esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 4 Thermal Hazards of Lithium-Ion Battery
### Thermal Hazards of Typical Battery Components
#### 4.1.3 Thermal Hazards of the Separator  
At present, the LIB separator is commonly made of polyethylene (PE), polypropylene (PP) or their composite materials. However, because of their low thermal deformation temperature (PE: within 85 \({}^{\circ}\)C, PP: within 100 \({}^{\circ}\)C) [93], the polyolefin separators will suffer severe thermal contraction when ambient temperature is relatively high, thus it is not suitable for the usage under high temperature environment. In order to meet the requirement of the complex working environment of the LIB, researchers have developed a variety of new composite separator materials and optimized their performance from the aspects such as preparation process improvement [94; 95; 96]. Among them, a new type of separator material obtained by the composite of polyaryl ether and thermoplastic resin has attracted extensive attention due to its superior ionic conductivity and high safety [97; 98]. [PERSON] et al. developed a series of poly aryl ether materials, including poly aryl ether ketone (PPEK), poly aryl ether sulfone (PPES), poly (phthalazinone ether sulfone ketone) (PPESK) and so on. Their glass transition temperatures were over 260 \({}^{\circ}\)C, and the 5% thermal weightlessness temperatures were close to 500 \({}^{\circ}\)C. Upon analysis using an electrostatic spinning process, the poly aryl ether materials with polyvinylidene fluoride (PVDF) as a composite separator of a LIB showed excellent electrochemical performance and thermal stability [99; 100].  
Although the high-temperature resistant separator can significantly improve the safety condition of the LIB, it cannot completely inhibit the occurrence of thermal hazard. According to the previous research, although the mass ratio of the separator is approximately 3%, it can release up to 33% of heat in the combustion process, see Figure 1, indicating that the combustion characteristics of separator directly affect the fire risk of the LIB [76]. Therefore, the research and development of a new separator material with high thermal safety is essential.
# Numerical Analysis of Fatigue Life of Wind Turbine Blades Reinforced with Graphene Platelets
## 2 Finite Element Model of a 5 MW Wind Turbine Blade
### Material Modeling of GPLRC  
The GPL-reinforced wind turbine blade of this study was implemented by replacing the conventional fiberglass composite with GPLRCs. The thickness of GPLRCs was the same as that of the conventional fiberglass composite. For material modeling of GPLRCs, effective material properties were calculated in terms of the base material properties of GPL, a reinforcing material, and epoxy and their volume fractions. Equations (1) and (2) show the effective material properties calculated based on the linear rule of mixtures. Specifically, \(V\) denotes the volume fraction of the material. Subscripts _eff_, \(GPL\), and \(m\) indicate effective material properties, GPL, and matrix, respectively.  
\[\
u_{eff}=V_{GPL}\,v_{GPL}+V_{m}\,v_{m} \tag{1}\]  
\[\rho_{eff}=V_{GPL}\,\rho_{GPL}+V_{m}\,\rho_{m} \tag{2}\]  
Meanwhile, mechanical properties that are significantly affected by the geometry of the nanofillers, such as stiffness and strength, must be calculated through more precise, effective material property modeling techniques. Therefore, the effective elastic modulus of GPLRCs \(E_{eff}\) was calculated via the Halpin-Tsai micromechanics approach [27]. Table 3 shows the base material properties of GPLs and epoxy taken for calculating the effective material properties.  
\[E_{eff}=\frac{3}{8}\cdot\frac{1+\xi_{L}\eta_{L}V_{GPL}}{1-\eta_{L}V_{GPL}}E_{m }+\frac{5}{8}\cdot\frac{1+\xi_{T}\eta_{T}V_{GPL}}{1-\eta_{T}V_{GPL}}E_{m} \tag{3}\]  
\[\eta_{L}=\frac{E_{GPL}-E_{m}}{E_{GPL}+\xi_{L}E_{m}}\,,\,\,\eta_{T}=\frac{E_{GPL }-E_{m}}{E_{GPL}+\xi_{T}E_{m}} \tag{4}\]  
\[\xi_{L}=\frac{2l_{GPL}}{t_{GPL}}\,,\,\,\xi_{T}=\frac{2w_{GPL}}{t_{GPL}} \tag{5}\]  
Here, \(L\), \(T\), \(GPL\), and \(m\) denote the longitudinal and transverse directions, graphene platelets, and matrix, respectively. In addition, \(l_{GPL}\), \(w_{GPL}\), and \(t_{GPL}\) are the length, width, and thickness of GPLs, respectively, which were set by \(l_{GPL}=2.5\) um, \(w_{GPL}=1.5\) um, and \(t_{GPL}=1.5\) nm [28].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Blade Span** & & & & & & **TE** & \\
**(m)** & **LE** & **LE Panel** & **Spar Cap** & **TE** & **Reinforcement** & **TE Panel** & **Shear Web** \\ \hline
0 & 1-2-3-2 & 1-2-3-2 & 1-2-3-2 & 1-2-3-2 & 1-2-3-2 & 1-2-3-2 & - \\
1.3667 & 1-2-3-2 & 1-2-3-2 & 1-2-3-2 & 1-2-3-2 & 1-2-3-2 & 1-2-3-2 & 8-9-8 \\
1.5 & 1-2-3-2 & 1-2-3-7-2 & 1-2-3-
## 3 Results and Discussion
### Above-Water BRDF Effects
While not strictly a comparison to a Lambertian bottom assumption, a discrepancy of 18% implies the potential for larger errors. To avoid across-swath BRDF effects in airborne imagery, the advice is therefore broadly the same as for avoiding surface glint [32]; thus flying toward or away from the sun will ensure the cross track view is at 90\({}^{\circ}\) to the solar plane, and a solar zenith angle > 30\({}^{\circ}\) both avoids glint and slightly flattens the BRDF response (Figure 10d,f).
# Optimal Mixture Designs for Heavy Metal Encapsulation in Municipal Solid Waste Incineration Fly Ash
## 1 Introduction  
Municipal solid waste incineration fly ash (MSWIFA) is classified as hazardous waste in numerous locations across the globe [1; 2; 3; 4; 5]. MSWIFA has high concentrations of soluble heavy metals, including Pb, Zn, Cr, Sb, Hg, Cu, Ni, and Cd. Therefore, the MSWIFA is a hazardous pollutant when considering environmental risk control [6]. Solidification, which can be defined as the use of a binder, which either chemically or physically binds toxic waste matter to prevent leaching is a recognized way to dispose of MSWIFA [7; 8]. Solidification processes using Portland cement have received significant attention and the encapsulation ability of cement hydrates including ettringite and C-S-H has been reviewed in the literature [6; 9]. The relatively high CO\({}_{2}\) emissions associated with Portland cement have led to the development of numerous more sustainable alternatives with low or zero cement content [10]. Such alternatives have the added advantage of using industrial by-products, which are often otherwise landfilled. The objective of this work is to critically evaluate and develop a sustainable binder material using ground granulated blast furnace slag (GGBFS), ladle furnace slag (LFS), and flue gas desulfurization (FGD) gypsum that can encapsulate the heavy metals in MSWIFA.  
GGBFS is a commonly used and well-known supplementary cementitious material for concrete. It has often been used as a component in mixtures for heavy metal encapsulation [10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. LFS is a lesser known material and a by-product of steel manufacture which typically has both amorphous and crystalline phases with large amounts of aluminum and calcium [20]. Although the composition and crystalline phase in LFS tend to be variable, a commonly found crystalline phase in LFS is mayenite (C\({}_{12}\)A\({}_{7}\)). In our previous work, we have investigated mayenite reactions and found that it can form Friedel's salt when it reacts with the chloride that is commonly present in MSWIFA [21]. Friedel's salt is a layered double hydroxide (LDH) material, with interesting anion-exchange properties which leads to a potential for high heavy metals encapsulation [21]. Due to these characteristics and potential hydrates that are formed with LFS, its use for heavy metal encapsulation warrants further study.  
The hypothesis of this work is that the use of GGBFS, LFS, and MSWIFA will lead to a binder that has significant amount of LDH formation, which will lead to a system that is sustainable, strong, and which will encapsulate the heavy metals in MSWIFA. While the specific systems studied here have not been investigated in the past, the hypothesis is in line with the literature which suggests that the use of such slags will favor the formation of hydrates and microstructures that will result in enhanced immobilization of heavy metals [22]. This work is part of a larger study on sustainable by-product binders and results on chloride binding and hydration in some of the systems studied here have been already published [21].
## 3 Results and Discussion
### CFD Results  
It is intended to evaluate the modifications induced by the elements of the building's surroundings of an area with heterogeneous morphological characteristics to identify the places with more favourable conditions for the use of wind energy.  
The geometric model for the CFD simulation was designed in the CFD simulation software itself (Ansys Fluent). The enclosure has the north wall of the volume as the fluid entry zone, which corresponds to the zone where the wind direction is more predominant. The enclosure fluid outlet zones correspond to the remaining sides of the enclosure, with the exception of the base, which is considered to be a wall. The buildings were characterized as contact regions, and in this way, they can influence the movement profile of the fluid, in this case, the air. To compute the geometric model, an automatic mesh from the Fluent workbench was parametrised with smooth transitions, five layers of inflation, and a medium relevance centre. In total, 65,390 nodes and 362,172 elements were computed. The fluid was defined as air, with a reference density of 1225 kg/cm\({}^{3}\) and an initial velocity in the volume (inlet) of 5 m/s.  
The supposed location of installation of the turbines is not affected by changes in the wind direction or wind speed from other buildings. It appears that the airflow from north to south increases. It goes from 5 m/s to about 6.8 to 7.8 m/s, representing an increase from 36% to 58%. The airflow behaviour can be seen in Figure 5.  
In this case, we can say that the shallow turbulence at the top of the building would not influence a turbine installed on the block above the top of the building. Through Figure 6, it can be seen that at the top of the building, the wind speed changes. There is a decrease to practically 0 m/s in the central and lateral zones, except for the block that is at the top.  
The block on the hotel's roof would be an excellent place to install any of the turbines adopted for the case study. Furthermore, the north corners of the top are a perfect area for vertical axis turbines, as they are unidirectional turbines. Hence, if the turbine to be installed is turbine 1, Aeolos-V 10 kW, the best proposal would be the installation of two of them in both north corners of the roof marked with an \"X\" in Figure 7. Alternatively, turbine 2, Aeolos-H 20 kW, would be installed on top of the block above the hotel marked with a dot in Figure 7. There is also the possibility of installing the vertical axis turbine at the top of the block. However, the difference observed in the production between the two turbines, 1 and 2, for the same height leads to discarding this option.  
Figure 5: Speed contours in the north–south plane in the flow of air through buildings.  
Figure 6: Top view of wind speed at the top of buildings.  
Figure 7: Scenario identification for the installation of wind turbines in the building’s rooftop.
# Anxiety Detection System Based on Galvanic Skin Response Signals
## 3 Related Work  
Research in anxiety detection has garnered significant interest due to the increasing prevalence of anxiety disorders and the need for effective monitoring tools. Various approaches have been explored, utilizing different devices and targeting a range of physiological signals to enhance detection accuracy. Notable signals include heart rate (HR), respiratory rate (RR), heart rate variability (HRV), electronic noses (E-nose), galvanic skin response (GSR), electrocardiograms (ECGs), skin temperature (SKT), electroencephalograms (EEGs), and sleep status. Many studies have recorded and collected their own datasets, employing diverse computational methods to test various approaches in different contexts. This section reviews these contributions, highlighting the advancements and methodologies used in the field of anxiety detection.  
[PERSON] et al. [15] validated the Apple Watch's ability to measure HRV during relaxed and stressed states. A comparison was also made between the Apple Watch and Polar H7 when identifying autonomic nervous system responses. They found that both the devices demonstrated good reliability and consistency; however, the Apple Watch exhibited gaps in the RR interval data, thereby affecting the frequency of the HRV measurement.  
[PERSON] et al. [12] developed an artificial electronic nose and a GSR analysis technique to detect academic stress in engineering students, and they achieved a high success rate when classifying stress levels. [PERSON] et al. [29] focused on using physiological signals (i.e., HR and GSR signals) to detect stress during password writing, and they developed a fuzzy logic-based decision system that achieved high accuracy in detecting stress in a short period. [PERSON] et al. [30] developed Anxolotl, which is an anxiety companion application that detects stress levels and panic attacks using wearable devices. This application provides real-time monitoring and notifications to help users manage their mental health.  
[PERSON] et al. [31] investigated the use of biofeedback techniques, including those based on GSR, HR, and skin temperature, to measure the anxiety levels of university students during examinations. They observed significant correlations between the biofeedback measurements and anxiety levels, indicating the potential effectiveness of biofeedback for anxiety detection. [PERSON] et al. [32] developed a questionnaire and utilized Fitbit data to predict anxiety, depression, and positive moods of company staff. In particular, ML techniques were used to process sleep features collected from Fitbit, exhibiting promising results for estimating anxiety and depression levels. In addition, [PERSON] et al. [33] designed a mood recognition framework using HR, skin temperature, and acceleration data collected through a smartphone application. Their classifiers outperformed baseline methods, with the bagged ensembles of a decision tree algorithm achieving the highest accuracy.
## 3 Results and Discussion
### Research Design and Discussion
#### 3.4.3 Sensitivity Analysis of Evaluation Index Weights
From the analysis of Figures 5 and 6, it can be seen that the two indexes of the same category, average price advantage, and payment convenience in capital flow coordination, had a higher weight sensitivity; the index of freshness delivery ability in logistics coordination had a higher weight sensitivity; the two indexes of delivery flexibility level and supply chain brand image in business flow coordination had a higher weight sensitivity; and the collaborative response speed and information processing reliability in information flow coordination had a higher weight sensitivity. Figure 5: Sensitivity intervals caused by changes in the weights of indexes in each criterion layer and their initial values. By combining the 17 indexes in the index layer, which corresponded with the target layer, we found that the two index weights of the average price advantage of the same category, coupled with the collaborative response speed, had the highest sensitivity, and their sensitivity changes had the greatest impact on the final evaluation results. By combining the four dimensions in the criterion layer, which corresponded with the target layer, it was evident that the index weights corresponding to capital flow had the most obvious impact on the comprehensive score; thus, the fresh agricultural produce supply chain platform and its members should realize the coordinated operation of the supply chain through the means and methods of revenue, price, and guaranteed capital flow at an early stage.
# A High Capacity Watermarking Technique for the Printed Document
###### Abstract  
Digital watermarking technology is an effective method for copyright protection of digital information, such as images, documents, etc. In this paper, we propose a high capacity text image watermarking technique against printing and scanning processes. Firstly, this method obtains the invariant in the process of printing and scanning under the mathematical hypothesis model of print-scan transformation. Then based on the print-scan invariant, the Fourier descriptor is used to flip the trivial pixel points with high frequency information on the character boundary. Next, considering the resolution of the print-scan equipment and its influence on the print-scan invariant, a quadratic quantization function is proposed to embed watermark information of multiple bits for a single character. Finally, the QR code (Quick Response code) is researched, which has large information capacity, robust error correction ability and high decoding reliability. By using the QR code as the watermark information, we can reduce the impact of bit error rate during watermark extraction, and the robustness of the watermark information can be improved. The experimental results show that the proposed text watermarking algorithm has the advantages of anti-print scanning, anti-scaling, large capacity and good visual effects.  
Text watermarking; anti-print scan; Fourier descriptor; large capacity; QR code +
Footnote †: journal: Accepted: 22 November 2019; Published: 25 November 2019
## 3 Preliminaries
### Fundamental Concepts of the Echo State Network
#### 3.1.1 Reservoir Computing  
Reservoir computing models are characterized by an architecture divided into a _reservoir_ and a _readout_. The reservoir represents the recurrent part that allows for encoding the temporal dependencies of time series and maps them into a vector with higher dimensionality called a hidden state. The readout can take the hidden state and provide the vector related to the prediction. The key concept that makes this framework efficient is related to the non-adaptability of the reservoir. Keeping the recurrent part of the network fixed can avoid the backpropagation through time (BPTT) learning function, which is related to issues of gradient vanishing and gradient exploding that are typical in RNN models [52]. The extremely high efficiency of the model allows us to use it in on-the-edge devices. The echo state network is a powerful reservoir computing model.
# Gaussian Process Modeling of Specular Multipath Components
## 3 Gaussian Process Regression
### Prediction  
We next review the generic principle of GP regression (GPR). For each SMC \(k\), we have a training database \(\mathcal{D}_{k}=\{\mathbf{\phi}_{k},\mathbf{y}_{k}\}\) with \(i=1\ldots N\) measurements \(y_{k,i}=\psi(\phi_{k}(\mathbf{p}_{i}))+\epsilon\) (or \(y_{k,i}=\zeta(\phi_{k}(\mathbf{p}_{i}))+\epsilon\)), recorded at training positions \(\mathcal{P}_{k}=\{\mathbf{p}_{1},\mathbf{p}_{2},\ldots,\mathbf{p}_{N}\}\) that are mapped to angles \(\mathbf{\phi}_{k}=[\phi_{k}(\mathbf{p}_{1}),\ldots,\phi_{k}(\mathbf{p}_{N})]\) and \(\epsilon\sim\mathcal{N}(0,\sigma_{\epsilon}^{2})\) represents the measurement noise with variance \(\sigma_{\epsilon}^{2}\) (which is connected to the power spectral density of the AWGN as described in Appendix B). The measurements are stacked into the training data vector \(\mathbf{y}_{k}=[y_{k,1},\ldots,y_{k,N}]^{\text{T}}\). After the hyperparameters are learned, the mean and variance can be predicted at test position \(\mathbf{p}^{*}\) with angle\(\phi_{k}(\mathbf{p}^{*})\). The mean and the variance of the angle-dependent amplitude \(\psi(\phi_{k}(\mathbf{p}^{*}))\) (or phase \(\zeta(\phi_{k}(\mathbf{p}^{*}))\)) conditioned on the data \(\mathcal{D}_{k}\) and \(\mathbf{\theta}_{k}^{\text{abs}}\) (or \(\mathbf{\theta}_{k}^{\text{ph}}\)) can be expressed in closed form [31]  
\[\mathbb{E}[\psi(\phi_{k}(\mathbf{p}^{*}))|\mathcal{D}_{k},\mathbf{\theta}_{k}^{\text{ abs}}] =\mu_{\text{GP}}(\phi_{k}(\mathbf{p}^{*}))+\mathbf{k}(\phi_{k}(\mathbf{p}^{*}),\mathbf{ \phi}_{k})^{T}\mathbf{K}(\mathbf{\phi}_{k})^{-1}(y_{k}-\mu(\mathbf{\phi}_{k})), \tag{16}\] \[\mathbb{V}[\psi(\phi_{k}(\mathbf{p}^{*}))|\mathcal{D}_{k},\mathbf{\theta} _{k}^{\text{abs}}] =\sigma_{k}^{2}+\sigma_{v,k}^{\text{abs}\;2}-\mathbf{k}(\phi_{k}(\bm {p}^{*}),\mathbf{\phi}_{k})^{T}\mathbf{K}(\mathbf{\phi}_{k})^{-1}\mathbf{k}(\phi_{k}(\mathbf{p}^{* }),\mathbf{\phi}_{k})\,, \tag{17}\]  
where \(\mathbb{V}[\cdot]\) is the variance operator and with  
\[\mathbf{k}(\phi_{k}(\mathbf{p}^{*}),\mathbf{\phi}_{k}) =[c_{\text{GP}}(\phi_{k}(\mathbf{p}^{*}),\phi_{k}(\mathbf{p}_{1})), \ldots,c_{\text{GP}}(\phi_{k}(\mathbf{p}^{*}),\phi_{k}(\mathbf{p}_{N}))]^{\text{T}}\,, \tag{18}\] \[\mathbf{\mu}(\mathbf{\phi}_{k}) =[\mu_{\text{GP}}(\phi_{k}(\mathbf{p}_{1})),\ldots,\mu_{\text{GP}}( \phi_{k}(\mathbf{p}_{N}))]^{\text{T}}\,, \tag{19}\]  
and \(\mathbf{K}(\mathbf{\phi}_{k})\in\mathbb{R}^{N\times N}\) with \([\mathbf{K}(\mathbf{\phi}_{k})]_{i,j}=c_{\text{GP}}(\phi_{k}(\mathbf{p}_{i}),\phi_{k}(\bm {p}_{j}))+\sigma_{e}^{2}\delta_{i,j}\), where \(\delta_{i,j}\) is the Kronecker delta. In Appendix B, the relation between the predicted variance and the PDP of the DMC and the power spectral density of the AWGN is analyzed in detail.
## 4 Learning Constraints in EVAE  
### Joint Encoder and Decoder  
To implement the learning constraints in EVAE, we derived the analytic forms of the joint encoder and decoder of the EVAE. The making of the joint encoder and decoder should satisfy the two following requirements: (1) analytic expressions should be flexible enough to include earning constraints, and (2) derived expressions need to be fully differentiable to enable the fast and accurate approximate marginal inference of variable \(x\).  
To this end, we considered the analytic expression of our joint encoder that allowed the smooth interpolation of the two component encoders in Equation (1):  
\[q((z_{b},z_{s})|x;\phi_{b},\phi_{s},\alpha)\propto q_{\phi_{b}}(z_{b}|x)\cdot q _{\phi_{b}}(z_{s}|x)\alpha\cdot\{\mathcal{R}_{e}(z_{b},z_{s})\}. \tag{1}\]Note that, here, we inserted a hyperparameter \(\alpha\) to exert direct control over the output of the discrepancy function, \(\mathcal{R}_{e}(z_{b}^{l},z_{s}^{l})\). Importantly, the range of \(\alpha\) is defined as \((0,+\infty)\) in this research. This prevents the proposed EVAE from degrading to two unrelated VAEs (the \(\alpha=0\) case).  
The introduced discrepancy function \(\mathcal{R}_{e}(z_{b},z_{s})\) can be further expressed in the following Equation (2):  
\[\mathcal{R}_{e}(z_{b},z_{s})=\frac{1}{L}\sum_{l=1}^{L}\exp\{-||z_{b}^{l}-z_{s} ^{l}||^{2}\}. \tag{2}\]  
As the samples are easier to work with, we further Monte Carlo-sampled these encoded representations from two encoders, \(z_{b}^{l}\sim q(z_{b}|x)\) and \(z_{s}^{l}\sim q(z_{s}|x)\), where \(l\) stands for the number of samples. The added exp function was to ensure that the measured representation differences were positive.  
Similar to the prior factorisation in the encoder case, the analytic expression of the joint decoder is \(p_{\theta_{b},\theta_{s}}(\xi|(z_{b},z_{s}))\), obtaining a similar form in Equation (3), as follows:  
\[p_{\theta_{b},\theta_{s}}(x|(z_{b},z_{s});\alpha)\propto p_{\theta_{b}}(\vec{ x}_{s}|z_{b})\cdot p_{\theta_{s}}(\vec{x}_{s}|z_{s})\alpha\cdot\{\mathcal{R}_{d} (\vec{x}_{b},\vec{x}_{s})\}. \tag{3}\]  
Note here that we also incorporated the same hyperparameter \(\alpha\) to scale the differences of the two reconstructed data instances, where the discrepancy function for the joint decoder \(\mathcal{R}_{d}(\vec{x}_{b},\vec{x}_{s})\) can be defined in Equation (4):  
\[\mathcal{R}_{d}(\vec{x}_{b},\vec{x}_{s})=\frac{1}{L}\sum_{l=1}^{L}\exp\{-|| \vec{x}_{b}-\vec{x}_{s}||^{2}\}. \tag{4}\]  
Here, \(\vec{x}_{b},\vec{x}_{s}\) represent the reconstructed data instances from \(VAE_{b}\) and \(VAE_{s}\), respectively, i.e., \(\vec{x}_{b}\sim p_{\theta_{b}}(\vec{x}|z_{b})\) and \(\vec{x}_{s}\sim p_{\theta_{b}}(\vec{x}|z_{s})\).