esa-sceva/satcom-chunk-collection huggingface dataset:
# Mdpj _sciences_
## 5 Pollutant Reduction Results
### CO\({}_{2}\) Reduction  
Among the studies exploring the potential to reduce CO\({}_{2}\), [PERSON] et al. [28] conducted laboratory tests within a glass chamber on an active green wall biofililtration system, examining the gas absorption capacity of multiple plant species under different light intensities, with and without ventilation. The results showed that species such as _C. comosum_, _E. aureus_, and _Gibasis_ sp. could absorb CO\({}_{2}\) even at lower light intensities, and substrate ventilation slightly increased the CO\({}_{2}\) removal rate. Notably, _C. comosum_ under high light intensity and substrate ventilation reduced the largest CO\({}_{2}\) volume, approximately 2999 mL h\({}^{-1}\) (with light level 250 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) and fan speed 3.5 m s\({}^{-1}\)). Similarly, [PERSON] and [PERSON] [23] found greater CO\({}_{2}\) reduction at higher light intensities in laboratory analyses, achieving a 4.8% absorption rate with a green wall system, albeit without assisted ventilation. [PERSON] et al. [22] presented field performance results of a living wall in an occupied urban building, with CO\({}_{2}\) levels reduced by 49.9-68.8 \(\upmu\)g m\({}^{-3}\), or about 12-17%, compared to measurements in a similar area without a living wall (Figure 9).  
Figure 9: Maximum percentage of CO\({}_{2}\) that studies have succeeded in reducing [22; 23].
## 3 Results and Discussion
### Signal-to-Noise Ratio  
Figure 7 shows the SNR simulation results for the proposed CSK-based UM-VLC channel model and compares it to the same reference channel model defined in Section 3.2. In Figure 7a-c, the SNR for the red, green, and blue PD is shown in the simulation space for the proposed model. Figure 7d shows the SNR for the reference model. Figure 7e presents the ECDF for the SNR of the three PDs and for the reference model. From these graphs, it is clear that the reference model can maintain a much larger SNR over the environment: most of the SNR values for the proposed model lie in the 10 to 30 dB range, whereas for the reference model, the range is 40 to 60 dB.  
Figure 7: Signal-to-noise ratio. (**a**) Red PD. (**b**) Green PD. (**c**) Blue PD. (**d**) Reference model. (**e**) Empirical CDF for SNR at red, green, and blue PD in the proposed model, and for SNR in the reference model.
## 2 Experimental Procedure  
### Materials and Methods  
The ionic liquids, 1-ethyl-3-methylimidazolium bis (fluorosulfonyl) imide (EMIFISI), 1-ethyl-3-methylimidazolium bis (trifluoromethylsulfonyl) imide (EMITFSI), \(N\)-trimethyl-\(N\)-butyl-ammonium bis (fluorosulfonyl) imide (N\({}_{1114}\)FSI), \(N\)-trimethyl-\(N\)-butyl-ammonium bis (trifluoromethylsulfonyl)imide (N\({}_{1114}\)TFSI), \(N\)-trimethyl-\(N\)-butyl-ammonium (nonafluorobutylsulfonyl) (trifluoromethylomethylsulfonyl) imide (N\({}_{1114}\)IM\({}_{14}\)), \(N\)-methyl-\(N\)-propylpiperidinium bis (trifluoromethylomethylsulfonyl) imide (PIP\({}_{13}\)TFSI) and \(N\)-butyl-\(N\)-methylpiperidinium bis (trifluoromethylomethylsulfonyl) imide (PIP\({}_{14}\)TFSI), were synthetized and purified according to an eco-friendly method, which has been described in detail elsewhere [17; 20; 21].  
The Na-conducting electrolytes were prepared by dissolving the sodium salt, namely NaClO\({}_{4}\) (Merck KGaA, Darmstadt, Germany, >99 wt.%) and NaTFSI (3M, Saint Paul, MN, USA, >99.9%), in the IL materials (NaX:IL mole ratio = 1:4) [17] by magnetic stirring for a few hours.  
The carbon-based working electrodes were prepared by mixing 70 wt.% Super C45 (IMERYS, Bodio, Switzerland) and 30 wt.% Na-carboxymethylcellulose (CMC, Dow Wolff Cellulos GmbH, Bomlitz, Germany) in deionized water. The obtained slurry was casted onto aluminium foil (20 um thickness), and the aqueous solvent was allowed to extensively evaporate at room temperature for 24 h (within an extractor hood). Then, disks with a 10 mm diameter (and 50 um thickness) were punched from the electrode tape and vacuum-dried (using an oil-free pump) at 150 \({}^{\circ}\)C (in a glass oven) overnight.  
The HC and NMO, prepared according to a methodology reported in detail elsewhere [18; 19], were punched as 10 mm diameter disks (supported on 20 um thick Al foil), and then vacuum-dried at 120 \({}^{\circ}\)C overnight. The active material content is equal to 75 (HC) and 80 (NMO) wt.%, with a mass loading around 1.0 (HC) and 2.0 (NMO) mg cm\({}^{-2}\). This corresponds to a capacity of 0.3 (HC, theoretical capacity equal to 300 mA h g\({}^{-1}\)) and 0.4 (NMO, theoretical capacity equal to 208 mA h g\({}^{-1}\)) mA cm\({}^{-2}\).  
Both the electrolyte and electrode materials, as well as the cell manufacturing, were stored and handled in an Ar-filled dry box (Jacomex, Dagneux, France, O\({}_{2}\) and H\({}_{2}\)O level < 1 ppm).
## 3 Calculation Mesh and Boundary Condition Setting
### Independence Analysis
#### 3.3.2 Time Step Independence Analysis  
The full name of the CFL condition is the Courant-Friedrich-Lewy condition. The CFL condition is an important parameter for the stability and convergence of numerical calculation, so it is a very important condition in computational fluid dynamics [39]. In the three-dimensional case, the CFL condition is:  
\[C=\frac{u_{x}\Delta t}{\Delta x}+\frac{u_{y}\Delta t}{\Delta y}+\frac{u_{z} \Delta t}{\Delta z}\leq C_{\text{max}} \tag{1}\]  
In the formula, \(u_{x}\), \(u_{y}\), and \(u_{z}\) are the velocities in the \(x\), \(y\), and \(z\) directions during the mesh movement; \(\Delta t\) is the time step; \(\Delta x\), \(\Delta y\), and \(\Delta z\) are the minimum mesh size of the calculation mesh in the x, y, and z directions. Generally, the value of \(C_{\text{max}}\) is 1, which physically means that the displacement of fluid in unit time step cannot exceed the unit length of the calculation mesh, otherwise the calculation may diverge.  
In this paper, two time steps of 0.001 s and 0.0001 s are selected for transient calculation, which can meet the CFL condition.  
At the same time, in order to give consideration to the computational efficiency of the flow field simulation, two time steps of 0.001 s and 0.0001 s are used to analyze the step independence, taking the pitching maneuver of the conventional submerged inlet as an example. Let the mesh in the internal rotation domain conduct a simple pitching maneuver around the y-axis. The pitching angle changes in a sine motion. The average angular velocity of the sine motion is 60\({}^{\circ}\)/s and the amplitude is 30\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mesh Scheme**} & \multicolumn{2}{c}{**Number of Mesh**} & \multirow{2}{*}{**Total Pressure at outlet**} & \multirow{2}{*}{**Error**} \\  & **(Million)** & & **of the Inlet (Pa)** \\ \hline
1 & 0.9 & 68,696.44 & 0.00639 \\
2 & 1.2 & 68,872.64 & 0.00384 \\
3 & 3 & 69,108.51 & 0.00043 \\
4 & 5 & 69,097.21 & 0.00059 \\ experimental result [38] & / & 69,138.49 & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different mesh schemes and experimental results.  
Figure 7 shows the calculation results of the total pressure recovery coefficient at the outlet of the inlet under two time steps of 0.2 to 0.5 s. It can be seen from Figure 7 that the calculation results of the two step sizes are almost the same.  
The average total pressure recovery coefficient in 0.2-0.5 s is shown in Table 2. When the step length is 0.001 s, the average total pressure recovery coefficient during 0.2 to 0.5 s is 0.966. When the step length is 0.0001 s, the average total pressure recovery coefficient is 0.968 during 0.2 to 0.5 s. Based on the calculation result with a step of 0.0001 s, the error of the calculation result with a step of 0.001 s is only 0.2%, which means that a step of 0.001 s is sufficient to accurately capture the flow field details. Considering the computational efficiency and accuracy, the step size used in the transient calculation is 0.001 s.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 5 Secure Multi-Party Computation Protocol Based on NFHE Scheme
### Performance and Comparison  
References [11; 12] are both single-bit SMC protocols. If \(B\) is the number of input bits, then the two schemes need to be repeated \(B\) times. Reference [16] and the SMC protocol in this paper both support multi-bit encryption, which only needs to be executed once.  
Compared with the protocol in reference [16], the protocol constructed in this paper has the following two improvements.  
(a) From the perspective of efficiency, the protocol constructed in this paper improves the ciphertext size \((n+1)^{2}\lceil\log q\rceil^{2}\) by modifying the expansion method of the GSW13 scheme, and the obtained cipher-text size is \(\frac{(n+1)^{2}\lceil\log q\rceil^{2}}{\lfloor\log p\rceil}\). In addition, under the multi-bit encryption, the ciphertext size is \(\frac{(n+8)^{2}\lceil\log q\rceil^{2}}{\lceil\log p\rceil}\), as shown in Table 1. Therefore, the performance of the protocol under the existing CRS model is the best.  
(b) Moreover, in terms of storage overhead, the ciphertext of the NFHE scheme based on the SMC protocol in this paper is a matrix, so the ciphertext expansion rate is also \(O(1)\). At the same time, since the size of the ciphertext is much larger than the size of the key, the protocol in this paper effectively reduces the storage overhead of the system by greatly compressing the size of the ciphertext.  
The main performance comparison of the existing secure multi-party computation protocols based on fully homomorphic encryption under the CRS model is shown in Table 1, where \"Basic\" represents the basic fully homomorphic encryption scheme used in the protocol; \"Rd\" represents the protocol Interactive rounds; \"CTE Ratio\" represents the ciphertext expansion ratio; \"Depth\" represents the complexity of the NAND gate; and the last column \"Ciphertext Size\" represents the size of the ciphertext. \(B\) is the number of input bits; \(N\) is the number of users; \(n\) is the lattice dimension; \(d\) is the NAND depth of the circuit to be evaluated; \(\omega<2.3727\) is a constant; \(q\in\mathbb{Z}\) is a modulus; and \(p\) is a power of 2.
# Analysis and Experimental Test of Electrical Characteristics on Bonding Wire
## 2 Bonding Wire Model
### Geometric Parameters  
S-loop wire is the most common bonding wire. S-loop wire that has a longer flat distance then breaks is mainly used in two conditions: when the actual loop length of wire is too long, or the welding wire arc height should be under control in some devices. S-loop can prevent the sweep and sag problem when epoxy compound flows during the transfer injection molding process because of its strong stability and stiffness. The geometric parameters of the S-loop wire are shown in Figure 2 (d is the diameter, L is the span, L1 is the flat distance of S-loop wire, H1 is the bonding height of the arch and H2 is the connection height).  
In this section, the influence of the geometrical parameters on the scattering parameters is discussed. The four geometric parameters factors are the ratio of the L1/L, the diameter d, the span L and the bonding height H1. Finally, by comparing the scattering parameters, the transmission characteristics of Q-loop, S-loop and M-loop are analyzed and derived.
## 4 Experimental Process and Result Analysis
### Comparative Experiments
#### 4.4.2 Comparison of the Presented Method with the Current State-of-the-Art Method  
To our knowledge, the current state-of-the-art methods are the long short-term memory network model studied by [PERSON] et al. [30] and the convolutional neural network model designed by [PERSON] et al. [32] when dealing with the GNSS positioning error estimation problem. Therefore, the proposed method is compared with two state-of-the-art GNSS positioning error estimation methods to show the superiority of the presented method in the second comparative experiment.  
The LSTM model is a method commonly applied to predict time series data. It usually consists of input gate, forget gate, output gate, and cell state. Compared with the MLP model, it requires additional gating units and loop connections. The CNN models are mostly used to process image data, and are not suitable for processing data containing geographic information. It can be seen from Table 4 that the RMSE value of the presented method is significantly smaller than that of the other two methods, especially compared with the LSTM method. The proposed method can adaptively choose which features to retain based on the importance of the input features. This characteristic can improve the representation ability and generalization performance of the model to a certain extent. Since the experiment was conducted with the same dataset, the results of the comparative experiment are convincing to a certain extent.
# Autonomous Obstacle Avoidance with Improved Deep Reinforcement Learning Based on Dynamic Huber Loss
## 3 A DRL Framework Based on Dynamic Huber Loss and Soft Updates
### Loss Function
#### 3.2.2 Smooth L1 Loss  
Smooth L1 loss is a loss function commonly used in regression tasks. It is a smoothed version of MAE and MSE. The expression for the Smooth L1 loss is as follows:  
\[L_{\text{Smooth L1}}(\theta)=\left\{\begin{array}{ll}\frac{1}{2}\varepsilon ^{2}&\quad\text{if }|\varepsilon|<1\\ |\varepsilon|-\frac{1}{2}&\quad\text{otherwise}\end{array}\right. \tag{11}\]  
For the neural network parameter \(\theta\), the gradient is as follows:  
\[\
abla_{\theta}L_{\text{Smooth L1}}(\theta)=\frac{\partial L_{\text{Smooth L1}}}{\partial\varepsilon}=\left\{\begin{array}{ll} \varepsilon&|\varepsilon|<1\\ sign(\varepsilon)&|\varepsilon|\geq 1\end{array}\right. \tag{12}\]  
When \(|\varepsilon|<1\), \(\
abla_{\theta}L_{\text{Smooth L1}}(\theta)=\varepsilon\), according to the relationship between the derivative of the Lipschitz continuous function and the Lipschitz constant, the Lipschitz constant \(L_{1}=1\) at this time; when \(|\varepsilon|\geq 1\), the gradient is \(\
abla_{\theta}L_{\text{Smooth L1}}(\theta)=sign(\varepsilon),g_{t}^{2}=1\), and its derivative is 0. However, from the perspective of Lipschitz continuity, the overall Lipschitz constant is relatively large at the boundary \(|\varepsilon|=1\) due to the gradient mutating from \(\varepsilon\) to \(sign(\varepsilon)\). It can be shown that it does not satisfy the condition for a smaller Lipschitz constant by constructing a counterexample, taking \(\theta_{1}=1-\varepsilon\), \(\theta_{2}=1+\varepsilon\), \(\varepsilon\to 0^{+}\) and calculating \(\frac{\|\
abla L_{\text{smooth, Li}}(\theta_{2})-\
abla L_{\text{smooth, Li}}(\theta_{1})\|}{\|\theta_{2}-\theta_{1}\|}\). When \(\varepsilon\to 0^{+}\), the ratio tends to infinity, indicating that there is no smaller Lipschitz constant. When \(|\varepsilon|\geq 1\), \(m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})sign(\varepsilon)\); when there are more \(|\varepsilon|\geq 1\) points in the dataset, \(m_{t}\) will fluctuate drastically between \(\beta_{1}m_{t-1}\pm(1-\beta_{1})\), and this fluctuation will affect the stability of the parameter update. According to the recursive formula, \(v_{t}=(1-\beta_{2})\sum\limits_{i=0}^{t-1}\beta_{2}^{i}+\beta_{2}^{i}v_{0}\); when \(t\) is large, \(v_{t}\approx\frac{1-\beta_{2}}{1-\beta_{2}}=1\), and its update stabilizes relatively quickly. This stabilization is due to the jumps in the gradient, which does not reflect the true gradient change well. Due to the large fluctuation of \(m_{t}\) and the rapid convergence of \(v_{t}\) to 1, the parameter updating step \(\Delta\theta_{SmoothL1}\) has a large variation amplitude in different iterations, and even the parameter updating deviates from the correct direction due to the abnormal fluctuation of \(m_{t}\), which affects the convergence speed and convergence accuracy.
### Degradation Mechanism Causing Physical Property Changes to Grout-Injection Materials  
Using only data based on the physical property changes of grout-injection materials, it is difficult to assess whether the grout-injection materials will be able to withstand the deterioration conditions and to maintain a stable waterproofing performance [5]. To address this issue, a new comparative evaluation criterion is proposed in this study through a technical evaluation criterion that will allow a facilitated degradation factor resistance performance comparison for the selection of an optimal type of grout-injection repair material in compliance to ISO TR 16475:2011 _Guidelines for the Repair of Water-Leakage Cracks in Concrete Structures_[6]. This standard identifies the required performance criteria of grout-injection materials' responses to the six types of degradation in below-grade concrete structures. Refer to Figure 2 below for the details of the six degradation factors.  
Figure 1: The six types of degradations applied to waterproofing membranes in below-grade structures.  
Currently there are six separate test methods (thermal resistance, chemical resistance, washout resistance, wet surface adhesion strength, hydrostatic pressure resistance, and substrate behavior resistance) outlined in the ISO TS 16774 _Test Methods for Repair Materials for Water-Leakage Cracks in Underground Concrete Structures_[7]. In reference to these existing test methods, a technical evaluation regime that independently measures the physical property changes of four types of grout-injection materials due to six environmental degradation factors is proposed. This evaluation regime addresses the aforementioned concerns and proposes a new comparison regime based on the test results of three different products for each of four types of grout-injection materials (acrylic resin, epoxy resin, polyurethane foam, and synthetic polymerized rubber gel).  
Figure 2: The six types of expected degradation conditions in the below-grade environment; (**a**) the cyclic thermal stress on concrete and waterproofing membrane, (**b**) a chemical attack on the waterproofing, (**c**) erosion due to hydrostatic pressure or water flow, (**d**) a membrane wet surface due to humidity, (**e**) hydrostatic pressure damage to the waterproofing membrane, and (**f**) the zero-span tension and shear stress applied on a waterproofing membrane.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 5 Simulation Results
### Path Loss  
During the transmissions in the acoustic model, energy loss occurs along the propagation paths. The primary mechanisms for the energy loss are: geometric spreading, absorption loss and scattering loss. Geometric spreading is defined as a sound wave moving away from the node; the area that the sound energy covers becomes larger, and as a result, the sound intensity decreases. The underwater medium for propagation is dense as compared to the terrestrial one, so when an acoustic signal propagates from the source node towards the MS and the distance between them is great, the power loss is caused by the geometric spreading, which is directly proportional to the square of the distance. When the source node transmits the signal, it may be converted to other forms and absorbed by the medium. In the case of acoustic signals, the absorption coefficient is calculated in [10]. Scattering is a physical process where non-uniformities in the medium, like particles and bubbles, force the signal to deviate from its trajectory. Due to the dense underwater channels, there are more chances of scattering. Scattering causes path loss, as well as end-to-end delay.  
In Figure 11, path losses are shown. In 3D-SM, path loss is less in comparison. The whole plot of 3D-SM does not change as drastically as AUV-PN and DDRP. AUV-PN is a cluster based with MS routing protocol; path loss linearly increases at a higher rate in comparison, up till the NLT. After that, around 70,000 s, its behavior slightly varies; however, when the number of dead nodes is increased, the path loss also increases because the network becomes sparse. In DDRP, during the NLT, there is a slight increase in the path loss; however, after the death of the first node, the path loss decreases, because nodes start dying, which leads to an increasing time and abrupt change in path loss when the network is sparse. In 3D-SM, the NLT is longer compared to the existing schemes due to balanced transmissions between nodes and the MS (or CNs). The network is designed in such a way that nodes transmit the sensed data when the distance from the MS (or CN) is the least and avoid path loss.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Features**}} & **1990** & **1990** & **1993** & **2008** & **2010** & **2012** & **2015** & **2016** & **2018** \\  & **[PERSON] et al.** [11] & **[PERSON] et al.** [19] & **[PERSON]** & **Vallance** & **[PERSON] et al.** [20] & **[PERSON] et al.** [21] & **[PERSON] et al.** [13] & **[PERSON] et al.** [13] & **[PERSON] et al.** [14] & **[PERSON] et al.** [22] \\ \hline Operation & Discharge & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ mode & Charge & Yes & No & No & No & Yes & Yes & No & Yes & Yes & No \\ \hline Cell & Fe & Yes & No & Yes & Yes & No & Yes & Yes & Yes & Yes \\ chemistry & Ni & No & Yes & No & No & Yes & No & Yes & Yes & No \\ \hline Cell & Planar & No & No & Yes & No & No & No & No & No & No \\ geometry & Radial & Yes & Yes & No & No & No & Yes & No & Yes & Yes \\  & Cloverleaf & No & No & No & Yes & No & Yes & No & No \\ \hline \multirow{4}{*}{Processes} & Porosity & Yes & No & Yes & Yes & No & Yes & Yes & Yes & Yes \\  & Na\({}^{+}\) & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\  & Transport & & & & & & & & & \\ MCl\({}_{2}\) & & Yes & No & No & Yes & Yes & Yes & No & No \\  & lattice & & & & & & & & & \\  & Heat & & & & & & & & & \\  & formation & & & & & & & & & \\ \hline Validation & & No & Yes & Yes & Yes & No & No & Yes & No & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature overview of _Na-NiCl\({}_{2}\)_ and _Na-FeCl\({}_{2}\)_ cell modelling. The aim of this work is to develop a model representing all relevant cell processes with a small calculation effort. In addition to the implementation of the second reactions (Equations (1) and (2)), the model should be capable of representing the charging and discharging of a _Na-MCl\({}_{2}\)_ cell with cylindrical geometry. With the advantages described above, the approximation approach of Orchard and Weaving [15] will serve as a basis for the presented model, while the requirements of a cell working with both active materials will be addressed.