esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### In Situ Hydrogen Permeation Monitoring Test  
#### 2.2.1 In Situ Hydrogen Permeation Monitoring Set-Up  
In this study, the hydrogen permeated into the hollow cylindrical steel specimen during exposure to the marine environments was monitored in situ using the hydrogen permeation monitoring set-up shown in Figure 2. Based on Devanathan-Stachurski technique [29], the developed set-up can evaluate the hydrogen permeated into steel via measuring the so-called \"anodic oxidation current\". To make it clear, the set-up shown in Figure 2 is referred to \"hydrogen permeation sensor\" and unless otherwise specified, the sensor mentioned below is a hydrogen permeation sensor. Inside the sensor was a closed three-electrode system: the inner surface of the hollow cylindrical steel specimen was the working electrode, a Pt wire as counter electrode, a Hg/HgO/0.2 mol/L NaOH electrode as reference electrode and the nitrogen deaerated 0.2 mol/L NaOH + 30 vol\(\%\) dimethyl sulfoxide (DMSO) solution as the inner electrolyte. The outer surface of the sensor was sealed with custom-built poly tetra fluoroethylene (PTFE) shells and epoxy resin except for the area reserved for exposure, as shown in Figure 2. During hydrogen permeation test, the inner surface with Pd-plated acted as so-called \"hydrogen detection side\"; while the outer surface of the sensor exposed to the marine environments acted as so-called \"hydrogen entry side\". The multi-core copper wires connecting the electrodes were led out through a waterproof connector, and the wire joints were sealed with epoxy resin inside the sensor.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**C** & **Si** & **Mn** & **P** & **S** & **Ni** & **Cr** & **Mo** & **Fe** \\ \hline
0.399 & 0.293 & 0.509 & 0.0146 & 0.0144 & 0.0804 & 0.903 & 0.204 & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the AISI 4135 steel used in this work (wt.%).  
Figure 1: Microstructure of AISI 4135 high-strength steel: OM micrograph **(a)**, SEM micrograph **(b)**; \(\alpha\)—Ferrite, P—Pearlite.  
Before the in situ hydrogen permeation monitoring test, the Pd-plated surface of the sensor was polarized at 0 mv (vs. Hg/HgO) and left for at least 24 h to ensure the polarization current density monitored decreased to below 0.1 \(\upmu\)A\(\cdot\)cm\({}^{-2}\). In addition, as shown in Figure 2, the sensor whose outer surface was fully sealed with epoxy resin and PTFE shell served as a control sample, for correcting the background current fluctuations caused by temperature variations. This control sample is also referred to as a completely sealed sensor in later sections. Since no surface of the steel was exposed to corrosive media because of the covering by epoxy resin and PTFE, it is regarded that there is not a electrochemical reaction (including corrosion reaction) occurring on the surface of the completely sealed sensor.
## 3 Results and Discussion
### Single-Factor Experiments
#### 3.1.2 Effect of Particle Size of Sample  
The particle size of the powdered _T. chinensis_ var. _mairei_ leaves was classified into five fractions using sieves (<40 mesh, 40-60 mesh, 60-80 mesh, 80-100 mesh, and >100 mesh). The effect of the particle size on the extraction yields of the polysaccharides and essential oil are shown in Figure 3A. When the particle size of sample was less than 80 mesh, with the decrease of particle size of the sample, the extraction yields of polysaccharides and essential oil were increased. It can be related to an increase in the specific surface area which allows more contact of the sample with the solvent and a better penetration of the microwave [32; 33]. However, when the particle size of sample was more than 80 mesh, with the decrease of particle size of the sample, the extraction yields of polysaccharides and essential oil were decreased. It may be smaller particle size was easy to absorb target compounds [34], thus decreasing the diffusion rate of target compounds from sample into solvent. It is clear from the results in Figure 3A that the highest yields of the polysaccharides and the essential oil were obtained when the particle size of the sample was 60-80 mesh.
# Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells
## 2 Mechanisms of Abnormal Formation Pressure
Hydrocarbon Generation and Thermal Cracking of Hydrocarbons  
Hydrocarbon generation: Kerogen in source rock transforms into less dense oil and gas, causing pore fluid volume to expand. After hydrocarbon expulsion, the fluid migrates to the reservoir, with the pore fluid bearing part of the overlying rock load, resulting in abnormally high pressure. 2. Transformation from Montmorillonite to Illite  
Montmorillonite is a very common elastic mineral found in shale, and its crystal structure contains abundant interlayer water. When montmorillonite is freshly deposited, it undergoes hydration. This hydration process continuously absorbs free water between the crystal particles, increasing the fluid volume in the pores. 3. Transformation from Gypsum to Anhydrite  
The transformation from gypsum to anhydrite releases crystallization water, while anhydrite absorbs water and converts back to gypsum. This process causes anhydrite to absorb water and expand, increasing rock volume and reducing rock porosity, which can also lead to higher pressure. 1. Minimal Change in Porosity  
Although the porosity remains essentially unchanged, abnormal high pressure can still occur. For example, mineral transformations (such as the conversion of montmorillonite to illite) may lead to the redistribution of fluids and an increase in pressure, even though there is little change in porosity. Additionally, chemical reactions between fluids can result in the generation of dissolved gases or minerals, thereby increasing the pressure of the pore fluids. For instance, reactions between groundwater and dissolved carbonate minerals can lead to the formation of gas bubbles, which causes fluid pressure to rise. These mechanisms are crucial for understanding the complexity of subsurface pressures and their implications for oil and gas exploration [21; 22].
## 4 Cases Studies
### Performance Analysis
#### 4.2.3 Comparison with EMD  
The adjacent singular value difference graph of EMD-SVD is presented in Figure 16, from which it is clear that the EMD-SVD method incorrectly estimated the number of sources as two.  
Figure 16: A line graph of adjacent singular value differences obtained by the empirical mode decomposition-singular value decomposition (EMD-SVD) method.  
Figure 15: The corresponding envelope order spectrums of extracted modes by EWT. The dashed red line in the figure represents the theoretically calculated fault characteristic order. The waveforms of the four mode envelope spectra are almost the same, the possible cause is the mode aliasing caused by the error of boundary detection.  
EMD was used to decompose the original signal in the same way. As a result, no fault features can be found in the corresponding envelope order spectra of the modes extracted by EMD, as shown in Figure 17.  
The results in this section clearly reflect the difficulties of EMD, such as the mode aliasing problem (which is evident from Figure 17; each component contains almost the same information), which is also a popular research topic by scholars.  
The results presented in this section demonstrate that neither EWT nor EMD can effectively extract the fault characteristic order in such a complicated environment. The problems revealed in the experimental results are in line with the authors' expectations, and prove the correctness of the experiment. The superiority of the method proposed in this paper is therefore well verified.
## 2 Materials and Methods
### EEG Measurement  
A Polymate Pro 6100 (Miyuki Giken Co., Ltd., Tokyo, Japan) was used for the EEG signal measurements. Active electrodes were applied to C3 and C4 according to the international 10-20 method. Each participant was seated in a 40-cm-high chair or wheelchair in a resting position. A 15-inch LCD screen displaying moving images was placed before each participant. The participant's hands were placed bilaterally on the thighs. A movie of a paralyzed patient extending the upper extremity of the paralytic side forward was shown, and the participant was instructed to \"Please imagine that you are moving your arm forward in the same way as the arm movement on the screen. However, do not actually move your hand.\" If the participant's arm moved, a supervisor stopped the participant's arm to minimize body movement. EEG signals were recorded during the MI (Figure 2).  
Figure 1: Diagram of the research overview. (**A**) Recording of EEG signals while the patient performs motor imagery. (**B**) Detection of ERD from the EEG using a classifier of the MSM and RP. (**C**) Measurement of reaction time, which is from the start of the EEG to when the arm starts to move. EEG, electroencephalogram; ERD, event-related desynchronization; MSM, Markov switching model; RP, relative power.  
The patients were asked to view the movie 20 times, with 10 s for one task (1 s for the presentation of a still image of the paralyzed forearm on a desk, 5 s for the movie of the upper-extremity movement, and 4 s for the interval gazing point). Before measuring the EEG signals, the patients were shown the same movie 20 times for the MI practice. The interval between the practice and EEG recording phases was 2 min. The participants were instructed to rest without thinking while looking at the gazing point. This was one task and had to be performed 20 times. The patients practiced first and were then tested (Figure 3).
## 5 Experiment and Discussion
### Displacement Tracking Experiments  
Comparative experiments were carried out in order to test the tracking performance of BSTDNDO. Figure 9 shows the reference signal. The constant load force was adjusted to 50 KN and applied to the cylinder during the experiments. The metering-out valve kept the maximum orifice opening. The parameters of BSTDNDO were \(k_{1}=55\), \(k_{2}=35\). \(k_{3}=230\), \(r=40\), \(\alpha=0.8\). \(\eta_{l2}=200\), \(\eta_{l3}=100\), and \(\eta_{l01}=100\). The max tracking error of BSTDNDO was 2.35 mm, as shown in Figure 10. Subsequently, comparative experiments were carried out. Because the sliding mode control (SMC) algorithm was robust and practical in engineering [34; 35; 36], it was chosen for this comparison. A large number of parameter sets were tested to make sure that the SMC controller works with the highest performance. Figure 11 shows the tracking error while using the SMC algorithm. The BSTDNDO controller had higher tracking accuracy than the SMC controller because the uncertainties and disturbances were observed by NDO and compensated during the controller design procedure (Figures 12 and 13). The max displacement tracking error was 3.62 mm and the maximum tracking error was reduced by 35%.
## 4 Numerical Results  
### Problem Data  
Figure 1 depicts the standard IEEE 69-bus radial distribution system adopted in this article [80]. The base voltage and power of the system are 12.66 kV and 1 MW, and its minimum and maximum permissible voltage limit is [0.9, 1.05] p.u. [81; 82; 83; 84; 85]. In addition, the network has 68 main lines, which are represented by the solid line in Figure 1. This network also includes 5 tie lines to reconfigure the network, which are presented with dashed lines in the figure. There is a breaker on each line. Each main line is located between bus n and n + 1, and the breaker on this line is marked with number n. For example, the breaker on the line between buses 15 and 16 is marked with the number 15. In addition, the tie lines are located between the buses (11, 43), (13, 21), (15, 46), (27, 65), and (50, 59), and the breakers on these lines are identified by numbers 69, 70, 71, 72, and 73. The data of distribution lines and distribution substations and peak data of \(P^{0}\) and \(Q^{0}\) are extracted from [80]. The hourly amount of \(P^{0}\) and \(Q^{0}\) can be found by multiplying the daily curve of the load factor and peak values of \(P^{0}\) and \(Q^{0}\). The daily load factor curve is drawn in Figure 2[28]. Parameters \(P^{1}\) (\(Q^{1}\)) and \(P^{2}\) (\(Q^{2}\)) are assumed to be 20% and 10% of \(P^{0}\) (\(Q^{0}\)), respectively. \(N_{ch}\) is considered to 5.  
This article incorporates wind turbine and solar cells as two kinds of renewable energy generation equipment. It is noteworthy that the generation output of wind turbine and solar cells relies on wind speed and solar irradiance, respectively [26]. Obviously, the output generation of these units is time-variant as the wind speed and irradiance varies over time. Hence, the expressions of their maximum capacity are time-variant [26]. Daily curves of generation power rates for the wind system and solar panel are drawn respectively in Figures 3 and 4 [86], and their maximum capacity is calculated by multiplying these curves by the nominal capacity of this type of DGs. It is assumed that these resources do not participate in reactive power control and were used only for energy generation purposes based on standard IEEE1547 [87]. Their capacity is equal to 700 kW, the wind turbines are located on buses 20, 27, 54, and 65, and the solar cells are placed on buses 34, 38, and 46. Moreover, in the said network, there is a DG of 4000 kW and 1000 kVAr in bus 60, which is a diesel generator. Additionally, the minimum power factor allowed for the said network is set to 0.9.  
Figure 1: Diagram of the test 69-bus radial distribution system [80].  
Figure 2: Daily curve of load factor [80].
## 4 Conclusions  
In this paper, we designed a Residual Network called ResNet18 and the optimizing feature extraction method. The three-dimensional fusion features along with the data augmentation strategy of SpecAugment showed its advantages. Besides, the center loss function and softmax with the embedding layer also contributed to the best performance with the aggregated features using an adaptable learning rate. The accuracy results of 94.3% on the ShipsEar dataset indicated that the proposed method achieved state-of-the-art accuracy. The results showed that the proposed method can extract high-level abstract information that was beneficial to classification. The proposed method provided good technical support for the target classification and recognition function of the Sonar system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **ResNet18** & **ResNet18** & **ResNet18** \\ \hline Feature & LM + MFCC + CCTZ & LM + MFCC + CCTZ & LM + MFCC + CCTZ \\ Loss function & Softmax & Uniform loss & Center loss \\ Class A & 0.967 & 0.979 & 0.964 \\ Class B & 0.892 & 0.938 & 0.947 \\ Class C & 0.874 & 0.894 & 0.915 \\ Class D & 0.922 & 0.945 & 0.950 \\ Class E & 0.923 & 0.924 & 0.938 \\ Average & 0.910 & 0.934 & 0.943 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The comparison of the different loss functions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy** \\ \hline Baseline [15] & 0.754 \\ RBM + BP [14] & 0.932 \\ RSSD [4] & 0.933 \\ ResNet18 + 3D & 0.943 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The comparison of recognition accuracy with other models on ShipsEar.  
The feature augmentation and target classification of the proposed method under the conditions of different sizes and different SNR are worthy of further study.  
Conceptualization, F.H.; methodology, F.H.; validation, F.H.; investigation, C.L.; writing--original draft preparation, F.H., and C.L.; visualization, C.L., L.G., and F.C.; project administration, F.H. and H.F.; funding acquisition, F.H. and H.F. All authors have read and agreed to the published version of the manuscript.  
This work reported herein was funded jointly by the National Natural Science Foundation of China for Young Scholar (Grant No. 61801471), National Key R&D Program of China (Grant No. 2016 YFC0302000), Youth Innovation Promotion Association CAS, the development fund for Shanghai talents, and Jiading Youth Talents Program.  
The study did not require ethical approval.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data available in a publicly accessible repository. The data presented in this study are openly available in available at [[http://atlantic.uvigo.es/underwaternoise/](http://atlantic.uvigo.es/underwaternoise/)]([http://atlantic.uvigo.es/underwaternoise/](http://atlantic.uvigo.es/underwaternoise/)) at 10.1016/j.apacoust.2016.06.008 in ref [16].  
The authors declare no conflict of interest.
# On the Use of Focused Incident Near-Field Beams in Microwave Imaging
## NF Beams and Results  
The example presented in Section 2.4 utilized a numerically modelled incident field. In this section, we consider realistic incident NF distributions. To this end, we consider two antenna systems that are able to create NF focusing: (1) a NF plate which can create a subwavelength focused NF distribution on a line (line focusing) [47] and (2) a Bessel beam launcher which is able to generate spot focused NF distribution (spot focusing) and maintain it over a distance away from the launcher [48]. Herein, we use these two antennas to illuminate the OI so as to demonstrate the idea presented in the previous section. It should be noted that all the results presented herein have been simulated using ANSYS HFSS (Electronic Desktop 2018, ANSYS, Canonsburg, PA, USA).
## 4 Conclusions  
We have demonstrated a photonic scheme of an absolute distance interferometer using a 1 GHz microwave synthesized by the 10 th harmonic of the pulse repetition rate of a fiber mode-locked laser. The photonic microwave offers an accurate length ruler with a 0.3 m wavelength stabilized to the Rb clock with a fractional instability of 10\({}^{-11}\) at 1 s averaging. However, the measured interferometric phase is disturbed by optical and RF electrical components comprising the interferometer system, which were identified by measurements and subsequently corrected by passive and active schemes of systematic error suppression. Experimental results revealed that the systematic error can be corrected to a residual level of 7.8 \(\upmu\)m in terms of the standard deviation (1 \(\sigma\)), which is a phase measurement error of \(\sim\)0.0084\({}^{\circ}\), comparable to the resolution of the lock-in amplifier used in phase measurement. It is expected that the measurement error can be enhanced to a sub-micron level by selecting a higher microwave frequency, along with high frequency RF components that will be available in the near future. It is also important to note that the scheme of photonic microwave interferometry can be a promising candidate for practical use of the frequency comb-based distance measurement with traceability to the time/frequency standard, which can be applied to high-precision machine axis control as well as long distance measurement space missions such as geodetic survey and inter-satellite ranging.  
Conceptualization, Y.-S.J. and S.-W.K.; methodology, W.K.; software, H.F.; validation, W.K., H.F. and Y.-S.J.; formal analysis, W.K.; investigation, H.F.; resources, K.L.; data curation, S.H.; writing--original draft preparation, Y.-S.J.; writing--review and editing, S.-W.K.; visualization, W.K.; supervision, Y.-S.J.; project administration, S.-W.K.; funding acquisition, S.-W.K. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Active systematic error correction. (**a**) Phase measurement circuit with feedback control for signal power compensation. (**b**) The result of optical power stabilization. (**c**) Position error after power compensation (blue) and its corrected error (green). BM: balanced mixer, HPF: high pass filter, LPF: low pass filter, PD: photodetector, RF: radio frequency, _ref_: reference, _mer_: measurement,_f_: pulse repetition rate, PI: proportional integral, STD: standard deviation.  
Funding:This work was supported by the National Research Foundation of the Republic of Korea (NRF-2012R1A3A1050386).  
[PERSON] acknowledges support from the KRISS (20011030) and [PERSON] appreciates support from the KIMM (NK224A).
# Wireless Networks under a Backoff Attack: A Game Theoretical Perspective
## 3 Network Throughput under Backoff Modification
### Discussion  
The results showed in Figure 1 show that:  
* The throughput of normal stations decreases significantly for low values of \(W_{2}\). This is independent of the number of malicious stations, the mechanism used (BA or RTS/CTS) and the payload size. This happens because the malicious stations use lower backoffs and hence, they have higher chances to win the contention procedure against normal stations. This causes that the throughput is not fairly distributed among stations. As \(W_{2}\) increases (i.e., the malicious stations behave more similarly to the normal ones) the throughputs difference becomes smaller.
* If there is only one malicious station, this station consumes the major part of the network throughput for low \(W_{2}\), because it usually wins the contentions. This is independent of the mechanism used (BA or RTS/CTS) and the payload size. Yet when there are more than one malicious stations, the total throughput becomes 0 for \(W_{2}=1\), because there are some stations trying to access the network that will always collide. As the \(W_{2}\) value increases, we observe that the throughput for the malicious stations also increases, presenting a maximum value which depends on the total number of stations in the network and the \(W_{2}\) parameter. Also, as \(n_{2}\) increases, the throughput a malicious station obtains decreases: it is better for a malicious station to be the only malicious station in the network.
* RTS/CTS mechanism provides higher throughput when using larger payloads: in Figure 1e-h, RTS/CTS curves are always above BA curves. The opposite happens when using short payloads.  
Hence, if in a network using CSMA/CA there is one or more stations which can modify the binary exponential backoff procedure used by 802.11, the throughput that each station gets can be seriously  
Figure 1: Throughput (\(S\)) results for the simulations using [PERSON]’s model with short payload, \(T_{pJ}\), (**a–d**) and long payload, \(T_{pJ}\), (**e–h**). In cases (**a**) and (**e**), there are no malicious stations; in cases (**b–d**) and (**f–h**) there are malicious stations. \(S_{1}\) is the throughput of normal stations, \(S_{2}\) the throughput of malicious stations.  
affected. This happens using both BA or RTS/CTS mechanisms. The results obtained in this section show that network fairness is seriously affected by a backoff attack; the next sections will propose a solution to this situation using game theory tools.