esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Approach
### Multi-Source Feature Fusion Based on RS
\[D_{b}=argmax(C_{b}(sitting),C_{b}(standing),C_{b}(walking),C_{b}( lying)) \tag{7}\]  
Therefore, if \(C_{b}(sitting)\) is the highest, \(D_{b}\) is classified as sitting; if \(C_{b}(standing)\) is the highest, \(D_{b}\) is classified as standing; and, similarly, for each behavior within the set. The Top-1 accuracy is effective for evaluating the recognition effect of mutually exclusive behaviors. However, it inadequately assesses the detection effectiveness of anomalous behaviors in scenarios where basic behaviors and abnormal behaviors overlap. To more comprehensively evaluate the anomaly alerting capability of the methods, an Anomaly-Focused Accuracy Rate (AFAR) metric has been designed based on the following principles:  
(1) Decoupling the influence of the driving states. This involves disregarding the accuracy of distinguishing between driving states (non-driving vs. distracted driving) in regard to the classification of anomalous behaviors, focusing instead solely on the identification of specific anomalous behavior categories (e.g., smoking, playing on a phone). (2) The differential penalty mechanism. Considering the practical application context, this mechanism imposes differential penalties for cross-category misjudgments (e.g., mis-classifying smoking as playing on a phone) and missed detections (the failure to detect anomalous behavior), based on the severity of the misjudgment consequences. This ensures that the evaluation not only considers correct identifications, but also appropriately penalizes errors, according to their impact. The AFAR is defined as follows:  
\[AFAR=\frac{N_{correct}}{N_{correct}+N_{confusion}+2N_{miss}} \tag{8}\]where \(N_{correct}\) represents the number of samples correctly classified into the anomaly behavior categories, \(N_{confusion}\) denotes the number of misclassifications, and \(N_{miss}\) indicates the number of anomaly samples not detected. Given that missed detections generally result in more severe consequences than misclassifications in real-world scenarios, the formula imposes a heavier penalty for misses to reflect their impact. This AFAR metric only evaluates the detection effectiveness based on anomaly samples. In order to prevent an overemphasis on anomaly classification at the expense of normal sample detection, an evaluation metric CCA that comprehensively considers both anomaly and normal behavior detection accuracies is introduced, which is given by:  
\[CCA=\frac{N_{correct\_normal}+N_{correct\_anomaly}}{N_{total}} \tag{9}\]  
where \(N_{correct\_normal}\) represents the number of normal samples correctly identified as not exhibiting any anomaly behavior, \(N_{correct\_anomaly}\) denotes the number of anomaly samples that are correctly classified, and Ntotal is the total number of samples. This approach ensures that the evaluation framework not only emphasizes the critical aspect of detecting anomalous behaviors accurately, but also maintains high standards for the correct identification of normal behaviors, thereby providing a comprehensive assessment of the model's performance.
###### Abstract  
When using magnetic memory detection technology to locate weld cracks and porous defects, the traditional zero-point polarity theory leads to misjudgments in defect location and difficulty in distinguishing between the residual stress and the magnetic signals generated by defects due to the influence of external noise and residual stress. Therefore, this paper considers the different mechanisms of magnetic signal generation in areas where crack- and porosity-type defects and residual stresses are located and discusses research focused on the detection of weld defect localization considering the influence of residual stresses. Using the mechanism of magnetic signal generation as a starting point, the three-dimensional magnetic modulus gradient polarity determination method is proposed to distinguish residual stress and defects' magnetic signals. Through the COMSOL simulation of a welding defect's finite element magnetic signal, the resulting magnetic signal is converted into a characteristic determination formula for characterization. To verify the accuracy of the simulated characterization, the 3D magnetic signal is extracted and verified manually. Finally, a double orthogonal wavelet transform is introduced to eliminate the random noise in the gradient of the three-dimensional magnetic modulus. The results show that the theoretical analysis, numerical simulation, and experimental results agree with each other. The three-dimensional magnetic modulus gradient values of cracks and pores are much higher than that of the defect-free residual stress area. The three-dimensional magnetic gradient modulus can locate defects and characterize the lengths of defects. The dual orthogonal wavelet eliminates noise interference while improving the accuracy of locating three-dimensional magnetic modulus gradient defects.  
weld crack and porosity defects; magnetic memory detection; COMSOL simulation; 3D magnetic modulus gradient; double orthogonal wavelet Article
## 3 Methods of Determining of Image Number Lines
### Methods Proposed by Authors  
The methods proposed by the authors of the article are based only on the values of the maximum and minimum pixel amplitudes and their differences. The mean values and variances of the amplitudes of pixels composing the analysed image are not calculated. Therefore, taking into account the time-consuming computation for images of large sizes, e.g., for sources of unwanted emission in the form of graphic paths of laser printers or monitors operating in higher graphic modes, these methods may be effective in electromagnetic infiltration processes. 3.1 The Maximum Value of the Difference between the Maximum and Minimum Pixel Amplitude Sums Calculated for Each Vertical Line of the Reconstructed Image--Method V  
Method V is not based directly on the maximum and minimum amplitude values of the pixels building the reconstructed image. The respective maximum \(Maximum_{Met\_V}(B)\) and minimum \(Minimum_{Met\_V}(B)\) values are calculated for the sums \(Sum_{Met\_V}(B,m)\) of pixel amplitude values calculated for each column of the analysed image according to formulas:  
\[Sum_{Met\_V}(B,m)=\sum_{b=0}^{B-1}l_{B}(b,m), \tag{21}\]  
\[Maximum_{Met\_V}(B)=\max_{m}(Sum_{Met\_V}(B,m)), \tag{22}\]  
\[Minimum_{Met\_V}(B)=\min_{m}(Sum_{Met\_V}(B,m)). \tag{23}\]  
Then, according to the adopted algorithm, the differences \(Dif_{Met\_V}(B)\) between the maximum value \(Maximum_{Met\_V}(B)\) and the minimum \(Minimum_{Met\_V}(B)\) of sums \(Sum_{Met\_V}(B,m)\) are calculated, according to the formula  
\[Dif_{Met\_V}(B)=Maximum_{Met\_V}(B)-Minimum_{Met\_V}(B). \tag{24}\]  
The next stage of the procedure requires the determination of the maximum value \(Maximum_{Dif_{Met\_V}}\):  
\[Maximum_{Dif_{Met\_V}}=\max_{B}(Dif_{Met\_V}(B)), \tag{25}\]which allows us to calculate normalized values:  
\[Dif_{Met\_V\_Un}(B)=\frac{Dif_{Met\_V}(B)}{Maximum_{Dif_{Met\_V}}}. \tag{26}\]  
The maximum value determined by (27) is assumed as the criterion for determining the correct \(B_{Corr}\) number of the image line:  
\[Dif\_max_{Met\_V\_Un}=\max_{B}(Dif_{Met\_V\_Un}(B))\to B_{Corr}. \tag{27}\]  
3.2 The Minimum Value of the Sum of the Differences of the Maximum and Minimum Amplitudes Calculated for Individual Vertical Lines of the Reconstructed Image--Method VI  
Method VI requires the calculation of the maximum \(Maximum_{Met\_VI}(B,m)\) and minimum \(Minimum_{Met\_VI}(B,m)\) values of the pixel amplitude for each column \(m\) of the reconstructed image according to the formula  
\[Maximum_{Met\_VI}(B,m)=\max_{b}(l_{B}(b,m)), \tag{28}\]  
\[Minimum_{Met\_VI}(B,m)=\min_{b}(l_{B}(b,m)). \tag{29}\]  
Next, a sum of differences \(Maximum_{Met\_VI}(B,m)-Minimum_{Met\_VI}(B,m)\) is calculated:  
\[Sum_{Met\_VI}(B)=\sum_{m=0}^{M-1}(Maximum_{Met\_VI}(B,m)-Minimum_{Met\_VI}( B,m)), \tag{30}\]  
which is calculated independently for each value of B.
## 4 Simulation Results and Discussion
### Scenario (4)  
In this scenario, load disruptions and wind turbine disruptions have been applied to the power system according to Figure 10. In this scenario, severe uncertainty related to the power system parameters is considered in the system inertia (\(H=-50\%\)). In Figure 12(a)-e, the FR of the power system to load disruptions, wind turbine disruptions, and severe uncertainty related to the system parameters are shown. According to Figure 12(a), the MFD and ST based on the proposed controller (optimized FOPD-FOPID controller using the DOSA) are equal to 0.00079 Hz and 3.93 s, respectively. The MFD and ST based on controller 1 are equal to 0.00172 Hz and 4.58 s, respectively; the MFD and ST based on controller 2 are equal to 0.0157 Hz and 25 s, respectively; and the MFD and ST based on controller 3 with SMES are equal to 0.0197 Hz and 46 s, respectively (Figure 12(b)-d).  
Figure 12: (**a**) The FR of the power system based on the proposed controller, Scenario (3). (**b**) The FR of the power system using controller 1, Scenario (3). (**c**) The FR of the power system using controller 2, Scenario (3), (**d**) The FR of the power system using controller 3, Scenario (3). (**e**) The FR of the power system using LFC based on controller 4, Scenario (3).  
According to Figure 13e, the FR of the power system is unstable against severe disruptions using LFC based on controller 4, and this control method does not have the ability to maintain frequency stability against severe disruptions related to the parameters of the power system. According to the results of scenario (4), the proposed controller performs better in reducing the deviations of the power system than the other mentioned controllers; it reduces the ST of the frequency deviations caused by disruptions in the power system and is resistant to the severe uncertainty related to the system parameters. In Table 4, performance results of different control methods for 4 scenarios are shown.  
Figure 13: **(a)** The FR of the power system based on the proposed controller, Scenario (4). **(b)** The FR of the power system using controller 1, Scenario (4). **(c)** The FR of the power system using controller 2, Scenario (4), **(d)** The FR of the power system using controller 3, Scenario (4). **(e)** The FR of the power system using LFC based on controller 4, Scenario (4).
# Cross-Sectional Shape Optimization of Cylindrical Elastomer Spring for Sensitive Cargo Container
## 2 NFEA and Shape Optimization  
### Mechanical Property Evaluation of Natural Rubber  
The model equation representing the hyperelastic behavior of the elastic body used in the spring assumes isotropy in large deformation [19; 20; 21; 22; 23; 24]. In order to increase the accuracy of NFEA, the material constant of the model equation with the highest correlation coefficient should be used.  
The stress-strain hysteresis of natural rubber for spring was obtained through an isometric displacement control test, as shown in Figure 2[19]. Figure 3 shows the hyperelastic behavior model of the strain-stress curve in Figure 2. These material constants are used as input data for the material properties of NFEA.  
Figure 3: Stress–strain curves of the hyperelastic model.  
Figure 2: Stress–strain curves of the natural rubber under equi-biaxial tension.  
Since [PERSON]'s model represents the highest correlation coefficient (0.991) among the hyperelastic model equations, it is commonly used to obtain the elastomer material constant [23]. The hyperelastic behavior of this elastomer is expressed as a strain energy density function (U), as shown in Equation (1), using [PERSON]'s model equation.  
\[\mathrm{U}=\sum_{i=1}^{3}C_{i0}(\overline{I_{1}}-3)^{i}, \tag{1}\]  
where \(U\) is the strain energy function; \(I_{1}\) is the first strain invariant; \(C_{i0}\) is the material constant, which are \(C_{10}=0.318\), \(C_{20}=-0.026\), and \(C_{30}=0.005\), respectively.  
This study developed a polyurethane spring for sensitive products instead of the existing natural rubber spring. The advantages of this polyurethane material over natural rubber are its high load compression properties, excellent abrasion properties, anti-cutting, and tear performance, durability against contaminants, and extended life, especially outdoors or in extreme environments [25; 26]. The hyperelastic behavior of this material is generally evaluated through uniaxial tensile, shear, and biaxial tests. This research intends to predict the material constants of the polyurethane spring having the same hardness value as natural rubber by using Equation (1) instead of additional experiments.
# Nanoelectrode Ensembles Consisting of Carbon Nanotubes
## 2 Materials and Methods
### CNT-NEE Fabrication  
CNT-NEEs were fabricated by alternating layers of HNBR with ribbons of spinnable CNTs, as shown in Figure 1. The spinnable or drawable CNTs were grown in a vertically aligned forest, typically with heights of \(\sim\)450 \(\upmu\)m [47; 45], and then drawn into ribbon-like films (Figure 1b). The CNT films were relatively dense (Figure 2a) but contained separated CNTs and bundles (Figure 2b). HNBR was chosen as the encapsulating polymer due to its strong interaction with CNTs, as previously reported by [PERSON] et al. [48]. Initially, a layer of HNBR dissolved in acetone was sprayed on the Teflon belt, which moved continuously on a motorized drum. The bottom side of the Teflon belt was positioned 10 mm away from a hot plate, allowing acetone to evaporate and HNBR to dry before collecting the CNT film. The spinnable CNTs were pulled out as a thin film onto the HNBR layer, and more HNBR was then sprayed over the CNT film, encapsulating the CNTs. To build up multiple layers (hundreds), the HNBR layer was allowed to dry before adding another layer of CNTs. The layers on the belt were cut and stacked to create a bulk material of aligned CNTs within HNBR, which was then cured under heat (160 \({}^{\circ}\)C) and pressure (4000 PSI). Using a cryomicrotome, the HNBR-encapsulated CNTs were sectioned orthogonal to the direction of the CNTs with \(\sim\)100 \(\upmu\)m thickness, in a similar manner to our prior work [44]. The resulting CNT-NEE had exposed open ends that were employed as nanoelectrodes.  
An electrical connection to the CNT-NEE was established by attaching a copper wire to the backside of the CNT-HNBR material using fast drying silver paint. Subsequently, an epoxy resin was placed over the copper wire and silver paint and around the edges of the CNT-HNBR material, which, after drying, acted as a dielectric protection layer. An illustration of the CNT-NEE assembly is shown in Figure 3a, and Figure 3b illustrates the nature of the nanoelectrodes at higher magnification.
## 3 CFD Modelling as Digital Twin
### Fluid Equations
#### 3.2.1 Continuity Equation  
The first principle is the continuity equation, which states that the amount of mass entering a control volume must equal the amount of mass leaving it plus any mass accumulation within the volume. Fluid motion is related to the density value (\(\rho\)). Mathematically, the continuity principle is expressed in Equation (4).  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\mathbf{u})=0 \tag{4}\]  
where \(\mathbf{u}\) is the velocity vector. The first term of the equation corresponds to the rate of change of mass conditions versus time, and the second term corresponds to a divergence term, which is related to the change in mass per unit time in a given infinitesimal volume.
## 2 Characteristics of SNR
Thus, it was concluded that the SNR is a function of the satellite geometry and the signal-reception environment and can thus be utilized as a criterion by which to determine whether the signal is affected by the multipath.
# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 4 Performance Evaluation
### Numerical Results
#### 4.3.5 Advanced Networking Mechanisms  
Please note that the performance of the proposed dynamic compute service may also heavily depend on underlying technology parameters. To this aim, Figure 17 quantifies the satisfied Interest fraction for different WiFi parameters, including disabled and enabled RTS/CTS (denoted by \"RTS\" and \"No RTS\") functionalities, single and multi-channel operational modes (denoted by \"S\" and \"M\") for mobile and static meshes (denoted by \"Mob.\" and \"Stat.\"). Recall that previously, we assumed that RTS/CTS functionality was disabled while all nodes operated using the same channel.  
Analyzing the results presented in Figure 17, one may observe that the use of the RTS/CTS scheme does not drastically affect system performance. The only case where it provides a noticeable effect is the case of static environment with single channel operation. The reason for this is that in these conditions, the major source of incorrect packet reception is interference. Enabling RTS/CTS functionality allows improving performance. When nodes are permitted to utilize multiple channels via adaptive channel selection functionality, the interference is negligible, and thus no gains are evident. Addressing the effect of adaptive channel selection, we would like to note that it produces the most effect in static conditions when RTS/CTS is disabled. In mobile conditions, most of the losses are mainly caused by node mobility and, thus, not affected by this functionality.
## 2 Materials and Methods
### Source of Datasets  
This study used two different datasets, an internal dataset and an external set, for meniscal segmentation. The internal dataset was obtained from the FastMRI initiative database provided by New York University (NYU) ([[https://fastmri.med.nyu.edu/](https://fastmri.med.nyu.edu/)]([https://fastmri.med.nyu.edu/](https://fastmri.med.nyu.edu/))) [20; 21]. The FastMRI dataset contains 10,012 consecutive DICOM (Digital Imaging and Communications in Medicine) images from 9290 patients from clinical knee MRI examinations, providing a wide range of clinical images representing different tissue contrasts and various imaging planes. This dataset is also notable for including k-space data [21]. The external set was obtained using the MRNet dataset of 1370 knee MRI examinations performed at Stanford University Medical Center [22].
## 3 Results and Discussion
### Keywords and Trending Topics of the Research on Pavement Management
#### 3.2.2 Pavement Performance Models
To address this problem, different solutions were suggested, like uncertainty management, that considers variability of the parameters and model uncertainty [100], and a new model that combines two different mechanistic theories [116]. Pavement deterioration models can also be defined as deterministic or probabilistic [77; 117; 118; 119]. Among probabilistic models, the Markov chains received great attention in the studied time interval [120]. It can be applied to any index or parameter, like the Pavement Condition Index (PCI) [121]. To obtain the Markovian transition probabilities, historical data have been deployed, if available, or the knowledge of experienced engineers [78]. When using a TPM obtained from experts' knowledge, it can be updated as soon as field data become available by means of a Bayesian approach [122]. For incomplete in situ observation data, the use of the exponential hidden Markov model has been suggested [123]. In addition, a semi-Markov model, without the \"memoryless\" property of Markov models, has been presented, where the sojourn time in each condition follows a Weibull distribution [124]. Nonetheless, there is no consensus on techniques for performance modeling because of the wide range of factors that must be considered, and the limitations and accuracy of field data. Consequently, researchers are exploring alternative methods, such as polynomial functions [125], and truncated Levy flight distribution [126]. Regression analyses were employed for various purposes: for macrotexture prediction in chip seals [127], for generalized models in Louisiana [128], and a multilinear regression for IRI and rutting modeling [129]. ANN has been deployed for Present Serviceability Index (PSI) of urban flexible pavements [130] and for cracking, raveling, rutting, and roughness for secondary roads in India [131]. Feed forward neural networks modeling method was used to forecast gravel loss in gravel roads in Botswana [132]. Flexible pavement rehabilitation treatments have been evaluated by means of aggregate and disaggregate post treatment performance models [133]. In case of limited or incomplete data, an autoregression method [134] or a hybrid technique [99] were suggested. Moreover, since 1968, the World Bank has conducted a series of projects about roads, and has developed various evaluation modules that include pavement performance models. They were distributed to more than 100 countries around the world, and they need to be adapted to local conditions, as performed in India [135]. The HDM-4 model for cracking performance, developed by the World Bank, has been incorporated to a new Portuguese maintenance optimization system [136]. Other models, derived from HDM-4, have substituted previously used AASHTO performance models in Visseau (Portugal) [137]. Finally, performance model idea has been exported to other fields, like paint pavement markings [138].
###### Abstract  
In recent years, the method of deep learning has been widely used in the field of fault diagnosis of mechanical equipment due to its strong feature extraction and other advantages such as high efficiency, portability, and so on. However, at present, most kinds of intelligent fault diagnosis algorithms mainly focus on the diagnosis of a single fault component, and few intelligent diagnosis models can simultaneously carry out comprehensive fault diagnosis for a rotating system composed of a shaft, bearing, gear, and so on. In order to solve this problem, a novel stacked auto encoders sparse filter rotating component comprehensive diagnosis network (SAFC) was proposed to extract domain invariant features of various health conditions at different speeds. The model clusters domain invariant features at different speeds through the self-coding network, and then classifies fault types of various parts through sparse filtering. The SAFC model was validated by the vibration data collected, and the results show that this model has higher diagnostic performance than other models.  
deep learning; stacked auto encoders; sparse filtering; domain invariant feature; multiple components +
Footnote †: journal: Applied Sciences
## 3 Network Model
### Transmission Model
#### 3.2.2 Asymmetric Link  
A symmetric link implies that if one node is able to receive a message from another node, then it can send a message to the same node. For a wired network, this is true, but for a wireless network, this is true only when the communication range of both nodes is the same or the distance between two nodes is less than the minimum communication range of both nodes. The latter is called a mutual inclusion graph (MG) in [41]. However, in any real case, the network is likely to be asymmetric, so the resulting network is directed. The irregular communication range leads to an asymmetric link. Figure 4a,b shows the symmetric and asymmetric communication model, respectively.  
The omnidirectional radio indicates that the node's antenna broadcasts in all directions. In WSNs, it is possible that radio communications can be modeled as a directional antenna, which can transmit in specific direction(s), as illustrated in Figure 4c. The work in [42] suggests that both unreliable and asymmetric links have a great impact on the global quality of connectivity in large-scale sensor networks.