esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thickness Swelling and Water Uptake  
The three-layers PBs made from cup-plant, and the three-layer PB made from spruce wood, both showed similar thickness swelling after 2 h (TS2h, Figure 11). Data also show that the spruce particles in the surface layer did significantly lower TS2h, as it is the case with the cup-plant PBs (3 LCP). The TS2h of single-layer cup-plant PB was almost twice the TS2h of the three-layer cup-plant PB. No TS2h difference was found between 3 LCP and 3 LCP. A lower thickness swelling of three-layer PB than single-layer PB was also  
Figure 10: Internal bonding strength (IB) of the measured particleboards (PBs). 3 LCP—three-layer cup-plant PBs, 3 LSP—three-layer spruce PB, 1 LCP—single-layer cup-plant PB, and 1 LSP—single-layer spruce PB (\(n=20\)).  
measured by [22], where Miscanthus or topinambour stalks were utilized for the core layer in three-layer PB.  
It is further shown that TS24h of both cup-plant PBs were significantly higher than the three-layer spruce comparison. It can be noted that in the 3 LCP the spruce surface layer has a positive effect on TS24h. In addition, TS24h of the three-layer cup-plant PB was significantly lower (\(p<0.05\)) than the TS24h measured for the single-layer cup-plant PB.  
Water uptake results after 24 h were different in a way with the three-layer spruce particleboards absorbing the least water, while the single-layer cup-plant had the highest water uptake. Spruce particles as the surface layer is reducing the water-uptake, meaning that these particles seemingly reduce water access. This could be linked to a less accessible pore structure in spruce, compared to cup-plant particles (see Figure 3).
# Finite-Time Set Reachability of Probabilistic Boolean Multiplex Control Networks
## 3 Finite-Time Set Reachability
### State Transfer Graph
Further, \(\mathcal{X}_{1(7)}\)=\(\mathcal{X}_{1(4)}\cup\{\delta^{0}_{2^{\mathrm{EM}}}\}\), \(\mathcal{X}_{2(7)}\)=\(\mathcal{X}_{2(4)}\cup\{\delta^{0}_{2^{n}}\}\), and \(\mathcal{E}_{(7)}=\mathcal{E}_{1}\cup\mathcal{E}_{2}\cup\mathcal{E}_{3}\), where  
* \(\mathcal{E}_{1}=\{(\delta^{j}_{2^{\mathrm{EM}}},\delta^{i}_{2^{\mathrm{EM}}}, p_{ij},\delta^{s}_{2^{\mathrm{EM}}})\in\mathcal{E}_{(4)}\mid j\in\Lambda^{c}_{d},i \in[1,2^{n}]\}\),
* \(\mathcal{E}_{2}=\{(\delta^{j}_{2^{\mathrm{EM}}},\delta^{0}_{2^{\mathrm{EM}}}, 1,\delta^{s}_{2^{\mathrm{EM}}})\mid j\in\Lambda_{d}\}\),
* \(\mathcal{E}_{3}=(\delta^{0}_{2^{\mathrm{EM}}},\delta^{0}_{2^{\mathrm{EM}}},1, \delta^{s}_{2^{\mathrm{EM}}})\). The STG of RLDS (7) may be determined intuitively from the STG of PBMCNs (4) as follows:  
* The edge starting from \(\widetilde{\mathcal{M}}^{c}_{d}\) in \((\mathcal{X}_{(4)},\mathcal{E}_{(4)})\) stay unaltered. * The edge starting from \(\widetilde{\mathcal{M}}_{d}\) in \((\mathcal{X}_{(4)},\mathcal{E}_{(4)})\) are substituted with the edges in \((\mathcal{X}_{(7)},\mathcal{E}_{(7)})\) directed toward \(\delta^{0}_{2^{\mathrm{EM}}}\) with probability one. * The edge \((\delta^{0}_{2^{\mathrm{EM}}},\delta^{0}_{2^{\mathrm{EM}}},1,\delta^{s}_{2^{ \mathrm{EM}}})\) is added. For the overall state, the transition rule of STG from PBMCNs (4) to RLDS (7) conforms with the overall state, but the set of the overall state nodes is \(\mathcal{X}=\Delta_{2^{\mathrm{EM}}}\). In addition, we utilize a numerical example to demonstrate the above-mentioned procedure. **Example 1**. : _Consider the PBMCNs that has a one-step transition probability matrix \(P^{\widetilde{x}}\)._  
\[P^{\widetilde{x}}=\left[ \begin{array}{cccccccc}0.4&0.1&0.4&0.1&0&1&0&0\\ 0.1&0.4&0.1&0.4&1&0&0&0\\ 0.4&0.1&0.4&0.1&0&0&1\\ 0.1&0.4&0.1&0.4&0&1&0\end{array} \right]\]  
_Supposing that \(\widetilde{\mathcal{M}}_{d}=\{\delta^{2}_{4},\delta^{4}_{4}\}\), we can obtain \(\widetilde{\mathcal{M}}^{c}_{d}=\{\delta^{1}_{4},\delta^{3}_{4}\}\). Then, \(\hat{D}_{\widetilde{\mathcal{M}}^{c}_{d}}=\delta_{8}[1\,0\,3\,0\,5\,0\,7\,0]\). The STG of the PBMCNs is shown in Figure 1._  
Figure 1: STG of the PBMCNs (4) in Example 1. _Here \(\widetilde{x}_{i}\in\widetilde{\mathcal{M}}_{d}\) are represented by the gray nodes, and the dashed arrows denote the edges that emerge from \(\widetilde{\mathcal{M}}_{d}\). The one-step global nonzero state transition probability matrix of the resulting RLDS (7) is presented as_  
\[P^{\bar{x}}=\left[ \begin{array}{cccccccc}0.4&0&0.4&0&0&0&0&0\\ 0.1&0&0.1&0&1&0&0&0\\ 0.4&0&0.4&0&0&0&0&0\\ 0.1&0&0.1&0&0&0&1&0\end{array} \right]\]  
_Figure 2 shows the STG of the RLDS._
# Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems
## 2 Method
### Proximal Operators  
When we solve the training problem (1) for a deep neural network with a large dataset, the most popular optimization algorithms include stochastic subgradient descent [49] and its variants such as AdaGrad [50], RMSProp [31], and ADAM [32]. These methods use small subsamples of training examples \(B\subset\{1,\ldots,n\}\), called minibatches, to construct stochastic subgradients in forms of:  
\[g(w):=g_{B}(w)+\
abla\Psi(w),\]  
where \(g_{B}(w):=\frac{1}{|B|}\sum_{i\in B}\
abla\ell(w;x_{i},y_{i})\) and \(\
abla\Psi(w)\) is a subgradient of \(\Psi(w)\). If we use the subgradient \(g(w)\) which includes the part \(\
abla\Psi(w)\) from the regularizer for updating learning parameters \(w\), it is unlikely that any updated weight value will be precisely the zero value, even though some values might be close to zero.  
Instead, we consider a mechanism to set the weight values to the exact zero value whenever necessary, by means of the so-called proximal operator of a convex function \(\Psi(\cdot)\). Given a vector \(z\in\mathbb{R}^{p}\), the proximal operator of \(z\) with respect to \(\Psi\) is defined as  
\[\operatorname{prox}_{\Psi}(z):=\operatorname*{arg\,min}_{w\in\mathbb{R}^{p}} \ \frac{1}{2}\|w-z\|_{2}^{2}+\Psi(w).\]  
When \(\Psi(w)=\lambda\|w\|_{1}\), the regularizer we use for sparse coding, the proximal operator can be computed in a closed form independently for each component,  
\[[\operatorname{prox}_{\Psi}(z)]_{i}=\operatorname{sgn}(z_{i})\max\{|z_{i}|- \lambda,0\},\ \ i=1,\ldots,p.\]  
This operator is also known as the soft-thresholding operator [51].
## 1 Introduction
In view of the nonlinear properties of ECDs, the governing equation for an SDOF-ECD system becomes nonlinear, which is difficult to calculate the system response analytically. On the other hand, as structural engineers are most familiar with classical viscous damping, it is essential to compare the performance of ECDs with FVDs, which are nonlinear in nature as well. This investigation aims to demonstrate how nonlinear ECDs affect the dynamic responses of SDOF systems and evaluate the seismic control performance compared with FVDs. Presented first are the nonlinear characteristics of a rotary axial ECD, where we demonstrate that its force-velocity relationship can be characterized by the [PERSON]'s model [29]. Secondly, the energy dissipation capacity of the nonlinear ECD is derived analytically when subjected to harmonic motion and its optimal critical velocity for energy dissipation are obtained, where its energy dissipation characteristics are compared with FVDs. Thirdly, steady-state responses of the SDOF-ECD systems under harmonic excitations are calculated using numerical simulations, their displacement and acceleration responses are compared with those of the SDOF-FVD systems. Fourthly, the seismic response control performance and energy dissipation capacity of the SDOF-ECD systems under real earthquakes are analyzed and compared with the SDOF-FVD systems. Some conclusion remarks are drawn in the final part.
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 3 Trust Model Concepts
### Trust Model Structure
These previous values are stored and inserted into the trust calculation, usually using an ageing algorithm that can be anything from a weighted average to something more complex. These historic trust values are often referred to as the reputation of the node. This step may not be utilized in all models, and the direction arrows in Figure 1 show this. **(5) Threshold calculation**. An area that is often lacking in most models is dynamic threshold calculation. The threshold level in most models decides whether a node's trust value renders it malicious or not. Dynamically calculating the threshold gives the model more flexibility to be applied in more than one specific network, as it will account for differences in network traffic, the number of nodes or other factors that could affect the average value of trust for a node. The majority of models tend to leave this phase out and instead employ a static threshold value method [38]. Dynamic threshold calculation should, however, be considered for the models, as it provides more flexibility in allowing them to be applied in more than a single specific network. The final stage in the diagram is the simple comparison of the trust value with the threshold value. This stage determines the final decision on the node's trust status. If a node becomes ejected from the network, some models have methods that allow these nodes to build up reputation again over time without having full access to the network and, later, to potentially rejoin the network. Models also sometimes face the 'cold start' issue, whereby trust calculations that rely on data and previous values do not work initially on startup, which must be considered. These points are discussed further below.
## 2 Dynamic Modeling of Trans-Medium Process  
### Preliminary  
In this section, the operating mode of the coaxial HAUV developed by us is briefly introduced. In order to focus on the control problem of the HAUV's cross-domain processes, we first analyzed the differences between the dynamic characteristics of the HAUV maneuvering underwater and in the air. Our goal is to provide hybrid dynamic models of trans-medium vehicles operating in the air, across domains, and under water. Therefore, the coaxial system's dynamic model can be divided into three categories (underwater dynamics, trans-medium dynamics, and aerodynamics), which are organically unified by designing a linearized switch function.  
As shown in Figure 1, the HAUV has two operating modes. It can maintain free floating for a long time to carry out long-term ocean observation tasks when carrying a float ball. When the float ball is abandoned, it can perform underwater maneuvering operations for underwater sampling and lurking tasks. In this study, the dynamic modeling and robust controller design for the most typical water-air cross-domain process (second operation mode) of the HAUV were investigated.  
The symbols of the physical quantities related to modeling are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acronyms** & **Full Name** & **Acronyms** & **Full Name** \\ \hline HAUVs & Hybrid aerial-underwater vehicles & ANFTSMC & Adaptive non-singular fast terminal sliding-mode controller \\ NFFSMC & Nonsingular fast terminal sliding & AV & Unmanned aerial vehicle \\  & mode control & & \\ SMC & sliding mode control & ADRC & Active disturbance rejection control \\  & Underwater gliders & ADSC & Adaptive dynamic surface control \\  & Unmanned surface vehicles & NDO & Nonlinear disturbance observer \\  & UUV & Unmanned underwater vehicles & TDAV & Trans-domain amphibious vehicle \\ \hline \hline \end{tabular}
\end{table}
Table 1: The acronyms and full names used in this paper.  
Figure 1: Schematic diagram of two operation modes’ motion processes of the coaxial HAUV.
# Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction
## 4 Discussion  
We observed that the proposed model outperformed the GRU [37] and SLI-ESN [3]; moreover, it is important to use different sub-predictors to process different components of original data separately. In particular, the results showed that the ARIMA \((2,1,0)\) model could obtain a better performance in predicting the trend component than the neural network GRU. In this section, we will discuss the reason as the following.  
The neural network needs to normalize the input data before, and then perform inverse normalization in the output step. The general methods to normalize are as follows:  
\[x^{\prime}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{17}\]  
where \(x^{\prime}\) is the normalized data; \(x\) is original input data; \(x_{\text{min}}\) is the minimum of \(x\); and \(x_{\text{max}}\) is the maximum of \(x\). Then, the inverse normalization formula is  
\[\hat{x}=(x_{\text{max}}-x_{\text{min}})*\hat{x}^{\prime}+x_{\text{min}} \tag{18}\]  
where \(\hat{x}^{\prime}\) is the prediction with the value within \([0,1]\) and \(\hat{x}\) is the inverse normalization value of \(\hat{x}^{\prime}\).  
As shown in Figure 7, the minimum value of the input data is \(a\), and the maximum value is \(b\). In the normalization, this numerical range \([a,b]\) of the input data is mapped to \([0,1]\) by Equation (17). After the operation of the network, the predicted value is obtained, but within \([0,1]\), and then the inverse normalization calculation is required.  
However, the network cannot predict the interval of the predicted value as \([\text{b, c}]\), and the current inverse normalization method still uses the normalized interval to perform the operation, that is, \(x_{\text{min}}\) is \(a\) and \(x_{\text{max}}\) is \(b\). This is the basic reason for the inaccuracy of using neural networks to make regression predictions.  
While the ARIMA model does not need the normalization and inverse normalization process, it can work well when data exhibit stable or consistent patterns over time. Moreover, the greater the slope of the trend component, the more obvious the advantages of using the ARIMA model. Therefore, we used the ARIMA model to model and predict the trend component, and obtained more accurate results than neural network GRU. The proposed methods proposed in this paper can combine other identification approaches [38; 39; 40; 41; 42] to study the modeling and prediction problems of other dynamic time series and stochastic systems with colored noises [43; 44; 45; 46; 47], and can be applied to other fields [48; 49; 50; 51; 52] such as signal modeling and control systems [53; 54; 55; 56].  
Figure 7: Normalization and inverse normalization of linear prediction using neural networks.
## 4 Discussion
### Limitations and Future Work  
This research has some limitations. First, although we experimentally manipulated the level of complexity and number of 3D virtual objects when memorizing MAR information, there were still some potential confounding factors related to AR properties, such as the illumination level of the environment [6], the depth condition [69], the viewing angle [70], the object theme [71], the ambient music [72] and user emotions [73], which may affect the VWM capacity. Second, the virtual objects used in this experiment were static and de-colored, but it is unclear whether the different forms of multidimensional encoding in 3D virtual objects (such as the spatial regularities [74], realism [75], and vanishing modes [76]) would affect VWM capacity. Third, previous study has shown that the performance and the precision of VWM declines with age [77], but all 31 participants in our experiment were from a younger age group. Therefore, based on the existing research results, future research could further explore the effects of those AR properties, multidimensional encoding forms and recruit participants from multiple age groups.
## 3 The Decoding-Complexity, Rate, and Distortion Relationship  
In order to develop an algorithm to control the bit rate and decoding-complexity of a video sequence, it is necessary to first determine the relationship between these two parameters and the distortion produced when a particular coding parameter combination is selected by the encoder for a given content. As such, a content-adaptive decoding-complexity-rate-distortion model is necessary, where the decoding-complexity can be determined for various decoding operations based on the coding modes and features selected by the encoder. To achieve this, the decoding-complexity estimation models developed in [24; 43; 44; 45] for both inter-predicted and intra-predicted coding units (CU) are used as a basis for this work, which will equip the encoder to compute the relative complexity of each decoding operation. This section describes the approach used to analyze the behavior of these three parameters and a content-dependent model that can be generated for the decoding-complexity-rate-distortion space.
## 3 Results and Discussions
In fact, any increase in temperature in this range leads to the stability of arsenic gas species due to the decrease in their formation free enthalpies. The liquid components of arsenic have the opposite behavior with temperature. The influence of temperature on lead volatility shows that any increase in temperature increases the vaporized quantity and the vaporization speed of lead. The effect of the variation of oxygen pressure, on lead and arsenic volatility are presented in Figure 8. In this study, we used a temperature 2200 K for the experiments. These Figures show that the increase of the partial pressure of oxygen supported the incorporation of Pb and As in the containment matrix. In fact, when the atmosphere of the furnace becomes more oxidizing, the vaporized quantity and the speed of vaporization of As and Pb decrease. Figure 6: Variation of the mole number of titanium components in the liquid and gas phase. When the atmosphere is oxidizing, Equations (6) and (7) represent the principal reactions responsible for As volatility:  
\[\text{Ba}_{3}(\text{AsO}_{4})_{2}=2\text{AsO}\left(\text{g}\right)+3\text{Ba}+3 \text{O}_{2} \tag{6}\]  
\[\text{AlAsO}_{4}=\text{AsO}\left(\text{g}\right)+\text{AlO}_{2}(\text{g})+1/2 \text{O}_{2} \tag{7}\]  
Equations (6) and (7) show that any addition of oxygen to the two reactions led to the formation of Ba\({}_{3}\)(AsO\({}_{4}\))\({}_{2}\) and AlAsO\({}_{4}\). We conclude that the increase of the partial pressure of oxygen supported the confinement of As in the containment matrix. Equations (8) and (9) represent the reactions of Pb vaporization when the atmosphere is oxidizing:  
\[\text{PbO}=\text{PbO}\left(\text{g}\right) \tag{8}\]  
Figure 3:0  
Equation (9) shows that any addition of oxygen to the system moves the equilibrium to the formation of PbO and strengthens the incorporation of Pb in the containment matrix. Figure 3:1 Influence of partial pressure of oxygen on the volatility of: (**a**) Pb, (**b**) As. Figure 3:2 Influence of temperature on the volatility of As. To study the influence of matrix composition on the volatility of As, three matrices were used. Matrix 3 represents the initial composition of waste and is given by Figure 3:3 Matrix 1 and 2 have the same composition to matrix 1, but in matrix 1 the mole number of Ba was reduced to 0.01 mole, whereas in matrix 2 the mole number of Ba was increased to 0.5 mole. Figure 9 depicts the influence of the composition of the containment matrix on the arsenic volatility. The increase of the quantity of Ba in the containment matrix strengthens the confinement of As in the matrix. In fact, the addition of the mole number of Ba in the confinement matrix led to the increase of the mole numbers of Ba\({}_{3}\)(AsO\({}_{4}\))\({}_{2}\) in the liquid phase. This molecule, which has a significant quantity (between \(10^{-3}\) mol and \(10^{-2}\) mol), is responsible for the confinement of As in the matrix.
## 2 Numerical Calculation Model
### Numerical Methodology and Boundary Conditions  
The time-dependent and Reynolds-averaged Navier-Stokes equations were used to solve the transient flow in the three-dimensional model [4]. The real gas model (RGM) was utilized (see Section 2.3) due to the extremely high pressure. The time-derivative terms were discretized by a second-order backward Euler scheme. The pressure-velocity coupling was solved by employing the pressure-implicit with splitting of operators (PISO).  
The movement of the piston was updated at each time step based on Equation (2) [25]. The suction and discharge pressure and temperature (table in Section 3) with turbulence intensity of 5% were imposed as inlet and outlet boundary conditions (Figure 3). Considering that the 2-1A and 2-1B hyper-compressors share one intake buffer tank, and then the mutual effect of the pressure pulsation of 2-1A and 2-1B, the dynamic pressure and temperature monitored at the same position of 2-1B transformed 180\({}^{\circ}\) were loaded to the 2-1A as the boundary condition, to achieve both highly accurate results and to avoid unnecessary computational resources.  
\[v=\frac{n\pi L}{60}\cdot(\sin\theta+\frac{\lambda}{2}\cdot\frac{\sin 2\theta}{ \sqrt{1-\lambda^{2}\sin^{2}\theta}}) \tag{2}\]  
where \(v\) is the velocity of a piston, \(L\) is the stroke, and \(\lambda\) is the ratio of the crank radius to the connecting rod.  
In addition, aiming to save on computational costs, the following assumptions were set:  
1. Leakage was disregarded;
2. The heat exchange during the operating cycle was neglected;
3. The pressure pulsation in the piping in front of the inlet buffer tank was not considered.
## 4 Training of NN Weights
 \tag{42}\]  
Define an ellipsoid set \(\Omega_{Ei}\) centered at \(W_{i}^{*}\) as  
\[\Omega_{Ei}(W_{i}^{*},\digamma_{i})=\{\hat{W}_{i}|\hat{W}_{i}^{T}\digamma_{ i}^{-1}\hat{W}_{i}\leq 1\}. \tag{43}\]  
Now, we propose the following theorem. **Theorem 1**. : _Consider the singularly perturbed model of an FJR (5), by using the weights updating law (39) and the NN-based adaptive controller \(u=u_{s}+u_{f}\) with \(u_{s}\) given in (26) and \(u_{f}\) given in (30), it can be guaranteed that: **(1)** The NN weights \(\hat{W}_{i}(t)\) remain inside the ellipsoid set defined in (43); that is, \(\hat{W}_{i}(t)\in\Omega_{Ei},\forall t\geq 0\). **(2)** The identification error \(\bar{\psi}_{fi}\) is bounded. **(3)** The sliding manifolds \(s_{s}\) and \(s_{f}\) are bounded, and the tracking error \(e_{1}\) is bounded by the predefined performance boundaries._  
A detailed proof of Theorem 1 is presented in Appendix A. **Remark 2**. : _In this study, we propose an adaptive PPC tailored for FJRs. Departing from conventional approaches that employ back-stepping or dynamic surface control methodologies [6; 7; 8; 9], our design leverages the SPT to reduce system complexity and simplify the controller synthesis process, thereby significantly enhancing its practical applicability to actual FJR systems. Furthermore, unlike other research efforts [10; 11] that solely rely on SMC or PPC to counteract disturbances, this work introduces an online-trained NN to approximate and mitigate unknown perturbations, significantly enhancing control precision. This innovative utilization of SPT, PPC and NNs constitutes the first key contribution of our paper._  
**Remark 3**. : _This paper presents a novel composite-learning OBE algorithm for online updating of NN weights. Diverging from conventional OBE methods in the literature [13; 32; 33; 34], which solely utilize identification error, our approach uniquely integrates tracking errors from both the slow and fast subsystems into the weight updating process. This innovative strategy yields substantial enhancements in identification and tracking precision, constituting the second major contribution of this study._