esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The basic flow of the proposed approach is shown in Figure 1c, which is calculated for the data from multiple observations comparing the GAP algorithm. For simplicity, the basic flow is illustrated only with two observation data. Some close-ups marked by red or blue rectangles in each frame are enlarged and presented below the corresponding frames. The close-ups marked by red rectangles are still background and close-ups marked by blue rectangles are motion foreground. Compared with the existing GAP algorithm, the proposed approach can provide high visual quality in the foreground and background of a scene simultaneously and enhance the fidelity of the reconstruction results. Figure 1: Basic flow of maximum a posteriori probability and Markov random field (MAP-MRF) based reconstruction approach. The close-ups marked by red rectangles represent the still background and the close-ups marked by blue rectangles represent the motion foreground. Proposed MAP-MRF-based algorithm uses more observation than other algorithms to utilize redundant inter-frame information. (**a**) the basic flow of generalized alternating projection (GAP) with total variation (TV) regularization; (**b**) the basic flow of GAP with wavelet and discrete cosine transform (DCT); (**c**) the basic flow of MAP-MRF-based approach. The proposed reconstruction algorithm adopts multi-regularization constraints to reconstruct the video. It considers regularization with both total variation (TV) and norm in the wavelet domain into a minimization problem for compressive sensing, and solves it using a modified accelerated generalized alternating projection algorithm. The weighting coefficient for different regularization is resolved by the motion characteristics of pixels. Different from the existing GAP with transform regularization, the proposed multi-regularization algorithm does not need select the groups and group weights in the transform domain. Moreover, most present algorithms only utilize the intra-frame information but not the redundant inter-frame information. Hence, this paper suggests the adoption of the sequence super-resolution (SR) algorithm to further improve the quality of the image sequence [14; 15]. The reconstructed image sequence is estimated by the belief propagation algorithm based on the proposed Bayesian model for CACTI. Table 1 summarizes frequently used notations in this paper. The paper has been organized into the following sections. Section 2 introduces the fundamental principles, forward model, and Bayesian model for CACTI image super-reconstruction; Section 3 introduces the proposal of a multi-regularization reconstruction algorithm and MAP-MRF-based reconstruction approach utilizing inter-frame redundant information; Section 4 describes the implementation of the proposed SR approach; and Section 5 involves the comparison and discussion of the proposed approach.
###### Abstract  
The vehicle-mounted transient electromagnetic method (VMTEM) has been proposed to detect tunnel internal defects in operational tunnels based on the ideal space state. However, the space environment of tunnel surrounding rock is different compared with conventional application fields, so the interpretation of detection data has certain inadaptability and unreliability. In this paper, three typical space states involved in the detection process of operational tunnels were analyzed. The diffusion law of the transient electromagnetic response signal under the condition of three typical space states was carried out, the proportional coefficient of different space states was determined, and the spatial transformation problem was also determined. Meanwhile, the results obtained by the numerical simulation calculation and numerical derivation calculation were verified by laboratory experiments. The results showed that the correction coefficient of full space to half space was 2.50, and that of three-quarters space to half space was 1.42. The detection process in operational tunnels involves the mutual transformation process of three typical space states. The critical distance that the spatial transformation does not affect is 300 m. In addition, the results of laboratory experiments verified the typical space proportional coefficient and spatial transformation phenomenon well. The results can provide essential ideas for the subsequent space correction of the detection environment in operational tunnels.  
vehicle-mounted transient electromagnetic method; detection environment; spatial transformation; proportional coefficient; numerical calculation; laboratory experiment Article
## 1 Introduction
velocity components compared to the results provided by a standard Reynolds stress model. The LES behavior can also be obtained without using explicitly the grid spacing, as is the case in the SAS framework proposed by [PERSON] and [PERSON] [29; 30]. The SAS formulation has been used by several authors to compute the flow through hydraulic turbines [17; 31]. [PERSON] et al. [31] showed that the SAS model on a grid with nine million control volumes improves the prediction of the velocity profiles in the draft tube diffuser for the part load operating condition compared to the standard SST model. Excluding the resolution of the turbulent eddies, Explicit Algebraic Reynolds Stress Models (EARSM) and Reynolds Stress Models (RSM) are able to capture the flows characterized by stagnation points and streamline curvature. Such models have been used for instance by [PERSON] et al. [32] to compute the flow in a high head Francis turbine including part load, BEP, and full load operating conditions. They concluded that the EARSM or RSM model does not improve the results compared to the SST model. Moreover, at part load condition, the RSM model was not able to converge. Based on the previous literature review, the linear eddy viscosity models are able to compute accurately the BEP of a high head Francis turbine, but have some difficulties in computing the flow at part load and SNL operating conditions. During the last 15 years, several improvements have been achieved in the development of URANS and hybrid RANS/LES models [33; 34; 35]. Consequently, the present paper focuses on the influence of some \"advanced\" URANS turbulence models regarding the dynamics of the flow at the SNL operating condition of a high head Francis turbine already investigating at part load by [PERSON] et al. [36] and at various operating points by the current authors [37; 38; 39]. The paper deals first with the description of the case study, the governing equations, and the numerical setup. In the BEP results, a detailed analysis regarding the influence of the mesh, the boundary conditions, and the numerical setup is carried out. Finally, the SNL operating point is computed with four \"advanced\" URANS models: the SST \(k-\omega\) model with production limiter and curvature correction, the SAS SST \(k-\omega\) model, the Organised Eddy Simulation (OES) SST \(k-\omega\) model, and the Baseline \(k-\omega\) model (BSL) EARSM model. The results are compared with the ones provided by the standard SST \(k-\omega\) model and with experimental data.
## 4 Analysis of Engineering Example
### The Accumulative Value of the Segments' Lateral Displacements  
The calculated lateral displacements of the ring segments and the monitored lateral displacements of the ring segments from the field are shown in Figure 8, the parameters used in this calculation are shown in Table 1. From Figure 8, we understand the following.  
1. When the segment is in the shield tail, there is a big difference between (2.267 mm) the simulated displacements and the field monitoring results. This is probably due to the simplification of the wrapping influence of the shield tail brush and sealing grease on the shield tail segments during the theoretical simulation.
2. In the process of segments leaving the shield tail around 1 to 4 rings, the simulated accumulated lateral displacement agrees well with the lateral displacement from the field. Compared with the monitored data, the simulated results are a little underestimated. The curvature radius in this project has already reached the turning limitation of the TBM. The poorly controlled shield posture and the improper selection of assembly position can cause the squeezing action of the second ring in the shield tail from the shield shell or the tail brush. The squeezing action also causes the increase of lateral offset of some rings, which just leaving the tail and are backfilled.  
The first ring leaving the tail is most influenced by this effect. From the field data, we can know that the lateral displacement increases rapidly when the ring leaves the tail.
3. In the process of segments leaving the shield tail around 5 to 8 rings, the simulated displacements coincide quite well with the monitored data. When the backfilling finishes, the accumulated displacements from the field tend to be stable. However, the simulated displacements still increase with low speed.
4. When the segments leaving the tail around nine rings, there is a big difference between theoretical results and field data. When the pea gravels form the ring, the lateral accumulated displacement declines slowly under the pressure difference of surrounding rocks and tends to be stable from the 17 th ring. For the theoretical simulation, the pressure difference of surrounding rocks from both sides of the shield line is ignored as the 3D tunnel is simplified as the 2D circular curved beam in the model. The simulated accumulated displacement increases slowly from the 11 th ring and tends to be stable from the 15 th ring.  
Finally, the simulated displacements agree well with the field data; the simulated results are more conservative. Therefore, this model is applicable in the prediction of the horizontal accumulated displacement of the shield segments in the small radius interval.
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
## 3 Results and Discussion
### Morphology Analysis of CAA-CS Polysaccharide Hydrogel  
Scanning electron microscopy (SEM) was utilized to characterize the microstructure of the CAA-CS series hydrogels; these hydrogels exhibit a continuous and porous three-dimensional network structure. The pore size of the CAA-CS polysaccharide hydrogel exhibits a certain positive correlation with the concentration of CAA. With the increase in the concentration of CAA (ranging from 1.5% to 2.5%), the cross-linking density of the hydrogel gradually elevates, leading to a decrease in pore size and a denser structure. This phenomenon might be ascribed to the fact that the increased concentration of CAA offers more acetoacetyl groups, thereby facilitating more efficient intermolecular cross-linking reactions. The SEM measurements in Figure 3a-f indicate that the average pore size of the polysaccharide hydrogels ranges from 100 to 200 um. This three-dimensional network structure not only provides a substantial specific surface area for drug loading but also offers an optimal spatial framework for cell scaffold applications. Moreover, the relatively large pore size facilitates cell growth and enhances the effective exchange of nutrients and gases [33]. Meanwhile, as the concentration of CAA increases, the porosity of the composite hydrogel decreases. The measured porosity of the CAA-CS series hydrogels ranged from 43% to 58% as shown in Figure 3g, indicating an enlargement of the internal pore size and a more abundant porous structure. Higher porosity indicates larger pore sizes or more developed pore structures. This trend is consistent with the results observed by scanning electron microscopy (SEM). A higher porosity indicates that a more abundant pore structure is formed inside the hydrogel, and the increase in CAA concentration promotes the crosslinking reaction between acetoacetyl groups and chitosan amino groups, thereby forming a more abundant three-dimensional network structure.
###### Abstract  
This study proposes a full integration method for the double capacitances and inductance-series (LCCL-S)-compensated inductive power transfer (IPT) of electric vehicles (EVs). The transmitter and receiver coils adopt the unipolar coil, and the compensation inductor is designed as an extended DD coil. Specifically, the use of an extended DD coil enhances the misalignment tolerance of the EVs. When the IPT system is in the misaligned state, a primary transfer path for magnetic flux is established between the transmitter and receiver coils, and a secondary transfer path is established between the extended DD coil and receiver coil. The distance between the two unipolar coils of the extended DD coil is optimized to maximize the magnetic flux on the secondary transfer path, thereby increasing the total power of the system misaligned state. Simultaneously, the most suitable turns and inner diameter of the extended DD coil are designed by using the finite element method (FEM) simulation tool. In order to verify the performance of the proposed integrated coil method, a 3.3 kW experimental prototype with a 100 mm air gap was constructed and compared with the conventional integration method under the same conditions. The experimental results show that the proposed magnetic coupling structure maintains at least a 63.6% well-aligned value at a door-to-door 150 mm misaligned state, and the output power of the system is 1.05 kW higher than that of the traditional integration method without extra control algorithms.  
electric vehicles (EVs); integration method; LCCL-S compensation; extended DD coil; misalignment tolerance
## 4 Modified Functional Observer with Unknown Input
### Problem Statement
To determine the control input \(u_{c}(t)\), it is required to first estimate the EID \(\hat{d}_{e}(t)\). However, to obtain the estimated EID \(\hat{d}_{e}(t)\), it is required to first obtain the control input \(u_{c}(t)\). To overcome this causality problem, we approximate the control input as \(u_{c}(t)\cong\hat{a}_{c}(t)=\hat{a}_{f}(t)-\hat{d}_{e}(t)\), which implies \(\hat{d}_{e}(t)=\hat{a}_{f}(t)-u_{c}(t)=\left[ \begin{array}{cc}0_{m\times( \kappa-m)}&I_{m}\end{array} \right]\dot{z}(t)-u_{c}(t)\), where \(\hat{u}_{f}(t)=\left[ \begin{array}{cc}0_{m\times(\kappa-m)}&I_{m}\end{array}  \right]\dot{z}(t)\). The aforementioned causality mechanism can then be implemented as depicted in Figure 1.  
Notice that the augmented state vector \(\omega(t)=\left[ \begin{array}{cc}x_{e}^{T}(t)&d^{T}(t)\end{array} \right]^{T}\in \mathfrak{R}^{(n+l)}\) in the original functional observer [11] is now modified to \(\omega(t)=\left[ \begin{array}{cc}x_{e}^{T}(t)&u_{f}^{T}(t)\end{array} \right]^ {T}\in\mathfrak{R}^{(n+m)}\) in this paper. This proposed modified functional observer compensates for the system disturbance, even if \(G_{i}\) and \(G_{o}\) are unknown. Since in models of the real physical system \(G_{i}\) and \(G_{o}\) are usually unknown, it is more realistic to estimate the equivalent unknown input instead of the disturbances directly.
# Effects of Three Different Withering Treatments on the Aroma of White Tea
###### Abstract  
White tea (WT) is a slightly fermented tea, and withering is a critical step in its processing. The withering treatment can affect white tea's aroma; different treatments' effects were investigated in this study. White tea was withered indoors (IWT), in a withering-tank (WWT), or under sunlight (SWT). Quantitative descriptive analysis (QDA) results showed that SWT had a more obvious flower aroma, and WWT had a more pronounced grassy aroma. Volatile compounds were extracted and subsequently detected with solvent-assisted flavor evaporation (SAFE) and headspace solid-phase microextraction (HS-SPME) combined in addition to gas chromatography-mass spectrometry (GC-MS). A total of 202 volatile compounds were detected; 35 of these aroma-active compounds met flavor dilution (FD) factor \(\geq\) 4 or odor activity value (OAV) \(\geq\) 1. The nine key potent odorants for which both conditions were met were _dimethyl sulfate_, _2-methyl-butanol_, _1-penten-3-one_, _hexanal_, _(Z)-4-heptenal_, _\(\beta\)-Myrene_, linalool, geraniol, and trans-\(\beta\)-ionone. These results were used with QDA to reveal that SWT had a stronger floral aroma mainly due to an increase of geraniol and linalool. Moreover, WWT had a stronger grassy aroma mainly due to increased _hexanal_. The results could be used to select processing methods for producing white tea with a superior aroma.  
white tea; withering treatment; volatile compounds; aroma extract dilution analysis; odor activity value +
Footnote †: journal: Physics Letters
# Machine Learning Detection of Cloud Services Abuse as C&C Infrastructure
## 5 Discussion  
### Experimental Results of All Features  
The evaluations were analyzed and compared with regard to detection accuracy. We present the detection accuracy of five classifiers, all of which utilize the extracted features for classification, as demonstrated in Table 5. The results show that the random forest classifier outperforms the other classifiers, achieving a high detection accuracy of 97.77% in the 70:30 split scenario and 97.80% in the 10-fold cross-validation scenario. The J48 and K-NN classifiers also demonstrate high accuracy, with detection rates of 96.41% and 93.12%, respectively, in the 70:30 split scenario, and 96.84% and 93.80%, respectively, in the 10-fold cross-validation scenario.  
However, the NB and SVM classifiers show significantly lower accuracy compared to the other classifiers, indicating that they may not be suitable for this dataset. The NB classifier has detection accuracies of 65.13% and 63.76% in the 70:30 split and 10-fold cross-validation techniques, respectively. The SVM classifier has even lower detection accuracies of 52.47% and 52.54% in the 70:30 split and 10-fold cross-validation techniques, respectively.  
Ultimately, when considering all extracted features, the results demonstrate that the RF, J48, and K-NN classifiers prove suitable for this dataset. Conversely, the NB and SVM classifiers are not recommended.  
Figure 12: Comparative analysis of wrapper feature selection: DT-SFSF vs. DT-SFSB vs. DT-RFE, highlighting the optimal feature count for maximum accuracy as indicated by dotted lines.
## 3 Results and Discussion
### Principal Component Analysis  
In our research, PCA was used as a multidimensional statistical analysis for the possible identification of VOs on dependent variables--FA composition. FA compositions serve as the main component. In Figure 5, PCA explained 90% of total variation with PC1 for 75% and PC2 for 15%. The PCA plot in Figure 5 shows that seven different VOs formed a distinct group, and each group could be distinguished. The group consisting of safflower, sunflower, maize, cottonseed and soybean oils were close together due to their similar FA compositions. Another group of oils comprised rapeseed and linseed oils. According to Figure 5, the principal scores among VOs were mainly related to linoleic acid C18:2n6c, where safflower, sunflower, maize, cottonseed and soybean oils possessed higher concentrations of linoleic acid. As shown in Figure 5, the group of linseed and rapeseed oils depend on higher concentrations of \(\alpha\)-linolenic acid C18:3n3 and oleic acid C18:1n9c as variables. In our research, we used PCA analysis to discriminate VO types by FAs. Other authors [71, 72, 73] used PCA to reveal factors of the FA composition of various types of VOs.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **SFAs** & **USFA** & **MUFA** & **PUFA** & **Omega-6** & **Omega-3** & **PUFAs/SFAs** & **Omega-6/Omega-3** \\ \hline SFAs & \(1.0000\) & \(-0.9966\) & \(-0.0393\) & \(-0.9010\) & \(0.1063\) & \(-0.3616\) & \(-0.9369\) & \(-0.4159\) \\ \hline USFAs & *** & \(1.0000\) & \(0.0301\) & \(0.9080\) & \(-0.1053\) & \(0.3626\) & \(0.9502\) & \(0.4622\) \\ \hline MUFAs & * & * & 1.0000 & \(-0.3915\) & \(0.0628\) & \(-0.1698\) & \(-0.2650\) & \(-0.4448\) \\ \hline PUFAs & ** & ** & * & \(1.0000\) & \(-0.1233\) & \(0.4050\) & \(0.9859\) & \(0.6120\) \\ \hline Omega-6 & * & * & * & * & 1.0000 & \(-0.9573\) & \(-0.1666\) & \(0.5022\) \\ \hline Omega-3 & * & * & * & * & ** & 1.0000 & \(0.4412\) & \(-0.2847\) \\ \hline PUFAs/SFAs & ** & ** & * & *** & * & * & \(1.0000\) & \(0.5906\) \\ \hline Omega-6/ & * & * & * & * & * & * & \(1.0000\) \\ \hline Omega-3 & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pearson correlation coefficient (r) was between FA parameters.  
Figure 5: Principal component analysis for FA compositions: (**a**) seven vegetable oils were displayed in loading plot; (**b**) PCA loading plot of FA compositions bi-plot.
# A Dynamic Federated Identity Management Using OpenID Connect
## 2 Related Work
When the RP provided the token and sent an API call to request the resource, the resource server (or the API) computed the trustworthiness level, leveraging the FCM tool of the identity provider (IdP) from which the token was issued. If the value of trustworthiness was above a certain threshold, the IdP could be trusted and resource access could be granted. The motivation behind this model was that existing identity federation models, such as SAML and OIDC, were limited by the complexity of their trust model. This complexity comes from the fact that these models are based on a preconfigured circle of trust (CoT), which is hard to scale and is not extendable. In addition, this model provided a confidence level in a user's identity trust, unlike OIDC which lacks that confidence. [PERSON] et al. [14] proposed a model based on a drafted SAML profile that allowed users to establish a trust federation dynamically between two organizations, resolving the limiting dynamic federation support in the current SAML implementation. The author claimed that the proposed dynamic SAML model allowed users to create the identity federation dynamically and in real-time with a specific lifetime threshold, meaning that the federation is removed once the threshold has passed. [PERSON] et al. [15] presented a multifactor authentication mechanism named Truste-dID system that was based on Mobile Connect and OpenID Connect standards to access sensitive mobile services on a smartphone. [PERSON] et al. [16] introduced a trust and identity management model based on the blockchain for cloud identity management. In another study, [PERSON] and [PERSON] [17] leveraged the smart contracts and zero-knowledge proof (ZKP) algorithms to avoid the threats that might arise by using a centralized third party for improving the existing claim identity model in a blockchain. Moreover, [PERSON] et al. [18] used a smart contract on a blockchain to eliminate the need for a third party management while enabling users to maintain a level of self-sovereignty. Our literature review indicated that no research until this time provided a dynamic identity federation model using OIDC. In fact, [PERSON] et al. [13] in 2018, and others [14], stated that the OIDC registration process is hard to scale and not technically extendable, OIDC does not provide a protocol that specifies a trust model to establish a trust relationship between two entities, and OIDC has limitations and cannot be deployed in a dynamic and open environment, such as the cloud. In mid 2019 until September 2021, the OpenID Foundation issued a new identity federation trust protocol named OIDC Federation 1.0 [10]. The OIDC protocol specified how an organization could dynamically discover and register with an identity provider by establishing a federated identity model along with establishing a trust model between those two entities. We believe that the recent release of the OIDC federation protocol introduced a new opportunity to utilize OIDC for dynamic identity federation.