esa-sceva/satcom-chunk-collection huggingface dataset:
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 6 Numerical Simulation Results Analysis
### Impact of the Unknown Sound Position on Estimation Results  
In previous simulations, it was assumed that the source position was known. However, in practical scenarios, deviation in the target position can occur. Under such conditions, it is necessary to evaluate the performance of radiated noise estimation at different assumed positions. Figure 7 presents the estimation results of the radiated noise power using channel transfer functions at different positions.  
It can be observed that the position with the highest estimated power corresponds to the actual position of the sound source, and the estimation result matches the true power of the radiated noise. It indicates that the proposed MLE method remains applicable even when the source position is unknown.  
Figure 6: Radiated noise estimation results at different frequencies, (**a**) estimation results, (**b**) estimation variances.  
Figure 7: Radiated noise estimation results at different assumed positions.
# Boundary SPH for Robust Particle-Mesh Interaction in Three Dimensions
## 2 Materials and Methods
### Single Particle-Triangle Scenario
#### 2.3.3 Calculating the Signed Distance  
The signed distance from particle to triangle can be combined into a single expression as provided by [PERSON] and [PERSON] [31]:  
\[d_{p,t}=\|(p-proj_{p,t})\|sign((p-proj_{p,t})\cdot N_{b}) \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Region** & **Check** & **Outputs** \\ \hline \(R_{c}\) & \(\begin{array}{c}(proj_{p}-v_{c})\cdot E_{1}\leq 0\text{ and }\\ (proj_{p}-v_{c})\cdot E_{2}\leq 0\end{array}\) & \(proj_{p,t}=v_{c}\) \\ \hline \(R_{1}\) & \(\begin{array}{c}(proj_{p}-v_{c})\cdot N_{1}\geq 0\end{array}\) & \(proj_{p,t}=v_{c}+\frac{(proj_{p}-v_{c})\cdot E_{1}}{E_{1}-E_{1}}E_{1}\) \\ \(N_{b}\) is \(E_{1}\)’s pseudonormal vector. \\ \hline \(R_{2}\) & \(\begin{array}{c}(proj_{p}-v_{c})\cdot N_{2}\geq 0\end{array}\) & \(proj_{p,t}=v_{c}+\frac{(proj_{p}-v_{c})\cdot E_{2}}{E_{2}-E_{2}}E_{2}\) \\ \(N_{b}\) is \(E_{2}\)’s pseudonormal vector. \\ \hline \(R_{t}\) & Other checks fail; default condition & \(\begin{array}{c}proj_{p,t}=proj_{p}\\ N_{b}\text{ is the face pseudonormal vector. }\end{array}\) \\ \hline \end{tabular}
\end{table}
Table 2: Region checks to identify which Voronoi region \(proj_{p}\) lies within.  
Figure 5: Example process of checking which Voronoi region \(proj_{p}\) lies in. Regions are colored to distinguish them from one another in the visualization, and the red dot represents \(proj_{p}\). The process starts with Panel (**a**) and shows that region \(R_{1_{t}}\) is rejected. Panels (**b**) and (**c**) show the same with regions \(R_{1_{t},2_{t}}\) and \(R_{1_{t},3_{t}}\), respectively. The algorithm ends in Panel (**d**) where \(proj_{p}\) is determined to be in region \(R_{t}\). Therefore, \(proj_{p,t}=proj_{p}\) and \(N_{b}=N_{t}\).  
This assumes that we are looking at a singular point without a radius to consider and that the signed distance is negative if the point is contained inside the mesh boundary. However, in the SPH scheme, we are dealing with particles with radius \(r_{p}\). To prevent the kernel truncation issue, we need to detect the moment the smoothing kernel intersects with the boundary manifold. Therefore, we provide an alternative form of the signed distance function in consideration with \(r_{p}\):  
\[d_{p,t}=\|(s-proj_{p,t})\|sign((s-proj_{p,t})\cdot N_{b}) \tag{4}\]  
where \(s=p-N_{b}r_{p}\), which represents the innermost point on the smoothing kernel of particle \(p\). We follow the convention that the negative sign indicates that the particle's smoothing kernel is submerged into the boundary manifold, while a positive sign indicates that the particle and its smoothing kernel are outside the boundary manifold.
## 4 Data Processing and Features
### Reinforcement Learning
#### 5.3.2 Deep Q-Learning (Q-Learning with Neural Networks)  
Deep Q-Learning saves all previous events in memory and defines future action using Q-Network output. Thus, the Q-network obtains the Q-value at state \(\mathrm{s_{t}}\), and the target network (Neural Network) calculates the Q-value for state \(\mathrm{S_{t+1}}\) (next state) to stabilize training and stop abrupt Q-value count increases by duplicating it as training data on each iterated Q-value of the Q-network [4; 9; 23; 32; 35; 39; 41; 42; 43; 48; 55; 56; 58; 60; 67; 70; 71]. Table 12 shows the advantages and disadvantages of using reinforcement learning in quantitative finance. Figure 10 shows the basic structure of deep Q-Learning. Deep Q-learning is a technique that is used to train artificial intelligence agents to function in settings that have distinct action spaces.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of Method** & \multicolumn{2}{c}{**Advantages**} & **Disadvantages** & **Article** \\ \hline \multirow{6}{*}{RL/DRL} & \multirow{6}{*}{\begin{tabular}{c} + \\ \end{tabular} } & Models that use & \multirow{6}{*}{\
\begin{tabular}{c} - \\ \end{tabular} } & \multirow{6}{*}{
\begin{tabular}{c} Large datasets are \\ required for \\ reinforcement learning to \\ obtain more accurate \\ benchmarks and \\ judgments \\ \end{tabular} } \\  & & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: Advantages and Disadvantages of using RL Techniques to predict stock market.  
Figure 9: Q-Learning—Simple Reinforcement Learning method that employs Q-values (action values) to enhance the learning agent’s behavior.
## 4 Discussion
### Top and Deeper Hardness Measurements  
The air-inhibited layer depth range, primarily composed of unreacted monomers, varies from 4 \(\upmu\)m to 60 \(\upmu\)m and can influence surface hardness due to its less-polymerized nature [2,44,45]. To minimize bias from this layer, microhardness at a 0.5 mm depth was used as the reference for top hardness in this study, as this represents the maximum expected Vickers Hardness Number (VHN). This initial microhardness depth measurement is well below the oxygen-inhibited layer. Previous research by [PERSON] et al. [22] and [PERSON] et al. [44] has shown that bottom surface hardness can sometimes exceed top surface hardness when unpolished due to a resin-rich layer. By using the 0.5 mm depth as a reference, potential surface irregularities affecting hardness measurements were reduced. Additionally, some bulk-fill composites tested here showed higher microhardness values at different depths than at the 0.5 mm reference, but no significant differences in the microhardness ratios were observed. This was noted in SDR at the 2 and 3 mm depths with Bluephase Style, Valo, and QTH LCUs; in Fitlek Bulk Fill and Venus at depths of 2 and 3 mm for all LED LCUs; in Sonicfill at a 2 mm depth for Bluestyle, Demi Ultra, and QTH; in Tetric Evo Ceram Bulk Fill up to a 5 mm depth with Demi Ultra; and in Xtra-Base with the QTH LCU [46]. [PERSON] and [PERSON] [47] also reported higher subsurface mechanical properties in low-filled resin composites, attributing this to material shrinkage towards the center of the restoration or enhanced polymerization at deeper levels where the overlying material insulates the exothermic reaction.
## Abbreviations  
AHRS Attitude and Heading Reference System  
AMSL Above Mean Sea Level  
BDS BeiDou Navigation Satellite System  
CAN Controller Area Network  
ENU East-North-Up coordinate system  
FAO Food and Agriculture Organization  
FMIS Farm Management Information Systems  
GGA Format of essential position data sent via serial port  
GLONASS Russia's Global Navigation Satellite System
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 3 Common Classification of Photocatalysts
### Non-Oxide Photocatalyst
#### 3.2.3 ZnS Series Photocatalyst  
ZnS is a group of II-VI wide band gap semiconductor materials with a band gap between 3.6 and 3.8 eV. ZnS has two different crystal structures--zinc blende and wurtzite. Among them, zinc blende, also known as \(\beta\)-ZnS, can stably exist at low temperatures, while wurtzite, also known as \(\alpha\)-ZnS, can exist stably at temperatures above 1024 \({}^{\circ}\)C. ZnS is difficult to oxidize and hydrolyze. More importantly, these properties are still present when the size of ZnS is reduced to the nanometer scale. Thus, ZnS nanomaterials exhibit good photocatalytic activity [110]. Furthermore, ZnS is easily fabricated, non-toxic, and widely used. To date, many research groups have successfully synthesized low-dimensional nanomaterials such as ZnS nanoparticles [111; 112; 113; 114; 115; 116; 117], nanowires [118; 119; 120; 121; 122], nanotubes [123; 124; 125; 126], and nanosheets [127; 128].  
Among the methods for enhancing the photocatalytic activity of ZnS, the following three are the most common approaches. (1) The specific surface area is increased by changing the morphology of ZnS [129; 130; 131]. The high specific surface area can increase the active site on the ZnS surface and can increase the contact area between ZnS and the reactant, thereby increasing the photocatalytic activity of ZnS. (2) By altering the electronic properties and band structure of ZnS through doping with other metals and non-metallic elements [132], the absorption rate of ZnS to visible light is improved, thereby improving the photocatalytic efficiency of ZnS. (3) ZnS forms a heterojunction with other semiconductors [133] or a composite structure with noble metals [134] to reduce the electron-hole recombination rate, thereby improving the photocatalytic performance of ZnS.
## 3 Results and Discussion
### Applications  
It is expected that the modeling approaches in this study would contribute to estimating the life of a media layer via the correlations of specific SS deposit (\(\sigma\)) with filtration coefficient (\(X_{\sigma}\)), the maximum specific SS deposit (\(\sigma_{m}\)), and SS removal rate constant (\(X_{\sigma}\)). The time variation of the SS in runoffs during a rainfall event was not considered, as mentioned above. However, the possibility of more improvement was confirmed by the successful description displayed in Figures 3, 5E and 6C, under irregular influent SS concentration.  
In addition, the result of this study would also contribute to the further study by providing important information about the major consideration for the description and prediction of the performance of stormwater filtration systems, which are the dependence of \(k_{a}\), \(k_{\sigma}\), \(\sigma_{m}\), \(k_{\sigma}\), and \(X_{\sigma}\) on filtration velocity, i.e., flowrate (Figures 3, 5E and 6C), and the dependence of \(k_{a}\), \(k_{\sigma}\), \(\sigma_{m}\), and \(X_{\sigma}\) on head loss (Figure 7).  
Moreover, the modeling approaches in this study would contribute to the evaluation of a stormwater filtration system in a laboratory. In many countries, a stormwater runoff treatment system is evaluated in a laboratory by relevant authorities before installation, because it is hard to monitor the performance on site [29, 30, 35, 36]. The major information to be obtained are the life and critical specific SS deposit. However, they can hardly be obtained by the results of experiments performed in a short period. Therefore, a number of laboratory studies have been performed to evaluate the performance of stormwater runoff filtration systems [11, 16, 37, 43, 44, 45, 46].
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
###### Abstract  
The Actuator Disc/Navier-Stokes (AD/NS) method has played a significant role in wind farm simulations. It is based on the assumption that the flow is azimuthally uniform in the rotor plane, and thus, requires a tip loss correction to take into account the effect of a finite number of blades. All existing tip loss corrections were originally proposed for the Blade-Element Momentum Theory (BEMT), and their implementations have to be changed when transplanted into the AD/NS method. The special focus of the present study is to investigate the performance of tip loss corrections combined in the AD/NS method. The study is conducted by using an axisymmetric AD/NS solver to simulate the flow past the experimental _NREL Phase VI_ wind turbine and the virtual _NREL 5 MW_ wind turbine. Three different implementations of the widely used Glauert tip loss function \(F\) are discussed and evaluated. In addition, a newly developed tip loss correction is applied and compared with the above implementations. For both the small and large rotors under investigation, the three different implementations show a certain degree of difference to each other, although the relative difference in blade loads is generally no more than 4%. Their performance is roughly consistent with the standard Glauert correction employed in the BEMT, but they all tend to make the blade tip loads over-predicted. As an alternative method, the new tip loss correction shows superior performance in various flow conditions. A further investigation into the flow around and behind the rotors indicates that tip loss correction has a significant influence on the velocity development in the wake.  
wind turbine aerodynamics; actuator disc; AD/NS; tip loss correction; blade element momentum +
Footnote †: journal: Applied Mechanics and Physics Letters
## 4 Discussion
### The Impact of Soil C and N Contents  
Soil C and N contents have also been mentioned in previous studies as one of the factors affecting GHG emissions from tree stems. The formation of N\({}_{2}\)O requires high soil N concentration and favorable conditions for oxidation-reduction reactions. In naturally wet peat soils, water has low oxygen (O\({}_{2}\)) content, reactions occur slowly and the amount of N\({}_{2}\)O released is low [66]. The results of a study carried out in peatlands in Finland show that drainage of peat soil rich in nutrients (including N) increases the O\({}_{2}\) content in soil and thus the rate of mineralization of N, creating favorable conditions for nitrification and promoting the formation of N\({}_{2}\)O. NH\({}_{3}\) not used by plants can be denitrified to NH\({}_{4}\)\({}^{+}\) in the reduction process, which also leads to the release of N\({}_{2}\)O. The lower the C:N ratio was, the more the N\({}_{2}\)O increase was detected when the C:N ratio exceeded 30, whereas, N\({}_{2}\)O emissions increased exponentially with decreasing C:N ratio when the study site was dry--the groundwater level was lower than 30 cm. Additionally, N concentration in peat correlated positively with N\({}_{2}\)O emissions, but the correlation was not as pronounced as for the C:N ratio [31]. In a study carried out in Estonia, in a grey alter stand, the soil NO\({}_{3}\)\({}^{-}\) concentration was positively and NH\({}_{4}\)\({}^{+}\) concentration was negatively related to N\({}_{2}\)O emissions from stems at different heights. A significant relationship was identified between the N\({}_{2}\)O fluxes from tree stems and nitrogen availability in soil [15]. In our study, higher C and N concentrations in soil were positively associated with N\({}_{2}\)O fluxes from silver birch and black alter stems.
# Cooperative Multi-Robot Task Allocation with Reinforcement Learning
## 4 Multi-Robot Task Allocation Methods
### Reinforcement Learning for MRTA
#### 4.1.2 Optimization  
We train the reinforcement learning model based on the policy gradient method with baseline. It is usual to use a critic network to estimate the value function. However, as noted in [22], using the baseline instead of a critic network is a better choice to solve combinatorial optimization. Therefore, we also construct baseline \(b(s)\) as the moving average of makespan while training. We also tried critic-network. However, training the value network made the training unstable. We optimized with Proximal Policy Optimizer (PPO) which clips the loss not to diverge. PPO takes the loss as the minimum value of the clipped advantage and the advantage:  
\[L(\pi_{\theta}|s_{t})=\text{min}(\frac{\pi_{\theta}(a_{t}|s_{t})}{\pi_{\theta _{old}}(a_{t}|s_{t})})\hat{A}_{t},\,\text{clip}(\frac{\pi_{\theta}(a_{t}|s_{t} )}{\pi_{\theta_{old}}(a_{t}|s_{t})},1-\epsilon,1+\epsilon)\hat{A}_{t}) \tag{19}\]  
\[\hat{A}^{t}=G_{t}-b(s_{t}) \tag{20}\]  
Figure 4: What the model learns. The representation of robots and tasks includes the relationship information between robots and tasks. In this figure, \(T_{1}\) is the most important task for the robot \(R_{1}\) and \(T_{3}\) is the most important task for the robot \(R_{2}\). The rectangle blocks in the cross-attention block are the vector representation of robot and task. The gradient on color denotes the importance of the task. More red is more important.  
Figure 3: The neural network architecture for the MRTA. The robot to be allocated is \(r_{2}\) and the remaining tasks are \((t_{1},t_{2},t_{3},t_{4})\). Tasks \(t_{4},t_{5}\) are finished tasks and they are not used in the model. After normalizing the logits, we sample action from the probability. Here the robot \(r_{2}\) is allocated to \(t_{k}\) where \(t_{k}\in\{t_{1},t_{2},t_{3},t_{4}\}\). Best viewed in color.  
We update the baseline \(b(s_{t})\) after the end of the episode as the moving average of terminal reward which is \(-\)_makespan_.  
\[b(s_{t})=\Big{(}\frac{i-1}{i}\Big{)}b(s_{t})+\Big{(}\frac{1}{i}\Big{)}R_{T}\ \ \ \text{for episode $i=1,2,3,\cdots$} \tag{21}\]  
The overall training algorithm of our model with baseline \(b(s_{t})\) is in Algorithm 2.  
```
1:Inputs: A differentiable policy parameterization \(\pi(a|s,\theta)\), Batch size \(B\), and number of gradient descent \(S\), discount rate \(\gamma\)
2:Initialize policy network \(\theta\)
3:\(b\gets 0\)
4:for\(i=1,2,\cdots\)do
5: Generate an episode \(S_{0},A_{0},R_{1},\cdots,S_{T-1},A_{T-1},R_{T}\), following \(\pi(\cdot|,\theta_{old})\)
6:\(b\leftarrow(i-1)/i\cdot b+1/i\cdot R_{T}\)
7:for\(t=1,\cdots,T\)do
8:\(G_{t}\leftarrow\sum_{k=t+1}^{T}\gamma^{k-t-1}R_{k}\)
9:endfor
10:for\(j=1,\cdots,S\)do
11:\(g_{\theta}\leftarrow\frac{1}{B}\sum_{i=1}^{B}L(\pi_{j}|s_{j},b)\
abla_{\theta }\log p_{\theta}(\pi_{j}|s_{j})\)
12:\(\theta\gets ADAM(\theta,g_{\theta})\)
13:endfor
14:\(\theta_{old}\leftarrow\theta\)
15:endfor
```  
**Algorithm 2** Policy Gradient with Surrogate Loss (full episode)
# Multifunctional Integrated Underwater Sound Absorption Materials: A Review
## 3 Corrosion Resistance and Hydrostatic Pressure Resistance Mechanisms of USAMs  
### Corrosion Resistance Mechanism of USAMs and Research Advancements  
The USAMs are required to withstand harsh environments when underwater vehicles are operating at great depths beneath the ocean. There are three recognized main causes of corrosion. (1) Atmospheric corrosion is caused by the interaction between a material and its surrounding atmospheric conditions, such as temperature, humidity, irradiance, chloride ion concentration, salinity, and pollutants. (2) Seawater is a highly corrosive electrolyte solution containing significant amounts of salts, including sodium chloride and salts containing potassium, bromine, iodine, and other elements. (3) Hydrostatic pressure restricts the corrosion mechanism in materials [68; 69; 70; 71; 72; 73]. As a result, marine corrosion causes structural damage and material decay, which impacts the sound absorption coefficient and reduces the underwater vehicle's service life. Therefore, investigating the suitability of sound absorption materials with anti-corrosion properties in harsh environments holds considerable promise for marine equipment applications.  
The protection of underwater vehicles against corrosion is essential to ensure their longevity and performance. Several methods have been utilized to achieve corrosion resistance, including coating protection [74; 75; 76; 77; 78], cathodic protection [79; 80], pitting corrosion method [81], joint protection act [82; 83], and selecting multifunctional materials with anti-corrosion properties. Engineers often integrate sound-absorbing materials and  
Figure 7: (**a**) Proposed overall structure and unit cell of acoustic metamaterial [65]. (**b**) The schematic diagram of single-layer MPPB and double-layer MPPB and metamaterial composed of 11 MPPBs in parallel array [66]. (**c**) The model simplification process of phononic glass [67].  
anti-corrosion materials to meet requirements. [PERSON], [PERSON] et al., prepared a multiwall carbon nanotube addition with a polyurethane coating on phosphate steel, and studied the anti-corrosion and underwater acoustic absorption properties [84]. However, this integration can lead to a substantial increase in cost, weight, and performance issues. Based on current research, ceramic matrix composite materials have shown strong resistance to corrosion compared to ordinary metal materials, resulting in better performance. He, [PERSON] et al., prepared a porous sound-absorbing ceramic matrix that showed a 28.9% increase in sound absorption coefficient for low- and middle-frequency ranges and anti-corrosion properties [85]. Foam glass-ceramics materials were prepared by [PERSON], [PERSON] et al., and demonstrated effective sound absorption and corrosion-resistant characteristics. All sintered samples have a noise reduction coefficient of 0.41, and the percentage change in mass was less than 0.4% after soaking for 35 days in a dilute sulfuric acid with at pH 5 [86].
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 3 Related Research
### Studies Relying on Standard Classifiers
For single-touch gestures, without sensor glove information, the Bayes Net classifier achieves an FAR of 11.96% while maintaining an FRR of 8.53%. The resulting performance is improved when sensor glove information is present to an FAR of 2.15% and an FRR of 1.63%. The paper also proposes an authentication mechanism, which uses sequences of events to strengthen the authentication for data limited to touch-input only. The results show that a touch sequence length of seven results in the best FAR/FRR combination. Furthermore, an authentication threshold of three is determined to produce the best results while still maintaining a good FRR. This means with those final parameters, where from seven consecutive strokes three have to be recognized as valid inputs from the authenticated user, an FAR of 4.66% and an FRR of 0.13% is reached. [PERSON] et al. presented an approach in [27] of continuous user authentication, which is based on Touch Dynamics data. In comparison to the previously evaluated systems, this one is training four different classifiers. The results of the study are reported in terms of accuracy, where vertical gestures gave better results than horizontal swipes. A combination of horizontal and vertical swipes resulted in worse accuracy than only relying on horizontal or vertical data. The used models are a combination from those used in [1,26], except for the k-NN classifier. The study used a total of 15 participants where, for every user, an average of 180 strokes was recorded. As evaluation results, the accuracy of the classifiers and the EER of the system were reported. The best overall accuracy was achieved using the Random Forest classifier, where vertical strokes were scoring 96.7%, horizontal ones 96.36% and combined input resulted in 95.03%. Furthermore, the results show that, when using vertical swipe features from two-handed device usage, the EER is lower (0.0041) than using one-handed device data (0.0309).