esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 CD, GFD and Metabolic Disorders  
### CD, GFD and Lipid Profile  
Newly diagnosed CD children showed elevated levels of total cholesterol and low-density lipoprotein cholesterol (LDL-C) and reduced high-density lipoprotein cholesterol (HDL-C) levels [18]. A recent study compared the lipid profile of 12 untreated CD children with that of 10 control patients showing a decreased level of serum triglycerides levels and very-low-density lipoprotein (VLDL) and an increased level of LDL-C in CD children compared with controls [19]. These results could be explained by fat malabsorption characterizing untreated CD patients. Recently, in an observational study of 30 CD children and adolescents having available lipid profiles before and after initiation of GFD, total cholesterol levels remained unchanged, while triglycerides decreased, and HDL-C increased [20].  
In adults, a recent systematic review [21] found three studies assessing lipid levels before and after the initiation of a GFD [9, 22, 23] and one study reporting the prevalence of dyslipidemia in celiac patients following a GFD [24]. In the first one (a prospective study), no significant changes were found in terms of levels of triglycerides in patients following one-year GFD [9]. On the other hand, the second study found increased serum triglyceride levels, a higher total cholesterol level, and a decrease in the HDL level in CD patients following a GFD for 4.7 years [22]. Finally, a cohort study of 185 patients with CD retrospectively showed a significantly increased cholesterol level and a significantly decreased HDL level after the initiation of GFD after more than one year of GFD; however, triglycerides levels did not statistically differ [23]. These findings demonstrated a worsening of lipid profile in CD patients following GFD. However, a cross-sectional study showed that a lower prevalence of dyslipidemia in CD patients compared with the control group [24].
## 3 The Proposed Solution  
As discussed above, our main design goal is to concurrently achieve security and privacy in V2I communications. In particular the proposed solution will support the following properties:  
* _Security._ Only authenticated nodes (vehicles) will be allowed to communicate with RSUs (_unforgeability_). In addition, no adversary, even as strong as the collusion of all the authorities, should be able to impersonate a legitimate node (_unframeability_).
* _Privacy. Vehicle anonymity_ must be assured, meaning that the identity of the vehicle should not be disclosed to RSUs or any external entity. In addition no single entity should not be able to trace the transmitted messages send to one or more RSUs with a particular sender (_message-vehicle untraceability_). Traceability should only be possible for a collusion of the CA, the SA and the relevant RSUs. Finally, an RSU (or any other external entity) should not be able to link together different messages coming from a single sender, even if the identity of the sender is not revealed (_message unlinkability_).
* _Efficiency._ The system must be efficient enough in terms of communication and computation overhead. The RSUs must be able to process multiple messages per second. For example, 100-200 messages/second are sufficient for RSUs to receive informed decisions about the current traffic conditions and unexpected events, even if some messages will be eventually lost in case of bursts. Vehicles must not require to be able to perform crypto operations that are not'mainstream' in terms of computational cost. For example although public key crypto is feasible, bilinear pairings are not within the current state of the art.
* _Privacy-preserving and efficient revocation._ Finally revocation should be both efficient and privacy preserving. The system should not require CRLs for revocation, as they can become a system bottleneck. At the same time, revocation of a node must not disclose the identity of the node as this would violate the privacy of previous communications.  
Trust Assumptions  
We are adopting a scenario involving two trusted authorities. Concerning the privacy properties, we assume that the Credential Authority CA and the Signing Authority SA are honest but curious. An interesting observation might be that although the CA is considered honest, its involvement is reduced in providing an appropriate AoN-PKEET scheme and inspect the behaviour of SA by checking the validity of the NIZKP provided. On the other hand, SA practically has the burden of correct protocol execution without knowing the real identity of the drivers. For the security properties (i.e., vehicle unforgeability and unframeabilty) we assume that all authorities may be compromised. Finally, to protect from outsiders we assume that all the communication is encrypted and integrity protected, using standard security mechanisms such as TLS.
## 3 Results and Discussion
### Sensory Evaluation of Steamed Bread
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **L\({}^{\star}\)** & **A\({}^{\star}\)** & **B\({}^{\star}\)** & **\(\Delta\)E\({}^{\star}\)** & **Hue Angle (h\({}^{\star}\))** & **Chroma (C)** \\ \hline WF & 92.32 \(\pm\) 0.35 ab & 7.44 \(\pm\) 0.05 a & 5.90 \(\pm\) 0.11 g & 0 g & 0.98 \(\pm\) 0.12 a & 9.50 \(\pm\) 0.10 a \\ WF\&MF & 92.47 \(\pm\) 0.22 b & 7.22 \(\pm\) 0.10 b & 6.17 \(\pm\) 0.02 f & 0.38 \(\pm\) 0.03 f & 0.87 \(\pm\) 0.02 a & 9.50 \(\pm\) 0.13 a \\
2\%DF & 92.24 \(\pm\) 0.18 ab & 7.00 \(\pm\) 0.06 c & 6.53 \(\pm\) 0.08 e & 0.77 \(\pm\) 0.06 e & 0.74 \(\pm\) 0.05 b & 9.57 \(\pm\) 0.07 a \\
4\%DF & 92.14 \(\pm\) 0.09 abc & 7.04 \(\pm\) 0.03 c & 7.10 \(\pm\) 0.04 d & 1.28 \(\pm\) 0.10 d & 0.63 \(\pm\) 0.03 b & 10.00 \(\pm\) 0.11 b \\
6\%DF & 91.96 \(\pm\) 0.15 bcd & 6.89 \(\pm\) 0.10 d & 7.34 \(\pm\) 0.06 c & 1.58 \(\pm\) 0.10 c & 0.55 \(\pm\) 0.08 cd & 10.06 \(\pm\) 0.06 b \\
8\%DF & 91.79 \(\pm\) 0.10 cd & 6.88 \(\pm\) 0.05 d & 7.58 \(\pm\) 0.05 b & 1.85 \(\pm\) 0.08 b & 0.51 \(\pm\) 0.05 d & 10.24 \(\pm\) 0.08 c \\
10\%DF & 91.76 \(\pm\) 0.13 d & 6.91 \(\pm\) 0.07 d & 7.73 \(\pm\) 0.03 a & 1.99 \(\pm\) 0.05 a & 0.48 \(\pm\) 0.04 d & 10.37 \(\pm\) 0.04 c \\ \hline \hline \end{tabular}  
* L\({}^{\star}\) (whiteness/darkness), a\({}^{\star}\)(redness/greenness), b\({}^{\star}\)(yellowness/blueness) values were measured after calibration through a white standard board, \(\Delta\)E\({}^{\star}\) indicates the total color difference. Values are represented as means \(\pm\) SD (standard deviation). Values in a column with different lowercase letters are significantly different (\(p<0.05\)). Sample WF was the control.  
\end{table} 
Table 3: Color evaluation of steamed bread with steamed millet flour and DF (\(n=6\)). indexes of the steamed bread deteriorated sharply, which might be due to the dilution of the gluten content and the rough taste of DF itself.
# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
## 3 Dataset and Methodology  
### Dataset  
Two publicly available datasets from real world are utilized in this study. (1) PeMSD04, which contains traffic-flow data from the San Francisco Bay Area. The dataset includes 340 sensors, and the selected experimental period covers two months, from 1 January 2018 to 28 February 2018. (2) PeMSD08, a real-time traffic-flow-sensor data set, includes data from 295 sensor detectors from a two-month experiment period from 1 July 2016 to 31 August 2017. The datasets were collected by the Caltrans Performance Measurement System (PeMS) [33].
# Robustness of STIRAP Shortcuts under Ornstein-Uhlenbeck Noise in the Energy Levels
###### Abstract  
In this article, we evaluate the efficiency of two shortcuts to adiabaticity for the STIRAP system, in the presence of Ornstein-Uhlenbeck noise in the energy levels. The shortcuts under consideration preserve the interactions of the original Hamiltonian, without adding extra counterdiabatic terms, which directly connect the initial and target states. The first shortcut is such that the mixing angle is a polynomial function of time, while the second shortcut is derived from Gaussian pulses. Extensive numerical simulations indicate that both shortcuts perform quite well and robustly even in the presence of relatively large noise amplitudes, while their performance is decreased with increasing noise correlation time. For similar pulse amplitudes and durations, the efficiency of classical STIRAP is highly degraded even in the absence of noise. When using pulses with similar areas for the two STIRAP shortcuts, the shortcut derived from Gaussian pulses appears to be more efficient. Since STIRAP is an essential tool for the implementation of emerging quantum technologies, the present work is expected to find application in this broad research field.  
STIRAP; shortcuts to adiabaticity; dephasing; Ornstein-Uhlenbeck noise Article
# Characterization of Ductile Crack Propagation by Fractal Energy Dissipation Rate
## 4 Experimental Validation  
The proposed fractal energy dissipation rate was validated against the relevant experimental data in the literature. The nonlinear best-fitting algorithm of [PERSON] and [PERSON] [34] was adopted to obtain parameters \(H\) and \(\gamma_{p}\). The experimental load-displacement curves from [PERSON] and [PERSON] [35] were reinterpreted using the fractal approach, where the load-loading-line displacement curves are available. The material used for the tests was HY80 steel available in a 27-mm-thick plate. The tensile test results showed that the 0.2% offset yield stress was 630 MPa, and the ultimate tensile stress was 735 MPa. The specimens were subjected to single-edge notched bending, as recommended by ASTM E1820-01 [26], with different initial crack lengths \(a_{0}\). The geometric parameters of the beam were \(L\) = 203 mm, \(W\) = 50.75 mm, \(B\) = 25.375 mm, and slenderness \(\lambda\) = 4:1. The initial crack-length ratios were \(a_{0}/W\) = 0.19, 0.40, 0.55, and 0.60. Details are presented in Table 1. Because the calculation of the \(R\)-\(\Delta a\) curve requires the correlation between the load and crack extension, the instantaneous crack extension was then determined directly using the normalization method suggested in ASTM E1820-01 Annex 15 [26]. Another series of compact tension tests were performed by [PERSON] et al. [36], where the material was a 20 MnMoNi55 steel alloy with different thicknesses. The geometric details are presented in Table 1. In Figure 2, the experimental results for the \(R\)-\(\Delta a\) curve from a previous report [36] as well as the fitting curves obtained using Equation (15) are plotted. The determined parameters \(H\) and \(\gamma_{p}\) are listed in Table 1. The domain dimension of energy dissipation is denoted by \(D\) = \(3-H\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & **Specimen** & **W** & **\(\mathbf{[mm]}\)** & \(\mathbf{\boldsymbol{T_{n}}}\)**[mm]** & \(\boldsymbol{a_{0}/W}\)**[mm]** & \(\boldsymbol{H}\) & \(\boldsymbol{D_{\Delta a}}\) & \(\boldsymbol{\gamma_{p}}^{*}\) \\ \hline \multirow{3}{*}{HY80} & SE(B)-A & 50.75 & 25.4 & 0.40 & 0.3258 & 2.6472 & 40.1 \\  & SE(B)-B & 50.75 & 25.4 & 0.55 & 0.4166 & 2.5834 & 48.2 \\  & SE(B)-C & 50.75 & 25.4 & 0.60 & 0.4528 & 2.5472 & 45.9 \\ \hline \multirow{3}{*}{20 MnMoNi55} & C(T)-0.5T & 25.00 & 8.0 & 0.67 & 0.2823 & 2.7177 & 104.5 \\  & C(T)-1T & 50.00 & 8.0 & 0.61 & 0.3749 & 2.6251 & 164.5 \\  & C(T)-4T & 200.00 & 8.0 & 0.60 & 0.3837 & 2.6163 & 98.3 \\ \hline \multirow{3}{*}{20 MnMoNi55} & C(T)-0.5T & 25.00 & 4.0 & 0.62 & 0.2163 & 2.7837 & 105.6 \\  & C(T)-1T & 50.00 & 4.0 & 0.61 & 0.3215 & 2.6785 & 104.0 \\ \cline{1-1}  & C(T)-4T & 200.00 & 4.0 & 0.60 & 0.3590 & 2.6410 & 119.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specimen geometries and fitted results.
## 3 Fault Diagnosis Transfer Model
### Feature Extractor  
The feature extractor comprises five feature extraction modules and five channel reconstruction modules. Each feature extraction module includes a one-dimensional convolutional layer, a normalization layer, and a maximum pooling layer, as illustrated in Figure 2. The parameters of each feature extraction module are detailed in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Module Sequence** & **Structural Parameters** \\ \hline First feature extraction module & Cov1d(64 \(\times\) 1, 64) + BN(64)+ ReLU+ Maxpool(2, 2) \\ Second feature extraction module & Cov1d(3 \(\times\) 1, 32) + BN(32) + ReLU+ Maxpool(2, 2) \\ Third feature extraction module & Cov1d(3 \(\times\) 1, 64) + BN(64) + ReLU+ Maxpool(2, 2) \\ Fourth feature extraction module & Cov1d(3 \(\times\) 1, 32) + BN(64) + ReLU+ Maxpool(2, 2) \\ Fifth feature extraction module & Cov1d(3 \(\times\) 1, 64) + BN(64) + ReLU+ Maxpool(2, 2) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of the characterization module.  
Figure 1: Structure of transfer learning fault diagnosis model.  
Figure 2: Structure of feature extractor module.  
The space formed by the source domain samples and the target domain samples is \(X_{D}^{i}=\left\{X_{s}^{i},X_{t}^{i}\right\}\in\theta^{l\times(N_{s}+N_{l})}\), where \(l\) is the data length for every smple. The convolution operation on the input data is expressed as Equation (5).  
\[X_{D,y_{cov}}^{i}=f\Big{(}X_{D,(y-1)out}^{i}*\omega_{y_{cov}}+b_{y_{cov}}\Big{)} \tag{5}\]  
where \(X_{D,(y-1)out}^{i}\) represents the output of the last feature extraction module, \(X_{D,y_{cov}}^{i}\) denotes the output of the current \(y\)th module, \(\left\{\omega_{y_{cov}},b_{y_{cov}}\right\}\) denotes the training parameters for the weights and biases in the convolutional computation, and \(f(\cdot)\) denotes the linear rectifier unit (ReLU) activation function. The input to the first feature extraction module is a data sample that has undergone signal preprocessing.  
Then, the samples after the convolution operation are input into the batch normalization (BN) layer for a normalization operation to prevent the order-of-magnitude difference of the input variables from affecting the operation results too much. The BN operation process is shown in Equation (6).  
\[BN\Big{(}X_{D,y_{cov}}^{i}\Big{)}=2*\frac{X_{D,y_{cov}}^{i}-X_{min}}{X_{max}-X_ {min}}-1 \tag{6}\]  
where \(\left\{X_{max},X_{min}\right\}\) shows the maximum and minimum values in the operational data. Following normalization, the maximum pooling operation is applied on the normalized \(BN\Big{(}X_{D,y_{cov}}^{i}\Big{)}\) to reduce the model parameters and the operation cost. The maximum pooling operation is shown in Equation (7).  
\[p\Big{(}BN\Big{(}X_{D,y_{cov}}^{i}\Big{)}\Big{)}=\max\Big{\{}BN\Big{(}X_{D,y_{ cov}}^{i}\Big{)}^{q}\Big{|}\text{s}(r-1)+1\leq q\leq sr\Big{\}} \tag{7}\]  
where \(s\) represents the length of the segment, and \(r\) is the starting point of the pooling position.
## 4 Analysis of Experimental Results  
### Analysis of Fracture Roughness on Slurry Diffusion Law  
Figure (a)a,b depicts the pressure variation curves of slurry with different fracture roughnesses as a function of diffusion distance. From the graph, it can be observed that under different fracture roughness conditions, the change in slurry pressure along the diffusion path follows a similar pattern. As the diffusion distance increases, slurry pressure decreases, exhibiting a non-linear distribution. When the fracture roughness is higher, the slurry pressure is lower. Furthermore, different roughness levels result in differences in slurry pressure at various stages along the diffusion path, with variations of 0.01 MPa, 0.017 MPa, and 0.003 MPa at the early stage, middle stage, and final stage, respectively. The drop in slurry pressure over the diffusion distance exhibits an \"initial increase and subsequent decrease\" trend. The larger fracture roughness leads to more significant slurry pressure losses and a more severe pressure drop. Additionally, at the initial and final stages of the diffusion path, the impact of the grouting or discharge orifics is more pronounced, resulting in minor numerical variations in slurry pressure and, consequently, smaller pressure differences. In contrast, the middle stage, with a varying fracture roughness, experiences more significant differences in slurry pressure.
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 2 Materials and Methods
### Projection of Future Land Use Change  
The analysis of future land use change is increasingly prominent, as it is crucial with the multiple consequences induced by increasing drought on water availability and demand, agricultural production, food security, and livelihoods. Therefore, the land use model is considered to be useful for a better understanding of drivers and resultant consequences of changes by predicting and projecting future land use [16]. In this study, the Land Change Modeler (LCM) built-in module with TerrSet software (formerly known as IDRISI) developed by Clark Labs at Clark University was applied. As inputs, land use maps from two different historical periods, i.e., 2010 and 2015 were used to derive the future land use and its changes from 2010 (historical) to 2100 (future) based on relevant driving factors and their causal influence on accelerated land conversion [17]. A modeling method relies on Multi-Layer Perceptron (MLP) neural network and Markov Chain (MC), in which the MLP was trained to model land use transitions through creating transition potential maps. The transition potential maps were created based on a set of descriptive environmental variables called drivers, i.e., agricultural areas, distance to urban areas, rivers, roads, as well as altitude, slope, and aspect of land [18]. Thereafter, the Markov Chain method was then applied to process the transition maps for the prediction process [19], based on the past trends of land use changes from 2010 to 2015. The Markov Chain method was selected as it is a powerful descriptive and predictive model for land use change and future land use distribution. It has been widely used with the potential to provide timely projection of land use changes, and the minimum data requirement is desirable. However, its main limitation would be the ignorance of interactions between contiguous land areas for broad scale application, and it cannot be used to determine its spatial positionor the characteristics of spatial change and distribution [20; 21]. There is one more aspect to be noted; the land use modeling process relies on the integration of both changes in environmental and socio-economic drivers. Unfortunately, the incorporation of socio-economic factors was restricted by the lack of spatial data and the difficulties in integration with other environmental data [22]; only environmental variables were then used as driver variables for changes on all spatial and temporal scales.
## Appendix A  
### Surface Charge Contribution  
Consider a slurry (volume \(V_{\text{S}}\)) with spherical amine gel particles (the volume of a single particle is \(V_{\text{g}}\), and radius, \(r_{\text{g}}\)) as shown in Figure 11. The number of gel particles in the slurry \(N_{\text{g}}\) is \(V_{\text{g}}\)/\(V_{\text{g}}\). Let \(V_{\text{X}}\) be a volume element occupied per single ionic group in the gel particle. The number of the volume element in the gel particle, \(N_{\text{VX,g}}\), is given by \(V_{\text{g}}\)/\(V_{\text{X}}\) (Equation (A1)). The concentration of fixed charge in the gel particle is given by \(N_{\text{VX,g}}\)/\(V_{\text{g}}\). When the element volume is assumed as a spherical shape, the radius (\(r_{\text{X}}\)) of the spherical element is given by Equation (A2). The number of the element volume at the outer surface of the gel particle (\(N_{\text{VX,gS}}\)) is approximately calculated by Equation (A3). Consequently, \(N_{\text{VX,gS}}\)/\(V_{\text{S}}\) becomes the concentration of the surface charge in the slurry. If the hydroxide ions in the slurry (except for gel phase) contribute to neutralize the surface charge, the pH of the slurry will be given by Equation (A4), where \(\beta\) indicates the contribution ratio of the surface charge relative to the total charge of the gel.  
\[N_{\text{VX,g}}=\frac{V_{\text{g}}}{V_{\text{X}}}\] (A1)  
\[r_{\text{X}}=\sqrt[3]{\frac{3V_{\text{X}}}{4\pi}}\] (A2)  
\[N_{\text{VX,gS}}\cong\frac{S_{\text{g}}}{S_{\  
Figure 16 indicates the effects of the gel size on the amount of surface charge and solution pH for \(N_{\text{VX,g}}\)/Vg = 0.05, \(V_{\text{g,S}}\)/\(V_{\text{S}}\) = 0.05 and \(N_{\text{VX,g}}\)/Vg = 0.005, \(V_{\text{g,S}}\)/\(V_{\text{S}}\) = 0.5, respectively. The solution will be shifted to alkaline pH depending on the size and surface charge of the gel particles. When some hydroxide ions exist to neutralize the proportion of the surface charge of the gel particles, the pH in the solution will be increased. The slurry pH increases with DMAPAA and with \(\beta\). The increase in pH in the fine crushed gel slurries and the decrease in pH with the hydrophobisation in the gel network were experimentally observed phenomena.
## 3 Materials and Methods
### Structural Semantic Feature Extraction
#### 3.2.2 Two-Line MSS Plumb LINE Extraction with Prior Constraint  
The direction of gravitational acceleration is obtained with the help of inertial measurement elements or when the initial values of the position and attitude are known. The gravitational acceleration \(g_{c}\) in the camera coordinate system is obtained by using inertial measurement elements. Among the three orthogonal vanishing points in the Manhattan world, only the vertical vanishing point is parallel to the direction of gravitational acceleration. The vertical vanishing point provides an a priori constraint for the extraction of the plumb line. Using this prior constraint, we can first conduct a preliminary screening before comparing the evaluation value of the candidate hypothesis with the current optimal result. Because the angle between the vertical vanishing vector and vector of gravity is close to zero, the vector cross product is calculated, and the fault-tolerant threshold \(\tau_{1}\) is set as follows:  
\[\left|min\left\{arccos\left(\frac{l_{i}\times g_{c}}{\|g_{c}\|}\right)\right\} \right|<\tau_{1},\ i=1,2,3 \tag{2}\]  
The candidate set of the plumb line can be screened out from the extracted line segments, and the vertical vanishing point can be calculated by using the 2-line MSS method. Assuming that the proportion of segments belonging to the vertical vanishing point in the candidate set of segments is 0.5, the probability of randomly selecting two segments belonging to the vertical vanishing point is \(p=\mu 0.5^{2}\). \(\mu\) is the regulating factor. At a confidence coefficient of 0.9999, the number of iterations required to obtain at least one inner 2-line MSS is:  
\[it_{c}=log(1-0.9999)/log(1-p) \tag{3}\]  
Any \(it_{c}\) line segment pairs are selected, and the plane coordinates of the intersection image of the line segments are calculated and then converted to the equivalent spherical coordinates:  
\[\left\{\begin{matrix}\phi=acos\left(Z/\sqrt{X^{2}+Y^{2}+Z^{2}}\right)\\ \lambda=atan2(X,Y)+\pi\end{matrix}\right. \tag{4}\]where \((x,y)\) are the image plane coordinates of the endpoint of the line segment. \(f\) is the principal length of the camera. \(\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}\) are spherical coordinates:  
\[\begin{cases}X=x-x_{0}\\ Y=y-y_{0}\\ Z=f\end{cases} \tag{5}\]  
With candidate points, the optimal estimation of the position of the vertical vanishing point is calculated by using the threshold value \(\tau_{2}\), and the set of vertical line segments is extracted.  
\[argmax\sum_{i=1}^{n}VP*l_{i}<\tau_{2} \tag{6}\]