esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Analysis of Response Surface
#### 3.2.2 Ferric Ion Antioxidant Reducing Power (FRAP)  
When the ethanol concentration was fixed at 80% (\(v/v\)), the reducing power increased with the increasing temperature and time up to 55 \({}^{\circ}\)C and 100 min, which then started to declined slowly as the temperature and time increased up to 65 \({}^{\circ}\)C and 120 min as shown in Figure 2a. Although a high temperature mobilizes antioxidant compounds up to a certain limit, it also promotes the possible concurrent decomposition of antioxidants which were already mobilized at lower temperatures [22]. At constant time, the maximum reducing power of the red pitaya peels was attained at around 55-57 \({}^{\circ}\)C and at an ethanol concentration of 75-80% (\(v/v\)), respectively (Figure 2b). As shown in Figure 2c, the reducing power was observed to be positively influenced by the synergism between the ethanol concentration and extraction time. This is probably due to the increased solubility of plant-derived antioxidants in the mixture of ethanol and water which compensates for their polarity very well as compared to mono-solvent systems [23]. The red pitaya peel extract displayed a maximum reducing power of 7.38 mM Fe\({}^{2+}\)/g DW at an ethanol concentration of 80% (\(v/v\)) for a duration of 100 min. The increments of ethanol concentration and extraction time beyond the threshold level displayed a decrease in the reducing power.  
Figure 1: The response surface (3D) and contour plots showing the effect of extraction temperature and time (**a**); temperature and ethanol concentration (**b**); extraction time and ethanol concentration (**c**) on the DPPH scavenging activity of the red pitaya peels.  
Figure 2: The response surface (3D) and contour plots showing the effect of extraction temperature and time (**a**); temperature and ethanol concentration (**b**); extraction time and ethanol concentration (**c**) on the ferric reducing antioxidant power of the red pitaya peels.
# Detection of Rock Mass Defects Using Seismic Tomography
## 4 Application
### Results
We further obtained the mean relative error, standard deviation of relative error, and the percentage of high-accuracy results (relative error \(<\) 10%), and plotted the distributions of these metrics across the synthetic experiment and field experiment (as shown in Figure 17). From Figure 17, it can be observed that, at the experimental scale, both the mean relative error and standard deviation are low. When the wave velocity tomography method is applied at the practical engineering scale, the mean relative error and standard deviation increase slightly but remain within acceptable ranges, with the mean relative error below 10% and the standard deviation below 25%. This indicates that the goat detection results from the field experiments exhibit low overall error and minimal fluctuation, yielding satisfactory results. Examining the detection results of individual sections, the accuracy is highest in the third, fourth, and fifth sections, where the mean relative error is generally below 7%, and the percentage of high-accuracy results reaches 91-100%. In contrast, the detection accuracy in the second section is lower, with larger fluctuations in relative error. Based on the factors influencing the accuracy of anomalous boundary identification discussed in Sections 3.1 and 3.2, combined with the characteristics of the goaf distribution in the second segment, it is inferred that the reduced accuracy may result from the irregular shape, the largeredge angles, and the scattered distribution of the gosafs in this section. These factors lower the velocity inversion accuracy, thereby affecting the boundary identification precision of the goaf. By integrating historical data with results derived from physical detection, a total of 12 potential slopes and 100 confirmed slopes were identified in the tantalum-niobium mine.
# Applications of Plasma Produced with Electrical Discharges in Gases for Agriculture and Biomedicine
## 3 Review of Plasma Applications in Agriculture and Medicine
### Plasma Application in Biomedicine  
Starting from the 1990s, the development of medical plasma has demonstrated remarkable progress due to the increasing involvement of representatives of various scientific fields, such as microbiology, bioengineering, biochemistry, bioelectronics, and theoretical and applied electrical engineering [150; 151; 152; 153]. The first principal plasma-in-medicine experiments (late 1990s) were related to the examining of bactericidal properties of plasma. The experiments were based on the DBD approach to inactivating bacteria on different surfaces and liquids [154; 155]. After the experiments, it was found that low-temperature plasma (LTP) can be used not only for inactivating pathogens, but also for disinfecting biological tissues and, in the long term, for wound healing. Due to the great interest in the new interdisciplinary field, around 2005, the establishment of a plasma community was noted [156].  
The first successful clinical trials relating to chronic sores were observed in 2010 [157; 158; 159]. These promising results began the new era of plasma oncology experiments and included in vitro and in vivo approaches for the assistance in killing cancer lines [160], therapy in oral biofilm-related diseases [161; 162], wound and tumour treatment [163; 164], virus and infection treatment [165,166]. Figure 4 presents the most important LTP applications in biomedicine and its expected effects.  
This section contains a brief review and exemplary experimental results from using both approaches--direct and indirect plasma exposures--for the antimicrobial, wound healing, tumour treatment, cell incubation and proliferation, dentistry, and veterinary medicine plasma applications.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Dedicated Applications
#### 2.7.1 Event-Driven  
This type of application is the most representative of WSN applications (see Figure 8). As an example, we can mention the detection of a forest fire or an earthquake, or the monitoring of the air quality in a given region. In this class of applications, sensor nodes must be able to detect an event that could happen at any time in any location in the area of interest. Thus, the sensor nodes must continuously collect this information and immediately react to abrupt changes in the sensed values. For this application category, no periodic control is required by the sink, and the data processing by the sensor nodes is simple: each node must compare the measured data with a fixed critical threshold. As soon as this value exceeds the threshold, the sensor node sends the data to the sink. In order to provide better performance, these applications must meet certain criteria, such as:  
* A high density of sensor nodes is required to ensure high probability of event detection. This implies that the network must have very good coverage.
* Good coverage of sensor nodes that usually depends on the type of event to be detected.
* An exact location of all sensor nodes in the network is required. This is to locate the event through the sensor node in which the event occurs. Note that distributed location algorithms could be used for this purpose.
* Good network connectivity linked to transmission coverage so that communication protocols allow the alarm sent by sensor nodes to arrive at the sink with high probability and in the shortest time.  
Figure 8: Event-driven applications.  
Figure 7: Dedicated WSN applications.
## Materials and Methods
### Bentchtop Transceiver Testing  
The goals of the benchtop transceiver testing were to determine if the amplifier design can achieve expected amplification and determine the Bit Error Ratio (BER) performance of the transceiver when the APD captures pulses with \(\sim\)100 nW of peak power. A 1.6 m erbium doped fiber in a backwards pumping configuration was determined as the amplifier design for MOCT from the results of the optical amplifier characterization. A diagram of the setup can be seen in Figure 12 and picture of the setup can be seen in Figure 13. This latest version of the FLAT has incorporated a collimator, receiver focusing lens, APD, comparator, TDC, Modulator, and a clock in order to test the transceiver  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline seed wavelength & 1531 nm \\ pump wavelength & 976 nm \\ ASE spectrum & 1520 nm to 1580 nm \\ fiber core diameter & 7.6 μm \\ doping concentration & \(37\times 10^{24}\) ions/m\({}^{3}\) \\ cluster size & 2 ions/cluster \\ fraction of ions in clusters & 14\% \\ homogeneous conversion type & 2 ion interaction \\ seed input loss & 4\% \\ pump input loss & 4\% \\ signal output loss & 35.7\% \\ pump output loss & 99.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters used for all fibers in the FLAT simulation.  
hardware. The signals on the PIN diodes and the APD are monitored with an oscilloscope. The TDC sends collected time stamps to a computer via USB cable. It should also be noted that the TDC and the modulator shared a reference oscillator, called the Miniature Atomic Clock, so clock discrepancy is removed.  
Some differences from this setup compared with the MOCT design include using a 500 mW pump laser, a seed signal with \(\sim\)60 \(\upmu\)W of peak power, and 2.5 ns pulse widths. Even though the power damage threshold of the WDM connectors was increased from the previous FLAT hardware, 500 mW is close to the maximum allowable backward propagating pump power. Splicing all of the fibers together would increase the allowable pump power and a higher power pump could be used. The seed peak power being injected into the erbium doped fiber entrance was measured to be \(\sim\)60 \(\upmu\)W and was strong enough to be amplified. Adjustments could be made to hardware settings to increase this if needed. The pulse width was kept at 2.5 ns to avoid signal attenuation due to the oscilloscope's bandwidth.
## 4 Finite Element Analysis
Normal behavior is based on hard contact; the friction formula for the tangential behavior is based on the penalty function, and the friction coefficient is 0.4. The stiffener and steel tube are bonded and constrained. The part that overlaps with concrete is buried. Rebar is based on a truss element and is buried in concrete. The meshing for the steel structure in the circular CFST column specimen is shown in Figure 16. Overall, the meshing of the specimen model is shown in Figure 17.  
Figure 16: Finite element mesh of steel structure. (**a**) CFST-N; (**b**) CFST-HS; (**c**) CFST-VS; (**d**) CFST-SR; (**e**) CFST-DT; and (**f**) CFST-MC. Figure 17: Finite element mesh of whole specimen. Figure 15: Damage variable curves. (**a**) \(\mathrm{d_{c}}\); and (**b**) \(\mathrm{d_{t}}\). Simulation results for hysteresis curves of the 6 circular CFST column specimens are shown in Figure 7. Figures 18 and 19 show the Mises stress contour plot of outer steel tube and the minimum principal stress contour plot of core concrete when the lateral drift reaches 1/16.7. The failure mode is essentially consistent with the test results. As can be seen from Figure 18a, compared with other specimens, specimen CFST-N has the most severe buckling at the bottom of column, and the maximum Mises stress reaches 461 MPa, which is close to the failure load. The steel ratio of CFST-MC increased by 4.42% compared with that of CFST-N, while the Mises stress of steel tube decreased by 17.9%. This is because the internal and external steel tubes are connected as a whole by welding steel plates, and work together to enhance the local stability of the external steel tubes. The stiffeners can significantly alleviate the buckling of the column root, and make the steel tube enter the yield area wider, which can make full use of the properties of steel. It can be seen from Figure 19 that when the lateral drift reaches 1/16.7, the concrete in the steel tube is under compression on both sides of the loading direction. With the increase of section steel ratio, the absolute value of the minimum principal stress of concrete is gradually increasing, and the constraint effect of concrete is stronger. It can be seen that the minimum principal stress of concrete in some areas is greater than the peak load input in the concrete constitutive model, which is because the concrete in the steel tube is under tri-axial stress state, and the bearing capacity is significantly improved. Figure 18: Mises stress concotour plot of outer steel tube. (**a**) CFST-N; (**b**) CFST-HS; (**c**) CFST-VS; (**d**) CFST-SR; (**e**) CFST-DT; and (**f**) CFST-MC. The peak bearing capacities of the 6 circular CFST columns are calculated with the finite element model and are compared with the test results, as shown in \[d_{t}=1-\frac{\sigma_{t}}{E_{0}(\varepsilon_{t}-\widetilde{\varepsilon}_{t}^{pl })} \tag{3}\]0 The error between the calculated results and measured results is within 5%.
## 3 Sliding Mode Control Theory and Terminal Sliding Mode Controller Design
### Terminal Sliding Mode Controller Design  
In previous research [8], we modeled the dynamic positioning system of the \"Xin Hongzhuan\" vessel and designed a controller based on terminal sliding mode control. While the controller demonstrated good performance, there is still room for further optimization. This study builds on that foundation, exploring how an improved optimization algorithm can enhance the controller's performance. Based on prior research, the design of the terminal sliding mode controller for the ship dynamic positioning system is as follows:  
Define the end die face:  
\[s_{2}=\dot{e}_{2}+\lambda sig(e_{2})^{a} \tag{5}\]  
where \(\lambda\) is the control gain, \(e_{2}\) is the system error, and \(\dot{e}_{2}\) is the derivative of the system error.  
When \(s_{2}=0\), the equivalence is:  
\[\begin{array}{l}\tau_{20}\qquad=\vec{\eta}_{d}\big{(}MR^{-1}\big{)}+\dot{\eta} \left(M\dot{R}^{-1}+DR^{-1}\right)-\\ \qquad\lambda\left(MR^{-1}\right)\left[a|e_{21}|^{a-1}\dot{e}_{21},\cdots,a|e_{2 n}|^{a-1}\dot{e}_{2n}\right]^{T}\end{array} \tag{6}\]the switching control law is:  
\[\tau_{21}=-\Big{(}MR^{-1}\Big{)}\Big{(}k_{2}\frac{s_{2}}{\parallel s_{2} \parallel_{2}+\delta}+\zeta_{2}s_{2}\Big{)} \tag{7}\]  
In this context, \(\delta\) is the parameter introduced to eliminate chattering, while \(k_{2}\) and \(\zeta_{2}\) are tuning parameters of the controller.  
Therefore, the synovial control law of the dynamic positioning terminal of the ship is obtained:  
\[\tau_{2}=\tau_{20}+\tau_{21} \tag{8}\]  
where \(\tau_{20}\) is the equivalent control term, and \(\tau_{21}\) is the switching control term. The detailed derivation process can be found in [8]  
Based on this control law, the controller model is designed as follows Figure 1, the dots (.....) means that there is no name or the name is already indicated in the box. u indicates the set parameter value [8]:
# YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective
## 3 Proposed Method
### Model Structure
#### 3.2.3 Multi-Branch Decoupling Detection Head  
In object detection, the conflict between classification and regression tasks is a common problem. Therefore, the decoupling of classification and localization heads has been widely applied in both one-stage and two-stage detectors. However, despite the evolution of the YOLO series in terms of backbone and feature pyramid, its detection head remains coupled, as shown in Figure 4. In this paper, a multi-branch decoupled detection head is used in the detection stage. The model's output includes predictions for 2D bounding box regression, object classification, projection of the 3D box's center point \((x_{c},y_{c})\) onto the 2D image, depth value and logarithmic variance \((\varphi_{z},\sigma_{z})\) of the depth value, scale of the 3D box in terms of length, width, and height \((l,w,h)\), and the values required to compute the heading angle \(\sin(\theta)\) and \(\cos(\theta)\). The decoupled detection head consists of the following branches:  
1. [label=(0)]
2. Reg2D(h, w, 4): Used to determine the regression parameters for each feature point, allowing adjustment of the predicted bounding boxes.
3. Obj(h, w, 1): Used to determine whether each feature point contains an object.
4. Cls(h, w, num classes): Used to determine the object class for each feature point.
5. CenterReg3D(h, w, 2): Used to determine the regression parameters for the 2D projection of the 3D object's center point on the image.
6. Depth(h, w, 2): Used to determine the depth value and logarithmic variance for the 3D object.
7. Dim(h, w, 3): Used to determine the 3D dimensions (length, width, height) of the object.
8. Theta(h, w, 2): Used to determine the viewing angle of the 3D object.  
Figure 4: Schematic diagram of Decoupled Head.  
Finally, the seven prediction results are stacked together. The results obtained for each feature layer are of size Out(h, w, \(13+1+\) num classes). The first four parameters are used to determine the 2D regression parameters for each feature point, allowing adjustment of the predicted bounding boxes. The fifth parameter is used to determine whether each feature point contains an object. Parameters six to thirteen are used to obtain the 9 degrees of freedom for the object in 3D space. Finally, the last num class parameters are used to determine the object class for each feature point.
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 3 Description of the Communication System  
As previously shown in Equation (4), the frequency of the current signal is one of the factors that determines the magnitude of the magnetic field generated by the coil. For this reason, modulated signals are used in coil data transmission applications.  
Modulation is the name given to the operation where certain characteristics of a wave called \"carriers\" are modified according to another wave called modulator, which contains information, so that the latter can be transmitted. The wave obtained is called the modulated signal.  
For this application, the modulation algorithm known as amplitude shift keying was used. It is a digital modulation technique consisting of switching on and off a carrier sinusoidal signal by means of a modulating signal. Its main advantage is that the processes of modulation and demodulation are relatively cheap.  
Taking into account the aforementioned considerations, a communication system based on magnetic field induction is proposed, which uses the serial protocol to communicate two devices submerged under water and the data signal is transmitted in a modulated manner. In order to prove the concept, unidirectional communication is presented. Below is a description of the transmitter and receiver systems.
## 4 Proposed Scheme
### Integration into Distributed Applications
#### 4.7.3 Application Example: IoT Authentication
The system enables auditability, as parameter adjustments allow investigators to identify devices during post-event analysis to detect fraud or identify unauthorized use. The proposed method provides privacy and an adaptive security level, while maintaining efficiency, thus meeting the requirements of IoT networks. Implementation on a blockchain platform creates the premises for distributed data storage and complete auditability. This makes this solution suitable for various ranges of related applications, such as smart energy subnets under each meter queried by this system and thereby, industrial fleet management.
# An Ultra-Wideband Vivaldi Antenna System for Long-Distance Electromagnetic Detection
## 2 Antenna Configuration Design and Performance
### Ultra-Wideband Narrow Beam Antenna
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline L & 440 \\ W & 146 \\ \(D1\) & 16 \\ \(D2\) & 35 \\ \(L1\) & 239 \\ \(L2\) & 385 \\ \(L3\) & 17.5 \\ \(I1\) & 3.2 \\ \(l2\) & 1 \\ \(\theta\) & 60\({}^{\circ}\) \\ \hline \end{tabular}
\end{table} 
Table 5: Array antenna parameters (mm). Figure 7: Model and size of 8-element Vivaldi array antenna. loss is less than \(-10\) dB in the whole band, except that 3.9 GHz is about \(-9.5\) dB. The main lobe beam width at 2, 3, 4 GHz respectively is shown in Table 6. The HPBW in the E-plane of the proposed array are shown in Table 6. The HPBW are all less than \(10^{\circ}\), and the gains are greater than 10 dBi, which are shown in Figure 9b. Phase linearity within the operational bandwidth is an important aspect of wideband antenna design. It is observed from Figure 9c that the group delay response of the transmitting antenna is almost flat, which indicates distortionless transmission. The maximum group delay of the receiving array antenna is within 2 ns, which is acceptable. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency/GHz** & **HPBW (Measured)/\({}^{c}\)** & **HPBW (Simulated)/\({}^{c}\)** \\ \hline
2 GHz & 9.8\({}^{\circ}\) & 8.3\({}^{\circ}\) \\
3 GHz & 7\({}^{\circ}\) & 5.6\({}^{\circ}\) \\
4 GHz & 4\({}^{\circ}\) & 4.2\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Narrow beam Vivaldi 8-element array E-plane pattern main lobe width. Figure 8: E-plane pattern of Vivaldi 8-element array with structure at **(a)** 2 GHz; **(b)** 3 GHz; and **(c)** 4 GHz.
## 2 Materials and Methods
### Phenolic Compound Analysis by HPLC-DAD  
Phenolic compounds (quercetin, chlorogenic acid, tannic acid, gallic acid, salicylic acid, and catechin) in honey samples were analyzed using the HPLC (Agilent Technologies, Santa Clara, CA, USA, 1260 infinity) with binary pump (G1312B) and DAD-1260 VL detector, as described by [30]. Zorbox SB-C18 (250 \(\times\) 4.6 mm, 5 \(\upmu\)m; Agilent, Santa Clara, CA, USA) column at 30 \({}^{\circ}\)C and mobile phase (A) Milli Q water with 4% acetic acid (HPLC grade) and (B) MeOH (HPLC grade) was used. Gradient program used for the binary solvent was 0-10 min 5-20% B; 10-15 min 20-40% B; 15-20 min 40-60% B; 20-25 min 60% B with 1.0 mL/min flow rate. A 10 \(\upmu\)L sample was injected to HPLC, and the DAD was set at 280 nm. Peak retention time of phenolic compounds in honey samples were compared with those of standards. Quantification was done using the calibration curve of the corresponding external standards. All samples were analyzed in duplicate.
# The Big Picture: An Improved Method for Mapping Shipping Activities
###### Abstract
* We present a complete configurable framework that is capable of creating effective density visualizations based on raw data. * We conduct extensive experiments to demonstrate the effectiveness of our approach, using real AIS data and satellite images from the European waters from a one-month period. The results indicate significant improvement over the straight-line-interpolation baseline technique, for the trajectory reconstruction, and highlight the frameworks ability to detect vessels that do not transmit AIS messages. The rest of the paper is organized as follows: first we mention related work regarding AIS manipulation, reconstruction and visualization, as well as works that deal with vessel identification through satellite imagery (Section 2). Then, we describe our process for creating density maps from raw AIS messages (Section 3). Following this, we describe the two mechanisms for improving the quality of the AIS dataset (Section 4) and present the subsequent results of the experimental evaluation (Section 5). A discussion on the results of our experiments and the effectiveness of our proposed framework is described in Section 6. Finally, we conclude our work with a discussion on the end results and possible future steps for further improvements on our method's accuracy (Section 7).