esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
All data analysis was executed in the \"R\" statistical environment [34]. Multivariate analysis was performed on binned spectra without any a priori knowledge of the metabolites present. Multilevel partial least square analysis (mPLS) [35; 36] was performed to obtain data reduction (R script developed in-house). Support vector machine [37] applied on the first nine mPLS components was used for classification purposes. Models were evaluated by means of 100 cycles of a Monte Carlo cross-validation scheme (in-house-developed R script). In brief, 90% of the pairs of data, selected at random at each iteration, were used as a training set to build the model. Then, the remaining 10% was tested, and sensitivity, specificity, and accuracy (calculated according to the standard definitions) were assessed.  
Univariate analysis was conducted directly on the spectral regions associated with the metabolites/lipoproteins present in all serum samples at concentrations above the detection limit (>1 uM). Metabolites and lipoprotein-related parameters were identified and quantified using the Bruker IVDr quantification platform [38]. Metabolites whose levels were lower than the limit of quantification (LOQ) were imputed with half the LOQ (Table S1). Nonparametric Wilcoxon signed-rank test was used to infer intraindividual differences between the two timepoints. The \(p\)-values were adjusted for multiple testing using the false discovery rate (FDR) procedure with Benjamini-Hochberg [39] correction at \(\alpha\) = 0.05. Wilcoxon rank-sum test was used to infer differences between metabolites/lipoproteins of free-from-disease and relapsed CRC patients. The \(p\)-values were not adjusted for multiple testing because the group of relapse patients is small, and therefore the correction would be too severe, increasing the risk of missing promising biomarkers. However, we are aware that this could increase the risk of a type I error.  
Univariate analysis on clinical data was performed using the Fisher test for categorical variables and the ANOVA test for continuous variables. Polyserial correlations between ordinal clinical variables (pT, N, grade, stage, ECOG PS) and metabolites were calculated using the function \"polyserial\" (R package \"polycor\"). Point-biserial correlations between dichotomous clinical variables (tumor localization, sex) and metabolites were calculated using the function \"biserial.cor\" (R package \"ltm\").
## 4 Digital Twin Modelling Methodology
### Input Parameters
#### 4.2.2 Geological Rock Parameters  
The Q-value describes the stability of the underground opening. The range of the Q-value is a numeric value on a logarithmic scale and lies between 0.001 and 1000, denoting very low to exceptionally good rock quality [56]. RQD, joint number (Jn), joint roughness (Jr), joint alteration (Ja), joint water reduction factor (Jw), and SRF are the deciding factors of the Q-value prediction of the rock. Based on these factors, the Q-value is predicted by Equation (3) [57].  
\[\text{Q}=\frac{\text{RQD}_{0}}{\text{J}_{\text{n}}}\times\frac{\text{J}_{\text {r}}}{\text{J}_{\text{a}}}\times\frac{\text{J}_{\text{w}}}{\text{SRF}} \tag{3}\]  
The determination of the Q-value is made possible using geological mapping in the underground structure, on the surface, or using core logging. The true Q-value determined in the excavation site is usually more accurate. However, the rock parameters are incorporated with the already available Q-value to predict TBM's performance accurately in Equation (4) [54].  
\[\text{Q}_{\text{TBM}}=\frac{\text{RQD}_{0}}{\text{J}_{\text{n}}}\times\frac{ \text{J}_{\text{r}}}{\text{J}_{\text{a}}}\times\frac{\text{J}_{\text{w}}}{ \text{SRF}}\times\frac{\text{SIGMA}}{\text{F}^{10}/20^{9}}\times\frac{20}{ \text{CLI}}\times\frac{\text{q}}{20}\times\frac{\text{\sigma}_{\text{\Theta}}}{ 5} \tag{4}\]  
where F = average cutter load through the same zone, SIGMA = rock mass strength estimate (MPa) in the same zone, \(\text{CLI}\) = cutter life index, q = quartz content in percentage terms, and \(\text{\sigma}_{\text{\Theta}}\) = induced biaxial stress on tunnel face. In this study, two Q-values are used to predict TBM performance. The site engineer determines one Q-value using geological mapping, while the TBM predicts the other. Using these two values for predicting PR compensates for most of the geological parameters influencing the TBM's performance.
## 2 Case Presentation  
We followed the guidelines of the Helsinki Declaration throughout this study. We obtained approval from the Institutional Review Board of the Chosun University (2-1041055-AB-N-01-2019-08).  
A 39-year-old Korean man was referred to the Department of Oral and Maxillofacial Surgery, with a complaint of swelling and pain in the left maxillary molar region and palatal area. There was no significant past medical or dental history. Clinical examination revealed diffuse swelling in the left cheek area with facial asymmetry. Extensive swelling was observed from the left maxillary canine to the first molar extending to the mid-palatal area. The size of the swelling was approximately 50 mm \(\times\) 50 mm. Clinical examination of the neck showed no cervical lymphadenopathy at the time of diagnosis. Panoramic radiography (ProMax(r) 2D S3, Planmeca Inc., Helsinki, Finland) revealed a radiolucent lesion with moderate to defined margins extending from the left maxilla to the maxillary sinus (Figure 1). There was no evidence of significant root resorption.  
The Computed-Tomography (CT) equipment used was an Aquilion One CT system (Aquilion ONE, Canon Medical Systems, Otawara, Japan). The scanning conditions were as follows: tube current 250 mA, tube potential 120 kV, scanning time 0.5 s/scan, and slice thickness 0.5 mm. CT images demonstrated an expansile lesion obstructing the entire left maxillary sinus just below the orbital floor. The soft tissue mass extended to the superficial fascia of the face and protruded into the nasal cavity. The lateral and medial walls of the maxillary sinus were damaged (Figure 2).  
Figure 1: Clinical examination: (**a**) intraoral photograph showing the lesion with swollen mass on the left buccal mucosa and palatal area; (**b**) panoramic view showing the lesion in the left upper premolar area.  
Figure 2: The Computed-Tomography (CT) image: (**a**) alveolar bone was damaged and the lesion extended to the palatal area; (**b**) the lesion filled the maxillary sinus and extended into the nasal cavity.  
Incisional biopsy was performed, and the patient was diagnosed with malignant fibrous histiocytoma. Based on the results of the biopsy, total maxillectomy of the left maxilla was planned.
# Two-Color Operation of a Soft X-ray FEL with Alternation of Undulator Tunes
## 4 Simultaneous Measurements of Two Colors
### Spectral Measurements  
FLASH2 provides an invasive grating spectrometer located at the beam line FL22 providing a wavelengths range of 10 nm to 62 nm. The spectrometer is equipped with an intensified camera (IDDC) which can select one bunch of a train [28]. Usually, only one wavelength is monitored by the spectrometer. Simultaneous observation of two colors may be possible with the help of higher grating orders as shown in Figure 6. Here, the 4 th grating order of the first color at 39 nm and in the 3 rd order of the second color at 38 nm are used. Higher grating orders are preferred due to better resolution but suffer in signal-to-noise ratio. The first color and the second color are recorded with 9.83 nm/126.13 eV and 12.78 nm/97 eV, with a bandwidth of 0.7% and 0.4%. Since the spectrometer records the vertical beam profile, the measurement can monitor the relative pointing of the two colors.  
The simultaneous detection of two colors in single shots allows for observing the correlation between the pulse energies. An example of such a measurement for wavelengths 15 nm and 17 nm is presented in Figure 7. One can observe, in fact, an anti-correlation which is a feature of the nonlinear regime of operation of the two-color scheme as explained above.  
Figure 5: Pulse energies of two XUV beams measured with the GMD+OPIS method. The first color (odd undulator segments) is scanned between 13.7 nm and 20.6 nm (shown in blue). The second color (even segments) stays at 22.6 nm (shown in red).The figure was published in [17].  
Figure 6: Sum of spectra with 3 rd grating order of color 2 (12.78 nm/97 eV) and 4 th grating order of color 1 (9.83 nm/126.13 eV). The photon beam intensity relation was due to experimental needs during the two-color test experiment.  
Continuous monitoring of spectral features is possible using the OPIS spectrometer. Figure 8 shows a spectral measurement with the FLASH2 OPIS, where one of the colors was kept constant while the other one was scanned. The measurement varied the first color from 13.6 nm to 20.6 nm in seven steps while the second wavelength was kept at 22.6 nm. We clearly observed the increase in resulting photon pulse energy with longer first wavelengths \(\lambda_{1}\). This online monitoring is needed as an influence of changing one wavelength to the other was observed.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 1 Introduction
* _Continuous authentication strategy adopting smartphone sensor behavior analysis:_ The unique user behavioral data acquired by accelerometer and gyroscope sensors in smart phone (UE) is processed and characterized by time and frequency domain features. Subsequently, appropriate activity recognition implementation is conducted, where the individual behavior profile is evaluated with the pre-defined biometric parameter to reveal the real-time personal activity level. In this case, continuous authentication is performed with the adopted biometric parameter periodically. Security analysis demonstrates that the proposed scheme is able to provide adequate security assurance. Moreover, performance analysis proves that the proposed design is efficient compared with the state-of-the-art authentication schemes. To the best of our knowledge, we are the first to design the D2D authenticating and key distributing method with biometric continuous authentication. Potential scenarios include disaster rescue and medical aid in harsh environment. The remaining contents of the paper are constructed as follows. Section 2 briefly introduces the corresponding research achievements. Section 3 illustrates the significant preliminaries and the designed system model so that the reader is able to acquire a better understanding. Section 4 introduces the proposed D2D certificateless authentication scheme in detail. Section 5 presents the proposed continuous authentication strategy. Section 6 proves the formal security analysis. Section 7 demonstrates the performance analysis. Finally, the conclusion is drawn in Section 8.
## 4 Case Study  
### Site Condition  
On 21 September 1999, Taiwan was struck by its most significant earthquake of the 20 th century, with a moment magnitude (\(M_{W}\)) of 7.6 and a local magnitude (\(M_{L}\)) of 7.3. The epicenter was located near Chi-Chi, which led to the event being named the Chi  
Figure 3: Flowchart of liquefaction hazard analysis.  
Chi earthquake. This catastrophic quake claimed over 2400 lives and caused extensive damage to buildings, bridges, dams, and infrastructure. One of the primary reasons for the severe structural damage was soil liquefaction and ground settlement induced by the earthquake. After the earthquake, Moh and Associates (MAA) [24,25] conducted a detailed site investigation at liquefaction sites and neighboring zones, including Yuanlin City in Changhua County. Of these site investigation data, one borehole labeled BH-29 with definite evidence of liquefaction is taken as an example in this study to demonstrate the build-up of fragility curves for soil liquefaction and its application. The borehole site of BH-29 is located near an old well filled with boiling sand during the Chi-Chi earthquake, as illustrated in Figure 4a. The state of the ground damage is classified as \"Medium damage\" or \"Moderate liquefaction\" according to the Summary Report of MAA [24] and the criteria shown in Tables 1-3.  
The soil profile of the BH-29 site is depicted in Figure 4b. Based on the site investigation, the soil up to a depth of 20 m below the ground surface predominantly consists of alluvial silty sands and silts, with the exception of clay layers found at depths of 7.9 to 9.8 m and 19.6 to 20.0 m. The SPT-N values are below 10 at shallow depths. The groundwater table is situated at a depth of 2.0 m.  
For the Chinan Plain in Taiwan, the relationship between peak ground acceleration (PGA) at soft and hard site conditions is illustrated in Figure 5[5]. Given that the BH-29 site is located near the Chinan Plain, the median relationship curve from Figure 5 is used to account for the effects of soft site conditions when assessing the seismic fragility of soil liquefaction. Furthermore, uncertainties regarding the SPT-N values and the groundwater table will be incorporated into the analyses.  
Figure 4: The old well and the soil profile of the BH-29 site near the well. (**a**) The old well filled with boiling sand [26]. (**b**) Soil profile of the BH-29 site.
## 3 Methodology
### YOLO for Structural Components Detection
#### 3.2.1 Architecture of YOLO  
As shown in Table 1, the neural network of YOLO used in this paper has 24 convolution layers with different core sizes and two fully connected layers, and the max-pooling technique is applied to extract the main characteristics as well as to reduce the computational complexity [45].  
Convolution layer: The purpose of convolutional layers is to extract features from structural drawing images by convolving the input data with convolutional cores of fixed size and to produce feature maps as an output. According to Table 1, the convolutional layers of YOLO in this paper include \(3\times 3\) and \(1\times 1\) convolutional cores. The \(3\times 3\) convolutional cores are used for convolution, while the \(1\times 1\) cores are used to reduce the number of input channels, decrease the parameters generated in the neural network, and lighten the computation burden when training YOLO.  
Max-pooling layer: The max-pooling layer segments the feature map according to the spatial position of the feature matrix in the feature map. The maximum value obtained from each segment area is then used as a new eigenvalue. Table 1 reveals that the filter of max pooling in this paper is \(2\times 2\) with stride 2. It means that the maximum values, taken respectively in each segment area, with 2 strides, will be as the new eigenvalue, and the new feature map is generated based on these eigenvalues. After max-pooling operation, the new feature map is reduced by four times while the important information is remained in the former feature map. In addition, the image is abstracted to a higher level, which allows the following convolutional layers to extract features from it at multiple scales.  
Fully connected layer: The fully connected layer mainly deals with the dimensionality reduction problem, which turns the input 2D feature matrices into feature vectors, to facilitate the prediction and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Layer** & **Name** & **Filter** & **Kernel Size/ Stride** & **Layer** & **Name** & **Filter** & **Kernel Size/ Stride** \\ \hline
0 & Conv.1 & 64 & \(7\times 7/2\) & 15 & Conv.13 & 256 & \(1\times 1/1\) \\
1 & Maxp.1 & / & \(2\times 2/2\) & 16 & Conv.14 & 512 & \(3\times 3/1\) \\
2 & Conv.2 & 192 & \(3\times  
classification of structural components in the output layer. In the neural network of YOLO, two fully connected layers are connected to the feature map, which has undergone the process of convolution and max pooling to generate an output tensor.  
Moreover, in order to increase the nonlinearity of each layer of the neural network in YOLO, a linear activation function is applied for the final fully connected layer, while the leaky rectified linear activation function [45] is utilized for the rest of the layers and is shown as:  
\[\varnothing(x)=\left\{\begin{array}{ll}x,&x>0\\ 0.1x&x\leq 0\end{array}\right., \tag{4}\]
# Research on Vehicle-to-Vehicle MIMO Wireless Channels in Various Tunnels
## 6 Conclusions  
In this paper, we have proposed a geometry-based channel model for describing the V2V communications in the practical V2V scenario. The correlation properties, such as the space CCFs, temporal ACFs, frequency CCFs, and Doppler PSDs have been derived and investigated. Numerous simulation results have shown that the propagation characteristics of the proposed V2V channel model are impacted by the physical properties of the tunnel scenarios, the Rician factor, as well as the motion properties of the transmitter and receiver. These observations in principle validate that the proposed modeling solutions are practical for characterizing the propagation characteristics of the V2V channels. As a future work, we can conduct a variety of measurements to further validate the effectiveness of the proposed channel model.  
Data curation, Z.L.; Investigation, H.L.; Methodology, G.S.; Project administration, T.L.; Writing--original draft, S.W.; Writing--review & editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 61971167 and 62101274, National Mobile Communications Research Laboratory, Southeast University, grant number 2021D03.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 1 Introduction
### Methodology  
The content analysis method was used for this review study. Relevant studies were sourced from the different scientific databases. Some of the searched databases includes Google Scholar, Science Direct, IEEE Explore, SpringerLink, and Web of Science. The following word combination or phrases were used to search for articles for this study: \"blockchain-based internet of vehicles\", \"Machine learning in internet of vehicles\", \"Blockchain-based intelligent transportation system\", \"Blockchain and ML convergence in IoV\", \"Blockchain in vehicular transportation\", \"Blockchain and deep learning integration in IoV\", and \"Blockchain based smart transportation system\". This was done to narrow our search to only studies on the applications of blockchain or deep/machine learning in the IoV systems. The resulting articles after sorting were analyzed and used for this review. The materials used were grouped into three categories:  
\begin{table}
\begin{tabular}{c c} \hline
**Acronyms** & **Definitions** \\ \hline OBUs & On Board Units \\ \hline PDR & Packet Delivery Rate \\ \hline P2P & Peer-to-Peer \\ \hline QR & Quick Response \\ \hline RAN & Radio Access Network \\ \hline R-CNN & Regions with Convolutional Neural Networks \\ \hline RNN & Recurrent Neural Networks \\ \hline RSU & Roadside Unit \\ \hline SA & Simulated Annealing \\ \hline SDN & Software Defined Networking \\ \hline SV & Support Vector \\ \hline SVM & Support Vector Machine \\ \hline SVR & Support Vector Regression \\ \hline TA & Trusted Authority \\ \hline TCNN & Temporal Convolutional Neural Network \\ \hline TPS & Transactions Per Seconds \\ \hline UAV & Unmanned Aerial Vehicle \\ \hline UL & Unsupervised Learning \\ \hline VANET & Vehicular Adhoc Network \\ \hline VCS & Vehicular Communication Systems \\ \hline V2P & Vehicle-to-Pedestrian \\ \hline V2I & Vehicle-to Infrastructure \\ \hline V2R & Vehicle-to-Roadside Unit \\ \hline V2S & Vehicle-to-Sensor \\ \hline V2V & Vehicle-to-Vehicle \\ \hline V2I & Vehicle-to Infrastructure \\ \hline WAVE & Wireless Access in Vehicular Environments \\ \hline YOLO & You Only Look Once \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._* Studies on blockchain applications for IoV
* Studies on ML applications for IoV
* Studies on the integration BC and ML for IoV
## 4 Results and Discussion
### Reconstruction of a 3D Temperature Field in Acoustic Thermometry Based on Tucker Decomposition  
In our model design, we utilize a grid of \(40\times 40\times 31\) temperature nodes to cover a space ranging from \(1\) to \(1.45\) m in height, with each node measuring \(0.025\times 0.025\times 0.025\) cubic meters. We apply the Tucker decomposition algorithm to reconstruct these 31 sections. Figure 13. displays the temperature field of the model, the reconstruction results, and the errors for experimental condition 5. Despite the loss of some details, the reconstructed temperature field retains the main characteristics of the model. The error range for most of the reconstructed temperature field lies between \(-100\) and \(200\,^{\circ}\)C, indicating that it is feasible to characterize the three-dimensional temperature field by reconstructing all sections. Notably, after an increase in the primary airspeed, the temperature field gradually loses its unique squared-circular shape, showing reduced regularity under higher primary air speeds compared to lower ones, as well as greater reconstruction errors in irregular high-temperature areas. The significant reconstruction error rate in experimental condition 5 is partly due to its absence in the prior dataset and partly due to the shape deviation in the temperature field caused by the increased primary airspeed. Therefore, categorizing the data and selecting the prior dataset based on the experimental conditions might represent an effective strategy to enhance the reconstruction results.  
Figure 12: Reconstruction error distribution in experimental condition 2 with Gaussian noise at \(1\) m. (**a**) Simulation (left) and reconstruction (right) of temperature distribution at \(z=1\) m in experimental condition 2. (**b**) Reconstruction error position distribution. (**c**) Numerical distribution of reconstruction error.