esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Template-Based Watermarking for DIBR 3D Images
## 3 Proposed Method
### Proposed Watermark Extraction Method
#### 3.3.2 Message Watermark Extraction  
The message is extracted from the image recovered by the template. Message extraction consists of six steps:  
1. Split the synchronized image into blocks: Divide the recovered image into blocks as in the embedding step.
2. Column-by-column 1D-DCT: The same process applied in the embedding step is applied.
3. Correlation: The correlation is conducted as follows, \[Corr(b)=\frac{\mathbf{s}^{\prime}\cdot\mathbf{w}_{b}}{L}=\frac{1}{L}\sum_{i=1} ^{L}s_{i}^{\prime}\cdot w_{bi}\] (12)  
Figure 8: Proposed extraction process. (**a**) Template decoding and image recovery from geometric distortions; (**b**) message watermark extraction.  
The notations are identical to those in the embedding step.
* Bit decoding: Bits are decoded from the correlation result. For example, if the correlation value with \(w_{3i}\) is the highest, then the bit decoded from the \(i\)-th column is 011. The length of the bit is determined by the length of the watermark pattern set, and in this case, the watermark pattern set length = \(2^{3}\). Different bits may be decoded for each column, and the majority voting method is used to determine the bit of the block.
* Restore messages by merging the bits: The messages are recovered by merging the bits extracted from each message block.
## 2 Simulation Methodology
### Interaction, Meshing, Boundary Conditions, and Load Scenario  
ABAQUS CAE 6.13 was used for analysing the entire structure in a finite element environment. In the absence of an automotive seat, careful attention was paid to implement the boundary conditions as realistically as possible. For the human hands, rotation around steering centre was allowed, but detachment from steering wheel was restricted. For the legs, angular movement was permitted around the leg-ankle interface, but detachment from brake and clutch positions was restricted. The head was restrained to the headrest, but lateral adjustment was permitted. Bottom surfaces of the thighs were tied up to the seat cushion position. All the adjacent portions were interconnected through 'Tie' constrains.  
Quadrahedral or hexahedral element is useful for analysing deformable bodies in ABAQUS. For this analysis 10-node tetrahedral element C3D10 was used for meshing. Linear perturbation and modal dynamic steps were taken into account to solve the analysis. Figure 3 shows the connection between the segments and the meshed body. Care was taken during mesh generation to ensure that there were at least few layers of elements across all the sections.  
The load primarily came from the acceleration of the moving vehicle. For Jaguar XK coupe [41], the acceleration value was measured as 4.5 m/sec\({}^{2}\), whereas for another racing car [42] the value was 28 m/sec\({}^{2}\). Initial acceleration values of non-racer automotive at 60 km/h and 40 km/h on multiple lane highway was calculated [43] as 1.083 m/sec\({}^{2}\) and 0.861 m/sec\({}^{2}\), respectively.  
The acceleration value for the passenger vehicle operated in a rural area was defined by [44]:  
\[a_{av}=ae^{bv} \tag{8}\]  
\[a_{\max}=c+dv \tag{9}\]  
\(a_{av}\) = average acceleration in m/sec\({}^{2}\);  
\(a_{\max}\) = maximum acceleration in m/sec\({}^{2}\);  
\(v=\) vehicle speed in m/sec;  
\(a,b,c,d=\) constants.  
Assuming the car was a non-racer type and picked up a speed of around 30 m/h from static condition, the value of accelerations was taken as 0.861 m/sec\({}^{2}\) for this analysis work, and accordingly the force cause of this acceleration was calculated. The road condition was assumed to be smooth, and the initial 10 s of the vehicle operation was accounted for. The calculated force was implemented to the human body in a seated posture in modal dynamic step to understand the behaviour of the human system under the effect of vibration. The modal dynamics step was used to understand the system behaviour after the eigenfrequencies were obtained from the earlier step. This modal dynamics step considered the damping of the structure and calculated the system response on the basis of the mode shapes obtained from the system.
# Multifractal Characterization and Modeling of Blood Pressure Signals
## 2 Data and Methodology
### Data Pre-Processing  
#### 2.2.1 Addressing Outliers  
When working with real-world signals, such as signals coming from noisy measuring instruments, a check on the existence of outliers and their removal is of paramount importance before starting any analysis. For the signal under analysis, a simple Hampel filter [44] for outlier detection and substitution is used. It is known that when a dataset contains outliers, even a single out-of-scale observation, the sample mean may deviate significantly [45]. The Hampel filter is based on the fact that a single large observation can make the sample mean and variance cross any bound; thence, a robust estimation of the outlier can be based on the median and the median absolute deviation (MAD) [46].  
Given a sequence of values \(\{x_{k}\}_{k=1}^{N}\) and a sliding window of length \(k\), the local median is defined as:  
\[M_{i}^{k}=\text{median}(x_{i-k},x_{i-k+1},\ldots,x_{i},\ldots,x_{i+k-1},x_{i +k}), \tag{13}\]  
and the standard deviation reads as:  
\[\sigma_{i}^{k}=\rho\cdot\text{median}(x_{i-k},\ldots,x_{i+k}),\ \ \ \text{with}\ \ \rho=\frac{1}{\sqrt{2}\cdot\text{erfc}^{-1}(1/2)}\approx 1.4826 \tag{14}\]Finally, the MAD is defined as:  
\[\text{MAD}=\frac{\sigma_{i}^{k}}{\rho} \tag{15}\]  
and, for a given threshold \(T_{\sigma}\), the Hampel identifier detects \(x_{i}\) as an outlier and replaces it with \(M_{i}^{k}\). Even if the Hampel filter treats the process underlying the signal as a sequence of i.i.d. random variables, it is considered extremely effective in practice [47]. Figure 1a shows the detected outliers together with the upper and lower bounds of the signal at hand, while Figure 1b shows the original signal and the processed signal thanks to the Hampel filter.
There are various mechanisms that could explain their positive effects on the human body, such as modifying the gut pH, decreasing the quantity of pathogenic bacteria in the gut, and competing for nutrients with pathogens, among others, and they have already been implemented in numerous gastrointestinal disorders [7], as well as in the clinical management of infections to the respiratory tract, urinary tract infections, bacterial vaginosis, and depression [8]. Even though recent randomized controlled trials, published in the past few years, have demonstrated clinical, microbiological, and inflammatory improvements of periodontal parameters after the administration of probiotics [9, 10, 11, 12, 13], conflicting outcomes exist in the literature, and non-surgical mechanical debridement (NSMD) remains the \"gold standard\" for managing periodontal disease [14,15]. Furthermore, there is no consensus on the optimal duration, dosage, and frequency of probiotic administration regarding the treatment of periodontal disease [13,14]. Probiotics may be applied as a non-invasive adjunct to periodontal therapy mainly to reduce inflammatory parameters [13], but to the authors' knowledge, current periodontal guidelines do not include probiotics as an adjunct to periodontal therapy. Nonetheless, probiotics have been shown to have a decreasing effect both on salivary _Streptococcus mutans_ levels in adolescents [16], as well as on halitosis,also known as bad breath, though with insufficient evidence [17]. Keeping in mind that probiotics have been discarded as an adjunct to periodontal therapy, the research question arose whether probiotics exert a beneficial influence on halitosis and other oral health parameters, such as salivary _Streptococcus mutans_ levels, oral immunity, and saliva quality. Moreover, there is a lack of evidence of the preventative potential of probiotics in relation to oral health. Hence, the aim of this systematic review is to evaluate the therapeutic potential of probiotics in patients with halitosis and to assess whether probiotics can also be implemented as a preventative tool in oral health.
## 4 Parametric Study
### Summary  
As summarized in Table 3 and mentioned above, the impacts of many factors are considered in detail in this study. The active earth pressure coefficient \(K_{\rm a}\) increases as \(f_{\rm a}\), \(\theta\), \(i\), \(T\), and \(k_{\rm h}\) increase and decreases as \(\varphi\) and \(c/\gamma H\) increase. The failure wedge angle \(\alpha_{\rm a}\) increases as \(\varphi\), \(\theta\), and \(c/\gamma H\) increase and decreases as \(f_{\rm a}\), \(\delta\), \(i\), \(T\), \(k_{\rm h}\), and \(k_{\rm v}\) increase.  
In current seismic design practices for retaining walls, the seismic amplification coefficient of backfill soils and the influence of soil cohesion are generally regarded as secondary factors and have been routinely overlooked. However, as demonstrated in Figures 8-14 and Table 3, the present parametric sensitivity analysis reveals that both the seismic amplification effect of backfill materials and their cohesive properties constitute non-negligible  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline  & \(f_{\rm a}\) & \(\delta\) & \(\varphi\) & \(\theta\) & \(i\) & \(T\) & \(k_{\rm h}\) & \(k_{\rm v}\) & \(c/\gamma H\) \\ \hline \(K_{\rm a}\) & \(\uparrow\) & \(\uparrow\) & \(\downarrow\) & \(\uparrow\) & \(\uparrow\) & \(\uparrow\) & \(\uparrow\) & \(\uparrow\) & \(\ast\) & \(\downarrow\) \\ \(\alpha_{\rm a}\) & \(\downarrow\) & \(\downarrow\) & \(\uparrow\) & \(\uparrow\) & \(\downarrow\) & \(\downarrow\) & \(\downarrow\) & \(\downarrow\) & \(\uparrow\) \\ \hline \end{tabular}  
* The trend changes as the horizontal seismic acceleration coefficient \(k_{\rm h}\) increases. \(\uparrow\): Increasing trend, \(\downarrow\): Decreasing trend.  
\end{table}
Table 3: Trend in seismic active earth pressure coefficients \(K_{\rm a}\) and failure angle \(\alpha_{\rm a}\) with various parameters.  
influencing parameters. Although these two factors exhibit contrasting effects on the increase in the active earth pressure coefficient--one acting as a positive contributor and the other as a negative suppressor, potentially creating mutual offsetting effects on the seismic-induced increase in the active earth pressure coefficient--it is imperative that reliable assessments be grounded in comprehensive computational methodologies that properly account for the combined influence of these critical parameters.
# Parallel Direct Solution of Flexible Multibody Systems Based on Block Gaussian Elimination
## 1 Introduction
Moreover, by considering the matrix bandwidth, the block matrix sparsity can be retained with specific block order arrangement. The numerical stability is guaranteed by the BGE stability criterion. This paper is arranged as follows. Section 2 details the formulation of multibody systems, with flexible components modeled by the Craig-Bampton method. Procedure details of algebraic-differential governing equations, implicit integration scheme, equation linearization and assembly, DOFs reduction by the Craig-Bampton method, rigid modes removing and modes orthogonalization, and flexible body formulation under floating frame of reference are illustrated sequentially. In Section 3, we propose a parallel direct solution scheme based on block Gaussian elimination. Coordinate reordering is applied to generate a small bandwidth within the Jacobian matrix and its submatrices, and the feasibility of the sub-block Jacobian matrix is studied. The block and sparse features of the presented Jacobian matrix are deeply investigated, and subsequently, we introduce the block Gaussian elimination method to solve the linear equations at an algorithmic level of parallelism. Section 4 verifies the effectiveness and efficiency of the algorithm through numerical examples, which include the vibration of an aeroengine stator system and the rotation of a crank-slider mechanism.
## 3 Mitochondrial Dysfunction in AAI-Induced Kidney Diseases
### Investigation Methods of Mitochondrial Function  
Studies on AAI nephrotoxicity consistently show mitochondrial dysfunction. Evaluations of AAI's effects on the ETC and cellular respiration have involved isolating mitochondria from kidney cells of both animals and humans. The isolation process involves several steps: mechanical disruption of tissues or cells through homogenization, differential centrifugation to remove debris and large cellular organelles, and subsequent isolation and collection of mitochondria. To proceed with further investigations, the Biuret method is used to measure protein concentration. Isolated mitochondria are then analyzed to assess the functionality of the ETC (Figure 2) [16; 17].  
The following assays measure the effects of AAI on respiratory chain complexes in mitochondria: measurement of oxygen consumption, respiratory chain complexes, membrane potential of mitochondria (MMP), and adenine nucleotide translocator (ANT) activity.  
There are also other assays performed on isolated mitochondria which assess the functionality and the impact of AAI on mitochondrial DNA, such as mitochondrial DNA (mtDNA).  
Figure 2: Schematic representation of the aristolochic acid I (AAI) impact on complex I of the Electron Transfer Chain (ETC).
## 2 Overview of the Benchmark Problem in RTHS
### Implementation Constraints  
As the control plant model is achieved from the physical devices available in the laboratory, virtual RTHS (vRTHS) must be subjected to the following implementation constraints:  
1. The compensator has to be in the discrete form.
2. The sampling frequency of the vRTHS test is 4096 Hz.
3. The servo-hydraulic actuator responses should be within the maximum capacity of 8900 N, a stroke of 7 mm, and a maximum velocity of 25 mm/s.  
Figure 2: Magnitude, phase, and time delay of the nominal control plant. (**a**) Magnitude; (**b**) phase; (**c**) time delay.  
4. The A/D and D/A converters have an 18-bit precision and a span of 3.8 V, and are modeled as a saturation block combined with a quantizer in the Simulink software tool.
5. Sensor conversion factors from voltage to displacement in mm and voltage to force in N are considered, and are 7.89 mm/V and 1096 N/V, respectively.
6. The measurement noise is considered and modeled as a Gaussian process whose statistics are obtained from experimental observations. The noise level is approximately 0.05% of the full span of the A/D converter.
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 3 Selection of Solvent Systems Used in PCL Electrospraying
### Solution Properties for Cone-Jet in PCL Electrospraying
#### 3.2.5 Dielectric Constant  
To date, few papers have studied the effect of dielectric constant on the process and outcomes of the electrospraying method. The effects of the dielectric constant on the cone-jet are similar to the electrical conductivity ones. Too high or too low dielectric constant is not conducive to achieving the cone-jet during PCL electrospraying process. Some research works published about the electrospinninging of polystyrene noticed that the yield of productivity was correlated to the dipole moment and the dielectric constant of the working solutions [143; 144]. Thus, based on the use of 18 solvents to prepare the working solution, it was concluded that the formation of the cone-jet was related to the use of solvent solution having high electrical conductivity and dielectric constant, moderate viscosity, surface tension, and boiling point.
## 3 Results
### Spatial Representativeness with Vegetation Index  
The multi-year averaged \(\text{EVI}_{\text{bias}}\) showed a significant monthly variation and ranged from \(-14.21\%\) to \(19.04\%\) in a circular window with an increasing size from 250 to 3000 m (Figure 8a). The \(\text{EVI}_{\text{bias}}\) in April and July ranged from 2.52% to 9.79% and fluctuated around 0. In January, March and November, the \(\text{EVI}_{\text{bias}}\) generally increased with fluctuations and ranged from \(-1.42\%\) to \(11.06\%\). While February and December had the highest \(\text{EVI}_{\text{bias}}\), which ranged from 3.22% to 19.04%, where the \(\text{EVI}_{\text{bias}}\) still increased with fluctuations. In contrast, May, August, September and October had a lower \(\text{EVI}_{\text{bias}}\). The \(\text{EVI}_{\text{bias}}\) showed a decreasing trend and ranged from 10.97% to 11.06%. In June, the \(\text{EVI}_{\text{bias}}\) was the lowest and ranged from \(-14.21\%\) to \(2.07\%\), which decreased at the beginning and remained constant after 1600 m.  
The percentage of the value of the multi-year averaged \(\text{EVI}_{\text{bias}}\) within the \(\pm 5\%\) and \(\pm 10\%\) thresholds were 68% and 95%, respectively, at a 250 m target area. Then, the percentages increased to 97% and 100%, respectively, at a 500 m target area. After that, the percentages decreased as the target areas increased, reaching 17% and 67%, respectively, at a 3000 m target area (Figure 8b).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Month** & **2003** & **2004** & **2005** & **2006** & **2007** & **2008** & **2009** & **2010** \\ \hline
1 & 0.7920 & 0.7814 & 0.7857 & 0.8259 & 0.7892 & 0.7924 & 0.7709 & 0.8016 \\
2 & 0.8093 & 0.8279 & 0.7722 & 0.8162 & 0.8241 & 0.7848 & 0.8241 & 0.7875 \\
3 & 0.8447 & 0.8141 & 0.8345 & 0.8199 & 0.8293 & 0.8361 & 0.8236 & 0.8228 \\
4 & 0.8433 & 0.8259 & 0.8559 & 0.8449 & 0.8502 & 0.8418 & 0.8474 & 0.8197 \\
5 & 0.8408 & 0.8567 & 0.8443 & 0.8694 & 0.8643 & 0.8569 & 0.8438 & 0.8541 \\
6 & 0.8649 & 0.8589 & 0.8645 & 0.8995 & 0.8532 & 0.8648 & 0.8786 & 0.8527 \\
7 & 0.8680 & 0.8690 & 0.8715 & 0.8189 & 0.8805 & 0.8696 & 0.8823 & 0.8883 \\
8 & 0.8647 & 0.8577 & 0.8520 & 0.8765 & 0.8698 & 0.8765 & 0.8546 & 0.8896 \\
9 & 0.8516 & 0.8529 & 0.8355 & 0.8288 & 0.8399 & 0.8363 & 0.8296 & 0.8357 \\
10 & 0.8250 & 0.8223 & 0.8250 & 0.8452 & 0.8221 & 0.8286 & 0.8330 & 0.8100 \\
11 & 0.8318 & 0.8284 & 0.8259 & 0.8072 & 0.8093 & 0.8175 & 0.7712 & 0.8212 \\
12 & 0.7482 & 0.7856 & 0.7375 & 0.8165 & 0.8023 & 0.7895 & 0.7961 & 0.7480 \\ \hline \end{tabular}
\end{table}
Table 1: Statistics of monthly FTVT from 2003 to 2010.  
Figure 7: Seasonal variation of FTVT from 2003 to 2010.
# Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
## 2 Theoretical Background of Wavelet Transforms
### Maximal Overlap Discrete Wavelet Transform (MODWT)  
MODWT, also called stationary wavelet transform (SWT), is an extended DWT representation using the \"a trous\" algorithm proposed by [17; 18]. Compared to DWT, the MODWT is designed to overcome the lack of translation-invariance. In fact, the MODWT is invariant to circular shifting of the time series under analysis, while the DWT is not. Using MODWT, filters are up-sampled at each level. For this reason, MODWT is considered as a redundant wavelet because the same number of samples is presented in each set of coefficients.  
The MODWT can handle samples of any size N, while the DWT restricts the sample size to a multiple of \(2^{j}\), where J is the level of the decomposition; Furthermore, while both the DWT and MODWT can be used for an analysis of variance based on wavelet and scaling coefficients, the MODWT wavelet variance estimator can be shown to be asymptotically more efficient than the same estimator based on DWT [17; 18; 19; 20; 21].  
Figure 3: Level 2 decomposition using wavelet packet transform where X is the original signal, \(A_{1}\) is the approximation component, \(D_{1}\) is the detail component, \(AA_{2}\) is the approximation component of \(A_{1}\), \(DA_{2}\) is the detail component of the \(A_{1}\), \(AD_{2}\) is the approximation component of \(D_{1}\), \(DD_{2}\) is the detail component of \(D_{1}\).  
Figure 2: Multi-resolution analysis, the x is the original time series, the \(A_{1}\), \(A_{2}\) present the approximation components, the \(D_{1}\), \(D_{2}\), are the detailed components, the HP is the high pass filter and LP is the Low pass filter.
# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 2 Materials and Methods
### Network Architecture
#### 2.3.4 Loss Function  
TSINet consists of two independent branches designed for dual tasks: predicting semantic labels of points for semantic segmentation and generating discriminative instance embeddings for instance segmentation. Accordingly, the overall loss function of TSINet is defined as the sum of the losses from these two branches:  
\[L\ =\ L_{sem}+L_{ins} \tag{3}\]  
where \(L_{sem}\) denotes the semantic segmentation loss, and \(L_{ins}\) represents the instance embedding loss.  
The typical cross-entropy loss is used in the semantic segmentation loss \(L_{sem}\), defined as  
\[L_{sem}=-\sum_{i=1}^{N}\sum_{j=1}^{C}x_{j}^{\prime}(i)log\big{(}x_{j}(i)\big{)} \tag{4}\]  
where \(x_{j}^{\prime}(i)\) is the one-hot encoding of the ground-truth semantic label for point \(p_{i}\), and \(x_{j}(i)\) is the predicted probability that point \(p_{i}\) belongs to class \(j\).  
Since the input tomato plant point clouds contain an unknown amount of instances, the instance embedding loss \(L_{ins}\) is formulated as a composite loss comprising three weighted components:  
\[L_{ins}=\alpha\cdot L_{pull}+\beta\cdot L_{push}+\gamma\cdot L_{reg} \tag{5}\]  
where \(L_{pull}\) encourages points from the same instance to be close in the embedding space, \(L_{push}\) enforces separation between different instances, and \(L_{reg}\) is a regularization term. Their formulations are as follows:  
\[L_{pull}=\frac{1}{|I|}\sum_{i=1}^{|I|}\frac{1}{N_{t}}\sum_{i=1}^{N_{t}}\left[ \left\|\mu_{t}-e_{i}\right\|-\delta_{s}\right]_{+}^{2} \tag{6}\]  
\[L_{push}=\frac{1}{|I|\left(|I|-1\right)}\sum_{t_{A}=1}^{|I|}\sum_{\begin{subarray} {c}t_{B}=1\\ t_{A}\
eq t_{B}\end{subarray}}^{|I|}\left[2\delta_{d}-\left\|\mu_{tA}-\mu_{B }\right\|\right]_{+}^{2} \tag{7}\]  
\[L_{reg}=\frac{1}{|I|}\sum_{t=1}^{|I|}\left\|\mu_{t}\right\| \tag{8}\]  
where \(\left[x\right]_{+}=max(0,\ x)\), \(|I|\) denotes the number of instances, \(N_{t}\) is the number of points in the t-th instance, \(\mu_{t}\) is the mean embedding (centroid) of the t-th instance, and \(e_{i}\) is the embedding of point \(i\). \(\delta_{s}\) is the margin for intra-instance compactness, while \(2\delta_{d}\) is the margin for inter-instance separability. \(\left\|\cdot\right\|\) represents a measurement of distance, like \(L2\).  
Finally, the semantic labels are obtained via an argmax operation over predicted class probabilities, while the instance labels are generated by applying mean-shift clustering on the learned embeddings.
## 2 Data and Methods
### Methods  
This paper measures the change of agricultural land and agricultural management scale in suburban Shanghai from three scales: (1) at the plot level, we use the landscape metrics method; (2) at the peasant household level (or property right level), we use the agricultural statistical method; and (3) at the city or regional level, we use correlation analysis, principal component analysis (PCA) and multiple linear regression methods to reveal the driving forces of the change of landscape aggregation degree of cultivated land.