnet is not active, and no electrical current is provided, but it is still in contact with the cantilever beam and the FRF is affected. Table 4 shows that if the MR damper is turned on and subjected to two electrical currents values, the damping ratio of the system increases proportionally.  
Figure 8: **(a)** Optical image of the experimental setup and magnetorheological (MR) damper installation; **(b)** Schematic of cross-section A–A, the assembling of capsule–MR fluid is placed above the electromagnet.  
Figure 9: MR damper device magnetic flux density distribution.  
The damping of the system depends on the yield stress of the magnetorheological fluid, which can be estimated by using the technical data sheet from the fluid manufacturer [38].
# Design of a Compact 1-MV-Class Radial Closing Switch for Tesla Type Generator
## 4 Optimization Techniques
### Optimization
#### 4.2.3 Insulator-Oil and Insulator-Water Interfaces  
The field distribution on both the insulator-oil and the insulator-water interfaces are also optimized using the same \"grooving\" technique as described above. Figure 11 shows the 3-D design of a grooved insulator and the corresponding electric field distributions along the two insulators surface.  
As presented, on the insulator-oil surface, the maximum electric field \(E_{max}\) is only 120 kV/cm, a value considered to be safe [7]. To prove this assumption, a detailed analysis follows. In ref. [7], a coaxial line with an outer diameter (\(D_{o}\)) of 250 mm and an inner diameter (\(D_{i}\)) of 91.5 mm is considered, and a maximum voltage (\(U_{max}\)) of 660 kV is applied with the surface of the insulator being smooth. Using the simple formula:  
\[E_{av}=\frac{2U_{max}}{D_{o}-D_{i}} \tag{3}\]  
the average field (\(E_{av}\)) for the system described in ref. [7] is estimated as 83 kV/cm and using an electrostatic software, the \(E_{max}\) on insulator surface is determined to be 125 kV/cm. For the coaxial line of the current generator, \(D_{o}\) is 430 mm; \(D_{i}\) is 190 mm; \(U_{max}\) is 1 MV. Therefore, using (3), \(E_{av}\) is estimated as 83 kV/cm and, using an electrostatic solver, \(E_{max}\) is obtained as 120 kV/cm. In addition, in the present design, the insulator surface is grooved, thus increasing the surface flashover path.  
In the case of the insulator-water interface, the \(E_{max}\) does not exceed 100 kV/cm, which is also lower than values reported in ref. [30] and therefore is safe. A similar detailed analysis as presented above follows. In ref. [30], the coaxial water-filled line has the following parameters: \(D_{o}\) is 380 mm, \(D_{i}\) is 220 mm, \(U_{max}\) is 600 kV, \(E_{av}\) is estimated to be 75 kV/cm, and \(E_{max}\) is calculated as 130 kV/cm. In comparison, the present design of the coaxial line connecting the water-filled load has the following characteristics: \(D_{o}\) is 500 mm, \(D_{i}\) is 120 mm, \(U_{max}\) is 1 MV, \(E_{av}\) is therefore estimated to be 52 kV/cm, and \(E_{max}\) is simulated to be 100 kV/cm. Same conclusions as above apply.
## 4 Results
### Accuracy results versus variability index  
The variability index was calculated for all LIDC nodules, specifically on those image instances that represented the slices containing the largest area of the nodule. The five number summaries for the distribution of the variability index had the following values: min = 0, first quartile (Q1) = 1.3165, median = 1.9111, third quartile (Q3) = 2.832, max = 85.5842. Then we calculated the five-number summary of the variability index for two subsets: the misclassified instances and the correctly classified instances with respect to each characteristic. Regardless of the characteristic, we learned that those instances with low variability index (\(<=\) 1.58) were correctly classified by the ensemble of classifiers and all those instances with high variability index (\(>=\) 4.95) were misclassified by the ensemble of classifiers. Given that variability index values greater than 5.12 (= Q3 + 1.5 \(\times\) (Q3 - Q1)) indicate potential outliers in the boundary delineation, we conclude that the ensemble of classifiers is able to correctly classify instances with large variability in the nodule boundaries.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 2 Single-Model Nonlinear Filtering Algorithms
### Gaussian Filters  
The EKF, UKF, CKF, GHF, CDF, and DDF belong to the class of Gaussian filters. These filters evaluate the expected values appearing in (15)-(19) differently using Gaussian approximation. In general, a filter has two steps: prediction (or time update) and measurement update. The prediction and measurement update steps are governed by (15)-(17) and (11)-(14), as well as (18) and (19), respectively. The EKF linearizes the nonlinear functions \(\mathbf{f}_{k-1}\) and \(\mathbf{h}_{k}\) for estimates \(\mathbf{\hat{x}}_{k-|k-1}\) and \(\mathbf{\hat{x}}_{k|k-1}\) using the Taylor series expansion, and then it computes the expected values in (15)-(19) [8; 20; 50]. It was observed in some cases that the EKF does not work well and diverges when the degree of nonlinearity is high, the measurement accuracy is low, and the measurement time interval is high. The Cartesian EKF diverges for the bearing-only filtering problem, which satisfies these conditions [20]. It is easier to approximate a pdf than to approximate a nonlinear function. UT [14] and cubature transformation (CT) [18] use this concept to represent the mean and covariance of a Gaussian-distributed random variable using sigma points and cubature points, respectively. When a nonlinear function such as \(\mathbf{f}_{k-1}\) or \(\mathbf{h}_{k}\) transforms a Gaussian random variable, the sigma points and cubature points are used to obtain the more accurate mean and covariance of the transformed random variable than those obtained by the Taylor series expansion. The UKF based on the UT and the CKF based on the CT yield more precise outcomes compared to the EKF in such cases. The PF or sequential Monte Carlo is an approximate filter that implements the recursive Bayesian algorithm in (5)-(7) using Monte Carlo simulations [19; 20]. There are many versions of the PF [19; 20]. In a PF, the posterior pdf is represented by a set of random samples or particles and associated weights \(\{\mathbf{x}_{k}^{i},w_{k}^{i}\}_{i=1}^{N}\), where \(N\) is the number of particles. As the number of particles increases, the discrete approximation of the pdf approaches the true pdf, and the particle-based estimates approach the optimal estimates. The number of particles depends on the nonlinear filtering problem. The sequential importance sampling algorithm is a commonly used method in a PF [19; 20].
## 4 Discussion
### Influence of Increase in Vulcanization Time  
In Figures 10-12, the tensile load-displacement response curves of samples according to the variation in vulcanization time are reported for direct comparison. The control sample is identified by the initials (cs) placed next to the specimen number.  
These figures are followed by Table 2, where the features of the compared samples are reported together with the experimental results in terms of the maximum recorded force, \(F_{\mathrm{max}}\), the corresponding displacement, \(d_{\mathrm{Fmax}}\), and the ultimate displacement, \(d_{\mathrm{u}}\).  
Figure 11: Tensile load-displacement response curves of samples 4, 5, 6 (G = 0.8 MPa).  
Figure 12: Tensile load-displacement response curves of samples 7, 8, 9 (G = 0.4 MPa).  
Figure 10: Tensile load-displacement response curves of samples 1, 2, 3 (G = 1.4 MPa).  
over a period of time even longer than the vulcanization time. This may result in degradation of the elastomer. The two described phenomena can explain the observed drop in the maximum load.  
The samples that showed cohesive failure are samples 1 and 4, which also performed the best in terms of maximum strength because the strength of the elastomer prevents premature detachment of the elastomer from the reinforcement layers. The other samples show adhesive or mixed failures.  
The conclusion regarding the influence of increasing the vulcanization is that this parameter does not improve the adhesion between elastomer and steel layers. On the contrary, for specimens 5 and 6, it reduced their capacity in terms of strength, which decreased to values lower than the strength required by UNI EN 15129 [15] under tensile action, as already pointed out (Figure 8).
## 4 Results and Discussions
### Vortex Formation with Bluff Splitter Body  
Results shown in Figure 13a-j include the velocity contour of 7, 10, 12, 14, 16, 18, 19, 20, 22.5 and 25 m/s wind speed at time 0.3 s. It is shown in all figures that with constant wind flow, either with a high or a low wind velocity, the model was able to generate a large area of vortex downstream with the introduction of a bluff splitter body. As shown in the contour plot Figure 13, when wind flow was obstructed by upstream bluff splitter body, it separates around the body leaving a low velocity high pressure vortex region illustrated as the blue region downstream of the bluff splitter body in the figure. It is observed that vortex formation length generated under different wind speeds is similar. This proves that similar bluff splitter body shape and structural model's aspect ratio value produces a similar vortex formation length, disregarding the flow speed.  
Whenever wind flow passes through a bluff splitter body, the vortex generated will usually cause wake propagation downstream in which the frequency and pressure force will excite and damaged downstream structures. It is mentioned by [PERSON] that a rigid plate installed downstream of a bluff body is able to suppress wake propagation [53]. It is noted in Figure 13 below that by switching the rigid plate to a more flexible PVEH plate provides an additional vibrational tendency under the influence of vortex and in a degree generating power while suppressing further propagation of vortex downstream into wake.  
Figure 12: Graph of the mesh independence study.  
Figure 13: _Cont._In order to further describe the importance of the bluff splitter body for generating a better quality vortex, Figure 14 below depicts a PVEH plate in magnified view placed in 7 m/s wind speed domain similar to all the settings as previous cases. Without the bluff splitter body, it is shown that unlike in Figure 13, there are no low velocity regions present surrounding the PVEH plate which represents vortex formation. The reason is that bluff splitter body provides enough width which effectively separates the flow when comes into contact. Besides, wide bluff splitter body provides sufficient area downstream for clear vortex formation around the PVEH plate as shown in the light blue area in Figure 13a-j. Without vortex formation in Figure 14, neither vortex frequency nor fluctuating force is induced thus the PVEH plate is not vibrating under the effect of flow for the period of 600-time steps conducted. In addition, the lift force in this situation is at a constant 1.2 \(\times\) 10\({}^{-4}\) which is comparably lower to the values of models with the bluff body intact and also it is not fluctuating creating frequency value.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 2 Problem Formulation
### Constraints
#### 2.2.2 Generation Limit Constraint  
The generation limit constraint of the unit is regarded as one of a physical constraint. This inequality constraint limits the unit output to a certain range due to the generation capacity of the corresponding unit. It is shown in the following formula:  
\[u_{j,t}P_{j,\min}\leq P_{j,t}\leq u_{j,t}P_{j,\max} \tag{5}\]  
where \(P_{j,\min}\) represents the minimum power generation, and \(P_{j,\max}\) denotes the maximum power capacity.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Cluster-Based Routing  
Cluster-based routing is a method borrowed from WSN. The LEACH (Low Energy Adaptive Clustering Hierarchy) routing protocol is a classic work in WSN, and the cluster-based routing method comes from it; many experiments have proved that the clustering method is more suitable for WBANs. When the number of nodes increases and the relative distance between the nodes increases, the clustering method can ensure network connectivity, balance the energy consumption of the network center and edge, adapt to the dynamic topology structure, and improve the robustness of the network. Clustering routing protocol divides the nodes in the network into clusters of nodes. Each cluster consists of several cluster nodes and a cluster head. The cluster head is elected by algorithm and is responsible for integrating and forwarding the information in the cluster, to reduce the overhead of direct communication.