esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Surface Displacement  
The surface displacement value occurring in the S and P tests consists of 2 components, as expressed in Equation (1).  
\[\sum\mathrm{S_{d}}=\mathrm{u_{ys}}+\mathrm{u_{ypv}} \tag{1}\]  
where, \(\mathrm{S_{d}}\) surface displacement; \(\mathrm{u_{ys}}\) displacement of backfill; \(\mathrm{u_{ypv}}\) pipe vertical deflection  
The surface displacement values derived from the S test were utilized in analyses to verify and calibrate the material parameters of the backfill, aligning with findings from the experimental studies.  
The \(\mathrm{E_{50}}\) of the backfill was computed from triaxial (CD) tests to be 24 MPa. However, the best agreement between the surface displacements obtained from the analyses and the test surface displacements was achieved by using the backfill material parameters in Table 1. These values were also used in the analyses for the P tests. Figure 5 presents the variation in surface displacements resulting from both the S and P tests.  
The surface displacement value for the S test is 8.76 mm, while for the P test it is 10.71 mm due to the 320 kPa surcharge stress. The data derived from the S and P tests show close agreement, with the surface displacement values obtained from the P test generally being greater than those found for the S test.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Backfill Soil Properties**} \\ \hline Unit Weight (kN/m3) & Model & \(\
u\) & c\(\mathrm{c}\) (kPa) & \(\mathrm{(^{\circ})}\) & \(\mathrm{E_{50}}\) (MPa) & \(\mathrm{E_{ood}}\) (MPa) & \(\mathrm{E_{ur}}\) (MPa) & Rint-1 \\  & & & & (kPa) & (\(\mathrm{c}\)) & (\(\mathrm{c}\)) & (\(\mathrm{m}\)) & (\(\mathrm{m}\)) \\ \hline
17 & H.S & 0.33 & - & 40 & 28 & 28 & 70 & 0.17 \\ \hline \multicolumn{8}{c}{**Pipe Properties**} \\ \hline Density (kg/m3) & Model & \(\
u\) & E & EA & EI & Rint-2 \\  & & & (MPa) & (kN/m) & (kN/m2/m) & pipe-soil \\ \hline
970 & L.E & 0.45 & 450 & 9000 & 0.30 & 0.50 \\ \hline \hline \end{tabular} Note: H.S, Hardening soil; L.E, linear elastic; E, modulus of elasticity; \(\mathrm{E_{50}}\), secant elasticity modulus; \(\mathrm{E_{ood}}\), tangent oedometric modulus; \(\mathrm{E_{ur}}\), unloading-reloading modulus; Rint, coefficient of interaction.  
\end{table}
Table 1: Parameters used in Plaxis 2D analysis.
# A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
## 3 Experiments and Analysis  
For testing the performance of the magnetic field imaging system, circular magnets, a magnet array, defective steel plates, magnetic suction wireless charging coils, and a metal sample were used in the study cases. This system measures the magnetic field perpendicular to the sample surface. The distance between the unpackaged Hall linear array and the object under test is 1 mm in each study case. The step size of each movement of the X-Y axis motorized rail is set to 255 \(\upmu\)m, which is the same size as the spacing between adjacent Hall elements. To facilitate research and analysis, we converted the test results of the magnetic field imaging system into 2D/3D magnetic field images.
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 4 Results and Discussion
### Limitations and Future Works
This aids in verifying the data integrity at each layer, especially when highly trustworthy predictions are needed. By implementing Merkle trees in different types of DNN structures, we can enhance the protection of data integrity by the model. However, it is crucial to carefully balance computational costs and performance overhead to ensure practicality and feasibility. In summary, while the Merkle-Transformer model has shown significant potential in processing financial data, it also faces many challenges and limitations. We hope that future research will address these limitations, continuously optimizing and refining the model to better serve the needs of the financial industry, and contribute new ideas and frameworks to the research of machine learning in the domain of financial security.
## 2 Dimensional Analysis of Cutterhead Rotation Speed
### \(\pi\)-Theorem Modeling  
Using the selected base variables, the remaining variables are combined into dimensionless groups as follows:  
\[\pi_{1}=\frac{\omega D}{v},\ \pi_{2}=\frac{V_{c}}{v},\ \pi_{3}=\frac{P}{D},\ \pi_{4}=\frac{T}{FD},\ \pi_{5}=\frac{F_{8}}{F} \tag{5}\]  
Based on Equation (4), the cutterhead rotation speed can be expressed as: \(\pi_{1}=f(\pi_{2},\pi_{3},\pi_{4},\pi_{5})\).  
By integrating the dimensionless groups mentioned above:  
\[\pi_{1}=\frac{\omega D}{v}=f(\frac{V_{c}}{v},\frac{P}{D},\frac{T}{FD},\frac{F_{ 5}}{F}) \tag{6}\]  
\(\pi_{4},\pi_{5}\) primarily represent the influence of load factors on cutterhead rotation speed. These groups are combined through a product transformation into a single dimensionless group, denoted as \(\frac{TF_{5}}{DF^{2}}\), which captures the impact of load-related operational factors on rotation speed. The other two dimensionless groups, \(\pi_{2}=\frac{V_{c}}{v},\pi_{3}=\frac{P}{D}\), represent the effects of speed and length factors, respectively, on the cutterhead rotation speed.  
From a mechanical and operational perspective, the interactions among the three dimensionless groups are minimal and can be approximated as linear relationships. Therefore, the model can be expressed as:  
\[\frac{\omega D}{v}=k_{1}\frac{V_{c}}{v}+k_{2}\frac{F_{8}T}{DF^{2}}+k_{3}\frac {P}{D} \tag{7}\]  
Equation (7) presents a linear model derived from dimensional analysis to describe the relationship between the cutterhead rotation speed \(\omega\) and key operational and mechanical parameters. Each dimensionless term in the model represents specific contributions: the first term, \(k_{1}\frac{V_{c}}{v}\), indicates the influence of the conveyor speed to advance speed ratio; the second term, \(k_{2}\frac{F_{8}T}{DF^{2}}\), captures the combined effects of thrust, torque, and cutterhead diameter as load-related factors; and the third term, \(k_{3}\frac{P}{D}\) reflects the impact of the penetration depth to cutterhead diameter ratio. This linear model not only simplifies the analysis of complex mechanical systems but also provides a robust theoretical framework for predicting cutterhead performance under various operational conditions.
# The Power of Machine Learning Methods and PSO in Air Quality Prediction
## 2 Materials and Methods
### Machine Learning Regressors  
Among the standard machine learning approaches, linear regression, random forest, support vector regression, and XGBoost were selected for use in the study. XGBoost [16] performs strongly on small datasets by preventing overfitting thanks to its regularization techniques, while SVM [17] can effectively learn non-linear relationships using kernel trick. Random forest [18] has the capacity to make accurate predictions on small datasets with its ensemble structure and robustness to noise. Linear regression [19] provides fast and effective results with its simple structure and low risk of overfitting in small datasets.
## 3 Results
### VLSI Implementation and Test  
In order to improve the energy efficiency of the proposed GPS signal acquisition scheme, two types of acquisition engines were implemented with the proposed MP/DD acquisition method. One is a coarse-grained acquisition engine (CGAE) used for narrowing the parameter scope in the first dwell. The other type is a fine-grained acquisition engine (FGAE) applied to final signal parameter estimation in the second dwell.  
The CGAE generates correlation results in the first dwell, which then preselects possible parameters according to the multi-peak method. Considering the initial parameter scope continuity, a matched filter is applied to CGAE and the circuit architecture is illustrated in Figure 4. The signal data is processed with matched filter, Doppler mixing, and then accumulated in static random access memory (SRAM); finally, the multi-peak selection module gives the narrowed parameter scope.  
The fine-grained acquisition engine is used to generate correlation results and estimates the final parameter from the preselected parameter scope, according to the maximum value strategy. Considering the discreteness of the preselected parameters, the fine-grained acquisition engine adopts many acquisition channels, and each channel includes an independently-configurable correlator and mixer, and its circuit architecture is shown in Figure 5.  
The acquisition process is carried out with the software scheduling. The coarse-grained acquisition engine gives the preselected parameter, and the fine-grained uses the preselected parameter to estimate the final parameter; the coarse-grained and fine-grained engines use the pipeline alternately to work, and thus the hardware achieves a high utilization rate.  
Figure 4: The very-large-scale integration (VLSI) architecture of the coarse-grained acquisition engine. SRAM: static random access memory.  
Moreover, the GPS signal acquisition engine was integrated in a System-on-Chip (SoC) to verify its performance. Its die photograph using a 40 nm complementary metal oxide semiconductor (CMOS) process and test environment are shown in Figure 6a,b. We verified the performance of the MP/DD algorithm and architecture. The test chip contained two operation modes--one is the conventional DD/MAX algorithm, and the other is the MP/DD algorithm proposed in this paper. As shown in Figure 7, the average acquisition energy of GPS acquisition was measured under two modes, and the channel number of FGAE was 160 in VLSI implementation.  
Figure 5: The VLSI architecture of fine grained acquisition engine (FGAE).  
Figure 6: (**a**) The die photo of Global Positioning System (GPS) signal acquisition engine with 40 nm complementary metal oxide semiconductor (CMOS) technology; (**b**) Test environment, including Sprint GPS simulator, power source, and test board.
# Synchronization and Application of a Novel Hyperchaotic System Based on Adaptive Observers
## 2 Materials and Methods
### The Proposed 4-Dimensional Hyperchaotic System  
The mathematical model of the proposed hyperchaotic system is shown in (1):  
\[\begin{array}{l}\dot{x}_{1}=-12x_{1}+30x_{2}\\ \dot{x}_{2}=bx_{1}-x_{2}-x_{1}x_{3}\\ \dot{x}_{3}=ax_{2}+x_{1}x_{2}-x_{3}+x_{4}\\ \dot{x}_{4}=-x_{1}b+x_{1}x_{3}-2x_{4}\end{array} \tag{1}\]  
In the system (1), the variables \(x_{1}\), \(x_{2}\), \(x_{3}\) and \(x_{4}\) represent states, while \(a\) and \(b\) are positive parameters. The system (1) demonstrates chaotic behavior for specific values of tuned parameters and the initial conditions that are provided. Notably, the system is distinguished by its independence from the initial conditions. The examination of various initial conditions reveals that under specific parameter values, the system can manifest chaotic, hyperchaotic, periodic and quasi-periodic behaviors.
## 3 Results and Discussion
performed Raman micro-spectroscopy of the nucleus and cytoplasm of human colon tissues for cancer diagnosis [33]. PLS-DA analysis, with leave-one-patient-out cross validation, yielded excellent results (98.4% sensitivity and 97.5% specificity) for spectra measured in the cytoplasm region, and good results (84.0% sensitivity and 87.0%  
Figure 5: Prediction values (1: healthy cells; \(-1\): cancerous cells) for the Raman nucleus spectra of the calibration (**a**) and test (**b**) set. The prediction values for Raman cytoplasm spectra are displayed in (**c,d**). The red ellipses correspond to cases whose uncertainty bars cross zero. The root mean square error from cross-validation (RMSEC) is 0.35 and 0.34 for the nucleus- and cytoplasm spectra, respectively. specificity) were obtained for spectra measured in the nucleus region. Unlike the results reported in the above papers, our results (i) consider cytological samples, (ii) provided excellent values of classification parameters and (iii) these parameters were estimated by means of an independent test set of spectra. As for colon cells, Raman spectra of healthy and cancer cells were investigated by [PERSON] [34]. She stated that PLS-DA yielded effective cell classification, with excellent sensitivity and specificity; nonetheless, the data were analysed by means of a cross-validation procedure, without creating an appropriate test set for estimating the classification performance. Overall, excellent classification results of healthy and cancerous colon samples (mainly biological fluids and tissues) had already been obtained previously by other authors by both PCA-LDA and PLS-DA analysis and cross-validation procedures. Therefore, the novelty of our work concerns (i) the use of two independent spectral sets, i.e., a calibration set for building and optimizing the classification models and a test set for estimating classification accuracy, and (ii) the analysis of cytological samples with Raman spectroscopy. The obtained results suggest the translation of the Raman technique into clinical settings for cytological diagnosis.
## 1 Introduction  
Searwater temperature is an important basic parameter of the ocean physical state equation, and is directly related to ecosystem protection, global warming [1]. Therefore, it is significative to monitor the seawater temperature in real-time, online, and in large scale. As an important passive component of taper fiber, optical microfiber coupler (OMC) has the advantages of large evanescent field transmission characteristics, strong light confinement ability, low loss, small size, multi-port optical transmission, etc. It has been applied in research fields such as communication, sensing, and laser [2, 3]. Especially in the field of physical parameter sensing and monitoring, it shows good application potential, and has been used for sensing of refractive index, pressure, current, magnetic field, microfluidic flowmeter, ultrasound, and alike [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]. In view of the advantages of optical fiber sensors such as high sensitivity, compact structure, easy for multiplexing and integration, and online monitoring of physical parameters, there is also a strong interest in the application of OMC in marine environmental monitoring. For example, in 2015, [PERSON] et al proposed an temperature and salinity sensor based on microfibers directional coupler; by coating 1% concentration Ethyl cellulose ethoce (EC) solvent on the coupling area of dual microfiber, the temperature sensing sensitivity of 1.17 nm/\({}^{\circ}\)C was achieved, simultaneously the dual parameter sensing function of OMC temperature and salinity was verified [14; 15]. In addition, our research group has carried out the research of OMC's three-parameter sensing characteristics of seawater temperature, salinity, and depth. We used the fusing and tapering OMC without any sensitization measures, achieved sensor response capability of 1596 pm/%o, 2326 pm/\({}^{\circ}\)C, 169 pm/Mpa, and pointed out the idea of three-parameters synchronous sensing demodulation [16]. However, in order to satisfy the monitoring requirements of the practical marine environment, it is critical to encapsulate and protect the sensor. But OMC is a four-port optical device, the excess ports and optical path become the burden of the sensor package design. Given this, it is necessary to further simplify the optical path structure and effectively solve the problem of sensor cross-sensitivity [17].  
In this paper, we propose a reflection optical path device based on optical microfiber coupler interferometer (OMCI), and theoretically analyzes its spectral transmission characteristics and seawater temperature sensing characteristics. Besides, the seawater temperature sensing experiment is validated. To the best of our knowledge, there have been few reports on related research work. Comparing other temperature sensor based on microfiber, the OMCI has a simpler structure which is convenient for packaging and multiplexing integration. More importantly, the temperature self-calibration function can be achieved because there are two sensing areas, which provided important potential for practical application in marine environment monitoring.
## 6 Results and Discussion  
### Wake Deficit  
Figure 15 shows the wake deficit for all atmospheric conditions for the Vestas turbine at different downstream locations. As expected, the deficit is larger for the stable atmospheric condition, followed by the neutral atmospheric condition, while it is significantly smaller for the convective atmosphere. At 6D and 8D, the wake has reached 85% and 90% of the free stream velocity, respectively, for the unstable atmospheric conditions. During the inspection of the wake meandering for this stability, it was also found that the wake tracking algorithm had trouble finding a well-defined wake center for all surfaces greater than 4D. This is thought to be due to the wake tracking algorithm struggling with finding a well-defined area of constant velocity resembling the rotor area beyond \(x=4\)D. This could be due to the wake breaking into multiple velocity contours, and this points towards the contour area approach not being suitable for detecting the wake center in the far wake for unstable atmospheres.  
Figure 15: Wake deficit for Vestas V80 for all atmospheric conditions at 4D, 6D, and 8D.
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 3 Application of the Methodology  
### Constructing a Bayesian Network Model for GDS Accident Scenarios  
The bow-tie (BT) model previously established for GDS accident scenarios in our earlier work [7] clearly outlines the logical interdependencies among basic events, critical events, and outcome events. This structured representation serves as a robust foundation for constructing the corresponding BN model in the present study. By strategically adapting the original BT model and adhering to the established mapping algorithm of BT to BN [28], we have developed a BN structure tailored specifically for analyzing GDS accident scenarios, as illustrated in Figure 5.  
This BN model begins with various REs that culminate in the release of natural gas (designated as the CE). It then progresses through potential accident scenarios (OEs), contingent upon the occurrence or non-occurrence of safety-barrier failure events (SBFEs) and the spatial confinement event (SCE) within the station. As depicted in Figure 5, the BN model encompasses 78 REs (filled in green), 1 CE (filled in yellow), 2 SBFEs (filled in blue), 1 SCE (also filled in blue), 51 intermediate events (IEs) (filled in white), and 6 OEs (filled in pink). The model employs distinct visual indicators to represent the conditional dependencies among events. Specifically, red arrows indicate situations where the simultaneous occurrence of all lower-level events triggers an upper-level event. In contrast, black arrows represent conditional relationships that do not require the concurrent occurrence of events. Moreover, the red dotted arrows associated with the SBFEs or SCE indicate that the occurrence of these events is negated or prevented.
# Numerical Investigation of the Hydrodynamic Characteristics of 3-Fin Surfboard Configurations
## 2 Materials and Methods
### Fluid Domain  
Figure 3a displays the 2 m \(\times\) 0.5 m \(\times\) 1 m rectangular domain used for the simulations and is a similar domain to those used in previous studies [1; 2; 3; 4; 7]. A domain of this size includes all details of the flow and wake whilst maintaining minimal computational power. Within this domain, the fins were set on a 1 m \(\times\) 2 m face with spacing to mimic the plugs of a standard performance surfboard, with the side fins having a toe angle of 3\({}^{\circ}\) (Figure 3b). The fins were placed with the centre fin 0.5 m from the inlet. The domain was set to have double inlets/outlets to avoid changing the mesh across different cases. The face closest to the leading edge and one of the lateral faces was set as the velocity inlets and the trailing edge face and the opposite lateral face were set as pressure outlets. The velocity was then set using the components in the x and z directions to reproduce the AOAs on the fins and remove any pressure gradients on the domain's lateral faces. The fin walls were set to no-slip conditions, as was the 1 m \(\times\) 0.8 m blue area around the fin plugs in Figure 3a. This area generates the boundary layer influence from the \"surfboard\" the fins are attached to. A surfboard shape would change how the flow approaches the fins as concaves and tail shapes affect the flow, as seen in studies by [PERSON] and [PERSON] [12; 13]. This research ignored the true effect of the surfboard to simplify the study. The final two faces were set as symmetry walls and were classified as far-field flow.  
The fluid was defined as water with a density of 998.2 kg/m\({}^{3}\) and viscosity of 0.001003 kg/(m.s). There is a wide range of speeds a surfer can reach on a wave; however, speeds recorded using an inertial sensor in a study by [PERSON] et al. show the average speed during standard manoeuvres (cutback/bottom turn) of 6.8 \(\pm\) 1.7 m/s [9]. Therefore, the flow velocity at the inlet was set at 7 m/s. In water, flow is incompressible for low-speed ap  
Figure 3: (**a**) Computation surface grid. Inset: fin wall and surrounding inflation layers; (**b**) standard surfboard fin plug configuration, showing lift and drag directions (dimensions in mm unless otherwise stated).  
plications. The effects of water chop have been ignored and ideal conditions were assumed to simplify each simulation. The Reynolds number, Re, is 7.73 \(\times\) 10\({}^{5}\) and calculated using  
\[Re=\frac{\rho\cdot V\cdot L}{\mu} \tag{3}\]  
where \(\rho\) is density (kg/m\({}^{3}\)), \(V\) is the free stream velocity (ms\({}^{-1}\)), \(L\) is reference length (m), \(\mu\) is dynamic viscosity (kg/ms). The reference length (\(L\)) is the chord length of the foil at the base of the fin. For external fluid flow, the critical Reynolds number is (\(Re_{\rm cr}\) = 5 \(\times\) 10\({}^{5}\)); therefore, the flow is turbulent [14].