esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Methods
### Dispersion Model for Leaked Liquid Hydrogen  
#### 2.2.1 Pseudo-Source Model  
In the FLACS code, the pseudo-source model, also known as the notional nozzle model, is utilized to evaluate high-pressure gas release, resulting in the formation of an under-expanded jet due to pressure differences. The pseudo-source model considers four states, as shown in Figure 2: state 0 represents the reservoir condition, state 1 represents the nozzle condition, state 2 represents the jet (outlet) condition before the Mach disk, and state 3 is the jet (outlet) condition after the Mach disk, where the velocity is significantly decreased due to the choke.  
The physical quantity of the gas leak at each state for the pseudo-source model can be calculated as follows:  
State 0: reservoir condition  
\[\rho_{0}=\frac{\mathrm{P}_{0}}{\mathrm{RT}_{0}} \tag{1}\]  
\[\mathrm{m}=\rho_{0}\mathrm{V}_{0} \tag{2}\]  
State 1: Nozzle condition  
\[\mathrm{P}_{1}=\mathrm{P}_{0}\bigg{(}\frac{2}{\gamma+1}\bigg{)}^{\frac{\gamma }{\gamma-1}} \tag{3}\]  
\[\mathrm{T}_{1}=\mathrm{T}_{0}\bigg{(}\frac{2}{\gamma+1}\bigg{)} \tag{4}\]  
\[\mathrm{\rho}_{1}=\frac{\mathrm{P}_{1}}{\mathrm{RT}_{1}} \tag{5}\]  
\[\mathrm{u}_{1}=\mathrm{c}_{1}=\sqrt{\gamma\mathrm{RT}_{1}} \tag{6}\]  
\[\mathrm{\dot{m}_{1}}=\mathrm{\rho}_{1}\mathrm{A}_{1}\mathrm{u}_{1} \tag{7}\]  
Figure 2: Schematic for the leak conditions of the pseudo-source model.  
State 2: Jet (outlet) before the Mach disk  
\[\rm{P_{2}=P_{a}} \tag{8}\]  
\[\rm{T_{2}=T_{1}+\frac{1}{2}\frac{u_{1}^{2}-u_{2}^{2}}{c_{p}}} \tag{9}\]  
\[\rm{\rho_{2}=\frac{P_{2}}{RT_{2}}} \tag{10}\]  
\[\rm{u_{2}=u_{1}+\frac{P_{1}-P_{2}}{\rho_{1}u_{1}}} \tag{11}\]  
\[\rm{A_{2}=A_{1}\frac{\rho_{1}u_{1}}{\rho_{2}u_{2}}} \tag{12}\]  
State 3: Jet (outlet) after the Mach disk  
\[\rm{P_{3}=P_{a}} \tag{13}\]  
\[\rm{T_{3}=T_{a}} \tag{14}\]  
\[\rm{\rho_{3}=\frac{P_{3}}{RT_{3}}} \tag{15}\]  
\[\rm{u_{3}=u_{2}\frac{f_{2}}{f_{3}}} \tag{16}\]  
\[\rm{A_{3}=A_{2}\frac{\rho_{2}u_{2}}{\rho_{3}u_{3}}} \tag{17}\]  
where \(\rm{\gamma}\) is specific heat ratio, \(\rm{\gamma=1.4}\), \(\rm{f_{2}}\) is the factor depending on the geometry which is characterized by the area porosity, and \(\rm{f_{3}}\) is the factor depending on the upstream Mach number. State 3 represents the properties at the leak source for pseudo-source model; the details have been published [25].
## 4 Case Study
### The Pre-Processing  
Using the domain knowledge of the satellite network, a filtering algorithm was developed to filter data rows in the dataset that contain spurious values. For instance, this included null values caused by the terminal breaking down, and some timestamped events that did not record the terminal ID were filtered out by the pre-processing module in our architecture. It is noted that data samples that omitted terminal ID accounted for around 13.73% of all terminal recorded data.  
PCA was then applied to the terminal dataset to reduce the dimensions and identify the lead variables of the terminal data. Using 58% data variability, five principal components (PCs) were discovered and used. The PCs consisted of transmit, receive, and signal-to-noise ratio (SNR) attributes. It should be noted that there were many SNR variables with each measuring an aspect of the network performance.
# Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments
## 1 Introduction
These aforementioned platforms focus on space activity simulation and have inadequate features for the analysis and visualization of a planet's surface information. In addition, these platforms require professional knowledge to operate, and would not be widely accepted by the general public such as the case of Google Earth [31]. The virtual geographic environments (VGEs) aim to provide integrated tools to understand the world better and have been widely used in multiple fields, such as air pollution experiments, silt dam planning, and group behavior simulation [8; 32; 33; 34; 35; 36; 37; 38; 39]. In fact, VSEs can be regarded as the extension of VGEs in the field of outer space, and some ideas derived from VGEs can also be used for reference. A digital simulation platform called Sino-InSpace was developed for the abstract representations, visualizations with multiple viewpoints, and analytical understanding of VSEs from Earth to the outer planets. Using this platform, space activities and the access and analysis of geological information can be supported. In addition, it can provide simulation technology for the development of VGEs, the improvement of public universe cognition, and space science exploration. The remainder of the paper is organized as follows. The orientation and data source of Sino-InSpace are analyzed in Section 2. Section 3 illustrates the key technologies including time-space datum, data organization and preprocessing, visualization scenario organization, and design of the visualization engine. Three types of application modes were designed, and related case studies are introduced in Section 4. Then, a user study with participants was conducted to evaluate the performance of the platform in Section 5. Finally, we discuss the features and future work of the platform in Section 6.
## 3 Results and Discussion
### Method Validation
#### 3.2.2 Selectivity  
The selectivity of an analytical method is defined as its ability to identify the compound of interest in the presence of other contaminations in the sample (impurities, degradation products, substrate components, etc.). In practice, this means that in the chromatograms the retention times of the analytes and the possible contaminations should not be identical. Therefore, a comparison between two blanks (with diluent ACN-H2O 40:60 \(v/v\) and honey extracts) and two standard solutions (with DIP, ACA and/or SAL) was made (Figure 4).  
Figure 4: Chromatograms of (**a**) a standard solution of DIP, ACA and SAL, (**b**) a standard solution of DIP and ACA, (**c**) a sample obtained from the honey–core formulation, (**d**) blank solvent, (**e**) blank honey solution.
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
###### Abstract
[PERSON] [29] employed the weighted response surface method to modify the single-ginder finite element model of a bridge and adopted the central composite design experiment and variance analysis to screen out the parameters with high significance, thereby establishing a weighted response surface surrogate model; the objective function was constructed from the test load test data, and the structural parameters were identified by combining the chaotic particle swarm optimization algorithm; the obtained results are promising. Among the models mentioned above, the neural network model has outstanding learning ability and widespread applicability [30], and it can directly update the structural parameters without solving the complex sensitivity matrix; in addition, it has a powerful nonlinear mapping function and strong robustness, which can fit the implicit function relationship between structural parameters and structural responses and better deal with data noise and ensure that the fitting results are not distorted [31; 32]. Wavelet neural network (WNN) [33] is a product of the perfect combination of wavelet analysis theory and neural network theory. Based on the theory of neural networks, the wavelet function is used as the excitation function of the WNN. It inherits the superiority of wavelet analysis and artificial neural network, which has excellent approximation and fault tolerance, and is widely used in many engineering fields. However, the WNN lacks application in the finite element model updating of structures, and the related literature is scarce, but its excellent function approximation ability enables the network to be used as a surrogate model. Therefore, using the WNN as the surrogate model to update the finite element model has both research and application significance. This paper focuses on model updating and proposes a WNN combined with the WDO algorithm as the surrogate model of the finite element model. In this paper, firstly, the theories of WNN and WDO and their realization are introduced; then, a structural finite element model updating method using a WNN as the surrogate model and WDO algorithm is proposed, and the method is implemented on the finite element model updating of Ningbo Bund Bridge; the updating parameters are studied and selected, and then, the updating procedure is carried out. It is demonstrated that the model updating method is applicable to the updating of the multi-parameter bridge model, which has practical significance in engineering and high efficiency in finite element model updating.
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 3 Scalable TCP for 5G MmWave Networks
### Problem Analysis  
Before proceeding with this study, we evaluated the performance of the existing TCP congestion control in mmWave networks [17; 21]. Based on previous studies, we looked closely at the behavior of S-TCP, as this has not received much attention in other studies. Figure 2 shows the throughput change for a scenario where CUBIC, HighSpeed TCP (HS-TCP) [31], and S-TCP experience one long NLoS. The cwnd increase mechanism of S-TCP ensures a sufficiently fast cwnd increase in transition from NLoS to LoS in the mmWave link. Through the value of the constant in Equation (1), S-TCP can increase the throughput faster than the conventional CCAs can in response to the relaxation of the channel state. On the other hand, TCP CUBIC enters the congestion avoidance phase, slowly increases cwnd for 9 to 15 s, and shows very poor link utilization. The S-TCP reaches its highest bandwidth in about a second, but the HS-TCP does not provide the maximum throughput until after about 13 s.  
The results of our preliminary study [21] and Figure 2 show that S-TCP among conventional CCAs can provide sufficient throughput at mmWave network. Because the link utilization of S-TCP in mmWave networks is improved significantly over the default algorithm CUBIC, we judged that S-TCP is worth applying to mmWave networks.  
Therefore, we aimed to achieve the design goal of 5G by providing high throughput through S-TCP congestion control and ensuring low end-to-end latency by applying CoDel AQM. However, the increase (more aggressive) and decrease (low reduction) mechanism of S-TCP can undermine TCP fairness and cause throughput imbalance problems between TCP flows. In addition, excessive packet loss may occur due to an aggressive cwnd increase of S-TCP. In Figure 2, S-TCP for 0-3 s performed several slowstart phases due to low cwndreduction, which could provide serious instability to links in situations competing with other flows.  
In this paper, we propose an mmS-TCP congestion control algorithm through a simple redesign of S-TCP that provides sufficient throughput for the 5G mmWave band and can maintain TCP fairness at the same time.
## 3 Materials and Methods
### Multi-Subject-Tracking System
#### 3.3.1 Capture Merged Skeletal Data  
First, each coordinate system of sensors was calibrated to a global coordinate system. The system was installed at a different site for the construction of the dataset. The calibration process was performed according to the process presented in [17]. The centroid trajectory of the sphere object was recorded using point cloud data (PCD) and the RANSCAC algorithm. In this process, the sphere object was first filtered by specific color information. However, there was noise interference during the estimation of the sphere object's centroid in different environments. To filter out this noise, a masking process that filters out predefined noise and region-limited filtering that removes PCD outside the limited region were adopted. Following filtering, a trajectory consisting of the sphere object's centroid position was constructed for each sensor. Then, a transformation matrix  
Figure 3: Overview of the multi-subject skeletal-tracking system.  
Figure 2: The data acquisition environment of the screening games.  
consisting of rotation and translation information was calculated by singular value decomposition. Then, all coordinate systems of the subordinate sensors were calibrated to master sensors using the calculated transformation matrix. Lastly, all coordinate systems were calibrated to a global coordinate system set by a user using the ARUCO marker corner position recognized by the marker detection algorithm.  
Following calibration, the skeletal data in the same coordinate system could be captured. Then, multiple skeletal data tracked by multiple sensors in real time were merged by the algorithm proposed in our previous study [17]. First, the misoriented joints were corrected using reference positions. These reference positions were joints with high-confidence values provided by the skeleton-tracking SDK. The misoriented joints were arranged by a simple distance comparison procedure. Then, the merging algorithm could merge these multiple joint positions accurately by filtering noise candidates. The density-based spatial clustering of applications with noise was used for filtering. The reference position, which is the average of the candidates with high-confidence values and the previous position of the corresponding joint, was used during the clustering process to give high-confidence candidates more weight in the merging process. Lastly, the Kalman filter was used to smooth the tremble errors of the joint's movement. As a result of the algorithm, the position error was lower than 52 mm for all joints when 4 sensors were used. By using the merging algorithm, the accurate skeletal data of each subject could be captured in real time.
## 3 Results
### Insecticidal Activities of EO and EO-PG  
Table 5 presents the LD\({}_{50}\) values for the insecticidal tests of EOPH essential oil and the EO-PG formulation at different temperature and humidity conditions. The findings indicate that the EO-PG formulation is generally more potent as an insecticide than the EOPH alone. This observation is supported by the lower LD\({}_{50}\) values observed for the EO-PG formulation. This enhanced potency is likely due to the diffusion effect of the essential oil in the gypsum material in the formulation, which can amplify the insecticidal properties of the product. The insecticidal activity of the EOPH and EO-PG formulation is substantially affected by two factors: temperature and humidity.
## 2 Materials and Methods
### Antifungal and Antibacterial Assays
#### 2.6.1 Antifungal Assay  
The antifungal assays were carried out with the reference strains _C. albicans_ ATCC 14,053 and _C. glabrata_ ATCC 15126. The antifungal effects of clotrimazole (10 \(\upmu\)g), the EOs and the tablets were evaluated according to Clinical and Laboratory Standards Institute Standard M44-A. Briefly, strain suspensions (10\({}^{6}\) CFU mL\({}^{-1}\)) were swabbed on Mueller-Hinton Agar added with 2% glucose and 0.5 \(\upmu\)g/mL Methylene Blue Dye (GMB). A filter paper disk (6 mm diameter) was placed on the agar surface and added with 10 \(\upmu\)L of pure EO in order to evaluate its antifungal activity. Moreover, to evaluate the antifungal potential of the produced tablets, the same diffusion assay was performed using the tablet (one of each EO in triplicate) in place of a paper disk. Tablet dissolution was also monitored every five minutes. Clotrimazole (10 \(\upmu\)g) disks were used as positive controls. Pure dimethyl sulfoxide (DMSO; Sigma-Aldrich, St. Louis, MO, USA; 10 \(\upmu\)L) and organic linseed oil (10 \(\upmu\)L) disks were used as negative controls. Plates were incubated at 37 \({}^{\circ}\)C for 48 h. All experiments were performed in triplicate. The sensitivity test for the EO was considered positive if it resulted in an inhibition halo that was larger than that induced by clotrimazole (positive control \(\geq\) 100%).
# A Comparison of Electrical Breakdown Models for Polyethylene Nanocomposites
## 1 Introduction
As the positive space charge packets move toward the cathode, the electric field in front of the charge packets gradually increases, and the material is broken down when the maximum distorted electric field in LDPE reaches the breakdown electric field. [PERSON] et al. [23] considered the formation and migration of space charges in LDPE, and established a polymer breakdown model based on the accumulation of space charges and the corresponding electric field distortion. The relationship between the DC breakdown electric field of LDPE and the thickness of samples was calculated. It was found that the breakdown electric field has an inverse power function relationship with the thickness. [PERSON] et al. [24] also used a breakdown model based on the electric field distorted by the space charges to calculate the breakdown characteristics of multilayer polymers with partial barrier contact, and found that partial barrier contact between multilayer structures enhanced the breakdown strength of multilayer dielectrics. From the viewpoint that the electric field force acts on the trapped charges and affects the molecular chain motion, and to comprehensively consider the charge transport, the long-range motion of molecular chains, and the electron energy accumulation, we established a charge trapping and molecular displacement breakdown (CTMD) model for polymer nanocomposites [13; 25]. The energy accumulation process of electrons in the free volume expanded by the long-range motion of the molecular chain was simulated, and the relation between the breakdown electric field of the polyethylene nanocomposites with the nanofiller content, the applied pressure, the thickness of the sample, and the ramping rate was obtained. The results are consistent with the results of the electric breakdown experiments. The above analysis shows that the strong trapping effect of traps and the strong interaction between molecular chains in the interface regions are the two key factors to improve the breakdown strength. However, which of the trap trapping effect and the molecular chain interaction is more influential is still unclear. To clarify the factors of the breakdown characteristics of polyethylene nanocomposites having the greatest influence, this study compared three breakdown models, namely, the electric field distortion, the electron energy gain in a fixed-scale free volume, and the energy gain of electrons in an expanded free volume caused by the motion of molecular chains. By comparing the simulation results with the experiments, the electric breakdown mechanism of polyethylene nanocomposites was clarified. In the present work, we determined the quantitative roles of trapping effects and molecular chain interactions on breakdown strength. This paper provides simulation methods and data support for the improvement in the breakdown strength of polymer nanocomposites.
## 5 Conclusions  
Via STA and XRD data, it was shown that PAA after synthesis exhibits a pyrochlore structure corresponding with Sb\({}_{2}\)O\({}_{5}\)\(\cdot\)3H\({}_{2}\)O. Using Raman spectroscopy, it was demonstrated that in the structure of PAA, the Sb-O polymeric chain vibrations are dominant, and ion carriers are mainly present in the form of H\({}_{3}\)O\({}^{+}\) ions. Using impedance spectroscopy, it was apparent that the membranes possess high grain conductivity, ~5 \(\times\) 10\({}^{-2}\) S/cm, and that the grain boundary conductivity exponentially increases with relative humidity increases at room temperature. Through STA, thermo-XRD, Raman and impedance spectroscopy techniques. it was confirmed that the conductivity of the 90 PAA and 80 PAA membranes changed non-linearly with temperature in the range of 25-250 \({}^{\circ}\)C, due to the stepwise adsorption and structural water removal from the membranes, accompanied by unit cell shrinkage. The proposed equivalent circuit model fitted well with the experimental data, and included: (a) a Warburg-type contribution, due to mesoporous charge diffusion and superficial binder conduction; (b) two constant phase elements, responsible for charge diffusion contributions arising from the interface between electrodes and the material, and along the sample grain surfaces; (c) a resistor in parallel with a capacitor and a geometric capacitance, representing grain, grain boundary, and geometric effect contributions due to the electrode's placement. All mentioned contributions, except for the Warburg-type one, were all of an electronic nature but non-dominant.  
The following are available online at [[https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)]([https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)) _3390/app112411877/s1, Figure S1: EDX spectra of 90 PAA sample (a) bright area corresponding to antimony oxide; (b) dark area corresponding to fluoroplastic.  
Conceptualization, all authors contributed in equal parts; methodology, all authors contributed in equal parts; investigation, all authors contributed in equal parts; data curation, all authors contributed in equal parts; writing--original draft preparation, all authors contributed in equal parts; writing--review and editing, all authors contributed in equal parts; visualization, all authors contributed in equal parts. All authors have read and agreed to the published version of the manuscript.
## 2 Related Work
### Classification Algorithms
With Laplace Smoothing, the prior probability is given by (where \(\lambda\) is the smoothing parameter)  
\[p_{\lambda}(C_{k})=\frac{\sum_{i=1}^{N}I(y_{i}=C_{k})+\lambda}{N+K\lambda} \tag{4}\]  
The conditional probability is calculated using  
\[p_{\lambda}\big{(}x_{1}=a_{j}|y=C_{k}\big{)}=\frac{\sum_{i=1}^{N}I\big{(}x_{1}= a_{j},y_{i}=C_{k}\big{)}+\lambda}{\sum_{i=1}^{N}I(y_{i}=C_{k})+A\lambda} \tag{5}\]  
where \(K\) denotes the number of different values in \(y\) and \(A\) denotes the number of different values in \(a_{j}\). Usually \(\lambda=1\). _BPNN_-The BPNN consists of an input layer, a hidden layer and an output layer. Given training set \(D=(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\), \(x\in\mathbb{R}^{d},y\in\mathbb{R}^{l}\), as shown in Figure 1, for the \(j^{th}\) node (neuron), \(x_{1}\), \(x_{2}\),..., \(x_{i}\) are the inputs of the neuron, which are connected by the weights of \(w_{j1}\),\(w_{j2}\),...,\(w_{ji}\) to adjust the proportion of the input. Take the linear weighted sum as input and \(\theta_{j}\) as decision variable, hidden layer \(y_{j}\) output is  
\[y_{j}=f\Big{(}\sum_{i=1}^{n}w_{ji}x_{i}-\theta_{j}\Big{)} \tag{6}\]  
The parameters are set through the training data to obtain a parametric model of the prediction error, and the parameters are updated using the Gradient Descent method. _DT Algorithm_-A DT consists of a root node, internal nodes and leaf nodes. The root node contains the entire data set. The internal nodes use different features to make category judgements and each leaf node represents the final judgement category. The complexity of the DT model is related to the number of layers of the tree. Under DT, information gain is  
Figure 1: Illustration diagram shows how a Back Propagation Neural Network works. the expected reduction in entropy of target variable \(Y\) for data sample \(S\), due to sorting on variable \(A\)  
\[G(S,A)=H(A)-\sum_{v\in Values(A)}\frac{|S_{v}|}{|S|}H(S_{v}) \tag{7}\]  
Next, the impurity (e.g., data partition) of \(S\) is given by  
\[Gini(S)=1-\sum_{i=1}^{K}\left(\frac{|C_{iS}|}{|S|}\right)^{2} \tag{8}\]