esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Status and Development Trend of Wastewater Treatment Technology and Its Low Carbonization
## 2 Conventional Wastewater Treatment Technology
### Pushed-Flow Activated Sludge Process  
The pushed-flow activated sludge process is also known as the traditional activated sludge process. The surface of the push-flow aeration tank is rectangular. Under the push of aeration and hydraulic conditions, the water in the aeration tank is evenly pushed to flow. The wastewater enters from the head end of the tank and flows out from the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Operation Mode of Activated Sludge Method** & **BOD** & **COD** & **TSS** & **Advantages** & **Disadvantages** & **References** \\ \hline \hline \multirow{4}{*}{Pushed-flow activated sludge process} & \multirow{4}{*}{90–95} & \multirow{4}{*}{90–95} & \multirow{4}{*}{90–95} & \multirow{4}{*}{1 The degradation efficiency of sewage is higher.} & The phenomenon of insufficient aeration at the head of the tank and excessing gas supply at the tail of the tank increases the power cost. & [21,22] \\  & & & & \\  & & & & \\  & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & &  
tail end of the tank, and the liquid flow in the front section and the liquid flow in the back section do not mix. The process flow chart is shown in Figure 1. In the process of aeration, with the change in environment from the head of the tank to the end of the tank,, the biological reaction rate, the F/M value, the quantity and quality of microbial community, the adsorption, flocculation and stabilization of activated sludge, and the settlement-concentration performance are all constantly changing [21,22].  
The traditional activated sludge aeration tank is rectangular, the water flow is longitudinal mixed push flow, the aeration time of the mixed liquid in the aeration tank is usually 4-8 h, the sludge concentration is generally controlled within 2-3 g/ L, the amount of returned sludge requires 25-50% of the water intake, and the removal rate of BOD, COD and suspended matter can reach 90-95% [38]. The pushed-flow activated sludge method has the advantages of high treatment efficiency and flexible treatment modes, but it also leads to an energy surplus. The activated sludge at the head of the tank always absorbs gas from the head of the tank to the end of the tank, which increases the power cost to a certain extent and has the problem of energy waste, which is not conducive to the low-carbon emissions advocated by the present concept of carbon-neutral and sustainable development.
###### Abstract  
Connected autonomous vehicles (CAVs) face constraints from multiple traffic elements, such as the vehicle, road, and environmental factors. Accurately quantifying the vehicle's operational status and driving risk level in complex traffic scenarios is crucial for enhancing the efficiency and safety of connected autonomous driving. To continuously and dynamically quantify the driving risks faced by CAVs in the road environment--arising from the front, rear, and lateral directions--this study focused s on the self-driving particle characteristics that enable CAVs to perceive their surrounding environment and make driving decisions. The vehicle-to-vehicle interaction behavior was analogized to the inter-molecular interaction relationship, and a molecular Morse potential model was applied, coupled with the vehicle dynamics theory. This approach considers the safety margin and the specificity of driving styles. A multi-layer decoder-encoder long short-term memory (LSTM) network was employed to predict vehicle trajectories and establish a risk quantification model for vehicle-to-vehicle interaction behavior. Using SUMO software (win64-1.11.0), three typical driving behavior scenarios--car-following, lane-changing, and yielding--were modeled. A comparative analysis was conducted between the risk field quantification method and existing risk quantification indicators such as post-encroachment time (PET), deceleration rate to avoid crash (DRAC), modified time to collision (MTTC), and safety potential fields (SPFs). The evaluation results demonstrate that the risk field quantification method has the advantage of continuously quantifying risk, addressing the limitations of traditional risk indicators, which may yield discontinuous results when conflict points disappear. Furthermore, when the half-life parameter is reasonably set, the method exhibits more stable evaluation performance. This research provides a theoretical basis for the dynamic equilibrium control of driving risks in connected autonomous vehicle fleets within mixed-traffic environments, offering insights and references for collision avoidance design.  
vehicle-to-vehicle interaction; risk quantification; molecular potential fields; networked autonomous vehicles; deep learning +
Footnote †: journal: Applied sciences  
Article
## 6 Discussion  
### The Cause of Spectral Variation of Different Chlorites in Qidashan Iron Deposit  
The spectral absorption features of chlorite are diagnostic to chemical composition variations. It can be inferred that the different absorption wavelengths of chlorite in the wall rock of BIFs and high-grade iron ore are due to their different chemical compositions. Previous studies have shown that the wavelength of Fe-OH is positively correlated with the relative amount of Fe\({}^{2+}\) and inversely with the Mg\({}^{2+}\) in the octahedral cation sites [6; 24; 42; 43]. In the Qidashan Iron Deposit, the relationship between the wavelength positions of Fe-OH, Mg-OH, and OH and the content of cations in the octahedral layer of chlorite shows that the Fe-OH absorption band is positively correlated with the Fe and inversely related with Mg and Mg/(Mg + Fe) (Figure 20b,c), which is consistent with the result mentioned above. The Fe-OH band appears to be only weakly influenced by AlVI (Figure 20a,d). The other absorption bands manifest similar characteristics to those of Fe-OH (Figures 21 and 22). The shorter wavelength of Fe-OH and/or Mg-OH indicates that chlorite in wall rocks around BIFs is Mg-rich (Q-4 and Q-13), whereas the longer wavelength is indicative of Fe-rich chlorite in wall rock around high-grade iron ore (Q-2 and Q-11).  
Figure 20: The relationship between the wavelength of Fe-OH band and the content of cations in the octahedral layer of chlorite: (**a**) the relationship between Fe-OH and Fe; (**b**) the relationship between Fe-OH and Mg; (**c**) the relationship between Fe-OH and Mg/(Mg+Fe); (**d**) the relationship between Fe-OH and AlVI (the overlap points have been offset for clarity).  
Figure 21: The relationship between the wavelength of Mg-OH band and the content of cations in the octahedral layer of chlorite: (**(a)**) the relationship between Mg-OH and Fe; (**b**) the relationship between OH and Mg; (**c**) the relationship between Mg-OH and Mg/(Mg+Fe); (**d**) the relationship between OH and AlVI (the overlap points have been offset for clarity)).  
Figure 22: The relationship between the wavelength of OH band and the content of cations in the octahedral layer of chlorite: (**(a)**) the relationship between OH and Fe; (**b**) the relationship between OH and Mg; (**c**) the relationship between OH and Mg/(Mg+Fe); (**d**) the relationship between OH and AlVI (the overlap points have been offset for clarity)).  
Compared with the absorption wavelength, the relationship between the absorption depth and the specific chemical composition is not obvious. The absorption depth is usually related to the abundance of the absorber, but it is also strongly affected by the grain size of the material [15]. Therefore, the absorption depth of chlorite should be carefully applied in the practical mineral exploration.
###### Abstract  
In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.  
Keywords:road network; traffic congestion; representation method; data compression; short-term traffic prediction; deep learning +
Footnote †: journal:
# Joint Maintenance Strategy Optimization for Railway
## 3 Optimization Model of the Joint Maintenance Strategy
### Joint Maintenance Probability Model  
In this subsection, a classical age-reduction factor is introduced to describe the effect of imperfect maintenance. The age-reduction factor was first proposed by [PERSON] et al. [41], which assumes that maintenance can shorten the actual working time. The age-reduction factor of the \(j\)-th imperfect maintenance is \(\alpha_{j}\in[0,\ 1]\). If \(\alpha_{j}\)= 0, it means that the maintenance restores the actual working time to zero. If \(\alpha_{j}\)= 1, it means that the maintenance cannot shorten the actual working time. The time of the \(j\)-th imperfect maintenance is denoted as \(i_{j}\Delta t\) (\(j=1,\,2,\,3,\dots\) ), and the actual working time \(T_{j}\) is expressed as  
\[\left\{\begin{array}{l}T_{1}=\alpha_{1}i_{1}\Delta t\\ T_{2}=\alpha_{2}[T_{1}+(i_{2}-i_{1})\Delta t]=\alpha_{2}i_{2}\Delta t+\alpha_{ 2}(\alpha_{1}-1)i_{1}\Delta t\\ T_{3}=\alpha_{3}[T_{2}+(i_{3}-i_{2})\Delta t]=\alpha_{3}i_{3}\Delta t+\alpha_{ 3}(\alpha_{2}-1)i_{2}\Delta t+\alpha_{3}\alpha_{2}(\alpha_{1}-1)i_{1}\Delta t \\ \vdots\end{array}\right. \tag{6}\]
## 6 Discussion
It will be interesting in future work to join a combination of this work with artificial intelligence algorithms, for example, where nodes could adapt in real-time to the traffic load and signature by deciding to allocate or remove slots dynamically to nodes according to their immediate need, especially as the current global problem evolves with the capture of huge amounts of data.
## 2 Research Aims, Scope, and Novelty  
The present research is focused on examining the behavior of existing buildings before and after each strengthening stage from the viewpoint of earthquake resistance and upgrading, including adding new parts (floors and/or wings). With this aim, numerical analyses and experiments are carried out on a full-size real building. An additional aim is to develop methods for validating the correspondence of building strengthening and upgrading with the modern standards' requirements. The proposed method advances the existing engineering knowledge in the field, confirms the existing techniques in engineering practice, and suggests effective ways of improvement.  
The novelty of this research is that for the first time, complex investigations are carried out for the entire process of building after each stage of strengthening and upgrading. This process includes experimental and analytical evaluation of a building's state before strengthening as well as during the strengthening and upgrading phases.  
Known approaches for monitoring buildings include updating numerical models to obtain higher accuracy of modeling. The proposed method is aimed at developing a simple procedure that is more convenient for designers. Following this procedure, the dynamic characteristics of the building are updated, and the strengthening and upgrading are carried out using the corresponding design spectrum [5]. This yields a more effective and easy design that makes the procedure less time-consuming. To verify the efficiency of strengthening, the dynamic parameters are monitored at each stage.  
The application of the research outcomes is focused on the building's strengthening by adding RC stiffening walls, columns, cores, etc. The research results allow for the correction of dynamic parameters that correspondingly enable the use of ordinary elastic analyses to obtain more accurate and reliable results, which represent the building's response to seismic forces.
## 3 Example and Discussion
### The Influence of External Water Pressure \(p_{1}\) on the Tangential Normal Stress of Lining  
To further investigate the impact of external water pressure on the bearing performance of the lining, this subsection considers an internal water pressure (\(p_{2}\)) of 1 MPa. The external water pressure (\(p_{1}\)) gradually increases from 0 MPa to 1 MPa with each increase of 0.2 MPa, while keeping other parameters constant. The results are presented in Figure 5.  
From Figure 5, it is evident that the tangential stress values of the surrounding rock exhibit sinusoidal variation. In the directions of \(\theta\) = 90\({}^{\circ}\) and 270\({}^{\circ}\), the tangential normal stress tends to be closer to a tensile stress state. As the internal water pressure (\(p_{2}\)) increases, the tangential stress value curve remains largely consistent, with little numerical variation. This suggests that changes in internal water pressure have a relatively low sensitivity to the tangential stress of the surrounding rock.  
Comparing Figures 6 and 7, it is apparent that the tangential normal stress of the lining at the \(L_{1}\) boundary is at a minimum in the \(\theta\) = 90\({}^{\circ}\) and 270\({}^{\circ}\) directions, while it reaches its maximum in the \(\theta\) = 0\({}^{\circ}\), 180\({}^{\circ}\) directions. As the internal water pressure (\(p_{2}\)) increases, the tangential normal stress of the lining at the \(L_{1}\) boundary gradually transitions from a tensile-compressive stress state to a complete compressive stress state, and an increment of \(\Delta p_{2}\) = 0.2 MPa leads to a variation in tangential normal stress of \(\Delta\sigma^{\theta}\) = 1.5 MPa (compression).  
Figure 5: Influence of external hydraulic pressure \(p_{1}\) on tangential normal stress of surrounding rock at \(L_{1}\).  
Figure 6: Influence of external hydraulic pressure \(p_{1}\) on tangential normal stress of lining at \(L_{1}\).  
Conversely, the tangential normal stress of the lining at the \(L_{2}\) boundary is at its maximum in the \(\theta=90^{\circ}\) and \(270^{\circ}\) directions, and at its minimum in the \(\theta=0^{\circ}\), \(180^{\circ}\) directions. With the increase in \(p_{2}\), the tangential normal stress of the lining at the \(L_{2}\) boundary undergoes a similar transition from tensile-compressive stress to complete compressive stress. Furthermore, an increment of \(\Delta p_{2}=0.2\) MPa leads to a variation in tangential normal stress of \(\Delta\sigma^{\theta}=2\) MPa (compression).  
Certainly, the conclusions drawn above are based on the given parameters, and it is essential to conduct calculations and evaluations tailored to the specific circumstances of each project. Factors such as the stiffness ratio between the surrounding rock and the lining, initial ground stress, and the timing of support installation are crucial considerations that can significantly influence the results. Therefore, it is imperative to customize the analysis according to the actual conditions encountered in each scenario to ensure accurate and reliable outcomes.
# Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization
## 2 Data and Methodology  
### Data  
The data used in this study represent an actively producing field in the Eastern Mediterranean region. The study area features two separate potential reservoir zones divided by a less-permeable layer. Its structure is characterized by alternating layers of sandstone and shale, with a portion of it bordered by an aquifer. Since 1997, a total of 12 vertical production wells have been engaged in oil extraction and reservoir pressure monitoring across both the upper and lower zones, yielding a significant dataset for further analysis. The data used for electrofacies prediction consists of well-log measurements including gamma-ray, density, and deep resistivity.
###### Abstract
The vegetation water content (VWC) is one of the most vital parameters in the WCM. To remove the effect of vegetation on SSM estimation, it is necessary to obtain the VWC. Numerous studies [26; 27; 28] have indicated the application of the Normalized Difference Vegetation Index (NDVI), as the vegetation descriptor allows for the calculation of the vegetation backscattering coefficient on the total backscattering coefficient with good accuracy. In addition, some studies [29; 30; 31] have demonstrated that the Normalized Difference Water Index (NDWI) can be used for VWC estimation instead of measuring by manual work. However, the NDVI is limited to a certain range of vegetation coverage, and the NDWI has a poor ability to remove the effect of background soil reflectance, although the NDVI and NDWI can reduce the effect of vegetation coverage. The Chlorophyll Absorption Ratio Index (CARI) measures the depth of chlorophyll absorption at 670 nm relative to the green reflectance peak at 550 nm and the reflectance at 700 nm to describe the chlorophyll content of vegetation. [PERSON] et al. [32] developed the Modified Chlorophyll Absorption Ratio Index (MCARI) based on the CARI, mainly by increasing the R\({}_{700}\)/R\({}_{670}\) ratio, in an attempt to minimize the influence of soil reflectance and canopy nonphotosynthetic substances. Chlorophyll is a plant biochemical parameter that is closely related to nitrogen, cellulose, water, and other vegetation biochemical parameters [33]. At present, there are few reports on VWC estimation using this index, and its potential for soil moisture estimation needs to be further explored. Therefore, we attempted to use the MCARI to describe the VWC and remove the effect of vegetation on SSM estimation in order to make positive contributions to agricultural water resource management and drought monitoring. Based on S1 SAR and S2 MSI data, this study took farmland in Daxing, Beijing, and Gu'an, Hebei, China, as the study area. Aiming at the problem that the sensitivity of radar signals in farmland vegetation coverage decreases in SSM estimation, a planting cover index was introduced, namely the MCARI. In this paper, a semi-empirical model of SSM estimation in a vegetation-covered area based on microwave and optical RS data was developed, and soil moisture inversion experiments under six inversion modes formed by the combination of the vegetation indices (NDVI, NDWI, and MCARI) and the polarization modes (VV and VH) were carried out to analyze the robustness of the proposed model, which provides a research idea for the cooperative inversion of SSM in vegetation-covered areas. The method was tested in semi-arid farmland crop cover areas in Daxing, Beijing, and Gu'an, Hebei, China.
# Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture
## 3 The Signal Model and Principle of Virtual Synthetic Aperture Radiation Source Location Method
### The Signal Model
The slot duration is \(T_{slot}=\frac{1}{2^{\mu}}\)ms. Because of the orthogonality of OFDM signal, the OFDM symbol duration without CP is \(T_{u}=\frac{1}{\Delta f}\). For \"Normal\" mode CP, number of symbols per slot is set as 14, so the OFDM symbol duration is \(T_{sym}=\frac{T_{slot}}{4}\). The difference between \(T_{sym}\) and \(T_{u}\) is the CP duration \(T_{c}\). Thus, the transmitted signal is given by  
\[s(t)=\sum_{m=0}^{\infty}\sum_{l=0}^{L-1}\sum_{k=0}^{K-1}c_{mlk}\exp\{j2\pi f_{ k}t\}\text{rect}\{t-1T_{s}-mLT_{s}\} \tag{5}\]  
Figure 2: The principle of generating the OFDM signal. where  
\(m\) denotes the frame number,  
\(l\) denotes the OFDM symbol number,  
\(k\) denotes the subcarrier number,  
\(c_{mlk}\) denotes the modulation symbols,  
\(K\) denotes the number of subcarriers, which is 4096 for 5G,  
\(L\) denotes the number of symbols per slot, which is 14,  
\(f_{k}\) denotes the subcarrier frequency, \(f_{k}=f_{0}+k\Delta f\),  
\(\text{rect}\{\cdot\}\) denotes a rectangular function representing the duration of signal. Thus, the received signal is expressed as follows:  
\[s_{r}(t)=s(t-\tau(t))=s(t-\frac{R(t)}{c}) \tag{6}\]
## 2 System Architecture and Problem Formulation
### Design of MOBA-CV-SARSA
Hence, the reciprocal value of delay time (\(\frac{1}{\text{f}_{1}}\)) will exceptionally be that of energy consumption (\(\frac{1}{\text{e}_{2}}\)). To uphold equal significance for both objectives, \(\omega_{1}\) is designated as 0.25 and \(\omega_{2}\) as 0.75. \[\text{reward}=\omega_{1}\frac{1}{\text{f}_{1}+\text{e}_{1}}+\omega_{2}\frac{ 1}{\text{f}_{2}+\text{e}_{2}}, \tag{16}\]  
Reward values (16) enable the utilization of the Q-value update formula to modify the Q-table within SARSA. The Q-table format employed in this investigation is depicted in Table 1. Initially, bats exhibiting diverse states are integrated into the Q-table's state table. Following this, \(\alpha\) and \(\gamma\) are designated as numerous sets of values, initializing all Q-values within the Q-table to 0. The respective Q-value undergoes an update upon the bat colony's state alignment with an entry in the Q-table. With each iteration, new bats emerge, prompting a comparison between these bats and the states archived in the Q-table. Should the new state be pre-existing, the associated state table is employed for Q-value (Q\({}_{\text{i}}\)) updates. Conversely, if the new state is absent from the Q-table, the new bat is appended, and \(\varepsilon\)-greedy facilitates the initial action selection and Q-value update. Figure 6: Initialization of bat group. When dealing with a multi-objective problem, a singular objective assessment approach is inadequate. This study presents a specialized method for multi-objective decision making to evaluate multiple objectives simultaneously. Within this methodology, a comprehensive comparison between the new bat (\(X_{\text{i}_{\text{new,bus}}}\)) and the optimal bat (\(X_{\text{l}_{\text{bus}}}\)) is conducted. The procedural details are elucidated in Algorithm 1, where 'M' denotes the total number of objective functions. In this investigation, M is set at 2, representing overall network latency and energy consumption. Initially, each objective function undergoes a comparison between the new bat and the optimal bat. If the new bat demonstrates superior performance over the optimal bat in the assessed objective function (fj), the count of dominated objective functions (d\({}_{\text{num}}\)) increases. When the number of objective functions dominated by the new bat matches the total count of objective functions, it signifies complete superiority over the optimal bat, prompting the replacement of the original bat with the new one. In sum, the pseudo-code of the aforementioned MOBA-CV-SARS is shown in Algorithm 1.