esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Network Topological Metrics
#### 3.2.2 Topological Importance Index  
In Equation (5), a service efficiency importance index is proposed to determine the recovery priority of failed nodes. To compare the effects under different recovery sequences, a composite topological importance index is constructed as a reference. It adopts 4 metrics, i.e., degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, to measure the importance of each node. The topological importance index is calculated based on the entropy weight method to more accurately assess the relative importance of each node in the network.  
The entropy weight method is an objective weighting approach that determines the weights based on the amount of information provided by the data itself. In this paper, the entropy weight method is used to calculate the weights of degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality for each node in the network. First, these centrality indicators are normalized to convert the different dimensions into comparable standard forms. Then, the variation value of each node in each centrality indicator is calculated to derive the information entropy. Information entropy reflects the degree of uncertainty of the information in each indicator. The smaller the entropy, the more information the indicator provides and the larger its weight. Based on the information entropy, the weights of each centrality indicator are calculated, as shown in Table 3, and the final topology importance index for each node is obtained. This process ensures that the importance of each indicator is objectively determined based on the distribution of the data itself, thereby reasonably evaluating the role and influence of nodes in the network.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Degree Centrality** & **Betweenness Centrality** & **Closeness Centrality** & **Eigenvector Centrality** \\ \hline
0.353 & 0.277 & 0.297 & 0.073 \\ \hline \end{tabular}
\end{table}
Table 3: The weights of centrality indicators.
# Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods
## 2 Materials and Methods
### In-SAR Techniques
#### 2.3.1 PS-InSAR  
The method of PS-InSAR is based on identifying point targets, named permanent scatters (PS), in the multi-temporal interferometric SAR images [13]. The PS are natural or man-made targets that give out a stable signal phase, such as artifacts, parts of buildings and rock outcrops. They usually remain consistent for years of SAR images collected over the region with the same sensor, displaying dominant scattering by an object generally smaller in dimension than the resolution cell and they are exploited for obtaining millimeter land deformation and improving DEM accuracy [11; 31]. The fundamental supposition of the PS method is that point objectives are all related in land scope and height across the SAR dataset, expecting to have an equal strength when processing images of different looks.  
The PS-InSAR main processing steps are as follows [32]:  
(1) PS connection graph  
The super master image was selected automatically using the optimal selection algorithm and then 19 interferograms were generated by phase differencing between each slave image and the super master image accordingly (Figure 3a). The perpendicular baseline was from 8.88 to 98.64 m, while the temporal baseline ranged from 24 to 732 d.  
(2) PS interferometric process  
Oversampling was done by a factor of 4 to detect a larger number of pixels and avoid aliasing effects, which could help to judge the subpixel location of the main PS in a resolution cell [24]. Then, the phase due to topography was removed using STRM DEM from every interferogram following the theory of D-InSAR. Next, preliminary PS candidates were selected on the basis of the amplitude dispersion index, which is the ratio of the standard deviation and the average of amplitude values for every pixel in the image dataset.  
(3) PS inversion  
The phase obscurity was settled by the application of a linear temporal inversion method [5]. The parameters of displacement sampling and minimum displacement velocity were set to 1 mm/year and \(-\)200 mm/year, respectively. Then, displacement and time measures for phase and time monitoring at each point were acquired. In this research, we only considered the PS points with a coherence value greater than 0.75.  
Figure 3: Temporal and spatial baseline distribution of synthetic aperture radar (SAR) interferograms of Sentinel-1A; (**a**) PS analysis; and (**b**) SBAS analysis. The blue lines represent the interferometric pairs, the yellow diamond stands for the super master image and the green diamonds indicate slave images.  
(4) PS geocoding  
Based on SRTM DEM, geocoding was executed in the original satellite line of sight (LOS) orientation with a max number of points of 100,000 in each shape file. As a result, the time series displacement of PS was converted from the SAR coordinate system to the WGS-84.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 3 Proposed Methodology
### Feature Extraction
#### 3.3.1 Retail Features  
Retail feature extraction refers to the process of extracting valuable features from retail data that are important for demand forecasting. In this framework, retail features are extracted by the blend of probabilistic and Apriori methods. The purpose is to find significant correlations and relationships between data from the retail industry.  
(a) Probabilistic Approach  
This method employs probability distributions to simulate and assess retail data. For instance, it could use probability distributions to present the likelihood of some retail events or patterns emerging. This allows a probabilistic interpretation of the data that is useful in  
Figure 1: The architecture of the proposed demand forecasting model.  
characterizing uncertainties common to retail environments. The probabilistic approach can handle noisy data and outliers effectively and provides confidence intervals, which helps in decision-making. The probabilistic approach adjusts well to changing market conditions. The frequency score of \(S_{i}\) is  
\[P\big{(}S_{i}\big{|}S_{j},K\big{)}=P(S_{i})\dots P\big{(}S_{i}\big{|}S_{j}\big{)} \dots\dots P(S_{m}\big{|}S_{m-1},S_{m-2}\dots S_{1}) \tag{2}\]  
where \(K\) is the supply chain product and \(S_{i},S_{j}\), and \(S_{m}\), etc. are the supply chain data.  
The frequent item set mining algorithm Apriori is used to mine association rules [46]. In terms of retail feature extraction, the Apriori algorithm would probably find frequent item sets that are groups of items (features), mostly present jointly in a given amount of related data. This aids in identifying relationships and dependencies among various retail elements. The Apriori approach can handle large datasets effectively.  
\[C\big{(}S_{i}\to S_{j}\big{)}=\frac{Sup\big{(}S_{i}\cup S_{j}\big{)}}{Sup (S_{i})} \tag{3}\]  
In this case, the confidence is written as \(C\) and support of every key scale to \(i\)_th_ named \(Sup(S_{i})\).
## 3 Results and Discussion of the Current Study
### Effect of Cement Content on the Flow Characteristics of Soil  
As illustrated in Figures 7 and 8, the results demonstrate that the coefficient of permeability decreases with the addition of the contaminant. The crude oil, as aforementioned, tends to act as a waterproofing agent in which the passage of water through the voids is feasibly minimised. The coefficient of permeability (k) decreases by about 28% when the contaminant filling the spaces is nearly 15% of the soil weight.  
It can be noted that the trend of the variation of soil conductivity with the admixture content is almost similar for all the contaminated soil samples. The results show a proportional relation between k values and the cement content, and the cement action does not  
Figure 8: Variation of the coefficient of permeability of the contaminated soil and additive content.  
Figure 7: Relationship between the coefficient of permeability of the soil and crude oil content.  
vary widely among all contaminant concentrations The cement particles adsorb the water well due to the relatively large surface area and soften the inter-particle voids, reducing the plugging effect of crude oil and thus increasing the water flow rate with increasing the cement content.
# Study of the Algorithm for Wind Shear Detection with Lidar Based on Shear Intensity Factor
## 2 Methods  
### Glide Path Scanning and Headwind Data Acquisition  
Glide path scanning focuses on the detection of wind field information along the glide path and identifies wind shear within this range [18]. The glide path is the flight trajectory of an aircraft during take-off and landing. As shown in Figure 1, to ensure the laser beam is always able to scan the glide path, the elevation and azimuth of the lidar system components need to change simultaneously when scanning the glide path. Since the angle between the glide path and the runway is 6 during take-off and 3 during landing, different elevation angle changes should be set according to the aircraft's take-off and landing state. Compared with the traditional PPI scanning method, the glide path scanning method has a smaller azimuth angle change range and requires fewer laser beams, so the scanning period is shorter and the efficiency is greatly improved. The data points closest to the glide path are seen as the data in the glide path at that height, so that the headwind on the glide path can be obtained in this way. When the lidar is installed on the landing point, lidar only needs to scan once to obtain the headwind on the glide path. Note that the wind data used for the improved algorithm here are headwind data, which is the wind speed in the direction of the glide path. Therefore, although wind direction is the important parameter when calculating wind shear [19], wind direction is not considered when calculating wind shear information in this paper.  
Figure 1: Schematic diagram of glide path scanning.
## 2 The Evolution and Trends of 6G Network Architecture
### The Design Fundamentals of 6G Networks
#### 2.1.3 Information-Centric and Intent-Based Networks (ICNs)  
Information-centric and intent-based networks (ICNs) are being intensively explored in the field of Next-Generation Protocols (NGPs) by organizations such as the Internet Engineering Task Force (IETF) and the Internet Research Task Force (IRTF). ICNs represent a significant departure from the current networking paradigm, which is TCP/IP-based [9]. This approach involves separating content from its location identifier, using an abstract naming convention instead of IP addressing. Various protocol proposals exist for implementing ICN. It was considered a candidate for 5G in the ITU-T Focus Group (FG) on International Mobile Telecommunications 2020 (IMT-2020) [10]. However, there are challenges with tunneling ICN traffic through mobile networks, as it contradicts the transparent and flat Internet topologies. The new International Telecommunication Union--Telecommunication Standardization Sector (ITU-T) FG aims to set requirements for the network of 2030. Furthermore, to connect the most recent advancements in networking design and operational management, intent-based networking and service design have evolved. In order to achieve the desired results, 6G will heavily rely on this networking infrastructure lifecycle management approach, which calls for taking into account higher-level business and service policies, end-to-end softwarized infrastructure configuration, continuous network and service state monitoring, and real-time optimization.
# A Comprehensive Review on Clay Soil Stabilization Using Rice Husk Ash and Lime Sludge
## 1 Introduction
### Stabilization History  
Soil stabilization has been used for centuries, demonstrating its longstanding history. Humans have been using various techniques to enhance the strength and stability of soil for construction purposes and to prevent soil erosion [4, 5]. Around 2500 BCE, the Egyptians used soil stabilization techniques to construct buildings and monuments. They mixed mud with straw to strengthen the soil properties and create more durable structures [6]. Ancient Greeks and Romans used lime and pozzolanic materials such as volcanic ash tostabilize the soil and enhance its strength and durability [7]. In ancient China, rammed earth construction was widely used, where soil was compacted in layers with wooden frames, creating durable structures like the Great Wall. They also mixed lime and clay to improve soil strength [8]. In ancient India, stabilization methods that included using lime, ash, and organic materials to strengthen soil were prevalent [9]. During the Middle Ages, lime stabilization became popular in Europe to improve the soil properties [10]. With the advent of the Industrial Revolution in the 18 th century, new methods and materials for soil stabilization emerged [11]. Cement was produced on a larger scale and started to be used to stabilize soil, while lime also remained in use [12; 13]. In the early 20 th century, engineers began experimenting with chemical additives for soil stabilization [14; 15]. Materials, including bitumen and asphalt emulsions, were deployed to improve soil properties and make them appropriate for road construction [16; 17; 18]. In the mid-20 th century, the development of geotextiles and geosynthetics provided new opportunities for soil stabilization [19]. These materials, typically made of synthetic fibers, were utilized to reinforce soil [20]. In recent years, soil stabilization techniques have made significant strides in their development. Innovations such as using polymer additives, cementitious binders, and a range of chemical agents have emerged to improve soil properties [21; 22; 23]. Today, the soil stabilization process is an essential aspect of construction and civil engineering projects [24].
## 1 Introduction  
The role of the medial patellofemoral ligament (MPFL) is to prevent lateralization and lateral dislocation of the patella [1; 2; 3; 4]. It is located in the second layer of soft tissues on the medial side of the knee with reported average length varying between 43 mm to 72 mm [1; 5; 6; 7; 8]. The femoral insertion is located between the medial epicondyle of the femur, adductor tubercle, and gastrocnemius tubercle; the patellar insertion is located on the upper-medial half of the patella [1; 5; 6; 7; 8].  
In case of recurrent patellar instability, surgical reconstruction of this ligament is often necessary. Multiple techniques for MPFL reconstruction are described and the goal of most of them is to provide isometry throughout the whole arc of motion [9; 10; 11]. However, some authors suggested that length change pattern of native, non-reconstructed MPFL is not isometry, even though most of the techniques for MPFL reconstruction try to produce isometric graft [12; 13; 14]. Due to discrepancy in results, it is unclear what length change pattern should be restored by MPFL reconstruction in patients after MPFL injury in order to best recreate native biomechanics [12; 13; 14]. It is possible that the biomechanics that we routinely try to restore during MPFL reconstruction are not the same as exhibited by native, non-reconstructed MPFL [12; 13; 14]  
While there are numerous literature reviews of MPFL anatomy, biomechanics, injury patterns, and reconstruction outcomes, due to authors' knowledge there is no review concerning sources of heterogeneity within studies evaluating MPFL isometry [15; 16; 17; 18]. The hypothesis of this review was that studies assessing MPFL length change pattern are highly heterogenous. The aim was to present a state-of-the-art review of sources of this heterogeneity and to provide future investigators with an approachable \"to-report list\" of potential sources of bias, as well as with suggestions and considerations for future research.
## 3 Simulations and Discussion
To clearly compare the spectral bandwidths of the photon pairs, all density plots in Figures 6 and 7 are displayed in a window with the size set to 100 nm \(\times\) 100 nm, and Figures 6d and 8 are displayed in a 15 nm \(\times\) 15 nm window. As shown in Figure 8, the contours of JSI = 0.5 exhibit circular shapes for all five crystals, and the calculated spectral purities are 0.997 for RTP and 0.995 for RTA, KTA, and CTA, respectively. This means that very high-purity entangled photon pairs can be generated in five non-poled bulk KTP isomorphs, as well as in the case of the well-known PPKTP [10]. In addition, in terms of SPDC efficiency, all five non-poled KTP isomorphs discussed in this study show advantages over the case of PPKTP using the first-order QPM, as discussed in Table 3.  
Figure 6: Type II SPDC in a non-poled KTP with the maximum \(d_{\mathit{eff}}\) (Table 1) (**a**) pump envelope (PE) function, (**b**) PM function, (**c**) joint spectral intensity (JSI), and (**d**) the contour line for JSI = 0.5 (blue solid line) shown with a circle with a diameter of 11 nm (red dashed line). The calculated purity is 0.996. Figure 7: JSIs of Type II SPDC in non-poled (**a**) RTP, (**b**) RTA, (**c**) KTA, and (**d**) CTA with maximum \(d_{\mathit{eff}}\) values (Table 3). The calculated purities are 0.997, 0.995, 0.995, and 0.995, respectively.
## 2 Why an Endothelial Model?  
The study of human endothelium and endothelial dysfunction has become a central issue in cardiovascular and clinical research. The vascular endothelium is involved in a wide variety of functions and in the pathophysiology of several diseases, including diabetes, hypertension, neoplasms (Kaposi's sarcoma) and CVD [12; 13; 14]. Moreover, vascular endothelium alterations are considered initial events in the pathophysiology of various diseases [12]. It is a vital autocrine and paracrine organ that maintains vascular homeostasis, and regulates local cell growth and extracellular matrix deposition [13]. Endothelial cells (EC) produce many different vasculoregulatory and vasculotropic molecules that act at local and distant sites [14]. The endothelium not only functions as a barrier between blood and organs, but it also participates in the transfer of nutrients, hormones and white blood cells, and in anti-inflammatory responses [13].  
This organ is also involved in the regulation of blood pressure, blood flow, and coagulation [14]. Because the endothelium participates in wound repair processes, wound-healing or Boyden chamber migration assays with EC can be used to examine this process under different experimental conditions [12; 15]. Additionally, EC play a role in angiogenesis and platelet binding to substrates or to the endothelial monolayer under flow conditions [15]. The HUVEC model has been particularly useful in the study of the effects of hemodynamic forces on the endothelium and atherosclerotic plaque formation, since the model allows to expose EC to shear stress controlling flow conditions, and thus, mimicking blood flow conditions in vivo [5].  
In vivo models have been widely used to investigate a broad range of biological mechanisms. However, these models are not always the best option, partly because of several technical difficulties. Particularly when attempting to elucidate the contribution of cellular and molecular factors under very specific and controlled conditions, in vitro models are preferred. In recent years, in vitro HUVEC models have been useful to study monocyte adhesion to the endothelium, endothelial damage and repair, the potential impact of atherosclerosis in early stages and atherosclerosis progression [15]. Moreover, studies in HUVEC have the potential to refine the stratification of cardiovascular risk and serve as a guide to monitor the effects of therapeutic interventions.
## 5 Concluding Remarks  
The relay is widely used in industrial production and daily life as a core device and in all aspects of the new energy industry. If a relay failure in an electric vehicle is not found, it may cause a greater safety accident. The reliability of relay is a significant guarantee for reliable operation of electric vehicles. The multi-dimensional GNN model was established, and the relay lifetime was predicted by the IFWA-GNN model. The purpose of this paper is to improve the safety and stability of the electric vehicles by improving the prediction accuracy of the model. The main conclusions of this paper are as follows.  
1. The temperature is an important factor affecting the performance parameters of relay, and the parameters are various with the different temperatures. A multi-dimensional grey model is applied to predict the relay lifetime. The five parameters of temperature, contact bounce time \(t_{T}\), dynamic contact resistance duration \(t_{D}\), dynamic contact voltage drop peak \(V_{m}\), and static contact voltage drop \(V_{C}\) are used as initial performance parameters.
2. IFWA and FWA are tested by three standard test functions. The test results show that the convergence performance of IFWA is better than FWA. IFWA has the shortest running time. For the three standard test functions, the total average run time of the IFWA is 22.04% faster than the FWA.
3. This paper proposes an IFWA-GNN model. The test results show that the prediction effect of FWA-GNN model is better than the three other models. The _AD_ value of IFWA-GNN model is 14.73% smaller than the _AD_ value of FWA-GNN model; the _RMSE_ value of IFWA-GNN model is 6.75% smaller than the _RMSE_ value of FWA-GNN model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AD** & _RMSE_ & **R2** \\ \hline GM & 24.06 & 31.78 & 0.86 \\ GNN & 32.46 & 41.37 & 0.81 \\ FWA-GNN & 19 & 22.21 & 0.98 \\ IFWA-GNN & 16.2 & 20.71 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The evaluation results.  
The relay lifetime is predicted by the model. However, there are shortcomings in the relay lifetime prediction by using the initial performance parameters. For example, the five parameters selected are deficient. The other performance parameters related to the final lifetime are not considered such as the air humidity, atmospheric pressure, etc. The redundant parameters are effectively removed from a large number of parameters, which needs to be solved in future studies.  
[PERSON], [PERSON] and [PERSON] designed the experiment and got the experimental data; [PERSON] and [PERSON] analyzed the experimental data; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
The authors declare no conflict of interest.