esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Analysis of Genetic Algorithm-Derived Vs30 Values  
To evaluate the reliability of the GA approach in estimating Vs30 values through HVSR Inversion, a comparison was conducted between the estimated and measured ReMi Vs30 values for each site in GMMA and for 27 ReMi sites in Leyte. These values were also categorized according to the NSCP Soil Profile Types (Table 5), which are essential for assessing seismic hazards and designing earthquake-resistant structures. The comparison assessed the accuracy of the estimated Vs30 values relative to the actual measurements, as well as the soil profile type match rate between the two methods.  
The relationship of the ReMi-derived Vs30 values with the estimated Vs30 values and the percentage of the soil profile type match across all sites were also examined via linear regression. The original form of the non-linear power function to be utilized is given by Equation (1), as follows:  
\[y=e^{b_{0}}x^{b_{1}} \tag{1}\]  
where \(e^{b_{0}}\) is a constant and \(b_{1}\) is the power exponent. By applying a natural logarithmic function to Equation (1), the linear regression equation shown by Equation (2) is obtained, as follows:  
\[y=b_{0}\times x^{b_{1}} \tag{2}\]  
where \(y\) represents the values of dependent variable, \(b_{0}\) the slope of the regression, the independent variable, and \(b_{1}\) is the intercept of the regression. The values of the slope of the regression and the intercept were determined using the least squares estimation. The  
\begin{table}
\begin{tabular}{c c c c} \hline
**Thickness (m)** & **S-Wave Velocity (ms\({}^{-1}\))** & **Density (gcm\({}^{-3}\))** & **Damping Ratio** \\ \hline
7 & 282 & 1.8917 & 0.0238 \\
11 & 360 & 1.9378 & 0.011 \\
0 * & 760 & 2.1514 & 0.0114 \\ \hline \end{tabular} \({}^{*}\)A thickness of 0 indicates that the thickness extends indefinitely.  
\end{table}
Table 4: Resulting Vs profile parameters from the HVSR Inversion of the selected site.  
\begin{table}
\begin{tabular}{c c c} \hline
**Soil Profile Type** & **Description** & **Range of Vs30 (m/s)** \\ \hline \(S_{A}\) & Hard rock & \textgreater{}1500 \\ \(S_{B}\) & Rock & 760–1500 \\ \(S_{C}\) & Very dense soil and soft rock & 360–760 \\ \(S_{D}\) & Stiff soil profile & 180–360 \\ \(S_{E}\) & Soft soil profile & \textless{}180 \\ \(S_{F}\) & Site-specific evaluation required & \\ \hline \end{tabular}
\end{table}
Table 5: Description and corresponding Vs30 range of the NSCP Soil Profile Types.  
performance of the regression model was assessed using both R2 (coefficient of determination) and RMSE (root mean square error). R2, which indicates how well the regression line fits the data, was defined by the regression sum of squares and the total sum of squares. The RMSE was determined by measuring the average difference between the predicted and actual values.  
The relationships between the natural site period and ReMi-derived Vs30 with the estimated Vs30 values were analyzed to assess whether the estimated Vs30 exhibited the expected inverse proportionality with the site period. A power regression model was employed in this analysis using Equation (1).
## 2 LEA Detection and Tracking  
Although most of visual-MIMO related works [2; 5; 9] emphasized the importance of LEA detection and tracking, they did not address detail method and the corresponding effect on the communication performance. In [2; 3; 4], they did not consider the effect of misrecognition on the SER performance and also did not analyze the temporal movement of an LEA associated with tracking. On the other hand, in our paper, the detection and tracking method of an LEA optimized for color-independent visual-MIMO is described systematically based on image processing. Our proposed LEA detection (including tracking) method is also most suitable for the GCM to enhance the SER performance. In many practical applications of visual-MIMO (e.g., vehicle-to-vehicle (V2V) application), both the transmitter and the receiver can move. Therefore, we propose the LEA detection and tracking method shown in Figure 2.  
The entire process consists of four steps. The first step is to specify the region of interest (ROI) for an LEA from the received image. Then, using the Harris corner detection algorithm [12], we extract the features of the LEA such as the corners of a rectangle. In the third step, a Kalman filter [13] is used to track the desired LEA. Finally, we correct the distorted shape of the LEA using perspective projection [14]. Figure 3 shows the original configuration and the distorted shape example of an LEA in the received image. As an experimental example, we used the LEA configuration of a square shape with a size of \(4\times 4\). The shape of the LEA on a received image can be distorted in the form of translation, rotation, and warping. We correct the distorted shape by utilizing the perspective projection technique, to increase the probability of right decision for each LED color (or symbol) in the array.  
Figure 3: LEA configuration and shape distortion example of LEA on a received image.  
Figure 2: Block diagram of the proposed LEA detection and tracking method.
# Analysis and Prospects of an Antarctic Krill Detection Experiment Using Drifting Sonar [PERSON]
## 2 Materials and Methods  
### Buoy Design  
The experimental buoy used in this study has dimensions of 350 \(\times\) 350 \(\times\) 350 mm and weighs approximately 8 kg. It is equipped with two temperature sensors and a small fish finder. The buoy is powered by batteries, and the batteries are charged by a solar panel. Additionally, the buoy is equipped with satellite communication capability, allowing the detected data to be transmitted back through the satellite system. The specific structure of the buoy is depicted in Figure 1.  
Figure 1: \(Cont\).  
The external structure of the buoy consists of a waterproof casing. The fish finder is located in the center of the bottom and can emit sound signals at frequencies of 83 kHz or 200 kHz. It can detect depths ranging from 1 to 250 m and is adjustable. On one side of the bottom of the buoy, there is a temperature sensor used to measure the temperature of the seawater. Above the fish finder is a battery that powers the entire buoy. Additionally, there is another temperature sensor above the battery. A GPS module on the top is used for positioning, and a satellite communication module can transmit all data including time, latitude, longitude, heading, speed, air temperature, sea surface temperature, and echo data. The layout of the entire buoy is compact, the structure is simple, and the functions of each module are clear and independent, allowing the buoy to successfully complete data collection tasks for each module. The buoy collects data every two hours during normal operation or can be activated and data can be collected through software on a computer. The specific process is shown in Figure 2.  
The materials used for the entire buoy casing are renewable, and injection molding technology is utilized. Injection molding is the most common and widely applied method in plastic processing. The method involves injecting molten plastic into a mold, which  
Figure 1: Buoy. (**a**) Buoy design. (**b**) Photo of the buoy.  
Figure 2: The workflow diagram of the buoy.  
cools and solidifies to form the desired casing shape. Parts produced using injection molding technology have high efficiency and accuracy, making them suitable for large-scale production. The overall fabrication technology of this buoy is mature and low-cost. Therefore, it meets the requirements for the large-scale deployment of Antarctic krill detection.
# Electromagnetic and Thermal Phenomena Modeling of Electrical Discharges in Liquids
## 4 The Concept of Discharges Modeling in Liquids  
Discharge model, designed to analyze the phenomena occurring in devices using HVED for the preservation of liquid food products, should enable analysis of coupled electromagnetic and thermal fields. Primarily, the model was used during the design of the laboratory discharges generator and planning future tests to implement this technique in industry. In particular, the model should allow:  
* Selection of the electrodes system in terms of their dimensions and distance;
* Determination of the voltage values to initiate the discharge;
* Determination of discharges energy;
* Determination of the duration of discharges;
* Determination of the temperature distribution after discharge in order to analyze the time required to initiate next discharge. This task was aimed to eliminate the possibility of subsequent electrical discharges in the same channel;
* Determination of the power supply circuit parameters based on the required voltage and energy of discharges.  
Characterization of all above mentioned parameters requires advanced models and calculating procedures using both field and circuit models. Field computations, carried out in the commercial FEM system, were used to compute the electrodes system variables, streamers propagation scheme, and temperature distributions. Power source parameters have been determined using circuit models. The calculation algorithm is shown in Figure 6. Both field and circuit models have been extended with Author's procedures and algorithms, characterized in subsequent section of this paper.
# High-Speed Underwater Optical Wireless Communication with Advanced Signal Processing Methods Survey
## 6 Conclusions  
In this article, we provided a structured review of recent progress in UOWC systems, which are in high demand to overcome the inherent limitations of conventional acoustic systems and provide high-speed wireless communication links in underwater environments. In particular, we focused on the signal processing aspect of UOWC systems, which has attracted intensive interest as a promising method to suppress signal generation, modulation, transmission, and reception impairments. We reviewed the recent progress on both ML- and NML-based signal processing techniques. Due to its simple structure, the traditional NML equalization techniques can efficiently solve the SNR reduction caused by ISI, thereby improving the data rate of UOWC systems in recent years. Due to the advancement and development of ML algorithms, ML-based equalization techniques have shown better capability in reducing the influence of nonlinear effects. However, ML-based schemes normally require complex algorithms and powerful hardware support (high-performance computers).  
In practical applications, water flow, temperature, and sunlight are changing all the time. Hence, considering the dynamic feature of underwater channels, designing adaptive signal processing techniques is important in the future, which is a key challenge in current studies. In addition, whereas the ML algorithm can achieve impressive results by suppressing both linear and nonlinear impairments effectively, the training process is complicated, requires a large number of computations, and takes a long time. Some recent ML research has begun to optimize algorithms to reduce the training epochs of ML-based equalizers to improve DSP efficiency and reduce computation costs. With the continuous development of hardware and continuous innovation of algorithms, the computation-efficient ML algorithms for UOWC systems will be a key research focus in the future. Moreover, due to the dynamic topology problem caused by water flow, the underwater sensor network needs a more intelligent way to ensure stable links. According to recent research, the UOWC network based on reinforcement learning can greatly improve communication success rate and stability, providing a promising solution for future UOWC applications.  
Conceptualization, C.F. and K.W.; software, Y.W.; writing--original draft preparation, C.F.; writing--review and editing, C.F., S.L., Y.W. and K.W.; visualization, Y.W.; supervision, S.L. and K.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Australian Research Council (ARC) under Grant DP170100268.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Hybrid Localization Algorithms
### Distributed Localization Algorithms
#### 5.2.2 Three-Dimensional Underwater Localization (3 DUL)  
A three-Dimensional Underwater Localization (3 DUL) is introduced in [70]. There are three buoys floating on the surface and many underwater sensor nodes are deployed at different depths. In addition, there are propelled autonomous robots freely floating with water currents. The underwater sensor nodes and the propelled robots are unknown nodes.  
3 DUL is a two phase protocol. During the first phase, unknown nodes estimates the distances to their neighbor buoys by using two-way message exchange technique and acquires their depth information. This phase of the algorithm is called Ranging. Once the distances to at least three buoys are estimated, the second phase of the algorithm, Projection and Dynamic Trilateration, is initiated. During this phase, the unknown node projects the buoys' positions onto its horizontal level. Then, it localizes itself through trilateration and labels itself as a reference node. 3 DUL does not require time synchronization. The biggest drawback of 3 DUL is the very long localization time.
# Revisiting the High-Performance Reconfigurable Computing for Future Datacenters
## 4 Revisiting the FPGA Virtualization
### Resource Level Virtualization
#### 4.1.2 Input Output (I/O) Virtualization  
I/O virtualization enables the access of different resources through the same interface or the sharing of resources among multiple applications. Figure 3 depicts the generic architecture where many virtual channels are represented with dashed lines, that do not equate to available physical channels. The middle layer in I/O virtualization plays multiple roles, like enforcing security mechanism,  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Author(s) [Ref.] Year** & **Language** & **Board** & **Frequency** & **Granularity** & **Size** \\ \hline [PERSON] et al. [69] 2011 & VHDL & Virtex 6 & 100 MHz & 8 / 32 / 64 bits & 30 \\ [PERSON] et al. [70] 2011 & Verilog & Virtex 5 & 155 MHz & 32 bits & \(4\times 4\) \\ [PERSON] [61] 2012 & Verilog & Stratix III & 150 MHz & 32 bits & \(2\times 2\) \\ [PERSON] et al. [63] 2012 & - & Virtex 6 & 400 MHz & 32 bits & 40 \\ [PERSON] et al. [71] 2013 & HLS Method & Zynq 7000 & 250 MHz & 32 bits & \(2\times 2\) \\ [PERSON] [72] 2016* & RISC V ISA & UltraScale & 375 MHz & 32 bits & \(10\times 5\times 8\) \\ [PERSON] et al. [73] 2016 & HLS Method & Zynq 7000 & 286 MHz & 32 bits & 8 \\ [PERSON] et al. [74] 2017 & C & Stratix V & 94 MHz & 32 bits & \(60\times 2\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of CGRA based Overlays (*only industrial work).  
Figure 2: Generic Overlay Architecture.  
monitoring resource utilization, ensuring Quality of Service (QoS) in datacenters, improving access time, and installing memory buffers.  
There are two possibilities for the design of control logic, either software or hardware. The software approach offers high flexibility and space efficiency [75; 76; 77]. On the other hand, the hardware module offers improved performance at the cost of consuming some reconfigurable resources [78; 79].  
Designers, as in [80], employed I/O virtualization to accelerate the storage up to 6x, beneficial for data intensive applications. Microsoft used the same to reduce the traffic of the network by directly handing over the requests to FPGA [4]. These are examples of the diverse use of I/O virtualization middle layer.
# A Fully Automatic DEXP Method for Gravity Data and Its Application on a Potash Salt Deposit
## 4 Application on Real Gravity Data
(**c**) The combination of residual gravity anomaly, and lower-value part of the imaging results obtained with the improved DEXP method and the inferred faults. (**d**) Extracted from the imaging result as Profile I, where the white cross points represent the low-extreme points. For Well Jx5, the depth of the low-extreme point is about 235.8 m; for Well Jx4, the depth of the low-extreme point is about 142.3 m.  
both the error and ratio fall within acceptable tolerances. Importantly, the inversed result of the improved DEXP method exhibits no significant disturbance, such as fake anomaly or \"zero lines\". These advantages distinguish it from the pre-DEXP method. Additionally, no additional pre-SI value is required, highlighting its automatic calculation capability. These findings indicate that the imaging result obtained using the improved DEXP method is desirable and reliable. Furthermore, they suggest that this method can provide a valuable layer of information for interpretation.
## 2 Materials and Methods
### Analysis of Volatile Organic Compounds in Vapor Samples  
The vapor samples collected onto a multi-bed sorbent tube were analyzed with thermal desorption-gas chromatography (TD-GC)/mass spectrometry (MS) system (TD: Unity-xr, Markes International; GC: GC-2010 Plus, Shimadzu, Kyoto, Japan; MS: GCMS-QP2020, Shimadzu, Kyoto, Japan) to quantify 19 volatile compounds. Among them, 19 compounds (methyl ethyl ketone, methyl isobutyl ketone, butyl acetate, isobutyl alcohol, benzene, toluene, p-xylene, m-xylene, o-xylene, styrene, dimethyl disulfide, trimethylamine, propionic acid, butyric acid, isobutyric acid, hexanoic acid, heptanoic acid, phenol, and m-cresol) were quantified using standard materials. In order to prepare standard solutions for the 19 compounds, standards were purchased as liquids (Sigma-Aldrich, Louis, MO, USA; all standards were 97-99.5% pure, except for Trimethylamine (25.0%)). Solutions were prepared by diluting each standard in 100% methanol (J.T. Baker) to cover a range of concentration levels (5, 10, 20, 50, and 100 ng/\(\mu\)L). Then, for calibration analyses, 1 \(\mu\)L of each standard solution was spiked into the sorbent tube using a 5 \(\mu\)L syringe (SGE Analytical Science, Victoria, Australia), while supplying pure (99.999%) N\({}_{2}\) gas at 50 mL/min for 4 min using a mini pump (MP-\(\Sigma\)30, SIBATA, Tokyo, Japan). Each sorbent tube containing volatile organic compounds in vapor samples and standard solutions was then placed in the thermal desorption (TD) and thermally vaporized at 320 \({}^{\circ}\)C for 10 min. These vaporized gas samples were preconcentrated in a cold trap (Carbopack C and Carbopack B combination) at -25 \({}^{\circ}\)C and then thermally desorbed at 320 \({}^{\circ}\)C for 10 min. A total of 25 compounds were separated on a DB-Wax column (film thickness: 0.25 \(\mu\)m, diameter: 0.25 mm, length: 60 m; Agilent J&W, Santa Clara, CA, USA) for detection by GC/MS. The MS system was operated in electron ionization (EI) mode at 70 eV. Ions were monitored in full-scan mode (range: 35-400 m/z).
# Polymer@gold Nanoparticles Prepared via RAFT Polymerization for Opto-Biodetection
## 2 Colloidal Gold Nanoparticles: Optical Properties and Related Chemical Practices
Nevertheless, the synthesis of monodispersed Au NRs using bromide-free surfactants has also been described [48]. Although colloidal Au NPs have colloidal stabilizers at the surfaces, additional surface modification is very often necessary to ensure further functionalization envisaging specific applications [4; 49]. The functionalization of NPs is of great interest because it mediates the interaction of the Au NPs with the surroundings, namely via surface charges and functional groups (e.g., carboxyl or amine groups), which allows for example the bio-labelling of Au NPs using antibodies or DNA strands. An example of the former strategy is the use of DMAP (4-(dimethylamino)pyridine), which can be attached to Au NPs for conferring positive surface charge [50]. Figure 4a illustrates the use of thiol compounds whose chemical affinity for Au is well known and is an example of the acid-base interplay between a soft Lewis acid and a soft Lewis base. Therefore, thiol ligands such as sodium 10-mercaptoethanolatesulfonate [51] or polymers containing thiol groups have been used to modify the surface of Au NPs. Others polymers such as charged polyelectrolytes can be used for surfaces modification via electrostatic interactions [51; 52; 53; 54; 55; 56; 57], see Figure 4b. Regarding the functionalization of the NPs using biomolecules (Figure 4c), two main strategies can be distinguished. One strategy involves electrostatic interactions between surface charged Au NP and charged biomolecules; the other strategy involves formation of covalent bonds between functional groups at the surface of Au NP and functional groups of the biomolecules. Alternatively, a biomolecule modified with a thiol group is directly used for ligand exchange providing, in one step, stability and functionalization; this strategy is commonly used for functionalization with DNA [58; 59]. Among the several methods of surface modification and functionalization of Au NPs, those using polymers have received increasing interest in the last few years. This is because polymers offer the possibility to prepare stable, robust and multifunctional shells keeping the optical properties of the Au core, but also the polymeric shell can be responsive to an external stimulus, such as pH or temperature [60; 61]. On the other hand, these endeavors require a better control on the polymerization techniques employed to coat the NPs surfaces, which makes RAFT a very attractive strategy.
## 2 Data and Methods
### Inversion and Modeling Data
#### 2.2.2 Geomagnetic Measurement Data  
There are four continuously recording geomagnetic observatories within 200 km of the mainshock. The nearest station (YUL) is only 55 km away from the epicenter, while the farthest (CHX)- is 180 km away. These stations are shown in the red triangles in Figure 1. These geomagnetic observatories provide reliable data for the correction of the daily variation and secular variation of the ground repeated magnetic survey data.  
In addition, there are 27 repeated geomagnetic field vector measurement stations within 250 km of the epicenter, with a spacing of approximately 70 km (shown as blue triangles in Figure 1). The total intensity (\(F\)) of the geomagnetic field was measured using a proton precession magnetometer (GSM-19T) with an absolute accuracy of \(\pm\) 0.2 nT and a resolution of 0.01 nT [4; 25]. A DI magnetometer with an accuracy of 0.2\({}^{\prime}\) and a resolution of 0.1\({}^{\prime}\) was used to measure the magnetic inclination (\(I\)) [39]. GPS and DI magnetometer were combined to complete the measurement of the magnetic declination (\(D\)). These stations have recorded data from March to May every year since 2010.  
In this paper, we use continuously recorded data from four observatories from 1 January 2018 to 30 June 2021, as well as a total of five ground magnetic vector observation data points during 2018-2021 and one month after the Yangbi earthquake.