esa-sceva/satcom-chunk-collection huggingface dataset:
# Activation of Persulfate for Groundwater Remediation:
## 2 Activation Mechanism of Persulfate for ISCO Application
### Heat Activation  
The activation of persulfate by heating can be described as Equation (4) [36]. As a result of heat absorption, sulfate radicals (SO\({}_{4}\)\({}^{\bullet-}\)) can be generated by the breakdown of sulfate bonds in persulfate [45]. The energies for heat activation under acidic, neutral, and basic conditions are in the range of 100-116 kJ/mol, 119-129 kJ/mol, and 134-139 kJ/mol, respectively [60].  
\[\mathrm{S_{2}O_{8}}^{2-}+\mathrm{heat}\to 2\mathrm{SO_{4}}^{\bullet-} \tag{4}\]  
Extensive bench-scale studies have reported that heat activation significantly enhanced the degradation of the organic pollutants by persulfate, with a considerable removal rate (Table 1). [PERSON] et al. (2013) studied the degradation of polycyclic aromatic hydrocarbons (PAHs) using persulfate activated by heat, alkaline, and iron activations [21]. The results showed that among the three activation methods tested the heat activation was the best and the removal rate of PAHs reached 99.1% at a temperature of 60 degrees C. According to [PERSON] et al. (2011) [61], only 7.6% of TCE was degraded after 10 h of reaction at 20 degrees C. When the temperature was raised from 30 degrees C (31.6%; 10 h) to 50 degrees C (100%; 2 h), TCE degradation was significantly accelerated and TCE was totally removed after two hours. This suggests that the increase of temperature can significantly enhance TCE removal and shorten the remediation time. While extreme temperature speeds up the activation of persulfate, it reduces the degradation efficiency of contamination by persulfate [24]. This has been demonstrated in the study of [PERSON] (2009) [24], where it was reported that the decomposition rate of perfluorooctanoic acid (PFOA) was 99.3% at a temperature of 90 degrees C, while it decreased to 94.1 at a temperature of 130 degrees C [24]. This is because extreme temperatures lead to the formation of significant amounts of radical oxidants that are released rapidly to consume most of the remaining persulfate, thus causing a lower degradation efficiency. To prevent excessive consumption of persulfate at the injection stage, excessive high temperatures should not be adopted in field practices.  
In addition, the optimum temperature that provided the best degradation efficiency appears to differ between pollutants. For instance, Bisphenol A was totally degraded by persulfate at 30 degrees C [62], PAHs were completely degraded by persulfate at 40 degrees C [21] and PFOA was completely degraded by persulfate at the temperature of 70 degrees C [24]. Therefore, the optimal temperature should be set according to the characteristics of pollutants when heat-activated persulfate is applied to the actual site remediation. For some pollutants, temperature in the field is difficult to increase to the optimum value, it is necessary to use a combination of other activation methods to achieve a considerable remediation.
###### Abstract  
This paper investigates the outage probability of a hybrid Radio Frequency-Underwater Wireless Optical Communication (RF-UWOC) system that employs the Decode-and-Forward protocol under composite fading channels. It is assumed that the RF link experiences Generalized K distribution fading along with atmospheric path loss, while the UWOC link endures generalized Gamma distribution turbulent fading, accounting for underwater path loss and pointing errors. Based on these assumptions, when intensity modulation with direct detection (IM/DD) and heterodyne detection (HD) are, respectively, utilized at the receiver, the average outage probability and its corresponding asymptotic expression for the considered hybrid dual-hop systems under high signal-to-noise ratios are derived. Subsequently, Monte Carlo simulations are conducted to validate the accuracy of the theoretical analysis results and to explore the influence of various key system parameters on the dual-hop systems.  
decode-and-forward; RF-UWOC; composite channel fading; outage probability; asymptotic performance Article
## 3 Materials and Methods
### Accurate Camera Pose Determination Using ICP with LIDAR and RGB-D Point Cloud Data
To address this problem, the optical flow method is used to detect and remove dynamic objects in RGB and depth images in real time, as shown in Figure 8. Because variable illumination and infrared reflection characteristics of the surface material of the object may produce noise in the depth data, a bilateral filter denoising method, which combines the spatial proximity and similarity of the pixel values, is adopted to preserve detail and remove noise from the depth image data. Figure 7: Process of accurate pose determination from LiDAR data. After selecting the keyframe, culling the dynamic objects, and performing noise removal, the RGB-D point cloud data is generated based on the depth information of the RGB-D camera and the rough pose of the tracking thread. Because the volume of RGB-D point cloud data is huge, which affects the efficiency of the ICP iterative algorithm, the voxel grid accordingly downsamples the point cloud to a resolution of 5 cm to acquire RGB-D sparse point cloud for matching with LiDAR point cloud. According to the rough pose provided by the visual tracking of the keyframe, the LiDAR point cloud data covering the complete experimental site is filtered to obtain the local LiDAR point cloud data, ensuring a high degree of overlapping with the RGB-D point cloud data at the current keyframe for ICP iteration. The degree of overlapping between the acquired local LiDAR data and the RGB point cloud depends on the cumulative error of the keyframe camera pose.
## 4 Application-Aware (AA) Scheduling Approach
Similarly, if the 2 requirements of Application 1 are better than the required values, the number of the removed timeslots will depend on the lowest number of required timeslots for each requirement. For instance, suppose that during a metric calculation cycle, the delivery rate of Application 1 was 10% higher than the requirement, and the delay was 30% lower than the requirement. The number of removed timeslots for Application 1 would be 1. To clarify how the new scheduling is constructed, suppose that during a metric calculation cycle, multiple applications were not satisfied; the timeslots would then be added depending on the application's priorities. Suppose that in the case of three running applications, Applications 1 and 2 were not satisfied, and two more timeslots should be assigned to Application 1 and one more timeslot to Application 2. Then, the Sch 0 scheduling depicted in Figure 2c turns into the scheduling depicted in Figure 5, where firstly, two more timeslots were assigned to Application 1 (which has the highest priority), and then one more timeslot was assigned to Application 2. Our approach is the first one to dynamically adapt to the application's requirements for scalable SDWSNs without any additional hardware. Moreover, the Sch 0 schedule, which is a part of the AA approach, considers the application's priorities, which could delay the need for adding more timeslots. This, in turn, could save both energy and the time required for rescheduling. The AA approach reduces the time required to ensure the application's QoS requirements since the number of added timeslots depends on the difference between the calculated metrics and the application's requirements. Our approach could also save on energy since it contains a mechanism to dynamically remove active timeslot (s) when possible. Finally, this approach assigns more shared timeslots per application if needed, without any dedicated timeslots, in which it is difficult to support large networks and more energy is consumed. Figure 4: Scheduling calculation procedure.
## 4 Benchmark Function Testing and Analysis
### Improvement Strategy Validity Test  
The previous experiments mainly compared the improved PSO with the other mainstream evolutionary algorithms and showed the excellence of the proposed ADGMPSO algorithm. To further confirm the extent of the influence and effectiveness of the improved strategy on ADGMPSO, it was compared experimentally to the standard PSO. PSO1 improved based on the cat initialization strategy, PSO2 improved based on the sine-based nonlinear decreasing inertia weights and asynchronous change learning factors, and PSO3 improved based on the adaptive dimensional Gaussian mutation. The experimental parameters were set consistently for 20 experiments and the results are presented in Table 7.  
Figure 5: The convergence curves of the 10 test functions.  
As can be seen from Table 7, PSO1 improved using the cat chaos initialization and had a performance that was several times better compared to the PSO algorithm for all three metrics due to its more uniformly distributed initial population. PSO2 had performances  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Func.**} & \multicolumn{2}{c}{**ADGMPSO-PSO**} & \multicolumn{2}{c}{**ADGMPSO-BOA**} & \multicolumn{2}{c}{**ADGMPSO-WOA**} & \multicolumn{2}{c}{**ADGMPSO-GWO**} & \multicolumn{2}{  
that were several or even tens of orders of magnitude better than the PSO for each function. This was due to the sine-based nonlinear decreasing inertia weights and the asynchronous change learning factor strategy, which enabled the algorithm to conduct a more extensive search of the solution space range in the initial phase and a detailed local exploration in the later stage. PSO3 used the adaptive dimensional Gaussian mutation strategy of the elite particles to perturb the optimal global solution with mutation, which enabled the algorithm to perform a more significant perturbation to explore a more comprehensive search space in the early stage, and smaller perturbations to facilitate local exploitation and to improve the algorithm's ability to overcome local optima. Since the multi-peaked function had multiple locally optimal solutions, the PSO3 algorithm showed a significant performance improvement compared to the PSO, as it converged to more accurate values by evading of the local optimum via mutation.  
The three proposed improvement strategies based on the original PSO algorithm demonstrated performance improvements in the benchmark function's optimal value, mean, and standard deviation. ADGMPSO combined the advantages of all three strategies and exhibited an optimal performance in finding the optimal value in the benchmark function test.
###### Abstract
[21] examined compartment fire phenomena by analyzing combustion efficiency according to GER and reported a decrease in combustion efficiency in the over-ventilated condition (\(\phi_{\mathrm{g}}\) < 1.0). The combustion efficiency continued to decrease from \(\phi_{\mathrm{g}}\) = 0.75 to the condition of \(\phi_{\mathrm{g}}\) = 1.27 where the flame was ejected. After flame ejection, the combustion efficiency increased with GER, showing a value close to 100%. [PERSON] et al. [22] and [PERSON] et al. [23] also observed this phenomenon; they reported that the CO yield rapidly increased in the section where the combustion efficiency decreased. Researchers have named this phenomenon \"plateau\" and \"under-ventilated condition,\" but the causes of this abnormal behavior have not been analyzed. In [PERSON]'s study [14] mentioned above, the abnormal behavior of ULT occurred in the range of \(\phi_{\mathrm{g}}\) = 0.6-1.2, similar to the findings of [PERSON] et al. [21]. With these similarities, given that the abnormal behavior of thermal physical quantities and the reduction in combustion efficiency occur due to incomplete combustion, it can be inferred that these are the same phenomenon. Additionally, a rapid increase in CO yield due to abnormal behavior can cause serious errors in fire risk assessments. Accordingly, it is necessary to closely analyze the causes of the phenomenon and changes in the internal environment. In this context, compartment fire experiments were conducted to understand the abnormal behavior occurring in the over-ventilated condition. A 1/4 reduced-scale ISO 9705 compartment was used for the experimental apparatus. A wide range (\(\phi_{\mathrm{g}}\) = 0.2-2.0) including over- and under-ventilation conditions was considered as the GER conditions for the compartment fire experiments. In order to examine the fire characteristics across a wide GER range, the temperature and chemical species concentration inside the compartment were measured. Outside the compartment, HRR and combustion efficiency were measured, as well as the temperature and species concentration of the discharge flow through an opening. Through this, the effects and causes of abnormal phenomena occurring in the fire growth stage were analyzed.
# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
###### Abstract  
Power battery scheduling optimization can improve the service life of the battery, but the existing heuristic algorithm has poor adaptability, and the capacity fluctuates significantly in the cycle aging process, which makes it easy to fall into the local optimal. To overcome these problems, we take the battery cycle life maximization as the goal, propose a reinforcement learning scheduling optimization model with temperature and internal resistance difference constraints, so as to determine whether to charge or discharge during battery cycle aging. We do this using the deep\(-\)learning\(-\)based battery capacity estimation model as the learning environment for the agent, using the Double DQN algorithm to train the agent, and proposing the principal component analysis method to reduce the dimension of the state space. These experiments, using multiple publicly available battery aging data sets, show that the principal component analysis method and the constraint functions reduce the computational time to find the optimal solution, providing the possibility of obtaining larger reward values. Meanwhile, the trained model effectively extends the cycle life of the battery, and has good adaptivity. It can automatically adjust parameters with the battery aging process to develop optimal charging and discharging protocols for power batteries with different chemical compositions.  
double DQN; scheduling optimization; constraints; cycle life; adaptability +
Footnote †: journal: Applied Sciences  
1
Footnote 1: email: xiong.H.Chen, [PERSON], [PERSON], [PERSON] Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints. _Appl. Sci._**2023**, _13_, 7702. [[https://doi.org/10.3390/app13137072](https://doi.org/10.3390/app13137072)]([https://doi.org/10.3390/app13137072](https://doi.org/10.3390/app13137072))
# A New Method for Rapid Optimization Design of a Subsonic Tandem Blade
## 2 CFD Setup and Procedure
### Setup for 2D Tandem Blade Model  
The baseline 2D tandem blade design parameters are shown in Table 1. Considering the inflow Mach number of 0.8 at the DE (design) point, the CD airfoil was selected for both FB and AB. The camber line and thickness distributions are shown in Figure 4.  
Figure 3: Convergence of the simulation for the 2D tandem blade model; incidence \(=-1^{\mathrm{o}}\), inlet Mach \(=0.8\). (**a**) Momentum and mass; (**b**) mass flow rate on INLET.  
Figure 2: Configuration of the double-stage compressor with tandem blades.  
An HOH topology structured mesh was generated in blade-to-blade surface with the AutoGrid5 software package, as shown in Figure 5. The total grid number was about 195,000 (the number along the axial; circumferential and spanwise: \(305\times 71\times 9\)), and there were 91 grids along the chord-wise of FB and AB. The first nondimensional wall distance was specified to ensure that y+ was less than 5, and the grid expansion ratio near the wall was kept at less than 1.2.  
The boundary conditions were obtained from the 50% blade height of the transonic tandem stator: at the DE point, the inlet Mach number was 0.8, and the Reynolds number based on the chord length was \(1.86\times 10^{6}\).  
Inlet boundary conditions: specify the total temperature--\(\text{Tt}_{11}\), total pressure--\(\text{Pt}_{11}\), three velocity directions (\(V_{z}\equiv 0\), \(V_{y}/V_{x}=\tan\beta_{11}\)).  
Outlet boundary conditions: specify the mass flow rate\(m_{22}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **FB** & **AB** & **Tandem** \\ \hline S (mm) & 17.1 & 17.1 & 18.5 \\ C (mm) & 20.5 & 19.7 & 39.3 \\ Inlet Metal Angle (\({}^{\circ}\)) & 56.1 & 51.3 & - \\ Outlet Metal Angle (\({}^{\circ}\)) & 43.4 & 9.7 & - \\ Solidity & 1.20 & 1.18 & 2.36 \\ AO & - & - & \(-\)0.10 \\ PP & - & - & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tandem airfoil geometric parameters.  
Figure 4: Tandem blade camber line and thickness.  
Figure 5: 2D Tandem blade model computation fluid dynamic (CFD) mesh.  
It can be seen from the flow continuity in Equation (4), the inlet velocity directions and outlet mass flow rate should change correspondingly to ensure that the inlet Mach number remains at 0.8 under different incidence conditions.  
\[m_{22}\ =\ m_{11}\ =\ [K*Pt_{11}*A_{11}*q(\lambda_{11})*\cos\beta_{11}]/\sqrt{ Tr_{11}} \tag{4}\]
# Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
## 3 Problem and Algorithm Description
### DC-KH Algorithm Description
#### 3.3.3 Single and Multi-Hop Transmission  
The vice-cluster head is mainly responsible for transmitting the information of the main cluster head to the sink node through the manner of single hop or multiple hops. In the initial stage, the vice-cluster head broadcasts its own ID number, the remaining energy, and so on. If A receives the message of B, and it selects A as the vice cluster head, then B sends L bit data to the sink node. Equation (3) is used to calculate the energy consumption model. To ensure the communication overhead and energy of the next hop node, the weight of the sub cluster head is calculated with Equation (39).  
\[W(i)=\partial\cdot\left[\frac{E(H_{j})}{E(H_{i})}\right]+(1-\partial)\cdot[d_ {i\to BS}^{4}/(d_{i\to j}^{2}+d_{j\to BS}^{2})] \tag{39}\]  
where \(\partial\) is determined according to actual situation. If \(W(i)\) is greater than 1, then the vice-cluster head is selected as the maximum next hop node. The vice-cluster head then directly sends the data to the sink node.
# Study on Wear Resistance of Ti-6 Al-4V Alloy Composite Coating Prepared by Laser Alloying
## 3 Results and Discussion
### Impact of Load Variation on Friction and Wear Properties
#### 3.2.2 Effect on Abrasion Loss  
Table 2 shows the comparison of the abrasion loss of Ti-6 Al-4V alloy and alloying coating under different loads. It is apparent from the table that the loss of the alloying coating under different loads is less than that of the Ti-6 Al-4V alloy. With the increase of the load, the abrasion loss of the material also increases, which coincides with the Archard wear theorem. Because the larger the load applied in the same wear time, the greater the positive pressure and shear force on the surface of the material, which make the plastic deformation and local peeling found on wear surface.
# Hybrid Renewable Systems for Small Energy Communities: What Is the Best Solution?
###### Abstract  
This research developed smart integrated hybrid renewable systems for small energy communities and applied them to a real system to achieve energy self-sufficiency and promote sustainable decentralized energy generation. It compares stand-alone (SA) and grid-connected (GC) configurations using a developed optimized mathematical model and data-driven optimization, with economic analysis of various renewable combinations (PV, Wind, PHS, BESS, and Grid) to search for the optimal solution. Four cases were developed: two stand-alone (SA1: PV + Wind + PHS, SA2: PV + Wind + PHS + BESS) and two grid-connected (GC1: PV + PHS + Grid, GC2: Wind + PHS + Grid). GC2 shows the most economical with stable cash flow (\(-\)\(\epsilon\)123.2 annually), low CO\({}_{2}\) costs (6367.2), and 91.7% of grid independence, requiring 125 kW of installed power. While GC options had lower initial investments (between \(\epsilon\)157k to \(\epsilon\)205k), the SA configurations provided lower levelized costs of energy (LCOE) ranging from 60.039 to 60.044/KWh. The integration of pumped hydropower storage enhances energy independence, supporting peak loads for up to two days with a storage capacity of 2.17 MWh.  
small energy community; smart hybrid energy solution; pumped- hydropower storage (PHS); wind; PVSolar; battery energy storage system (BESS); grid independence Article
## 5 Probabilistic Arithmetic Automata
### Basic PAA Concepts  
Probabilistic arithmetic automata (PAAs), as introduced in [1], are a generic concept useful to model probabilistic chains of operations. In this section, we sum up the definition and basic recurrences needed in this article.  
**Definition 4** (Probabilistic Arithmetic Automaton, PAA): _A probabilistic arithmetic automaton is a tuple \(\mathcal{P}=\big{(}\mathcal{Q},q_{0},T,\mathcal{V},v_{0},\mathcal{E},\mu=( \mu_{q})_{q\in\mathcal{Q}},\theta=(\theta_{q})_{q\in\mathcal{Q}}\big{)}\), where \(\mathcal{Q}\), \(q_{0}\), \(\mathcal{V}\), \(v_{0}\), \(\mathcal{E}\) and \(\theta\) have the same meaning as for a DAA, each \(\mu_{q}\) is a state-specific probability distribution on the emissions \(\mathcal{E}\), and \(T:\mathcal{Q}\times\mathcal{Q}\rightarrow[0,1]\) is a transition function, such that \(T(q,q^{\prime})\) gives the probability of a transition from state \(q\) to state \(q^{\prime}\), i.e., \(\big{(}T(q,q^{\prime})\big{)}_{q,q^{\prime}\in\mathcal{Q}}\) is a stochastic matrix._  
_A PAA induces three stochastic processes: (1) the state process \((Q_{t})_{t\in\mathbb{N}}\) with values in \(\mathcal{Q}\), (2) the emission process \((E_{t})_{t\in\mathbb{N}}\) with values in \(\mathcal{E}\), and (3) the value process \((V_{t})_{t\in\mathbb{N}}\) with values in \(\mathcal{V}\) such that \(\mathcal{V}_{0}:\equiv v_{0}\) and \(\mathcal{V}_{t}:=\theta_{Q_{t}}\left(V_{t-1},E_{t}\right).\)_  
We now restate the PAA recurrences from [1] to compute the state-value distribution after \(t\) steps. For the sake of a shorter notation, we define \(f_{t}(q,v):=\mathbb{P}(Q_{t}=q,V_{t}=v)\). Since we are generally only interested in the value distribution, note that it can be obtained by marginalization: \(\mathbb{P}(V_{t}=v)=\sum_{q\in\mathcal{Q}}\,f_{t}(q,v)\).  
**Lemma 2** (State-value recurrence, [1]): _The state-value distribution is given by \(f_{0}(q,v)=1\) if \(q=q_{0}\) and \(v=v_{0}\), and \(f_{0}(q,v)=0\) otherwise. For \(t\geq 0\),_  
\[f_{t+1}(q,v)=\sum_{q^{\prime}\in\mathcal{Q}}\,\sum_{(v^{\prime},e)\in\theta_{q }^{-1}(v)}\,f_{t}(q^{\prime},v^{\prime})\cdot T(q^{\prime},q)\cdot\mu_{q}(e), \tag{1}\]  
_where \(\theta_{q}^{-1}(v)\) denotes the inverse image set of \(v\) under \(\theta_{q}\)._  
The recurrence in Lemma 2 resembles the Forward recurrences known from HMMs.  
Note that the range of \(V_{t}\) is finite for each \(t\), even when \(\mathcal{V}\) is infinite, as \(V_{t}\) is a function of the states and emissions up to time \(t\), and state set \(\mathcal{Q}\) and emission set \(\mathcal{E}\) are finite. We define \(\mathcal{V}_{t}:=\text{range}\,V_{t}\) and \(\vartheta_{n}:=\max_{0\leq t\leq n}\,|\mathcal{V}_{t}|\). Clearly \(\vartheta_{n}\leq(|\mathcal{Q}|\cdot|\mathcal{E}|)^{n}\). Therefore all actual computations are on finite sets. When analyzing the number of character accesses of a pattern matching algorithm, we have \(\mathcal{V}_{t}\subset\{0,\ldots,m(n-m+1)\}\), as at most \((n-m+1)\) search windows are processed, each causing at most \(m\) character accesses. Thus, \(\vartheta_{n}\in O(n\cdot m)\).