esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Analysis and Results
### Seismic Microzoning Based on Geomorphology with NEHRP Standards  
In Sections 6.1 and 6.2, the study of geomorphological classifications and matching of the geomorphological units to the V\({}_{\text{S30}}\) value was reported. The point feature data were transformed into polygonal features to create a seismic microzoning map according to the NEHRP provisions. The NEHRP defines five classes of soil, i.e., A, B, C, D, and E, based on the soil rigidity, which represents the V\({}_{\text{S30}}\) value.  
The V\({}_{\text{S30}}\) values gathered are represented with the classified PBC geomorphological units. The available NEHRP classes in the study area were C and D. The C class has a V\({}_{\text{S30}}\) value of between 360 and 760 m/s (referring to dense soil or soft rock), while the D class is between 180 and 360 m/s (referring to stiff soil). When referencing NEHRP class D geomorphological units, weak rock, incised terraces next to eroded rock, delta plains, and low-lying fluvial plain areas (unit numbers 11, 12, 13, 14, 15, and 16, respectively; see Figure 7) are included, which are mostly areas along the river due to high sedimentation processes. Class C covers the incised terrace next to the foot of the mountain, moderately eroded mountains, and volcanic regions (units 7, 8, and 15, respectively; see Figure 7).  
The final output, Figure 9, shows NEHRP class D in red, and the orange area is class C. The gray regions present mountain areas which could not be covered by the microt  
Figure 8: V\({}_{\text{S30}}\) based on OBC geomorphological units; the red lines indicate the NEHRP classes.  
Figure 7: V\({}_{\text{S30}}\) based on PBC geomorphological units; the red lines indicate the National Earthquake Hazards Reduction Program (NEHRP) classes.  
when comparing the geo-signature with the geomorphological unit that included microtremor data, it was similar to the units in class C.
### Results for Subcase A  
Figure 2 shows the sound source distribution for different frequencies of the subcase A obtained by the present data processing method based on Clean-SC algorithm. It can be seen from Figure 2 that the program used in this paper can accurately identify the location of the noise source, and give a very clean image of the sound source.  
Figure 1: Layout of the planar array with sixty-four microphones.  
Figure 3 shows the comparison between the identification results of four sound sources and the benchmark data for subcase A. The results show that the magnitudes of four sources are accurately identified using the present array data processing method with an error less than 0.5 dB.  
Figure 3: Comparison between the present results and the benchmark case for subcase A.  
Figure 2: Sound source distribution for subcase A at different frequencies.  
Figure 4 shows the difference between the present results and benchmark data. For more extensive comparison, the results from other groups are also shown in the figure, including Brandenburg University of Technology Cottbus (BTU), University of Adelaide (UniA), PSA3, and Delft University of Technology (TUD) [37]. It can be seen from Figure 4 that the present array data processing method has a good accuracy in a wide frequency range with an error less than 0.5 dB. In the frequency range above 3000 Hz, the error is less than 0.2 dB. The results from other groups provide results generally within an error of 1 dB with two exceptions. The first one is the Orthogonal Beamforming (OB) method from BTU (BTU ORTH). The OB method assumes that the sources have different levels of intensity [39]. Therefore, it is expected that the OB method obtains relatively higher error with several equal sources. The other exception is the Global Optimization (GO) method [40] from TUD with the differential evolution as the optimization method. The underlying reason for the large error at higher frequencies might be due to the larger number of local optima. A different optimization method could be used to improve the results. In addition, the implementation details such as the stopping criterion for the iteration in Clean-SC and DAMAS, the different grid resolutions and the different steering vectors used can also contribute to the differences.  
Figure 4: Comparison of the present results with other studies for subcase A.
## 4 Simulation of Experimental System  
In order to simulate the microwave wind tunnel experiment, especially to simulate the reflected power back to the antenna, a simplified simulation model from that shown in Figure 1 was applied and is shown in Figure 12. The lossy bars located in the middle of the tunnel were removed and open boundaries were applied in both \(+z\) and \(-z\) directions. The limitation on the total size of the array was the ratio between the cross-section area of the array in the _xoy_ plane and the total cross-section area of the wind tunnel. Let this ratio be denoted as \(\zeta\) then, \(\zeta\) is expected to be smaller than 0.01 to avoid disturbing the airflow. The dielectric materials of the bars in the reflect array are low-loss, which have a loss tangent of \(\sim\)0.002. Little microwave power can be absorbed inside the bars, which is only 1% of the incident power. However, \(\sim\)50% of the incident power is absorbed in the test block, as shown in Figure 3. This ensures the durability of the reflect-array.  
Considering that the height of the array in the \(x\) direction is 9 mm and the size of the tunnel in the xoy plane is 600 \(\times\) 600 mm\({}^{2}\), the total length of the array in the \(y\) direction has to be no larger than 400 mm. As a result, there can be 13 dielectric bars lined up to form the array, as shown in Figure 12a, and the total length in the \(y\) direction is 13 \(\times\) 27 = 351 mm, which is smaller than 400 mm. The only port in the simulation model in Figure 12a is the input port for the microwave antenna; so, the simulated results of 'outgoing power from all ports', as shown in Figure 12b,c, are actually the outgoing power from the antenna, i.e., the reflected power back to the antenna. It can be observed in Figure 12b that the reflected power has a maximum value of 0.0024 W in the frequency range near 9.7 GHz. The port can accept 0.5 W in total; so, the reflected power is about 5% of the incident power without the reflect array. However, as shown in Figure 12c, the peak of the reflected power disappeared after applying the reflect array and several small peaks were found. All of them have peak values no larger than 0.00066 W near 9.7 GHz. Compared to 0.0024 W without the reflect array, a decrease of \(\sim\)6 dB was obtained, which is acceptable for the steady operation of the microwave source. The structure was optimized under 9.7 GHz and obtained acceptable returned-back power in the frequency range from 9.62 GHz to 9.78 GHz, as shown in Figure 12. So, the structure works in a small bandwidth of \(\sim\)1.6%. If the operation in another frequency is necessary, it does not need to perform another optimization. The operation frequency can be adjusted by scaling up or down the geometrical parameters altogether, which is a typical method in microwave techniques.
# A Survey of Machine Learning Methods for Time Series Prediction
## 3 Tree-Based Machine Learning Architectures
### Gradient-Boosted Decision Trees
#### 3.2.3 CatBoost  
CatBoost [84], developed by Yandex in 2017, is designed with a specific emphasis on handling datasets with categorical features. One of its key innovations is a unique implementation of target encoding which takes the concept of traditional target encoding, where categorical values are replaced with the mean of the target variable for each category, and instead constructs the encoding process using only previous data to avoid data leakage. Another distinguishing feature of CatBoost is its use of symmetric decision trees, where all leaves at the same depth use identical splitting criteria. This structure not only accelerates training, but also significantly reduces the inference time, an essential advantage in some time-sensitive applications of real-world forecasting.
## 3 Proposed TSR Methodology
### Traffic Sign Detection (TSD) Step  
In this study, we opt for the best traffic sign detection method in terms of performance. For this reason, we rely on the best TSD techniques, which offer the best detection rates (DR) as well as processing times achieved through applying the GTSDB dataset. Table 5 confirms that the Haar cascade technique achieves the highest sign detection accuracy of 99.05%.  
The Haar cascade is a machine learning-based approach used in the detection step of our TSR system to identify 43 types of road signs divided into four categories. Hundreds of positive samples were collected in a folder describing the same traffic sign category, and several arbitrary negative images (without traffic signs) were gathered in another folder.  
This technique begins by extracting Haar-like features from input images and then building cascaded classifiers embedded into the AdaBoost classifier, a strong detector able to discard negative images quickly. After the training process, an XML file describing a road sign category was created to localise the traffic signs on a real-time video or on a captured frame.  
As an example, Figure 4 describes the Haar cascade process, which gives a speed limit sign detector, which is then applied to input images in order to detect speed limit signs, as shown in Figure 5.  
Figure 4: Speed limit Haar cascade detector.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TSD Methods** & **Techniques** & **DRs (\%)** &
\begin{tabular}{c} **Detection Time** \\ **(ms)** \\ \end{tabular} \\ \hline Colour-based methods & MSER in HSV [23] & 96 & - \\ \hline Shape-based methods & EDC Circles [8] & 84.64 & 36 \\ \hline Colour and shape-based methods & HSV + HOG [2] & 95.03 & 236 \\ \hline Machine Learning-based methods & Haar cascade [31] & 99.05 & - \\ \hline Deep Learning-based methods & Modified YOLOv2 [28] & 96.69 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Best obtained results with the GTSDB dataset.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 7 Conclusions
This enhanced security is achieved by dynamically adapting access permissions based on risk scores, which are calculated by considering various factors such as user attributes, environmental conditions, and past behavior. The proposed model's ability to adapt to changing circumstances and mitigate evolving threats makes it particularly valuable in healthcare settings, where the sensitivity of patient data requires a robust and adaptable security solution. Compared to existing studies from the literature, the proposed model offers a more robust and adaptable approach to managing access in healthcare systems, addressing the limitations of traditional access control models. However, the research also acknowledges limitations, such as the need for more extensive case studies to further validate the model's effectiveness in diverse real-world scenarios. For future work, we will focus on refining the risk assessment mechanism by incorporating more sophisticated machine learning algorithms and exploring the integration of other security technologies, such as blockchain and federated learning, to further enhance the model's resilience and adaptability. Additionally, we will investigate the model's scalability and performance in large-scale healthcare systems and explore its applicability in diverse real-world scenarios. Conceptualization, H.F.A. and Y.Y. ; methodology, H.F.A. ; software, Y.Y. ; validation, H.F.A. and Y.Y. ; formal analysis, H.F.A. ; investigation, Y.Y. ; resources, H.F.A. ; writing--original draft preparation, H.F.A. ; writing--review and editing, Y.Y. ; visualization, Y.Y. ; supervision, H.F.A. ; project administration, H.F.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data available in a publicly accessible repository. The authors declare no conflict of interest.
## 1 Introduction
In this paper, we propose the power-efficient MIMO configurations for the bi-directional vertical FSO link with the increased pointing error as the distant transceiver arrangement. The diversity gain is improved by minimizing the increased pointing error as the proposed system configuration. The proposed scheme can be utilized to achieve power-efficient transmissions for FSO based wireless back-haul networks.
## 2 Study Area and Datasets
### Datasets
#### 2.2.3 Hydrological Models  
The Global Land Data Assimilation System (GLDAS) [52] Noah2.1 model provides 4-layered soil moisture values of 0-2 m below the surface and snow water content data on the Earth's surface. We integrate soil moisture and snow water in the estimation of the shallow-surface TWS (GLDAS-TWS). We use three sets of GLDAS hydrological models with different spatial resolutions. The monthly models with a grid interval of 1\({}^{\circ}\) from January 2020 to December 2020 are used to simulate the vertical deformations at each GNSS station across the CONUS. The monthly models with a grid interval of 0.25\({}^{\circ}\) from January 2018 to August 2022 are used to evaluate the consistency of the TWS estimated using the GNSS (GNSS-TWS) and TWS derived with GFO (GFO-TWS). The models with a temporal resolution of 3 h and grid interval of 0.25\({}^{\circ}\) from 1 January 2019 to 31 January 2019 are used to extract daily shallow-surface TWS variations caused by a heavy precipitation event in January 2019 in the California watershed.
# A Public Infrastructure for a Trusted Wireless World
## 2 Background
### Near Field Communication (NFC)  
Near field communication (NFC) is a more simplistic implementation of radio frequency identification technology. Involving two wireless devices, NFC operates via short-range frequencies within five to ten centimeters. There are two modes: active and passive. An active mode device starts the communication. This device is referred to as the initiator. The initiator generates its own power and sends information by amplitude shift keying. Within the passive mode, the device is referred to as the target and uses the radio frequency field from the initiator as the power for its communication [5].
## 3 Results and Discussion
### Evaluation of the Pockels Coefficient of the Interfacial Water
#### 3.4.3 Magnitude and Phase of Voltage Distributed in the Electric Double Layer of Water
However, even in this case, since all the data points in the Cole-Cole plot reside in the Re \(Z>0\) and Im \(Z<0\) region, similarly to Figure 9, it should show such behavior that \(X\) dominates for the low frequencies and \(-Y\) grows with increasing frequency, as usual. Since it is difficult to solve the phase puzzle, therefore, we cannot determine the sign of the Pockels coefficient of interfacial water on Ag surface. However, the following can be argued modestly about the sign. It is reported that the transmission spectra (interference fringes) are blue-shifted and \(\Delta n\) is negative (\(r_{13},r_{33}>0\)) for the interfacial water on the transparent oxide electrode [3,21], while the transmission spectrum is characterized by the red-shift on the Pt electrode [8], and \(\Delta n\) is most likely to be positive (\(r_{13}<0\)). From this, since Ag is a noble metal as well as Pt, and it is more likely that the \(\Delta n\) of the interfacial water on the Ag electrode is positive \(\Delta n>0\) (\(\Delta n<0\)) for the positive (negative) applied voltage, so that the Pockels coefficient is negative (\(r_{33}<0\)). Figure 11: **(a)**Typical data for raw electromodulation transmission change spectra of normal incidence on 330-nm thick Indium Tin Oxide (ITO) electrode in 0.1 M LiCl aqueous solution with an AC voltage of 1 V (amplitude) at 210 Hz. The \(X\) signal is due to blue shift of the transmission spectrum. **(b)** Raw electromodulation reflection change spectra of 87\({}^{\circ}\) incidence on 400-nm thick TCO electrode in 0.1 M NaCl aqueous solution with an AC voltage of 2 V (amplitude) at 30 Hz. The \(X\) signal is due to blue shift of the reflection spectrum [21]. **(c)** Raw electromodulation transmission change spectra of normal incidence on 20-nm thick Pt electrode in 0.1 M NaCl aqueous solution with an AC voltage of 2 V (amplitude) at 21 Hz. The \(X\) signal is characterized by red shift of the transmission spectrum [8].
## 4 Computer Simulation Experiments
### Algorithm Precision Analysis
#### 4.2.1 Strong Signal Environment Evaluation  
The _C_/_N\({}_{0}\)_ of the simulated signal was set to 55 dB-Hz. In the real environment, the outdoor GPS signal has a _C_/_N\({}_{0}\)_ of approximately 35-55 dB-Hz [17]. The _C_/_N\({}_{0}\)_ of 55 dB-Hz is almost the maximum value of the real GPS signal, which represents a strong signal environment. The length of the sampled signal is approximately 10 minutes.  
Figure 7 presents the _C_/_N\({}_{0}\)_ estimation results that were obtained using four averaging times. The estimation results of the two classical _C_/_N\({}_{0}\)_ estimation methods are similar. In addition to the initial transition phase, the estimation results of the ASTKF method are more stable than those of the NWPR and the VSM.  
Table 3 presents a statistical comparison of the estimation results. In the strong signal environment, the standard deviation of the ASTKF estimation results is smaller than that of the results of the other two methods using the same averaging time. In addition, according to Table 3, the estimated standard  
Figure 7: Simulation strong signal _C_/_N\({}_{0}\)_ estimation results: (**a**) 0.5 s averaging time; (**b**) 1 s averaging time; (**c**) 3 s averaging time; and (**d**) 5 s averaging time.  
deviation of ASTKF using the averaging time of 0.5 s is smaller than that of NWPR and VSM using the averaging time of 5 s. Therefore, compared with the NWPR and the VSM, the ASTKF C/N\({}_{0}\) estimation algorithm can adopt a smaller averaging time, which facilitates the reduction of the lag of the estimation result. In the strong signal environment, using the ASTKF method, after the averaging time exceeds 0.5 s, continuing to increase the averaging time does not significantly improve the estimation accuracy of C/N\({}_{0}\).
## 1 Introduction
These studies delve into a comprehensive analysis of temperature drift mechanisms in GMM-FBG current sensors, leveraging finite-element multi-physics simulations to elucidate the interplay of solid mechanics, magnetostatics, and thermal expansion. In the present study, a new practical structure for the GMM-FBG current sensor is presented and investigated in terms of its temperature drift mechanism. The effects of elevating temperature on GMM-FBG sensing performances are elucidated through finite element multi-physics simulations of mechanical-electromagnetic-thermal correlations. According to the temperature drift characteristics, a general formula for calibrating GMM-FBG current measurements at various temperatures is eventually obtained by fitting the temperature drift curves of thermal strains.
## 2 Experimental Investigation
### Instrumentation  
Strain gauges were placed at different positions for measuring the strains in the end-plate, steel tube and steel beam. All the readings were recorded by a DH-3816N static strain test and analysis system. The load applied to the beam end was recorded by the MTS loading system. The arrangement of the displacement transducers (LVDTs) used in the test is presented in Figure 5. LVDT1 and LVDT2 were adopted for monitoring the rotation of the column (\(\theta_{\mathrm{c}}\)). LVDT3 and LVDT4 were adopted for monitoring the rotation of the beam (\(\theta_{\mathrm{b}}\)), which were 30 mm away from column flange in the specimens with a strong beam and 180 mm away from column flange in the specimen with a weak beam, considering local beam buckling. LVDT5 was adopted for monitoring the displacement of the loading point at the beam end. The relative rotation between the column flange and beam can be derived from Equations (1)-(3).  
\[\theta_{\mathrm{c}}=\frac{u_{1}-u_{2}}{1300} \tag{1}\]  
\[\theta_{\mathrm{b}}=\frac{u_{3}-u_{4}}{H} \tag{2}\]  
\[\theta=\theta_{\mathrm{b}}-\theta_{\mathrm{c}} \tag{3}\]  
where \(u_{1}\), \(u_{2}\), \(u_{3}\), and \(u_{4}\) are the corresponding displacements measured by LVDT1-LVDT4; and \(H\) is 350 mm for specimens ST-1-ST-5 and 180 mm for specimen ST-6, which is the horizontal distance between LVDT3 and LVDT4.  
Figure 4: Loading protocol.  
Figure 5: Arrangement of LVDTs (unit: mm). (**a**) For specimens ST-1–ST-5; (**b**) for specimen ST-6.
# Tertiary Waves Measured during 2017 Pohang Earthquake Using an Underwater Glider
## 4 Discussion  
Early models of the Slocum underwater glider were equipped with a non-pumped CTD. The non-pumped CTD has a large salinity uncertainty because the flow rate in the conductivity cell is not constant. The salinity measurement uncertainty of a non-pumped CTD was about 30 times greater than that of a pumped CTD [27]. To obtain good quality ocean observation data, recently-produced Slocum underwater gliders are equipped only with a pumped CTD. In terms of the overall operating time of the underwater glider, the pumped CTD noise is higher and it occurs for a longer time than noise from other sources. It is the main noise source in underwater glider operation.  
When an acoustic source emits in a test tank, acoustic waves are reflected by the walls, the surface, and the bottom. The multiple reflections give rise to a reverberated field. Measuring underwater sound power in reverberant environment such as a tank has advantages of speed but is limited in accuracy and frequency range, but it has been used to good effect in specific applications. For small sources (or small noisy components) like underwater glider, it is possible to make measurements of radiated noise in reverberant tanks [28].  
For the seismic observation of seafloor and coastal earthquake disaster risk assessment, a comprehensive analysis of seismic sources is needed. This requires simultaneously observing underwater phenomena caused by seafloor earthquakes. Moreover, submarine earthquake underwater acoustical observation technology using underwater gliders is very synergistic because it can provide basic research data on natural, anthropogenic, and ecological underwater sources in the ocean. In addition, underwater sound observations using underwater gliders can be of great assistance in developing earth observation systems by providing submarine platforms ensuring stable operational technology and linking with existing earthquake observation networks.  
To this end, improving the method for estimating a glider's underwater trajectory is important because it is a challenging task due to several errors in the linear interpolation method. It is not realistic because the position is estimated by dividing the distance between the real GPS fixed position by transit time, and the glider's direction and speed remain constant. In other words, the trajectory looks like a straight line. Our future work aims to provide a better method for estimating a glider's optimal underwater trajectory, focusing on the importance of the ocean currents and underwater wireless communication technology to transmit the acoustic data observed by the underwater glider to the shore in real time through the OSG such as WaveGliders in [20].