esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Basic Theory
### Displacement Reconstruction  
The vortex-induced vibration displacement of the flexible riser can be obtained based on the vortex-induced vibration strain information on the outer surface of the riser by using  
Figure 5: Four directions for the arrangement of the sensors.  
the modal analysis method. The vortex-induced vibration displacement at node \(x\) on the riser can be expressed in the time domain as:  
\[y(x,t)=\sum_{i=h}^{k}p_{i}(t)\varphi_{i}(x),\quad x\in[0,L] \tag{6}\]  
where \(\varphi_{i}(x)\) is the modal vibration shape of the \(i\)th displacement of the riser in the local coordinate system, and \(p_{i}(t)\) is the time history of the \(i\)th displacement weight.  
The relationship between the bending displacement of the beam \(y(x,t)\) and the bending strain \(\varepsilon(x,t)\) is shown as follows:  
\[\frac{\partial^{2}y(x,t)}{\partial x^{2}}=-\frac{\varepsilon(x,t)}{R} \tag{7}\]  
where \(R\) is the radius corresponding to the FBG sensors attached. In this test, no matter the bare pipe, buoyancy module or staked pipe, the radius used in Equation (5) is the same, for all FBG sensors were attached to the outer surface of brass pipe.  
With Equations (6) and (7), we can obtain bending strain \(\varepsilon(x,t)\) as follows:  
\[\varepsilon(x,t)=-R\sum_{i=h}^{k}p_{i}(t)\varphi_{i}^{n}(x) \tag{8}\]  
When selecting the strain information of \(n\) discrete points on the riser model for displacement reconstruction, we can perform matrix calculation after discretizing Equations (6) and (8):  
\[[S]_{n\times t}=-R[F^{n}]_{n\times m}[P]_{m\times t} \tag{9}\]  
\[[D]_{k\times t}=[\Phi]_{k\times m}[P]_{m\times t} \tag{10}\]  
where \(S\) is the strain matrix, \(D\) is the displacement matrix, \(\Phi\) is the mode shape matrix, \(P\) is the modal weight matrix, \(n\) is the number of sampling points of strain on the riser model, \(k\) is the displacement on the riser model that is ready to be obtained, \(m\) is the number of participating modes, and \(t\) is the number of samples in the time series. The known quantity in Equation (9) is the strain data of \(n\) discrete points at time \(t\), and the unknown quantity is the modal weight of the number of participating modes \(m\) at time \(t\). In order to obtain the unique solution of the equation system, the number of discrete points \(n\) must be greater than the number of participating modes \(m\).  
Multiply \([\Phi^{n^{\prime}}]_{n\times m}\) at both ends of Equation (9) and solve the inverse matrix \([\Phi^{n^{\prime}}]_{m\times n}^{T}[\Phi^{n^{\prime}}]_{n\times m}\) to obtain the displacement weight \(\mathbf{P}\) of each participating mode:  
\[[P]_{m\times t}=-\frac{1}{R}\Big{(}[\Phi^{n^{\prime}}]_{m\times n}^{T}[\Phi^ {n^{\prime}}]_{n\times m}\Big{)}^{-1}[\Phi^{n^{\prime}}]_{m\times n}^{T}[S]_{ n\times t} \tag{11}\]  
When the displacement weight \(P\) of each participating mode is obtained, the vortex-induced vibration displacement of the riser can be obtained from Equation (8), and the vortex-induced vibration velocity and acceleration at each node can also be obtained by applying the central difference method.
# Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models
## 3 Method
### Selection of External Variables  
Various economic indicators can be applied to container volume forecasts. First, we chose from the various external variables presented in relevant studies and economic indicators that represent domestic economic fluctuations. Next, we implemented the VAR model to explore the influence of external variables on container volume prediction. The VAR model consists of a federated equation for the analysis of the effectiveness of a variable's transient impact [13].  
The selection criteria are correlations and trends that have proven influence according to previous research works [5,6]. The correlations and trends are considered together so that they can be applied in deep learning model applications and time-series decomposition methods.  
Figure 5: Comparison of container volume and trends for the years 2017–2018 (x: date, \(y\): TEU).  
Figure 6: Comparison of container volume and trends for the years 2019–2020 (x: date, \(y\): TEU).  
The selected indicators are the manufacturing production index (MPI), industrial product index (IPI), import-export price (IEP), import-export weight (IEW), and consumer price index (CPI). However, the weight and price of exports and imports are used as a single variable by summing the two indicators. As shown in Table 2, the import-export weight and import-export price variables are highly correlated with each other, as well as with the container volume. Thus, we combined indicators by distinctive characteristics of them, which are weight and price. Based on our selection standards, five external variables were selected, which are represented by a correlation between the container volume data in Table 2. MPI, IPI, IEP, and IEW showed reasonable predictions in the short term (3 months), whereas long-term (6 months) prediction was limited in terms of accuracy improvement. As the duration of prediction increases, the prediction performance decreases simultaneously. However, only CPI, which has a high correlation and cointegration relationship, exhibited a performance with less than a mean absolute error (MAE) of 5 %; the other indicators did not show accurate predictions. Although CPI performed better in VAR model prediction, trend followability is an area of concern in container prediction.
# Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM
## 2 Related Works
### Depth and 3D Skeleton Dynamic Hand Gesture Recognition  
In recent works, along with the advances in hand pose estimation and the technology of depth-based cameras, skeleton-based recognition has gained more traction. In [11], they extracted the features of the distances, angles, elevations, and adjacent angles of fingertips by employing the data of direction, normal position, the central location of the palm, and fingertip position. [PERSON] et al. built the mo-capsystem to make hand pose annotations and gather 6D object poses from RGB-D video data for hand recognition [31]. [PERSON] et al. [12] published an approach with results better than the results of depth-based methods. They calculated the shape of connected joints descriptor from the connected joints of the hand skeleton and used a Fisher vector to encode it. A temporary pyramid was used to model Fisher vectors and skeleton-based geometric features before extracting the final feature.  
There are various methods that are based on deep learning, for dynamic hand gesture recognition using skeleton-like information. [PERSON] et al. [32] performed training on a bidirectional Recurrent Neural Network (RNN) using the movement features of fingers and hand and skeleton sequences. [PERSON] et al. proposed parallel convolutions to handle sequences of the hand skeleton.  
[PERSON] [33] proposed a new way based on CNN and LSTM by fusing two streams of the hand shape and skeleton model.  
Collectively, all the above methods have problems in recognizing gestures at some distance from the camera, with variable illumination.
# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 2 Structure, Health States, and Maintenance Strategies of the Offshore Wind Turbine  
### Structure of the OWT  
To facilitate mathematical modelling, the structure, health states, and maintenance strategies of the OWT are defined in this section based on [18; 19; 23; 24]. Due to the fact that gear-driven wind turbines currently have a large market share, a gear-driven OWT composed of six subsystems is considered. The six subsystems are the rotor system, main bearing system, drivetrain system, braking system, power system, and turbine structures, respectively.  
1. The rotor system is composed of the blades and the hub;
2. The main bearing system provides safe support to the rotor system and enables rotation to occur with minimal friction;
3. The drivetrain system consists of the main shaft and a gearbox that is also called accelerator;
4. The braking system locks the wind turbine position in non-operational mode; for example, when the maintenance of the wind turbine is in progress or the wind turbine is shut down under extreme weather conditions. The braking system is also used to slow down the rotor when the wind speed is beyond cut-out speed, i.e., a protective shutdown of the turbine is required when wind speed exceeds cut-out speed of 25 m/s [10; 12];
5. The power system consists of a generator, a converter, and a transformer;
6. The turbine structures include a nacelle, tower, and foundation.  
Since the failure of these subsystems will lead to the shutdown of the turbine, their reliability will have a direct impact on the O&M of the OWT. For simplicity, the health state of these subsystems is classified into three categories in this study, i.e., healthy, fault, and failure. When the subsystems are in a healthy state, they will operate normally. When a minor fault is detected from a subsystem by the CMS, it is assumed that the subsystem is still able to work with the minor fault, but more care should be taken to monitor the development of the fault. This is the fault state. When the minor fault develops into a failure and is detected by the CMS, the wind turbine will be shut down immediately to prevent causing secondary damage or catastrophic consequence. For example, the broken blade may hit the tower, causing the entire wind turbine to collapse; the failure of the braking system may catch fire and lead to the burning of the entire nacelle of the wind turbine, etc. [25].
# Design and Analysis of the Rolling and Jumping Compound Motion Robot
## 3 The Kinetic Analysis of the Rolling and Jumping Robot
### Steering Dynamics Equation  
The robot steered in the form of sliding free rolling on the horizontal plane, shown in Figure 12. The included angle between the major shaft of the robot and the horizontal plane was \(\beta\); the friction force in motion was \(f\); the mass of the rolling module was m\({}_{0}\); the center of mass was C; the distance between the center of mass and the geometric center of the shell was d; the counterweight mass was m; the distance between the limit position on both sides of the counterweight and the shape center was \(l\); the length of the swing arm was \(L\); the turning radius was \(r\), and the geometric radius of the robot was \(R\).  
Figure 11: (**a**) Schematic diagram of rolling movement; (**b**) View of Y\({}_{\Delta}\)Z\({}_{\Delta}\)-plane of the schematic diagram of rolling movement.  
Figure 12: (**a**) Schematic diagram of steering movement; (**b**) View of X\({}_{\rm C}\)Z\({}_{\rm C}\)-plane of the schematic diagram of steering movement.  
According to the triangular geometric relationship, the turning radius \(r\) can be approximately expressed as:  
\[r=\frac{R}{\tan\beta} \tag{5}\]  
When the robot system is in a balanced state with a constant inclination angle, the components of the gravity moment generated by the counterweight and the rolling module along \(\mathcal{X}_{\mathrm{C}}\) are \(M_{P}\) and \(M_{\mathrm{C}}\) respectively, which can be obtained from the movement balance condition:  
\[M_{p}-M_{c}+fR=0 \tag{6}\]  
Since \(M_{\mathrm{C}}\) is 0:  
\[M_{P}+fR=0 \tag{7}\]  
Take the moment along the \(\mathcal{X}_{\mathrm{C}}\)-axis:  
\[M_{p}=mg\sqrt{L^{2}+l^{2}}\sin(\gamma+\beta) \tag{8}\]  
According to Equations (5)-(8), in the steering movement, \(r\) and \(L\) meet the following constraints:  
\[r=-\frac{mg\sqrt{L^{2}+l^{2}}\sin(\gamma+\beta)}{f\tan\beta} \tag{9}\]
## 3 The Ionospheric Scintillation Compensation-Based Modified BP Imaging Method
### Basic Process and Analysis of BP Imaging Algorithm
GNSS satellites usually use atomic clocks, and the clocks are calibrated regularly, so they are relatively accurate. For radar applications, it is generally considered that the clock error of GNSS satellites can be ignored. In the GNSS-BSAR system, the impact of receiver clock error is mainly considered. The requirements for the frequency stability of the local oscillator of the receiver under ultra-long synthetic aperture time can be analyzed below. Taking a ground-based fixed receiver as an example, assuming that the synthetic aperture time \(T_{a}\) of the system are 300 s and the azimuth phase accuracy requirement is \(\Delta\phi\leq\pi\), then the maximum frequency error allowed by the system is \(\Delta f=\Delta\phi/2\pi T_{a}\approx 0.002\)Hz. The requirements are quite strict and cannot be met by most conventional GNSS receivers. Therefore, the influence of the local oscillator drift of the receiver must be considered. In addition, according to the analysis in Section 2.3, the ionospheric scintillation effect will also introduce phase error. In summary, under the long synthetic aperture time of GNSS-BSAR, the time-frequency errors and ionospheric scintillation error between the receiver and satellite transmitter will cause Doppler phase deviation, ultimately leading to azimuthal defocus. The modified phase compensation scheme for step 3 of current BP process is described in the following part.
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### GANs in Histological Image
#### 5.4.5 Color Normalization  
Color normalization standardizes image appearance and significantly enhances downstream analytical outcomes, as shown in [PERSON] et al. [65]. In the paper, CycleGAN was applied for color normalization and reported performance metrics directly linked to segmentation accuracy, PQ = 0.5527 and AJI = 0.5797.  
In a broader application, [PERSON] et al. [92] compared cGAN and CycleGAN for color normalization, domain transformation, and data augmentation. The results showed that cGAN achieved Mutual Information (MI) = 0.26 \(\pm\) 0.07, Normalized MI (NMI) = 0.10 \(\pm\) 0.03, Hierarchical Clustering (HC) = 0.75 \(\pm\) 0.13, and Bhattacharyya Distance (BCD) = 0.31 \(\pm\) 0.06. In contrast, CycleGAN yielded MI = 0.24 \(\pm\) 0.08, NMI = 0.09 \(\pm\) 0.03, HC = 0.61 \(\pm\) 0.26, and BCD = 0.35 \(\pm\) 0.15, suggesting cGAN slightly outperformed CycleGAN in this context for preserving domain-specific features.  
Moving toward more advanced models, [PERSON] et al. [58] developed a Cross-Stain Transfer Network (CSTN) for both color normalization and virtual staining. Implemented on an NVIDIA RTX 3090 GPU, CSTN produced an FID of 101.37, Kernel Inception Distance (KID) of 6.129, and Average Hue Ratio (AHR) of 1.98, reflecting moderate visual alignment between synthetic and reference staining styles.  
Likewise, [PERSON] et al. [89] introduced Restore-GAN, which achieved outstanding quantitative performance with SSIM = 0.949 \(\pm\) 0.020 and PSNR = 30.678 \(\pm\) 1.624, confirming its effectiveness in preserving morphological structure while correcting stain variability.  
Further contributing to this field, [PERSON] and [PERSON] [90] proposed a combined LSGAN and TredMiL approach, obtaining NMI = 0.800, BCC = 0.750, and WsCC = 0.590, reinforcing the feasibility of integrating adversarial learning with structural constraints to ensure consistent chromatic representation.
## 4 Security Threats
### Distribution of Sharing Rules  
#### 4.3.1 Rules Never Received Claim  
As mentioned above, every spectrum sharing protocol utilizes its own preferred techniques for keeping its clients up-to-date with the current spectrum allocation rules. The DSA network, in turn, must provide its nodes with uninterrupted access to such rules towards assuring both their normal and stable operation. However, an adversarial node may deny that he ever received either the whole, or a special part of the aforementioned rules, aiming at hiding his maliciousness behind this \"unawareness\" claim [28]. For instance, in case a previous allocation is more favorable than the current one, a node may choose not to update its configuration, and thus not to adapt its operation to the new conditions, so as to continue gaining the same profits as before. If such behavior is detected by an existing security mechanism of the DSA network, the only way for the adversary to escape any potentially induced punishment would be to pretend to be the victim of a \"fictitious\" attack which prevented him from accessing the respective rules.  
Obviously, each DSA network must be equipped with techniques ensuring that all nodes can gain access to the latest fresh rules and, additionally, that none of them can repudiate their reception [53]. Otherwise, an adversary could easily invert his position and pretend to be the victim, even if he is the offender.
## 2 Multi-Infeed LCC-HVDC System  
HVDC converters are classified into two major types: (1) line-commutated converter (LCC-HVDC) and (2) voltage source converter (VSC-HVDC).  
In LCC-HVDC, the DC side current is kept constant with minimum ripple. The polarity of DC side voltages determines the direction of the average power flow. A large inductor, connected in series, retains the current continuity similar to a current source. The DC side voltage (Vdc) in an LCC is proportional to the AC side voltage (Vac) multiplied by \(\cos(\theta)\). The \(\theta\) is the angle between the fundamental frequency component of the phase current and the phase voltage at the AC side. The \(\theta\) is controlled by adjusting the switching time of thyristors. The AC current is directly proportional to the DC side current because it is created from the switching of the DC current. The polarity of the DC side voltage changes with a firing angle beyond \(90^{\circ}\); however, this cannot affect the polarity of the DC current. The turn ON/OFF time of valves is controlled by controlling the firing angle \(\alpha\), which produces firing pulses for valves. One of the most important features of the LCC is the possibility to control the DC side voltage by controlling the firing instant.  
In VSC-HVDC, the DC voltage polarity remains the same, and the direction of the average power flow depends on the polarity of the DC current. Typically, the VSC DC terminals and a large capacitor are in parallel, which denotes voltage sources. The VSC entails reverse-conducting electronic switches, which are widely available in the power electronics market, such as IGBT.
## 3 Results
### Functional Properties  
The survey for the Cention-N alkasite material's functional properties showed that, at T0, 54 restorations (98.18%) qualified as clinically excellent and one (1.82% as clinically good, whereas at T1 (3 months) 48 restorations (87.27%) qualified as clinically excellent and seven (12.73%) as clinically good. At T2 (6 months), 40 restorations (72.73%) qualified as clinically excellent, 12 (21.81%) as clinically good, one (1.82%) as clinically sufficient, one (1.82%) as clinically sufficient, and one failure was reported (1.82%). At T3 (12 months), 40 restorations (72.73%) qualified as clinically excellent restorations, 11 (20.01%) as clinically good, two (3.63%) as clinically sufficient, and two (3.63%) failed restorations were reported (Table 4).  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Aesthetic Properties**} \\ \cline{2-10}  & \multicolumn{4}{c}{**Cention-N**} & \multicolumn{4}{c}{**GIC**} \\ \cline{2-10}  & \multicolumn{4}{c}{**n (\%)**} & \multicolumn{4}{c}{**n (\%)**} \\ \hline
**FDI CRITERIA** & **T0** & **T1** & **T2** & **T3** & **T0** & **T1** & **T2** & **T3** \\ \hline
1—CLINICALLY EXCELLENT & 55(100) & 45(81.8) & 30(54.55) & 26(47.27) & 56(98.25) & 37(64.91) & 25(43.85) & 20(35.09) \\
2—CLINICALLY GOOD & 0(0) & 10(18.18) & 23(41.83) & 26(47.27) & 1(1.75) & 20(35.08) & 31(54.39) & 32(56.14) \\
3—CLINICALLY SUFFICIENT & 0(0) & 0(0) & 1(1.81) & 1(1.81) & 0(0) & 0(0) & 0(0) & 3(5.26) \\
4—CLINICALLY & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) \\ UNSATISFACTORY & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) \\
5—CLINICALLY POOR & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) & 0(0) \\ FAILED & 0(0) & 0(0) & 1(1.81) & 2(3.63) & 0(0) & 0(0) & 1(1.75) & 2(3.51) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of aesthetic properties based on FDI criteria. T1 (3 months), T2 (6 months), T3 (12 months).  
The survey for the Fuji IX GIC functional properties showed that, at T0, 53 restorations (92.98%) qualified as clinically excellent and four (7.02%) as clinically good, with the same data reported at T1 (3 months). At T2, 45 restorations (78.94%) qualified as clinically excellent restorations, nine (15.79%) as clinically good, one (1.75%) as clinically sufficient, and two failures (3.52%) were reported. At T3, 36 restorations (63.15%) qualified as clinically excellent restorations, 16 (28.07%) as clinically good, three (3.52%) as clinically sufficient, and 2 failures (3.52%) were reported (Table 4).  
The functional properties of both materials have decreased over time, but no statistically significant difference was observed regarding the decrease in clinically excellent restorations between timepoints considering one material or the other. At different timepoints, no statistically significant differences were observed in terms of functional properties.
# Effects of Polypropylene Fiber on the Liquefaction Resistance of Saturated Sand in Ring Shear Tests
## 2 Materials and Methods
### Test Procedure  
The moisture content in the mixing process was kept at 10% proportion of the dry weight of sand. Prior to the addition of fiber, the prepared dry sand was mixed with required water because fiber easily mixed with wet sand. Thereafter, the predetermined fiber was added into the wet sand and mixed thoroughly with small increments by a stirrer for about 5 min. When the polypropylene fiber monofilament was uniformly distributed in the sand without fiber aggregates, we considered that the uniform sand-fiber matrix was obtained. Then, the mixture was divided into four equal parts and transferred into the shear box successively. Each part was compacted with the designed densities to form a sample with a height of 8 cm. Detailed information of the prepared specimens is given in Table 1.  
The specimen was saturated using carbon dioxide saturation method [19; 36; 37]. The prepared specimen was treated with CO\({}_{2}\) percolation first to expel the air inside the specimen. Then, the CO\({}_{2}\) was expelled totally by de-aired water infiltration. Thereafter, the saturation degree was evaluated, and we checked the saturation degree by using the \(B_{D}\) parameter, which was extended from Skempton's \(B\) parameter to use in the direct-shear state for determining the levels of soil saturation [38], and was denoted as:  
\[B_{D}\ =\ \frac{\Delta u}{\Delta\sigma} \tag{2}\]  
where \(\Delta\sigma\) (kPa) = increment of normal stress and \(\Delta u\) (kPa) = increment of pore pressure. After saturation, the specimen was firstly consolidated with a normal stress of 50 kPa in the drained situation and then an increase of normal stress \(\Delta\sigma\) = 50 kPa was applied and the increment of pore pressure \(\Delta u\) was measured in the undrained situation to calculate the value of \(B_{D}\) by Equation (2). If \(B_{D}\geq\) 0.95, we considered the specimen as saturated. After checking the saturation degree, the initial stress states were applied under the drained condition. The initial normal stress was first loaded slowly to consolidate until a steady value of axial strain, and then initial shear stress was loaded slowly to reach the designed value. A static initial shear stress was applied to the specimens in order to achieve uniform stress during the entire loading process. In order to investigate the liquefaction resistance of fiber reinforced sand under different loads, two different shearing methods, shear-torque-controlled (STC) and cyclic-torque-controlled (CTC),