esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Microbiome Influence on Atopic Dermatitis
### Gut Microbiota in AD Patient  
In a typical healthy gut, Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria are present. These categories are quite consistent overall, but the specific types of bacteria within them can vary from person to person.  
[PERSON] et al. discovered that certain groups of bacteria, such as Tenericutes, Mollicutes, Clostridia, Bifidobacteriumaceae, Bifidobacterium, Bifidobacterium, Christensenellaceae R7, Bacilli, and Anaerostipes, were linked to a reduced risk of developing atopic dermatitis (AD) [13]. On the other hand, bacteria groups such as the Eubacterium hallii group, Clostrtidaee_1, Bacteroidetes, Bacteroides, Anaerotruncus, an unknown genus, and Lachnospiraceae UCG 001 appear to be potential factors that could increase the risk of AD [13].  
[PERSON] et al. noticed differences in the gut bacteria of children with atopic dermatitis (AD). Children with transient AD had lower levels of _Streptococcus_ bacteria, but higher levels of _Akkermansia_. Conversely, children with persistent AD had the opposite pattern [132]. Moreover, in children with AD, the Clostridium genus became more abundant. There were also changes in the functional genes of their gut bacteria related to energy metabolism and short-chain fatty acid (SCFA) production, with a decrease in these functions. This conclusion was supported by another study by [PERSON], who used metagenomic analysis and arrived at a similar finding [133].  
[PERSON] et al. showed that, in children with early-stage AD, higher levels of _Enterococcus_ and _Shigella_ were present. Interestingly, these higher levels of these bacteria have been linked to increased production of serotonin, which, in turn, worsens skin pigmentation. Furthermore, the abundance of _Bifidobacterium_, a beneficial type of bacteria, tends to decrease in these children [134].  
Several other studies are being conducted to clarify the relation between the gut microbiota and AD, enhancing the knowledge about the microbiota profiles in AD patients.  
Several studies are confirming the idea that the immune responses in AD are influenced by the gut microbiota [135]. Figure 3 summarizes this information and focuses on three key aspects: (1) the anti-inflammatory effects of short-chain fatty acids (SCFAs), (2) the impact of tryptophan metabolites on the aryl hydrocarbon receptor (AHR) pathway, and (3) the role of toll-like receptor signaling, which can be influenced by the gut microbiota and is highly relevant to AD. Briefly, the gut microbiota, in particular Bifidobacteria, produces short-chain fatty acids, which activate SCFA-sensing G-protein-coupled receptors and inhibit histone deacetylases. These phenomena reduce inflammatory responses and promote TH1/TH2 equilibrium. This equilibrium is also helped with the production of D-tryptophan, which, in turn, is a microbial metabolite, which can also activate aryl hydrocarbon receptors, inhibiting inflammatory responses and promoting the skin barrier. TH1/TH2 balance is also promoted by molecular patterns associated with the presence of pathogens produced by the gut microbiota.
# A Survey on Variable Neighborhood Search for Sustainable Logistics
## 4 Results and Findings
### VNS Version Analysis
AVNS has also played a significant role in multiobjective optimization for sustainable supply chain management. In [27], an adaptive multiobjective VNS was used to optimize a two-echelon multi-vehicle location/routing problem with time windows for sustainable food supply chains. Extending this approach, ref. [28] integrated the strategic allocation of sustainable orders with a supply chain network under stochastic demand. Furthermore, ref. [29] conducted a comparative analysis of a multiobjective AVNS against other evolving algorithms to design a sustainable supply chain network integrated with vehicle routing. Pollution location inventory routing problems have also been tackled with adaptive VNS variations. Ref. [31] applied an adaptive version of BVNS, and subsequent works [23; 24] explored additional adaptations. A hybrid combining AVNS and ALNS was used in [15] to address a multi-level location/routing problem with environmental considerations. In urban logistics, AVNS has been effectively applied to optimize waste collection and routing systems. Ref. [21] proposed an AVNS for sustainable urban recycling with heterogeneous electric vehicles, while ref. [25] extended this methodology with AGVNS to optimize electric truck routing for milk collection. For green manufacturing systems, ref. [22] developed a double-adaptive GVNS to address unmanned electric vehicle routing and scheduling. Similarly, ref. [16] utilized AVNS to solve a green flexible job shop problem, aiming to minimize both makespan and total energy consumption. Beyond supply chains and urban logistics, AVNS has been hybridized with other methods for innovative applications. Ref. [30] combined AVNS with ABC to develop a customer-oriented solution for multi-task green scheduling with diverse time-of-use pricing in cloud manufacturing. Ref. [17] employed a self-adaptive VNS to design a sustainable freight transportation network incorporating cross-docks. In healthcare logistics, ref. [18] applied AVNS to a home healthcare routing problem with heterogeneous electric vehicles and synchronization. Additionally, [26] demonstrated the use of AVNS for vehicle routing problems with time windows and carbon emissions in logistics distribution. Finally, ref. [20] optimized the locations and routing for battery swap stations for capacitated electric vehicles using AVNS. This diverse body of research illustrates the growing relevance of AVNS and its variants across diverse sustainable logistics applications, from supply chain design to waste collection and beyond. Its hybridization with other optimization methods has further enhanced its ability to address complex, real-world challenges while balancing economic, environmental, and operational objectives.
## 6 Conclusions  
Uniaxial creep tests were conducted on carbonaceous slate samples (\(\beta\) = 0\({}^{\circ}\), 15\({}^{\circ}\), 45\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\)). A nonlinear fractional creep model was proposed. The conclusions are as follows:  
1. The instantaneous axial strain, \(\epsilon_{i}\), the axial creep strain, \(\epsilon_{c}\), the duration time of decelerating creep stage, \(t_{d}\), and the steady creep strain rate, \(\dot{\epsilon}_{s}\), increased with the rise in the loading ratio, \(k\). Their variations follow a power law relationship, with the \(R^{2}\) values all exceeding 0.95. The value of \(\dot{\epsilon}_{s}\) is less than 1.5 \(\times\) 10\({}^{-4}\)/h when \(\beta\) < 45\({}^{\circ}\), while it exceeds 1.5 \(\times\) 10\({}^{-4}\)/h in the cases of \(\beta\) \(\geq\) 45\({}^{\circ}\).  
2. The long-term strength, \(\sigma_{L}\), of carbonaceous slate showed a U-shaped pattern with the variation in \(\beta\). The maximum of \(\sigma_{L}\) occurred at \(\beta\) = 90\({}^{\circ}\), and the minimum was observed at \(\beta\) = 15\({}^{\circ}\).  
3. A fractional nonlinear creep model (FNC model) was developed, which can reflect the decelerating, steady-state, and accelerating creep stage of carbonaceous slate. The sensitivity analysis reveals that the larger the fractional order, \(n\), is, the \(t_{d}\) and the \(\dot{\epsilon}_{s}\) increase. \(\eta_{2}\) and \(E_{2}\) primarily affect the decelerated creep stage, while the steady-state rate exhibits a rapid increase with the rise of \(\eta_{1}\).  
4. To further validate the newly established model, a comparison is made with the traditional Nishihara model. The \(R^{2}\) of the FNC model all exceed 0.965, which are higher than that of the Nishihara model (\(R^{2}\)\(\leq\) 0.911). The FNC model can effectively cope with the impact of the sudden increase in strain.  
The results provide a theoretical basis for the creep mechanism of layered rocks and the long-term stability analysis of carbonaceous slate tunnels.  
**Author Contributions:** Y.Z.: methodology, software, formal analysis, writing--original draft. X.W.: investigation, software, writing--review and editing. B.L.: supervision, funding acquisition, resources, writing--review and editing. H.X.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China [Grant NO: U22A20234].  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The authors wish to acknowledge senior engineer [PERSON] for his help with the data collection and test. The authors also are very grateful to editors and reviewers for their constructive recommendations.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# An Assessment of CyGNSS v3.0 Level 1 Observables over the Ocean
## 4 Discussion  
### Posting Rate  
As was seen in Figure 1, the data posting rate appears to have changed to 2 Hz from July 2019, approximately simultaneously on all CyGNSS satellites (although FM02 appears to change in August, after an earlier abortive attempt in June). It should be noted that this change does not appear to be currently documented, although publications in the literature note the potential that this change offers for improved observations over land waterbodies, e.g., rivers [23], in terms of on-ground resolution. Over ocean, no change is seen in the time-series of average NBRCS at U10 \(\sim\)7 m/s, although a corresponding change in DDM SNR along with increased NBRCS variability are expected due to the shorter time available for incoherent accumulation of the signal (500 ms).
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 2 Background and Motivation
### Motivation for Dimension Augmentation  
Given the scarcity of historical data within individual resource pools and the presence of multiple correlated resource pools, an opportunity arises to enhance the dataset of a target resource pool through cross-pool data augmentation. This approach, which we refer to as dimension augmentation (DimAug), aims to improve the generalizability of the forecasting model.  
However, directly concatenating time series data from disparate pools may introduce artificial temporal features, potentially degrading model performance due to inconsistencies or misleading information. To address this issue, DimAug incorporates techniques to ensure that the augmented data remain consistent with the underlying patterns of the target resource pool. Specifically, DimAug employs a method that aligns the temporal features of the source and target data, thereby minimizing the risk of introducing artificial trends.  
By leveraging cross-pool data augmentation, DimAug seeks to overcome the limitations imposed by the few-shot problem and improve the accuracy and reliability of capacity forecasting in cloud data centers.
# Detecting Malicious Devices in IPSEC Traffic with IPv4 Steganography
## 3 IPsec-Based Network Steganography  
The utilization of IPsec in steganography is a topic that has yet to be explored in the literature. So far, only one example of the covert channel with IPSec has been proposed in the literature [54]. This lack of exploration is particularly notable when considering the development of a concealed storage channel, primarily due to the constraints posed by the limited number of available header fields. Nevertheless, despite these challenges, and although IPsec is widely regarded as a very secure protocol, there is substantial potential to enhance network nodes' security and authorization capabilities by implementing steganography in the IPv4 protocol. Additionally, around 2015, the U.S.'s NSA reportedly discovered vulnerabilities in its security by breaching IPsec or tampering with Diffie-Hellman algorithm keys. Additionally, in 2018, a team of researchers found out that reusing a key pair across different versions and modes of IPsec IKE can lead to cross-protocol authentication bypasses, enabling the impersonation of a victim host or network by attackers. If the attack was completed, they could complete the handshake with the victim's device [55].  
Our innovative approach introduces a novel method that enhances the integrity of transmitted packets on the IP header level and enables the potential to manage incoming traffic proactively. In detecting corrupted information, our method empowers network devices to initiate actions such as blocking incoming traffic. Furthermore, our approach facilitates communication with end hosts, alerting them to altered traffic within the packet's path by providing information about the MAC address of the corrupted network device.
# Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM
## 3 CAV Trajectory Prediction  
Quantifying and predicting the risks of CAVs requires establishing a car-following model and an acceleration model based on molecular force fields. These processes also rely on predicting the future trajectories of vehicles, making deep-learning methods a suitable choice for trajectory prediction. Traditional studies often used vehicle motion models for trajectory prediction, but these models tend to have significant deviations in long-term forecasts. To achieve predictions closer to reality, deep-learning models can be employed. A fusion model that combines the strengths of CNN and LSTM networks enables a more accurate prediction of vehicle trajectories. This integrated model is referred to as the CNN-LSTM model.  
In the CNN-LSTM model, the LSTM and CNN components are constructed on two separate paths, ensuring no correlation between them. Figure 1 illustrates the framework of the proposed method. As shown in the figure, the data first pass through both the LSTM and CNN paths for processing. By leveraging the unique characteristics of each model, the required relationships are effectively extracted.
## Materials and Methods
### Reverse Arrangements Test  
The RA test is performed by dividing the input signal into adjacent 100, 200, 500 and 1000 ms segments with 10 sub-segments and calculating the mean value \(y_{i}(i=1,2,3,\ldots,n)\) for each sub-segment. The number of reverse arrangements (\(A\)) in the sequence \(y_{1},y_{2},y_{3},...,y_{n}\) are counted when \(y_{i}>y_{j}\) for \(i<j\). In other words, \(A\) is the number of times that the value of the first data point \(y_{1}\) in the segment is higher than each subsequent data point value, \(y_{2},y_{3},...,y_{n}\). This process is then repeated for \(y_{2},y_{3},...,y_{n-1}\)[23]. A z-score can then be calculated using the following equation [32]:  
\[z=\frac{A-\left[\frac{n(n-1)}{4}\right]}{\sqrt{\frac{n(2n+5)(n-1)}{72}}} \tag{1}\]  
where \(A\) = total number of reverse arrangements in the EMG signals, and \(N\) = total number of data points. The null hypothesis for this test was that the mean value \(y_{i}\) in the signal are independent observations from a random variable. The alternative hypothesis was that the mean values \(y_{i}\) that make up the signal are related and are part of a significant trend underlying the signal. To reject the null hypothesis at \(p\leq 0.05\) requires a z score of \(z\geq 1.96\) or \(z\leq-1.96\).
# PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems
## 3 Field Dependency of the Wavefront  
A description of the wavefront aberration of a circular rotationally symmetric optical system can be adopted from [22], originally from [19]  
\[W(\rho,\theta;H,\varphi)=\sum_{p}^{\infty}\sum_{n}^{\infty}\sum_{m}^{\infty}W_ {klm}H^{k}\rho^{l}\cos^{m}(\varphi-\theta), \tag{8}\]  
where \(k=2p+m\) and \(l=2n+m\). Symbol \(W_{klm}\) is used for the expansion coefficients; the coordinates are defined in Figure 2c. The field dependency of \(A_{n}^{m}(H,\,\varphi)\) can be solved by comparing wavefront aberrations using Equations (7) and (8) as  
\[\sum_{n=0}^{k}\sum_{m=-n}^{n}A_{n}^{m}(H,\varphi)Z_{n}^{m}(\rho,\varphi-\theta )=\sum_{p}^{\infty}\sum_{n}^{\infty}\sum_{m}^{\infty}W_{klm}H^{k}\rho^{l}\cos^ {m}(\varphi-\theta). \tag{9}\]  
Equation (10) can be obtained by expanding Equation (7) and rewriting terms \(\cos^{m}(\theta,\varphi)\) into terms containing a set of goniometric functions \(\cos(m\theta),\,\sin(m\theta),\,r\in\mathbf{N}\) and comparing the coefficients in this form with expanded Zernike polynomials (as in Table 1). The resulting coefficients \(A_{n}^{m}(H,\,\varphi)\) describing the space variation of Zernike polynomials are presented in [22]. Coefficients \(W_{klm}\) are then used for describing the aberration of the space-variant optical system. The order of the aberration terms is defined by [PERSON] in [19] as  
\[order=(sum\,of\,the\,powers\,of\,\rho\,and\,\theta)-1. \tag{10}\]  
Figure 3: The set of Zernike polynomials used here. Table 1 includes the field dependency of these polynomials.
## 1 Introduction
In this study, our objective was to utilize remote-sensing data, meteorology data and an ecological model to identify intermittent drought events that occurred from 2009 to 2010 in south-western China and quantify the drought impacts on NPP, ET and WUE in different drought class.
# Stiffness Estimation and Equivalence of Boundary Conditions in FEM Models
## 2 Equivalence Method Based on a Series of Static Analyses
### Equivalent Stiffness Coefficients Calculation  
The red dashed line in Figure 1b shows a section plane in which part of the model is replaced by a bushing connector connected to ground. In Abaqus, connection type BUSHING provides a connection between two nodes that allows independent behavior in three local Cartesian directions and that allows different behavior in two flexural rotations and one torsional rotation. The equivalent FE model can be seen in Figure 2b. Figure 2a shows a separate (removed) part of the model that was used to calculate the stiffness coefficients of the bushing. These coefficients were determined by a series of six linear static analyses in which the residual part of the beam was loaded in all six degrees of freedom, respectively. The load was represented by a unit force and a unit moment acting at the reference point (RP). The transfer of the load from this point to the cross-sectional area of the beam was realized through a distribution coupling. In addition, the pretension was defined in the bolts. The preload force was set 10 kN. Since this is a contact task, frictional contact interactions were defined between all components. The friction coefficient was set to 0.2. In the normal direction, so-called hard contact was enforced. The underside of the base was fixed. The finite element mesh was formed by hexagonal elements with an average size of 1.5 mm.  
The unknown stiffness coefficients of the bushing were calculated using the following formulas.  
\[k_{x,y,z}=\frac{F_{x,y,z}}{\delta_{x,y,z}}\left(\mathrm{N/mm}\right), \tag{1}\]  
where \(k_{x}\), \(k_{y}\), \(k_{z}\) are equivalent translational stiffness in x-, y-, z- direction, respectively; \(\delta_{x}\), \(\delta_{y}\), \(\delta_{z}\) are maximum translations in x-, y-, z- direction, respectively; \(F_{x}\), \(F_{y}\), \(F_{z}\) are loading forces acting in x-, y-, z- direction, respectively, and  
\[k_{\varphi_{x},\varphi_{y},\varphi_{z}}=\frac{M_{x,y,z}}{\varphi_{x,y,z}} \left(\mathrm{N.mm/rad}\right), \tag{2}\]  
where \(k_{\varphi_{x}}\), \(k_{\varphi_{y}}\), \(k_{\varphi_{z}}\) are equivalent rotational stiffness about x-, y-, z- axis, respectively; \(\varphi_{x}\), \(\varphi_{y}\), \(\varphi_{z}\) are maximum angular deflection about x-, y-, z- axis, respectively; \(M_{x}\), \(M_{y}\), \(M_{z}\) are loading moments acting about x-, y-, z- axis, respectively.
# Calculation Method of Rotational Constraint Stiffness for a New Tower-Pier Connected System
## 2 The Plane Rotation Model of UHS
### Parametric Analysis of RCS
#### 3.2.1 Influence of Vertical Force on RCS  
1. Critical torque analysis  
When the radius of curvature is R\({}_{0}\) = 1.5 m, the friction coefficient of contact surface is \(\mu\) = 0.15 (for steel and steel) and the central angle is \(\theta\) = 23.5\({}^{\circ}\), the vertical force is loaded by six cases according to 10 MN, 20 MN, 30 MN, 40 MN, 50 MN, and 60 MN for the calculation of the FE model, and the the load-displacement curve of each case is obtained, as shown in Figure 6.  
Figure 6: Load–displacement curve in six cases with different vertical forces: (**a**) the vertical force F = 10 MN; (**b**) the vertical force F = 20 MN; (**c**) the vertical force F = 30 MN; (**d**) the vertical force F = 40 MN; (**e**) the vertical force F = 50 MN; (**f**) the vertical force F = 60 MN.  
According to the load-displacement curve in Figure 6, when the angle displacement increases sharply and the torque remains unchanged, the torque at this moment is considered as the critical torque. The comparison graph between the critical torque calculated using the FE method and that calculated by Equation (6) is shown in Figure 7. It can be seen from the figure that the difference between the two methods is within 5%, so the FE model can be considered correct and it can be employed to perform the RCS analysis of UHS.  
2. FE-based RCS Analysis  
The slope of the linear rising segment of the above load-displacement curves is taken as the RCS of UHS, as shown in Figure 6. It can be seen from Figure 6a that when the vertical force is 10 MN, the slope of the linear rising segment takes the value of k = 123.609 kN-m/rad. Similarly, the RCS parameters under the vertical force of 20 MN, 30 MN, 40 MN, 50 MN, and 60 MN can be obtained from Figure 6b-f. Furthermore, the relationship between the RCS and vertical force can be obtained as shown in Figure 8.  
Figure 8 shows that the RCS increases almost linearly with the increase in the vertical force of the bridge tower. Therefore, RCS can be calculated through the interpolation method according to the vertical force at each construction stage. The related curve of RCS and the vertical forces is fitted using the least square method in order to obtain the following expression equation:  
\[K_{\text{M}}=123.609F=\alpha\times F \tag{8}\]  
where the unit of RCS \(K_{\text{M}}\) is kN\(\cdot\)m/rad, and the unit of vertical force \(F\) is kN. \(\alpha\) is a coefficient with dimensions measured in m/rad.  
Figure 8: The rotational constraint stiffness of UHS.  
Figure 7: The critical torque obtained by the finite element method and theoretical calculation.