esa-sceva/satcom-chunk-collection huggingface dataset:
# Flame Retardance and Smoke Suppression of CFA/APP/LDHs/EVA Composite
## 1 Introduction  
The ethylene vinyl acetate (EVA) polymer is widely used as an insulator for wires and cables. However, the EVA not only is easily flammable, but also releases the toxic CO gas during combustion [1; 2]. To reduce flammability and the release of CO for EVA, the addition of flame retardants is effective [3; 4]. Brominated flame retardants are the most effective and show a good ratio of property to price, however, their uses have been limited to the consideration of life safety and environmental problems due to a large amount of smoke and hydrogen bromide produced during burning [5; 6]. The layered double hydroxides (LDHs) is a large class of anionic clays with a general formula of [M\({}^{2+}\)\({}_{1-x}\)M\({}^{3+}\);(OH)\({}_{2}\)]\({}^{x}\)+[A\({}^{n-}\)]\({}_{x/n}\cdot m\)H\({}_{2}\)O, where M\({}^{2+}\) and M\({}^{3+}\) are divalent and trivalent metal cations, \(x\) = M\({}^{3+}\)/(M\({}^{2+}\)+M\({}^{3+}\)) molar ratio and A\({}^{n-}\) is an \(n^{-}\) valent interlayer anion [7; 8]. During combustion, LDHs lose the adsorbed water and interlayer water, which can dilute and cool the combustible products within the flame zone. Meanwhile, the mixed metal oxide and char residue generated by the polymer/LDHs composite can protect the polymer from the air exposure and in turn suppress smoke production [9; 10]. Furthermore, since the composition of the metal layers of LDHs can be varied, there are enormous potential applications as flame retardants [11]. Based on the above advantages of LDHs, we have paid significant attention to the design and preparation of LDHs as a non-halogenated additive of EVA [12; 13; 14; 15].  
A triazines derivative (CFA) containing hydroxylethylamino, triazine rings and ethylenediamine into groups was synthesized by our group [16]. It is used both as a charring agent and as a foaming agent in intumescent flame retardants (IFR) due to its excellent flame retardant properties and water resistance. To improve the flame retardance and thermal stability of polymer (polyppropylene, EVA, polyethylene), CFA and ammonium polyphosphate (APP), systems with different synergistic agents (such as zinc borate, zeolites, organic montmorillonite) have been investigated in the previous report [17; 18; 19; 20; 21; 22]. However, the research on the combination of LDHs and CFA/APP as synergistic flame retardants in EVA composites has never been reported. In the present work, a new composite flame retardant CFA/APP/LDHs was used in the ethylene vinyl acetate (EVA) matrix. Furthermore, the schematic of CFA/APP/LDHs and EVA was shown in Figure 1. The LOI, UL-94 and HRR results confirm that the synergistic flame retardant effects between CFA/APP and LDHs. The release yield and production rates of CO\({}_{2}\) essentially mirror the HRR data. Moreover, the relationship between the CO\({}_{2}\) and CO release is observed by the cone calorimeter test.
## 3 Solar Radiation Prediction Time Horizons
### Time Horizon
#### 3.1.2 Forecasting Solar Radiation  
Long-term predictions: correspond to a horizon above 48 or 72 h, reaching a limit of 7 days. The larger the horizon, the greater the prediction errors, making it difficult to make reliable predictions of atmospheric variables above those 7 days [34; 35; 36]. Time horizons represent prediction analysis, as, as has been observed in the literature, ML models are able to demonstrate that they are the best alternative in time series data analysis. However, the need to reduce error correction has been modified to make way for ML regression model models using search optimization algorithms (SOA) that require accurate selection of their parameters to improve their performance [37; 38].
## 5 Experiments and Evaluation
### [PERSON]'s Kappa  
Cohen's kappa coefficient is used to measure the reliability of the model. [PERSON]'s kappa coefficient is a statistical method for finding the reliability between evaluators by measuring the agreement of measurement category values between two observers [36]. Equation (3) shows the [PERSON]'s kappa coefficient where \(\mathbf{P_{observed}}\) is the probability of a match between evaluators and \(\mathbf{P_{chance}}\) is the probability of a match by chance.  
\[\mathbf{\kappa}=\frac{\mathbf{P_{observed}}-\mathbf{P_{chance}}}{1-\mathbf{P_{ chance}}} \tag{3}\]  
[PERSON]'s kappa coefficient grades are shown in Table 7, which follow the interpretation of [PERSON] and [PERSON] [37].  
\(\mathbf{\kappa}\), representing [PERSON]'s kappa coefficient, has a value between 0 and 1. As a \(\mathbf{\kappa}\) value becomes closer 1, the reliability of a model increases. A [PERSON]'s kappa coefficient, \(\mathbf{\kappa}\), between 0.000 and 0.200 indicates slight agreement, while a value greater than or equal to 0.8 indicates almost perfect agreement.  
A previous study also used [PERSON]'s kappa coefficient to measure the reliability of a binary classification model [38]. The reliability of a model is determined by measuring the agreement between the label of the test data and the label predicted by the deep learning model. Figure 8 illustrates the result of generating a confusion matrix using the COVID-19 test data consisting of 500 sentences for measuring the reliability of the proposed model.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{\kappa}\) & **Strength of Agreement** \\ \hline \(>\)0.000 & Poor \\ \hline
0.000–0.200 & Slight \\ \hline
0.201–0.400 & Fair \\ \hline
0.401–0.600 & Moderate \\ \hline
0.601–0.800 & Substantial \\ \hline
0.801–1.000 & Almost Perfect \\ \hline \end{tabular}
\end{table}
Table 7: Kappa grades.  
Figure 7: Increasing performance in proportion to the size of training datasets.  
In the confusion matrix, four kinds of results can be taken as follows:  
* True Positive (TP): a result that correctly indicates the positive;
* True Negative (TN): a result that correctly indicates the negative;
* False Positive (FP): a result that wrongly indicates the positive when in fact the result belongs to the negative;
* False Negative (FN): a result that wrongly indicates the negative when in fact the result belongs to the positive.  
Through the confusion matrix, [PERSON]'s kappa coefficient \(\kappa\) can be expressed as shown in Equation (4).  
\[\kappa=\frac{2\times(\mathbf{TP}\times\mathbf{TN}-\mathbf{FN}\times\mathbf{ FP})}{(\mathbf{TP}+\mathbf{FP})\times(\mathbf{FP}+\mathbf{TN})+(\mathbf{TP}+ \mathbf{FN})\times(\mathbf{FN}+\mathbf{TN})} \tag{4}\]  
In this paper, the experiment result produced \(\kappa\) = 0.5737 when calculated using Equation (4), which shows moderate agreement according to the interpretation of [PERSON] and [PERSON].
## 4 Results and Analysis
### Case of Soybean Futures  
Similarly, the EMD, WPT, ITD and VMD methods are applied to decompose the soybean futures prices in this subsection, whose decomposition results are given from Figures 19-22, respectively. Based on the decomposition results, we use the four proposed \"decomposition and ensemble\" hybrid models to forecast the soybean futures prices during the period from 10 August 2010 to 29 July 2016, totally 300 values, regarding the PSO-BPNN model as the benchmark model and ARIMA model as the comparative model. The comparison of the one-day-ahead forecasting results is drawn as shown in Figures 23 and 24 and Table 5, respectively.  
According to these figures and table, we can reach conclusions similar to the empirical research on the wheat and corn; i.e., that the VMD-PSO-BPNN model is still the best model to forecast the soybean futures prices among all models proposed in this paper. Based on the value of MAPE, the order of these six models, from big to small, is the PSO-BPNN model (1.20%), ARIMA model (1.11%), EMD-PSO-BPNN model (1.01%), ITD-PSO-BPNN model (0.91%), WPT-PSO-BPNN model (0.70%) and VMD-PSO-BPNN model (0.57%), which shows that hybrid models with decomposition methods, especially the VMD method, have obvious advantages in forecasting the soybean futures prices in contrast with the PSO-BPNN model and ARIMA model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Criteria**} \\ \cline{2-4}  & **MAE** & **RMSE** & **MAPE (\%)** \\ \hline PSO-BPNN & 4.31 & 5.93 & 1.14 \\ EMD-PSO-BPNN & 3.94 & 5.08 & 1.06 \\ WPT-PSO-BPNN & 2.44 & 4.62 & 0.65 \\ ITD-PSO-BPNN & 3.59 & 5.08 & 0.96 \\ VMD-PSO-BPNN & **2.12 \({}^{*}\)** & **2.82 \({}^{*}\)** & **0.57 \({}^{*}\)** \\ ARIMA & 4.27 & 5.91 & 1.13 \\ \hline \hline \end{tabular} \({}^{*}\) The smallest value of each column is marked in boldface and with an asterisk.  
\end{table}
Table 4: Forecasting performance evaluation of corn.  
Figure 19: Decomposition results of the EMD method (soybean).  
Figure 20: Decomposition results of the WPT method (soybean).  
Figure 21: Decomposition results of the ITD method (soybean).  
Figure 23: One-day-ahead forecasting results of different models of soybean.  
Figure 24: Error graphics of different models of soybean.  
Figure 22: Decomposition results of the VMD method ( soybean).
## 4 Performance Results
### Performance Indicator
#### 4.1.3 Precision Score  
Precision represents the percentage of data that are actually true among the data that the model classifies as positive. The formula for calculating the precision score is as follows.  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]
## 4 Discovering Inconsistent Data Based on WFT-Nets  
From a model checking point of view, the states of data flow are the key to discovering inconsistent data. And considering the source and development of the error, the original reachability graphs of WFT-nets should be extended. In addition, this section proposes a corresponding data refining procedure to capture such error. Then, based on it, such error can be discovered with graph traversal.
## 4 Computer Simulations  
The proposed signal processing algorithm was verified using computer simulations. To see the shot-noise limited performance of the proposed signal processing algorithm, the normalized AFWLI fringe scans, \(i_{S}(n)\) and \(i_{R}(n)\) were computer-generated using Equation 4 and Equation 5 respectively and shot noise was added to the AFWLI fringe scans, \(i_{S}(n)\) and \(i_{R}(n)\) instead of white noise. In the computer simulations the zero order fringe peak position \(p_{0,S}\) (and \(p_{0,R}\)) for \(i_{S}(n)\) (and \(i_{R}(n)\)) were randomly selected as real number. \(i_{S}(n)\) and \(i_{R}(n)\) were cross-correlated into \(i(n)\). Then, \(p_{t}\) is calculated as (\(p_{0,S}\)-\(p_{0,R}\)) and zero order fringe peak \(p_{d}\)= \(n_{d}\) of \(i(n)\) becomes the integer part of \(p_{t}\). The coherence length of \(i_{S}(n)\) and \(i_{R}(n)\) were chosen as \(L_{C}\)=26\(\lambda\) to simulate the coherence length of the commercially available SLD like OKI OE350S from Oki semiconductor. For all the computer simulations presented in this article parameters are fixed as follows, unless the specified parameter is varied for a certain range and circumflex notation \(\wedge\) on the top of the parameter denotes the estimated value of that particular parameter calculated by the proposed signal processing algorithm.  
* Sample rate \(f_{S}\)=16 [samples/fringe]
* _SNR_=30 dB
* Effective Coherence length \(L_{C,\textit{eff}}\)= \(\sqrt{2}\times 26\)\(\lambda\approx 36\lambda\)
* Size of fine tuning step \(\Delta n\)=1/1000 [fringe]
## 2 MANTiS-32 Wireless Measurement System Design
### MANTIS-32 Wireless Networking Architecture  
MANTIS-32 serves as a wireless data aggregation hub, designed to collect structural vibration data from distributed MANTIS-6050 sensor (InvenSense Inc., San Jose, CA, USA) modules and transmit the acquired signals to a centralized PBM system via Wi-Fi communication. Engineered as a high-fidelity dynamic measurement platform with robust wireless capabilities, MANTIS-32 is optimized for real-time structural condition assessment and modal parameter identification. Its modular design ensures ease of deployment and scalability, enabling flexible integration across a wide spectrum of SHM applications, including small- to medium-span bridges, structural test specimens, and industrial plant systems.  
As illustrated in Figure 3, MANTIS-32 is configured to receive data from up to eight MANTIS-6050 sensor (InvenSense Inc., San Jose, CA, USA) nodes via RS485 communication, managed by an onboard PIC16F15344 microcontroller (Microchip Technology Inc., Chandler, AZ, USA). Inter-component communication within the system is established using the I2C protocol, allowing seamless coordination between sensor modules and the  
Figure 2: Design of the MPU-6050 sensor module.  
of the GPS-based timestamp.  
To enable high-precision temporal synchronization among multiple hubs and sensor nodes, the system incorporates a NEO-6M GPS module. This module communicates with the ESP32-C3 microcontroller through an interface, providing Real-time Location (UART) and time data. The integration of GPS-based timestamp facilitates accurate alignment of distributed sensor data, thereby enhancing the reliability of time-domain correlation and improving the fidelity of subsequent modal analysis. Wireless data transmission is executed by the ESP32-C3 microcontroller, which communicates with the PBM system through a designated AP using Wi-Fi. This architecture supports synchronized, high-throughput, and low-latency data transfer, forming a comprehensive and scalable framework for real-time wireless structural health monitoring.
## 1 Introduction
This makes it difficult to assess the degree of battery aging comprehensively and accurately by relying only on the SOH. Therefore, it is necessary to consider the SOH and RUL of the current battery to make a comprehensive diagnosis of the degree of aging throughout its life cycle. However, current common evaluation methods usually estimate only one of them, while estimating both separately will inevitably increase computational and algorithmic complexity. Based on the previous analysis, a method is proposed for estimating SOH and predicting RUL of lithium-ion batteries by charging feature extraction and ridge regression. First, three sets of health feature parameters are extracted from the charging voltage curve, and the Pearson correlation coefficient is applied to analyze these health features for maximum battery capacity. Then, the ridge regression method is applied to develop the battery aging model and estimate the SOH. On this basis, a multiscale prediction model is developed to predict trends in health features as charge-discharge cycles increase. The results of the multiscale prediction model are integrated with the battery aging model to estimate SOH through multiple steps, enabling RUL prediction. Finally, the accuracy and adaptability of the proposed method are confirmed by two battery datasets procured under varied operating conditions. Experimental results show that SOH and RUL are accurately predicted for the proposed method with high accuracy and reliability. The remainder of this paper is structured as follows: Section 2 presents battery degradation datasets and explains the process of extracting health features. Section 3 describes the rationale for the selected models and algorithms, as well as the overall framework for estimating SOH and predicting RUL. Section 4 provides experimental validation of the method, followed by discussion and analysis of the results. Section 5 summarizes the work carried out in this paper.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 5 Evaluation
### Impact of Proposed Algorithm on RA KPIs
#### 5.4.1 Reliability  
Figure 6 depicts the SP of the proposed algorithm against the others. To describe the test results for the learning curve as well as the performance on improving SP, the figure shows different positions where the metric is calculated. The position can be random;  
Figure 5: Convergence plot of \(Q\)-based and DQN-based algorithms: stability of reward values despite a random access network environment. Reward values are computed for every other episode. (**a**) Comparison between RL approaches for reward values in random access network environment; (**b**) reward values for DQN-based algorithm: different learning rates.  
however, we test the initial position as well to test how the SP improves, which helps to also visualize how the algorithm is learning. We observe that, in the initial positions, because the network is starting, and previously-stored information is not available, the deep RL algorithm proceeds to exploit as expected, and in the remaining positions, we observe that the deep RL method outperforms the other algorithms in terms of reliability. The increase in SP is attributed to the algorithm's efficient way of selecting a less congested TRxP by exploiting its own stored knowledge.
## 2 _Introduction_
Besides the good performance on mechanical properties of the sintered activated-ceramic product that can be achieved, the geopolimerization that uses kaolin/metakaolin as a source of alkali-activated materials (AAM) also exhibited good bioactivity of the products [32; 33]  
The geopolymerization process is characterized by the following stages: (1) dissolution of oxide minerals from alumina-silica-rich source materials under high alkaline conditions; (2) transport/orientation of dissolved oxide minerals followed by gelation; and (3) polycondensation to form a stable 3D network of a silica-voluminate structure. Numerous aspects influence the geopolymerization process, including the chemical and mineralogical composition of the starting materials, curing temperature, water content, and alkaline component concentration [31]. To obtain the reactive or amorphous phase required for the geopolymerization reaction, thermally heated kaolinite clay in the range of 550-850 \({}^{\circ}\)C remains the primary source of metakaolin. The amount of reactive SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) in the produced metakaolin is dependent on the purity of the kaolin clay used as the starting material [34]. In previous work, metakaolin-based geopolymer with up to 30 wt% quartz sand was found to be more compact, exhibited less drying shrinkage, and possessed greater mechanical strength than the specimen without quartz [35]. In medical applications, kaolin has, over time, been used as an additive, adsorbent, and agent for blood clotting because it enables charge interactions with clotting factors, besides having huge potential to accelerate haemostasis. Kaolin was reported to be mechanically reliable, in addition to its non-toxicity [36]. Sintering of geopolymers always results in the formation of sintered ceramic bodies with improved properties [37; 38; 39; 31]. A particular procedure is required in order to manage the purity, particle size, particle size distribution, and heterogeneity of ceramic products which employ naturally occurring rock sand minerals as the raw materials. These parameters have a substantial impact on the ultimate qualities of ceramics. The process of the sintering reaction is affected by impurities and the mineralogical structure of the kaolin minerals. Extrusion, slip casting, pressing, tape casting, and injection molding are the most popular techniques used to produce a green body prior to further heat treated to form a rigid, final product. Conventional sintering remains a popular method for HAp ceramic fabrication due to its simplicity. However, it still struggles to preserve their compatibility and prevent the degradation of the HAp structure during the high-temperature sintering. The objective of this research was to identify the effect of the mix design input on the preparation of a low sintering temperature of MK/HAp bioceramics. This will lead to an understanding of the chemical composition, which may act as a self-fluxing agent during geopolymerization, and thus its contribution to the phase stability and mechanical property.
## 2 Materials and Testing Program
### Direct Shear Box Tests  
The direct shear testing was carried out in accordance with Australian standard AS 1289.6.2.2-1998. The vertical stress applied to the specimen during shearing in the direct shear box was 35 kPa because this is equivalent to the self-weight of the pavement above the subgrade. A shear rate of 0.5 mm/min was adopted, and the shear stress and vertical displacement were monitored during shearing. To examine the influence of wetting, selected specimens were inundated for 24 h to mimic infiltration processes that would take place after rainfall periods and shearing resumed subsequently. The specimens were inundated at three stages of shearing (a) prior to shearing to evaluate as compacted conditions, i.e., new build, (b) once the soil reached yield mimicking most of the stress states the soil is likely to experience during the service life of the infrastructure. The shear tests aim to investigate the stress-strain behaviour of the soil at the subgrade level near the pavement shoulders (i.e., vertical stress of 35 kPa) and assess whether any potential  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Hydrated Lime**} \\ \hline
**Chemical Entity** & **Proportion** \\ \hline H\({}_{2}\)O & 0.1–2.5\% \\ Ca(OH)\({}_{2}\) & 90–95\% \\ Mg(OH)\({}_{2}\) & 0.5–1\% \\ SiO\({}_{2}\) & 0.5–3\% \\ Al\({}_{2}\)O\({}_{3}\) & 0–2\% \\ FeO & 0–0.4\% \\ \hline \multicolumn{2}{c}{**Portland Cement**} \\ \hline Portland Clinker & \textless{}97\% \\ CaSO\({}_{4}\) 2H\({}_{2}\)O & 2–5\% \\ CaCO\({}_{3}\) & 0–7.5\% \\ CaO & 0–1\% \\ Cr (VI) & \textless{}10 ppm \\ \hline \multicolumn{2}{c}{**Blast Furnace Slag:**} \\ \hline
**Constituent** & \\ \hline CaO & 35–42\% \\ SiO\({}_{2}\) & 33–38\% \\ Al\({}_{2}\)O\({}_{3}\) & 10–15\% \\ MgO & 7–12\% \\ FeO & \textless{}1\% \\ MnO & \textless{}1\% \\ Cr\({}_{2}\)O\({}_{3}\) & \textless{}0.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of hydrated lime and Portland cement (data from [26]) and blast furnace slag chemical composition (data from [27]).  
localised failures could arise from the wetting process and loss of strength. The direct shear apparatus was preferred because of its ease in inundating specimens compared to triaxial testing.