esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Methodology
### Computational Model and Grid Generation  
This study focused on the supercritical airfoil OAT15A. The basic parameters are described as follows: chord length c = 230 mm, thickness of 12.3%c. The OAT15A airfoil transonic shock buffet experiment was conducted in the S3 Ch continuous wind tunnel of ONERA [21]. The freestream Mach number for the experiment is 0.73, and the AOA ranged from 2.5 degrees to 3.91 degrees. Transition strips were placed at 7% chordwise positions on both the upper and lower surfaces of the airfoil. The results indicated that the transonic shock buffet occurred on the upper surface of the airfoil at an AOA of 3.25 degrees. The buffet frequency remained constant with increasing AOA, with a primary frequency of f = 69 Hz at 3.5 degrees.  
In this study, a C-H grid topology was employed for grid partitioning, as depicted in Figure 1. Regarding the selection of the streamwise dimension of the computational domain, [PERSON] [19] suggested a comparison between the airfoil thickness (e) and the step height (H) in the wake flow. For separated flow behind a step, a minimum streamwise dimension of 4H is required to capture the maximum wavelength numerically. In the case of airfoil flow at small angles of attack, where the flow is separated, the height of the separation region can be set as half of the trailing edge curvature (e). Thus, the streamwise dimension of the computational domain should be no less than 2e. For the OAT15A airfoil, with a maximum thickness of 12.3% chord length, based on the above analysis, the streamwise dimension of the computational domain is set to a 26% chord length in this section.
# Security Scheme Based on Parameter Hiding Technic for Mobile Communication in a Secure Cyber World
## 3 Proposed Security Scheme
### Initial Attach with GUTI
#### 3.3.2. MME Changed  
The second case is shown as Figure 3. The second case is the one that the information about UE is transmitted to new MME because the information about UE is saved in old MME, even though MME is changed. Processes that transmit GUTI are the same as that in other cases, and new MME receives UE information from old MME using information such as GUTI, NAS-MAC, and NAS sequence number.
###### Abstract  
Targeted magnetic resonance imaging (MRI) contrast agents offer platforms for the specific detection of many diseases, including cancer. This study explores the applicability of luteinizing hormone-releasing hormone-conjugated PEG-coated magnetite nanoparticles (LHRH-MNPs) to the enhancement of triple negative breast cancer (TNBC) detection. In vitro MRI studies were first performed, showing the consistent darkening effect of both MNPs and LHRH-MNPs in T2-weighted maps. Using a mouse model with an induced subcutaneous tumor, MNPs and LHRH-MNPs were injected into xenograft MDA-MB-231. This was done through intratumoral and intravenous injections, respectively, enabling direction comparisons of the two nanoparticles. Intratlumorally injected LHRH-MNPs maintained T2 signals within the breast tumors up to two weeks, revealing long-term tumor enhancement ability, while the signal started to recover towards the contrast of the original tumor before injection in the case of MNPs at 24 h post injection. For intravenous administration, LHRH-MNPs continued to darken breast tumor 24 h following injection, whereas contrast enhancement was not obvious in animals injected with MNPs. These results show the potential of LHRH-MNPs as negative contrast agents for the specific detection of TNBC.  
triple negative breast cancer (TNBC); luteinizing hormone-releasing hormone (LHRH); magnetite nanoparticles; magnetic resonance imaging (MRI); cancer detection +
Footnote †: journal: Medical and Aerospace Engineering, Princeton University, Olden Street, Princeton, NJ 08544, USA; [EMAIL_ADDRESS] (J.H.); [EMAIL_ADDRESS] (J.O.); [EMAIL_ADDRESS] (K.M.)
## 4 Results and Discussion
### Stepwise Intelligent Fault Diagnosis Based on SAE
#### 4.2.1 Description of Data in Stepwise Process  
The data of stepwise diagnosis process is arranged as Table 2 according to the Figure 4 to distinguish two states at once. Moreover, the experimental data is preprocessed by statistical filter and then is reconstructed into the train data and the test data.  
* There are five kinds of sliding bearing signal (normal state, contact rubbing, oil whirl, static unbalance, and dynamic unbalance) in this experiment and they are one-dimension data whose length is 1,000,000; moreover, the statistical filter is utilized to filter the noise and obtain the new data that marked as normal state \(N^{\prime}_{983,040\times 1}\), contact rubbing \(C^{\prime}_{983,040\times 1}\), oil whirl \(O^{\prime}_{983,040\times 1}\), static unbalance \(SU^{\prime}_{983,040\times 1}\) and dynamic unbalance \(DU^{\prime}_{983,040\times 1}\);
* The data can be reconstructed into a matrix and the number of rows of the matrix is 8192 (\(2^{13}=8192\)), and it fully contains the amount of data generated by one rotation of the sliding bearing; the column of the matrix change with the stepwise intelligent diagnosis to keep same size of two groups (two states of bearing). For example, the length of the contact rubbing state is 983,040 (\(8192\times 120\)), and the other three states are 327,680 (\(8192\times 40\)); similarly, the length of the oil whirl state is 983,040 (\(8192\times 120\)), and the other two states are 491,520 (\(8192\times 60\));
* Two sets of data (group1 and group2) in stepwise fault diagnosis are marked as \(G^{1}_{8192\times 120}\) and \(G^{2}_{8192\times 120}\). The train data \(G^{1}_{train}\) (\(100\times 8192\)) is above 80% in the first half of \(G^{1}\) and similarly \(G^{2}_{train}\) (\(100\times 8192\)) is also above 80% in the first half of \(G^{2}\) and the remaining data is test data. Therefore, the train data and the test data in this experiment are \(X_{train}=\left[G^{\prime 1}_{train};G^{\prime 2}_{train}\right]\) (\(200\times 8192\)) and \(X_{test}=\left[G^{\prime 1}_{test};G^{\prime 2}_{test}\right]\) (\(40\times 8192\)), respectively;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Group 1** & **Group 2** \\ \hline
1 & N (983,040) & C+O+SU+DU; (each state is 245,760) \\
2 & C (983,040) & O+SU+DU; (each state is 327,680) \\
3 & O (983,040) & SU+DU; (each state is 491,520) \\
4 & SU (983,040) & DU (each state is 983,040) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of experimental data.  
Figure 9: Experimental results with statistical filter.  
* The data labels are marked as [0, 1] according to the group 1 and group 2, respectively. Then the label data are expanded to vectors according to \(X_{train}\) and \(X_{test}\) and they are marked as \(Y_{train}\) (\(200\times 1\)) and \(Y_{test}\) (\(40\times 1\)). Therein, the first half of the vectors is 0 and remaining values are 1.
###### Abstract  
Objective: The aim of the present work is to study the microstructure, wear behavior, physical properties, and micro-hardness of the aluminum matrix AA6061 reinforced with TiC and B\({}_{4}\)C nanoparticles with different concentrations of 2.5, 5, 7.5, 10, and 12.5 wt.%. Methodology: Al/B\({}_{4}\)C and Al/TiC nanocomposites were fabricated with a powder metallurgy route. A dry sliding wear test was performed with a pin-on-disc machine. The wear test was performed at the applied loads of 3, 6, 9, 12, and 15 N at a constant time for about 10 min. The microstructural analysis of the fabricated nanocomposites was examined via field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD) analysis. The obtained data: The results of this work show that increasing the applied load leads to a decrease in the wear rate of the aluminum matrix and its nanocomposites. The wear rate of the aluminum matrix without any additives is about 7.25 \(\times\) 7\(\times\) (9/cm), while for Al/TiC and Al/B\({}_{4}\)C, it is 5.1 \(\times\) 10\({}^{-7}\) (9/cm) and 4.21 \(\times\) 10\({}^{-7}\) (9/cm), respectively. An increment in B\({}_{4}\)C percent increases the actual density, while an increment in TiC percent minimizes the actual density at 2.90 g/cm\({}^{3}\) and 2.51 g/cm\({}^{3}\), respectively. An increment in B\({}_{4}\)C percent decreases by 4.61%, while the porosity slightly increases with increases in TiC percent of 6.2%. Finally, the micro-hardness for Al/B\({}_{4}\)C is about 92 (HRC), and for Al/TiC, it is about 87.4 (HRC). Originality: In the present work, nanocomposites were fabricated using a powder metallurgy route. Fabricated nanocomposites are important in engineering industries owing to their excellent wear resistance, low thermal distortion, and light weight compared with other nanocomposites. On the other hand, Al/B\({}_{4}\)C and Al/TiC nanocomposites fabricated with a powder metallurgy route have not previously been investigated in a comparative study. Therefore, an investigation into these nanocomposites was performed.  
nanocomposites; powder metallurgy; microstructure; physical properties; micro-hardness; wear
## 2 Materials and Methods
### Sampling Typology  
The sampled environmental matrices are marine water, river water, and sediments. For the sake of brevity, abbreviations of the names of the samples reported in brackets have been used: marine sediments (SM), deep sea waters (AP), superficial marine waters (AS), coastal sediments (SC), coastal waters (AC), river sediments (SF), and river waters (AF).  
The total number of expected samples is 160, divided as follows:  
SF + AF: 16 samples (8 + 8).  
SM + SC: 51 samples (42 + 9).  
AP + AS + AC: 93 samples (42 + 42 + 9).  
The labeling of the samples was performed according to the following scheme:  
* The marine coastal sampling points (AC, AS, AP, SC, SM) are identified by an identification code of the transect: \"TX_\", where the X indicates the identification number of the transect. For river samples, the code becomes \"FX_\", with X the identifier of the point.
* The three digits of the bathymetric depth in meters (unsigned) are added to this prefix: from \"000\" for the points on the coast and along the river, to \"100\" for the bathymetric depth 100 m.
* Finally, the second underscore \"_\" and two letters indicate the type of sample.  
For example, _T0_040_AP_ indicates the deep seawater sample taken a few meters from the seabed of the bathymetry point 40 m along transect 0.
## 3 Numerical Modelling and Analysis of Ultrasonic Inspection of Grounded Flat Steel
### SH0 Wave Propagation Characteristics and Attenuation Characteristics in Grounded Flat Steel  
The grounding flat steel of a power transmission tower is buried underground for a long time, and the surrounding environment is complex; consequently, the propagation process of the SH0 wave in the grounding flat steel is affected by soil pressure, temperature, transmission distance, and other factors, resulting in attenuation of the ultrasonic signal. To investigate the influence of the detection distance on SH0 wave propagation attenuation, we assume that the grounded flat steel is under the same ambient temperature and the soil pressure without considering the soil around the flat steel to absorb the attenuation of the guided wave. The propagation cloud of the SH0 wave in the flat steel at different moments obtained by simulation is shown in Figure 5.  
During the SH0 wave propagation process, there are no other form of ultrasonic waves except for boundary echoes, and mode change does not occur. Due to the superposition of  
Figure 5: SH0 wave propagation displacement clouds in flat steel in different moments.  
the boundary echo on both sides of the grounded flat steel, the ultrasonic waves propagate faster in the central beam region than in the side flaps.  
In the model, the signal receiving points are set at 200, 400, 600, 800, and 1000 mm from the excitation source and the amplitude of the guided wave signal at each receiving point is solved. The guided wave signal at each site and the characteristic curve of SH guided wave attenuation in the flat steel are shown in Figure 6a and 6b, respectively.  
The amplitude of the guided wave decreases with the increase in the guided wave propagation distance. The equation for the linear attenuation coefficient \(\alpha\) of the guided wave is as follows:  
\[\alpha=[\lg(A_{1}/A_{2})]/L \tag{4}\]  
where \(A_{1}\) is the signal amplitude of the guided wave at the start receiving point, \(A_{2}\) is the signal amplitude of the guided wave at the end receiving point, \(L\) is the distance between the start and end monitoring points, and the attenuation coefficient of the SH0 wave in the flat steel is 0.0016 dB/mm, indicating that the long-distance detection of grounded flat steel by ultrasonic SH0 guided wave is feasible.
## 3 Boost Converter  
Figure 3 shows a boost converter that includes its main non-idealities (losses). Thus, \(r\) stands for the internal inductor resistance and \(R_{S}\) for the power switch resistance in \"on\" state (closed). The leakage resistance of the power switch in \"off\" state (open) is so large that it can be considered an open circuit. \(R_{D}\) is the forward (on) resistance of the freewheeling diode (the leakage resistance in off condition is so large that it can be considered an open circuit) and \(V_{\gamma}\) is its threshold voltage. The capacitor is considered an ideal element, as its series resistance has a very low value and its leakage resistance is very large (for capacitors of certain quality) whereby it can be neglected in parallel with the load.  
Figure 3: Boost converter circuit including its main non-idealities (losses).
## 5 Evaluation
### Performance Trade-Offs of Delegated Policy Evaluation  
Figure 16 illustrates the added value of local policy evaluation versus delegated policy evaluation. This experiment specifically focuses on the authorization for data sharing between the Mobile Health Tracker microservice and the Medical Records service. The amount of health parameters to be processed on the left-hand side (data intensive policy) is 100-times larger than on the right-hand side (data non-intensive policy). This reflects a diabetes patient that manually measures his/her blood glucose values up to eight times a day, versus a diabetes patient with a Continuous Glucose Monitoring (CGM) system measuring as often as once per minute. In the local policy evaluation setting, all relevant data are first transferred from the Mobile Health Tracker service to the Medical Records service. The authorization  
Figure 15: Systematic scalability analysis of delegated policy evaluation with the Universal Scalability Law (USL).  
policy in Figure 13 is computed at the latter service. This is how the traditional policy evaluation engine works, centralizing all the data and attributes in order to evaluate the authorization policy. Given that the computational complexity to process the health parameters is the same for either microservice, Figure 16 clearly demonstrates on the left-hand side the effectiveness of evaluating the data-intensive policy where the required data reside.  
The performance improvement of delegated policy evaluation for policies that are less data intensive may not be so outspoken (or even less optimal, as shown on the right of Figure 16) as local policy evaluation. However, the fact that sensitive data do not leave the boundaries of the microservice is clearly an advantage from a security and privacy point of view. For scenarios where security and/or privacy breaches are not a concern, one can find a trade-off point where there is no significant difference between either approaches for policy evaluation. While this trade-off point can be determined empirically in a static setting, any additional load on the involved microservices may shift this trade-off point in either direction.
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 2 Method  
According to the realizing domain, FWI can be divided into time domain, frequency domain, and Laplace domain. Time domain FWI is relatively easy to implement, but it cannot effectively achieve multi-scale inversion from long wavelength to short wavelength, and the inversion tends to fall into local small values [27]. The laplace domain FWI method [28] reduces the possibility of inversion problems falling into local minima. In contrast, the frequency domain FWI has a natural multi-scale. With low-frequency inversion to obtain the initial model and mid- to high-frequency inversion of medium wave-number and short wavelength information, it can obtain more accurate physical properties of the underground medium. Therefore, this article mainly introduces the frequency domain FWI method into the field of tunnel ahead prospecting. Combined with the special environment of the tunnel, more accurate wave velocity information of the rock in front of the tunnel face is obtained.
## 3 Proposed Vision-Based Arching Detection Method  
The process of arching detection in this paper includes five steps, as shown in Figure 7. The first step is to preprocess the inspection data by the CA-DTW mile-point alignment method, which is introduced specifically in Section 2.2. Then, inspired by human vision, the original sequences are converted into 2D images (step 2), and the data augmentation is used to balance the number of \"Arching\" and the number of \"Normal\" (step 3). The fourth step is to design the architecture of the CNN-based detection model and optimize the model parameters. The fifth step is to train the detection model repeatedly and test its performance using the established dataset. Through the above steps, the effective arching detection can be achieved. In this section, the details of the dataset establishment and CNN-based detection model are mainly introduced.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Inspection Date**} & \multicolumn{2}{c}{**Unaligned**} & \multicolumn{2}{c}{**Aligned**} \\ \cline{2-5}  & **E/mm** & \(\rho\) & **E/mm** & \(\rho\) \\ \hline
19 January & 860.41 & 0.7735 & 473.28 & 0.9265 \\
10 February & 1936.98 & \(-\)0.1641 & 515.89 & 0.9131 \\
23 February & 1075.79 & 0.6300 & 569.94 & 0.8925 \\
7 March & 1199.81 & 0.5939 & 572.70 & 0.8748 \\
8 July & 1850.71 & 0.0192 & 663.82 & 0.8380 \\
15 July & 1584.62 & 0.3777 & 808.91 & 0.8151 \\
23 July & 1978.14 & 0.0718 & 985.99 & 0.7553 \\
8 August & 2000.36 & 0.0576 & 841.28 & 0.7838 \\
23 August & 1971.47 & 0.0310 & 754.74 & 0.8167 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Deviation statistics of sequences compared to the reference sequence.  
Figure 6: Several arching segments of 15 July 2017 taking the data 8 January 2017 as reference. (**a**) The segment between K1086 + 600 and K1086 + 650; (**b**) The segment between K1094 + 870 and K1094 + 920.
## 2 Related Work
[34] mounted the smartphone on the bicycle and collected the stream data of the accelerometer and GPS sensor, then BFRS were detected based on abnormal changes using the selected threshold. However, it is the result based on the direct detection from acceleration, which can be affected by the noise of the original recordings. Different from the previous work, [PERSON] et al. [7] applied the continuous wavelet transform (CWT) to extract features of acceleration time series using the collected multi-sensor data from smartphones. It took the spatial transformation for the three-axis acceleration dataset, computed the coefficient matrix of CWT, and detected the BFRS based on the threshold of the coefficient. There are also some BFRS detection methods based on multi-sensor data of smartphones that utilize machine learning methods, such as random forest, SVM, KNN [23; 25; 35], or even the deep learning methods [36; 37]. To encode the BFRS feature from collected multi-sensor recordings from smartphones, [PERSON] et al. [17] designed the vibration detection system based on smartphones, accelerometers, and cameras, installed them in the vehicle, conducted the short-time Fourier transform to acceleration, and detected BFRS using the CNN. However, the acceleration recordings were collected at a specific speed with fixed multiple sensors, which limited the feasibility of the application. As described from the above methods, the sensor-based method can collect the actual road conditions and detect BFRS without the limitations of intuitive vision-based BFRS detection methods, with the advantage of multi-sensors and crowd-sourced smartphone datasets. The utilization of non-professional multi-sensor smartphones offers significant advantages, such as accessibility and crowd-sourced data collection, enabling widespread monitoring of road surface conditions. However, the inherent noise in data collection, stemming from varied sensor quality and the randomness of smartphone orientation, presents substantial challenges. These factors lead to non-homogeneous, discrete-sampled stream data that vary under different driving conditions. Feature encoding and detection are crucial in this context as they can effectively handle the uncertainty and variability of data quality, extracting meaningful patterns and insights from the noisy, complex datasets collected by smartphones. To deal with the non-homogeneous, discrete-sampled multi-sensor stream data from smartphones, the bump feature is analyzed based on the spectrum model; BFRS is detected using different feature channels and is further represented as cluster centers. In addition, the specific deviation and computation of IRI are not going to be conducted in this article, as are the shape and measurement accuracy of BFRS, since the multi-sensor stream data from the crowd-sourced smartphone device just has limited accuracy and cannot be taken as the replacement of professional surveying tools.
## 6 Results
### Classification Performance
#### 6.1.1 Statistical Comparison  
To evaluate the statistical significance of the obtained results, we first applied the Friedman test as proposed by [PERSON] [58] by calculating the Friedman asymptotic significance and \(p\)-values for eight measures (Table 5). The results show statistically significant differences between the methods for all eight metrics. To statistically compare the CNN-TL-DE method with the two baseline methods CNN and CNN-TL, the Wilcoxon signed-rank test was applied. If the Wilcoxon test resulted in statistically significant difference between the two methods, the method with the better average rank can be regarded as the better method. Our proposed CNN-TL-DE method significantly outperforms the other two methods in all six classification performance metrics (acc, AUNP, precision, recall, \(F\)-1 score, and [PERSON]'s kappa). On the other hand, the CNN-TL-DE method lags significantly behind with regard to computational complexity (consumed training time and number of epochs).  
Figure 4: Comparison of AUNP results for the compared methods over 10 folds.  
Figure 5: Comparison of [PERSON]’s kappa results for the compared methods over 10 folds.