esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Model  
### Model Assumption  
During a pressure decay test, gas replaces water on one side of the membrane, i.e., the permeate side or the feed side. The pressure decay test occurs with one side of the membrane in gas and the other in water, creating a hydrodynamic gradient across the membrane that varies vertically along the height of the membrane. When the increase in the pressure in the gas chamber balances the sum of resistances due to hydrostatic pressure, surface tension, and membrane elastic pressure, gas flows across the defect and a bubble begins its growth process. The bubble surface moves as a result of the difference in pressure between the inside and the outside of the bubble. The bubble rises during bubble growth, and the neck is formed in the final period. The bubble detaches itself when the bubble neck is closed. As a result, the pressure in the gas chamber declines, leading to pressure decay.  
Figure 1 shows the bubble formation model of a pressure decay test setup for a membrane system. It assumes the following: (1) The air in the pressurized volume of the membrane system is in the quasi-equilibrium state. (2) The gas pressures inside the bubble and in the membrane gas chamber are both uniform within their respective volumes. (3) The liquid is incompressible. (4) The air flowing through thin-walled orifice is an unsteady isothermal flow process for high-pressure air flow. For a specific moment, the flow can be seen as a steady flow, and the flow loss can only be a local loss. (5) As has been developed by [PERSON] et al. [16], once the diffusive airflow rate is known for a given integral membrane, and assuming ideal gas behavior for air, the pressure change and the pressure decay rate due to diffusion can be estimated. For this model, once the membrane has defects, the flow through the defect is many times more than the diffusion flow through the pore; the effect of diffusive flow on the pressure change and the pressure decay rate can be neglected.
## 2 Experimental Set-Up and Results
### Second Layer of Outer Stepped-Metal Coating by Electroplating  
Figure 2 shows the schematic of the stepped-metal electroplating equipment. Regulating the screw column can control the length of the stepped-metal coating. Different electroplating solutions can develop different packages for this sensor. The SMC-FBGs are divided into three types shown in Figure 3 Type A: a copper layer by electroless-plating and then a stepped-nickel layer by electroplating. Type B: a nickel layer by electroless-plating and then a stepped-copper layer by electroplating and Type C: a nickel layer by electroless-plating and then a stepped-nickel layer by electroplating. The subscript \(i\) (\(i\) = 1, 2, 3) means FBG, the first layer metal coating and the outer stepped-metal coating, respectively. \(L_{i}\) refers to the length and \(d_{i}\) to the diameter. Due to the different deposition rate between the copper layer and nickel layer, deposition time was adjusted to get the same thickness of coatings. For Type A,  
Figure 1: Diagram of electroless-plating.  
it took about three hours to get the inner copper layer and thirteen hours to get the stepped-nickel layer. For Type B, it took about two hours to get the inner nickel layer and fourteen hours to get the stepped-copper layer. For Type C, it took about two hours to get the inner nickel layer and twelve hours to get the stepped-nickel layer. These three types SMC-FBGs are of \(d_{1}=125\) um, \(d_{2}=130\) um and \(d_{3}=400\) um, respectively. Let \(h_{1}\) be the thickness of inner uniform metal layer and \(h_{2}\) be the thickness of outer stepped-metal layer, \(h_{1}=(d_{2}-d_{1})/2\), \(h_{2}=(d_{3}-d_{2})/2\), \(h_{1}=2.5\) um, \(h_{2}=135\) um.
## 5 Evaluation Results
### Results of Performance Analysis
#### 5.3.2 Latency with Cold Starts
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Platform** & **Function Invocations** & **Cold Starts** &
\begin{tabular}{l} **Percentage of Cold** \\ **Starts** \\ \end{tabular} \\ \hline AWS & 175,477 & 156,847 & 89.383\% \\ GCP & 175,357 & 21,253 & 12.120\% \\ Azure & 36,176 & 1392 & 3.848\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of cold-start testing (adapted from [54]). Considering these optimizations, we might conclude that latency from cold starts, while a factor, is relatively manageable, the incidence of which may be controllable depending on the platform used and if tactics such as keeping containers warm are employed. There still do exist those times for which a cold start will occur and thus we might start by optimizing the size of the bundle used to deploy a container. This will likely be the lowest-cost solution to mitigating higher cold-start latency compared to other potential solutions such as increasing the amount of allocated memory or the runtime used.
## 4 Results
### Results of Spatiotemporal Evolution Analysis
#### 4.4.1 Dynamic Evolution Results  
The density estimation results (Figure 5) reveal the dynamic evolution characteristics of the three-dimensional coupling coordination degree (D) of RGTI, NQP, and CRCI from 2013 to 2021. From a temporal perspective, the density distribution of D values exhibits a significant rightward shift. In 2013, the distribution peak was concentrated around 0.4, indicating that most regions were at the \"on the verge of imbalance\" or \"barely coordinated\" level. By 2021, the peak had notably shifted upward to approximately 0.7, suggesting that the overall coordination level had progressed into the \"primary coordination\" to \"intermediate coordination\" range. This systematic rightward shift reflects the increasing investment in green technological innovation and the enhancement of innovation capacity, which have deepened the penetration of low-carbon technologies in the construction industry. Additionally, the formation of new quality productivity has provided effective support for the absorption and transformation of green technologies, further strengthening the impact of technological innovation on carbon reduction.  
In terms of distribution morphology, the D-value density curve evolves from a unimodal to a bimodal structure. The unimodal distribution observed in 2013 gradually transitions into a bimodal pattern by 2021, revealing significant structural differentiation in regional coordinated development. Innovative regions such as the Beijing-Tianjin-Hebei (BTH) area and the Yangtze River Delta (YRD) have leveraged robust innovation systems and advanced industrial foundations to achieve rapid improvements in CRCI, driven by the synergy of RGTI and NQP. In contrast, less developed regions with scarce innovation resources and weak industrial bases remain constrained by limited technological absorption capacity and inefficient innovation transformation, resulting in persistently lagging coordinated development levels. This regional divergence is not only reflected in the expanded range of D-value distributions but also highlights systemic differences in regional innovation capacity and resource allocation efficiency.  
This study further reveals that the bimodal evolution of the kernel density curve profoundly reflects the \"Matthew Effect\" in China's green transition process. High-coordination regions continuously maintain their leading positions through cumulative innovation advantages, while low-coordination regions face multiple constraints in their efforts to catch up technologically, exacerbating regional disparities. This spatial polarization poses new challenges for the formulation of regional coordinated development strategies. Based on these findings, future research will employ the Dagum G  
Figure 5: The 3D and 2D kernel density estimation plots for RGTI, NQP, and CRCI coupling coordination.  
conduct a more refined analysis of spatial heterogeneity in coupling coordination degrees across China's four major regions: eastern, central, western, and northeastern.
## 4 Discussion
### Sample Evaluation of Ship-Handling Training  
As the purpose of the proposed model is to evaluate the trainee's ship-handling simulation results, short simulation experiments were conducted. The scenario is for the ship handling training requiring participants to follow the route as closely as possible. The course-altering angles vary from 5 to 39 degrees, and the distances between the waypoints vary from 0.1 to 0.72 miles. The initial speed was set to 13 knots; without controlling the engine, only manual steering control was allowed. A total of 70 participants conducted the training. Out of the 70 participants, the result of the best participant who made the slightest distance deviation from the charted route is visualized in Figure 11, and the average steering angles are marked in Figure 12.  
The simple ship-handling training focused on steering ability, which prevents the ship from deviating from the charted route. When the trainee's trajectory showed the minor average deviation of 5.750 cables in Figure 11a, the rudder steering angles changed dramatically in Figure 11b. This fact infers that training evaluation should approach various aspects besides the specific threshold-based evaluation. Figure 12 overlaps the trainee's  
Figure 11: Sample results of simulated ship-handling training: **(a)** trajectory and **(b)** rudder steering.  
Figure 12: The trainee’s ship-handling training results overlaid on the experts’ trend line of average steering angles.  
results on the experts' average steering trends. As the diamond marks colored by each elapsed time denote, some marks are matched in colors, and others are not. Table 2 shows specific results comparing the trainee's training data to the experts' trend data.  
According to the other evaluation methods using thresholds, an average deviation of 5.750 cables may be allowed to pass the training. However, in Table 2, the trainee failed in four out of sixteen course alterations to keep average steering angles within the range of the experts' average steering angles. The result denotes that twenty-five percent of the course alteration is not close to the experts' yet. The simulated ship-handling training could be evaluated more objectively when the trainee passes the threshold-based criteria and achieves a similar steering ability to experts.
## 2 Materials and Methods
### Empirical Formula and Path Loss Prediction Models Used in This Study  
Equation (1) presents the conversion of the transmitted output power of the base stations in (kW) to (dBm), while (2) was used to calculate the measured path loss along the routes of measurement [12; 31].  
\[P_{t(\text{dBm})}=10\text{log}\left[\left(P_{t(\text{kW})}*1000\right)\right]+30 \tag{1}\]  
\[L_{\text{dB}}=P_{t(\text{dBm})}+G_{t(\text{dBm})}-P_{r(\text{dBm})} \tag{2}\]where  
\(L_{\text{(dB)}}\) is the measured path loss;  
\(P_{t}\) is the transmitted power;  
\(G_{t}\) is gain of the transmitting antenna;  
\(P_{r}\) is the power received (received signal strength (RSS) in this study).  
The details of the predicted models are presented as follows:  
(1) **Hata's Model (Okumura-Hata)**  
This model [35] is an improvement over the Okumura model. It was formulated in the year 1980 based on the data provided by [PERSON] and is valid within a frequency range of 150-1500 MHz and a distance of 0-20 km from the base station. The path loss for urban areas \(L_{\text{(U)}}\) is as presented in (3):  
\[L_{\text{(U)}}=\left[\begin{array}{c}69.55+26.16\text{log}(f)-13.82\text{log }(h_{b})-\alpha(h_{r})\\ +(44.9-6.55\text{log}(h_{b})\log(d)\end{array}\right] \tag{3}\]  
where  
\(L_{\text{(U)}}\) is the path loss in dB;  
\(f\) is the carrier frequency, 150-1500 MHz;  
\(h_{b}\) is the transmitting base station's tower or mast height, ranging between 20 and 200 m;  
\(h_{r}\) is the receiver antenna's height, ranging from 1 to 10 m;  
\(d\) is the distance between the transmitter and the receiver (Tx-Rx), ranging from 1 to 20 km;  
\(a(h_{r})\) is the correction factor for the receiver height and is given as follows:  
\[a(h_{r})=\left\{\begin{array}{c}\big{(}\big{[}1.1\text{log}_{10}(f)-0.7\big{)} \big{]}h_{r}-\big{(}1.56\text{log}_{10}(f)-0.8\big{)}\\ \text{For suburban or rural area}.\\ \\ 8.29\text{log}_{10}(1.54h_{r})^{2}-1.1\\ \text{For an Urban area}:\ \ f\leq 200\text{ MHz}.\\ \\ 3.2\text{log}_{10}(11.75h_{r})^{2}-4.97\\ \text{For an Urban area}:\ \ 200\leq f\leq 400\text{ MHz}.\\ \end{array}\right. \tag{4}\]  
The Hata path loss equations for suburban areas and open areas are presented in (5) and (6), respectively:  
\[L_{\text{SU}}(\text{dB})=L_{U}-2\bigg{[}\text{log}_{10}\bigg{(}\frac{f}{28} \bigg{)}\bigg{]}^{2}-5.4 \tag{5}\]  
Open areas:  
\[L_{\text{O}}(\text{dB})=L_{Hata}(\text{U}rban)-4.78\big{(}\text{log}_{10}(f) \big{)}^{2}-18.33\text{log}_{10}\left(f\right)-40.94 \tag{6}\]  
The variables are as defined for (3).  
(2) **COST-231 Model**  
This is an extension of the Okumura-Hata model [36] to cover a wide range of frequencies between 0.5 and 2 GHz. It is used for medium to small cities:  
\[L_{\text{(dB)}}=\begin{array}{c}46.3+33.9\text{log}_{10}(f)-13.82\text{log }_{10}(h_{b})+\\ \big{[}44.9-6.55\text{log}_{10}(h_{b})\big{]}\text{log}_{10}(d)-\alpha(h_{r})+ C_{m}\end{array} \tag{7}\]where \(a(h_{r})\) is the receiver antenna correction factor, as presented in (4), while \(C_{m}=0\) dB for medium-sized cities and suburban areas and 3 dB for urban areas.
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
###### Abstract  
With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccurate assessment results and weak systemic assessment method are the main problems in air traffic situation assessment research. The aim of our research is to present an objective and accurate situation assessment method for air traffic systems. The paper presents a model to assess air traffic situation based on the complex network theory and ensemble learning. The air traffic weighted network model was introduced to systematically describe the real state of an air traffic system. Assessment criteria based on the complex network analysis method can systematically reflect the operational state of an air traffic weighted network system. We transformed the air traffic situation assessment into a binary classification, which makes situation assessment objective and accurate. Ensemble learning was introduced to improve the classification accuracy, which further improves the accuracy of the situation assessment model. The model was trained and tested on the dataset of the East China air traffic weighted network in 2019. Its average classification accuracy is 0.98. The recall and precision rates both exceed 0.95. Experiments have confirmed that the situation assessment model can accurately output air traffic situation value and situation level. Furthermore, the assessment results are consistent with the real operational situation of the air traffic in East China.  
air traffic network; situation assessment; complex network; ensemble learning Article
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks
### Adaptive Convolutional Network
#### 3.5.3 Training Procedure for the Model  
The HGE-ACN framework is a training method that aims to efficiently learn spatial-temporal patterns from wearable sensor data. The training process involves dataset preparation, preprocessing, graph construction, model initialization, feature extraction, graph convolution, adaptive convolution, loss function and optimization, batch processing, backpropagation, early stopping, and model evaluation. The input data are from a wearable glove with six-axis IMU sensors and five-finger curvature sensors. The raw sensor signals are normalized, synchronized, and transformed into structured feature vectors. A spatial-temporal graph is built, with vertices representing keypoints and edges representing spatial and temporal relationships. The Graph Convolutional Network (GCN) and Adaptive Convolutional Networks (ACNs) are initialized with random weights and edge weights computed using the Gaussian similarity function. The model's classification output is obtained using Softmax, cross-entropy loss is used for optimization, and the Adam optimizer is used with a learning rate of \(10^{-3}\). The training process includes batch processing, backpropagation, early stopping, and model evaluation. The HGE-ACN model achieves high recognition accuracy, robustness, and real-time performance.
# Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things
## 5 Conclusions  
This paper provides an SDN-oriented framework to improve the green management of an IoT domain. In particular, we address a general architecture design with a specific focus on the forwarding strategy operated by the SDN Controller, which dynamically adapts it to the network status in order to optimise the network responsiveness and energetic footprint. To this purpose, we characterise an optimisation problem, jointly based on nodes energy consumption and e2e flow delivery, and we address a practical solution implement in the SDN Controller in the DRM, with a limited control message overhead. We conducted a comprehensive and realistic simulation campaign, pointing out that the performance achieved by the proposed approach is better than SDN alternatives based on a hierarchical CP, as in LOA. Specifically, the SDN-DRM Controller presents the best trade-off between delay and battery consumption, without excessive overhead for comparable network sizes. Therefore, the proposed framework introduces a more effective and more flexible CP for a reference SDN IoT architecture. Future developments of the present work are planned mainly in terms of introduction of network-related machine learning techniques to predict future operative conditions to optimise in advance the weighting factors of the DRM approach.  
Figure 9: Objective function analysis.  
**Author Contributions:** Conceptualisation, M.B. and F.C. and L.P.; methodology, M.B. and F.C.; software, M.B.; validation, M.B. and F.C. and L.P.; formal analysis, F.C.; investigation, M.B. and F.C. and L.P.; resources, F.C. and L.P.; data curation, M.B.; writing--original draft preparation, M.B. and F.C. and L.P.; writing--review and editing, M.B. and F.C. and L.P.; visualisation, M.B.; supervision, R.F.; and project administration, R.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Review of Degradation and Life Prediction of Polyethylene
## 6 Conclusions  
In modern society, polyethylene materials are utilized in a variety of applications, but as their use increases, their initial performance qualities tend to change. Temperature, light, pressure, chemical attack, mechanical stress, microorganisms, and other factors can all affect the degradation of polyethylene materials. This influence is frequently synergistic, making polyethylene degradation extremely complex. Many researchers have developed various kinetic methods for predicting the lifetime of polyethylene materials, and this paper describes the most common and widely used kinetic methods.  
The isoconversion method derives from the Ozawa-Flynn-Wall (OFW) method and the Friedman method, both of which do not require a mathematical model and instead use several curves at different heating rates to calculate the kinetic parameters at the same conversion rate and obtain the activation energy. The multi-curve method is another name for the equal conversion rate method. With the introduction of the Kissinger-Akahira-Sunose (KAS) method, the accuracy of the equal conversion rate method has improved. Kissinger and OFW are model-free analyses, which means that the activation energy is calculated without taking into account the kinetic model of the reaction process. [PERSON]'s method has the advantage of not being limited to linear changes in heating rate and exhibiting simplicity, adequacy, and accuracy. The advanced isotransformation rate method is now widely acknowledged as one of the most precise methods for estimating activation energies from TGA experiments.  
Due to the numerous applications of polyethylene materials in people's lives, the extensive use of polyethylene materials can lead to environmental pollution. Extending the service life of polyethylene can promote the development of the 5 Rs [146] for reducing environmental pollution, therefore, the life prediction of polyethylene and other polymeric materials is critical.  
Conceptualization, Y.W.; Writing--original draft, G.F.; Funding acquisition, Y.W.; Investigation, Y.W. and D.Y.; Resources, Y.W. and N.L.; Supervision, Y.W. and H.L.; Validation, Q.L.; Writing--review and editing, J.T. and G.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (no. 2022D01C389), the Xinjiang University Doctoral Start-up Foundation (no. 620321029), and the Science and Technology Planning Project of State Administration for Market Regulation (no. 2022 MK201).  
Not applicable.  
The data are not publicly available.  
The authors declare no conflict of interest.
## 4 Target and Frequency Sweeping Jamming Classification Experiment
### Experimental Results  
In the experimental section, the concept of five-fold cross-validation was performed to evaluate the performance of the proposed technique. A total of 100 iterations of the SVM classifier were performed using a five-fold cross-validation approach. The mean and standard deviation of the performance evaluation indicators are computed. The experimental results with different SVM kernel function are shown in Table 4.  
To visualize the experimental results more easily, Table 4 is plotted as a histogram, as shown in Figure 8. As can be seen from Table 4 and Figure 8, support vector machines with all three types of kernel functions meet an accuracy rate of 98% or more, with support vector machines using Gaussian kernel functions enabling a maximum recognition accuracy of 98.81%. The recognition precision all indicators reach 98% or more, with a maximum of 99.43% when using polynomial kernel functions. The recall indicator is fully satisfied reaching over 97%, with a maximum of 99.21% with a Gaussian the kernel function. The false alarm rate measures the chance of survival of a radio fuse in a complex electromagnetic battlefield environment, with a lower false alarm rate indicating better battlefield survivability. The false alarm rates of the proposed methods in this paper all remain below 2%, with the lowest rate being 0.57% when using a polynomial kernel function, and the highest rate being 1.58% when using a Gaussian kernel function.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**SVM Kernel**} & \multicolumn{4}{c}{**Evaluation Indicators**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & **FalseAlarm** \\ \hline Gaussian & 98.81\% \(\pm\) 0.0037 & 98.42\% \(\pm\) 0.0068 & 99.21\% \(\pm\) 0.0030 & 1.58\% \(\pm\) 0.0068 \\ Linear & 98.58 \(\pm\) 0.0037 & 99.14\% \(\pm\) 0.004