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
### Adsorption-Regeneration Activated Sludge Process  
The adsorption-regeneration activated sludge method is also known as the biological adsorption method or contact stabilization method. The main feature of this operation mode is that the adsorption and metabolism of activated sludge for the degradation of organic pollutants are each carried out in their own reactors. The wastewater is fully recycled in the regeneration pool, and the activated sludge with strong activity enters the adsorption pool at the same time. The two contact fully in the adsorption pool, and most of the organic matter in the wastewater is absorbed by the activated sludge, and purified. The sludge separated from the secondary sedimentation tank enters the regeneration tank, where the activated sludge metabolizes and degrades the organic matter, and microorganisms proliferate. When the microorganisms enter the endogenous metabolic stage, the activity and adsorption function of the sludge are fully recovered, and it then enters the adsorption tank together with the wastewater [25; 39]. The technological process is shown in Figure 3. For the treatment of municipal wastewater, the BOD load (Ns) is 0.2-0.6 kg BOD5/(kg MLSS- d), the volume load (Nv) is 1.0-1.2 kg BOD5/(m\({}^{3}\).d), the sludge age (mean residence time of biosolids) (\(\theta\)r) is 5-15 d, the mixed liquid suspended solids concentration (MLSS) is 1000-3000 mg/L, the mixed liquid volatile suspended solids concentration (MLVSS) is 3200-5200 mg/L, the adsorption pool concentration is 600-1200 mg/L, the regeneration pool concentration is 2400-7000 mg/L, and the adsorption pool reaction time is 0.5-1.0 h. The removal rates of BOD, COD and TSS can reach 80-90% under the conditions of 3-6 h of regeneration tank, 25-100% sludge reflux ratio (R) and 3-5 h aeration time (t). Its treatment effect on sewage is lower than that of the traditional activated sludge process, and it also has the disadvantage of poor treatment effect on wastewater with high dissolved organic matter [40]. Its advantages are that its energy consumption and sludge utilization are considerable, and it can more fully use the sludge, combined with the regeneration pool to carry out sludge conversion operation, reduce the discharge and disposal of sludge, and to a certain extent achieve the purpose of energy saving.  
Figure 2: Process flow chart of completely mixed aeration tank.
## 6 Learning Algorithms (Optimizers)
### Exponential Moving Average (EMA)  
Given a sequence, \(x_{0},x_{1},\ldots\), consider a new sequence generated from it as follows:  
\[y_{t}:=\beta y_{t-1}+(1-\beta)x_{t},\quad t=0,1,\ldots; \tag{86}\]  
where \(y_{-1}=0\) for initialization, and \(\beta\in[0,1)\) is the rate of exponential decay, frequently set to at least 0.9. The expansion of (86) produces the following pattern:  
\[y_{t} :=\beta^{2}y_{t-2}+(1-\beta)(x_{t}+\beta x_{t-1})\] \[=\beta^{3}y_{t-3}+(1-\beta)(x_{t}+\beta x_{t-1}+\beta^{2}x_{t-2})\] \[=\cdots=\beta^{t+1}\underbrace{y_{-1}}_{0}+(1-\beta)\sum_{i=0}^{ t}\beta^{t-i}x_{i}, \tag{87}\]  
where the weights under summation change according to a geometric progression. The \(\beta\) values close to 1 have the effect of \"long memory\" because many past terms, \(x_{i}\), contribute to \(y_{t}\). This can be described as exponential smoothing over a long period. Instead, \(\beta\) values closer to 0 translate onto a \"short memory\"--weights further in the past fade out quickly.  
Note that (87) is a _biased_ (not normalized) average because weights do not add up to 1, unless \(\beta=0\), which would imply \(y_{t}=x_{t}\) for all \(t\). More strictly, for a fixed \(t\), the sum of weights is \((1-\beta^{t+1})/(1-\beta)\)--a potential correcting normalizer.
## 2 Finite Element Model  
### Material Parameters  
Experimental research has focused on exploring the milling characteristics of Nomex honeycomb composite (NHC) structures using the THU Ultrasonic 850 machine tool, which was developed by Tsinghua University in China (refer to Figure 1) [20]. This specialized machine tool incorporates ultrasonic vibration technology to enhance machining efficiency and precision when working with composite materials like Nomex honeycomb. By utilizing the THU Ultrasonic 850, researchers can investigate and optimize milling parameters such as feed rate, spindle speed, and vibration amplitude to achieve the desired machining outcomes. This experimental approach not only advances the understanding of NHC machining processes but also contributes to the development of innovative manufacturing techniques for aerospace and other high-performance applications. In order to validate the obtained results of the experiment, a numerical approach using the finite element (FE) method is proposed. In the context of machining simulation, it is essential to precisely establish the geometric characteristics of the part and the cutting tool. This approach is crucial to guarantee the accuracy and reliability of the results, which makes it imperative to carefully define the mechanical properties of the structure as well as the cutting tool. The materials forming the Nomex honeycomb structure are composed of aramid fibers and phenolic resin, recognized for their high mechanical and thermal resistance. The geometric characteristics of the cell are shown in Figure 1.  
In accordance with the experimental methodology, milling was conducted using a specific cutting tool designated as a high-speed steel (HSS-W18 Cr4V) ultrasonic circular milling cutter (See Figure 2) [20]. For this purpose, the design of the UCK tool was designed, considering the geometric parameters used throughout the experimental step (See Figure 3).  
Figure 1: (**a**) Dimensions of the Nomex honeycomb structure (NHC); (**b**) Dimensions of the alveolar cell [21].  
Figure 2: UCK cutting tool: (**a**) UCK used in milling simulation; (**b**) UCK used in the experiment phase.
## 5 Numerical Simulations
### Internal Stability Verification  
In this simulation, the leader vehicle maneuvers at a constant velocity of 25 m/s (i.e., 90 km/h). By utilizing our proposed internal stability analysis method, the kernel and offspring hypersurfaces of the CTCR diagram are depicted in Figure 3. The kernel hypersurfaces (KHs) and offspring hypersurfaces (OHs) are represented as red and blue, respectively. The stable region is marked as the shaded zone in Figure 3b, which is an exact and exhaustive stability map for the vehicular platoon with sensing delay and communication delay. It is remarked that there is no stable region outside the shaded zone.  
In order to verify the results, we consider two different points \"a\" (i.e., \(<\tau_{1}\), \(\tau_{2}>\) = \(<\) 0.4 s, 2 s \(>\)) and \"b\" (i.e., \(<\tau_{1},\tau_{2}>\) = \(<\) 2 s, 2 s \(>\)), which are inside and outside the stable region, respectively. The spacing error and velocity of the vehicles for the point \"a\" are exhibited in Figure 4, and those for the point \"b\" are exhibited in Figure 5. It is seen that the spacing errors and velocities of the follower vehicles converge in Figure 4, such that the internal stability is guaranteed. On the contrary, the spacing errors and velocities diverge in Figure 5, so the instability occurs. Thus, the simulation results verify the effectiveness of our proposed internal stability analysis method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Value** & **Unit** \\ \hline \(T\) & Time constant of the drivetrain & 0.4 & s \\ \(d\) & Standstill spacing & 10 & m \\ \(h\) & Time headway & 2 & s \\ \(v_{\max}\) & Maximum velocity & 55.6 & m/s \\ \(v_{\min}\) & Minimum velocity & 0 & m/s \\ \(k_{r},k_{v},k_{a}\) & Controller gains & 0.2, 0.9, 0.05 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicular platoon and controller parameters.  
Figure 3: CTCR paradigm of the vehicular platoon: (**a**) Spectral delay space representation. (**b**) Delay space representation.  
Furthermore, we compare the computational efficiency of our proposed Dixon resultant matrix-based method to that of the other two state-of-the-art methods, i.e., the Sylvester resultant matrix-based method [21] and ACE combined with Kronecker summation [22]. The three methods are all combined with the CTCR paradigm to compute the ETDMs for the identical vehicular platoon. The three programs are simulated in an identical computer, which has a 2.93 GHz Intel Core 2 Duo and 4 GB RAM, and has Matlab 2017a installed. Simulation time is exhibited in Table 2. It is seen that our proposed method consumes the least simulation time. The simulation time of our proposed method is less than that of the Sylvester resultant matrix-based method by two orders of magnitude, and less than that of ACE combined with Kronecker summation by three orders of magnitude. In conclusion, the computational burden of applying the CTCR paradigm to compute the ETDMs is elegantly reduced by our proposed method.
## 8 Routing Protocols of Multi-UAV Systems
### Geographic Position-Based Routing Protocols  
Sometimes called georouting, these types of protocols rely on the geographical location services of the UAVs [20]. They are more adapted for highly mobile UAVs in large FANETs [19]. In this type of protocol, each UAV determines its GPS position and shares it with its neighbors. A sender UAV must know the position of the receiver UAV, as shown in Figure 17. Based on this knowledge, messages can be routed without knowing the network topology beforehand. The paradigm of these protocols is similar to reactive protocols since the routes are used only when necessary. These protocols combine geoservice techniques [20; 77] with the Greedy Forwarding approach [78] and they consist of Greedy Perimeter Stateless Routing (GPSR) [91], Geographic Load Share Routing (GLSR) [92], Geographic Position Mobility-Oriented Routing (GPMOR) [93], and Reactive Greedy Reactive (RGR) [78].  
GPSR was originally proposed for MANET and allows us to calculate the shortest route for sending messages. From the location information of each UAV, it uses the greedy method [94] to find the nearest neighbors of the UAV that receives the message. Then, it calculates the shortest route to send the message through one of the closest neighbors of the receiver UAV. If no route is found, then the sending fails, without any possibility to recover the message.  
[PERSON] et al. [92] proposed GLSR, an extended version of GPSR that uses a prediction function to select the next promising gateway UAV to compute multiple routes from the sender to the receiver UAV. In another work [93], the authors proposed GPMOR, an adapted version of GLSR for a highly mobile flying ad-hoc network. The proposed protocol uses Gauss-Markov stochastic processes to model and predict the movement of the UAVs. This approach makes it possible to anticipate the most optimal routes for sending messages.  
To increase the deliverability of the messages and minimize the bandwidth consumption, [PERSON] et al. [78] introduced the RGR protocol. This protocol is based on both the AODV routing protocol and greedy forwarding method. Since it is a reactive protocol, it calculates \"on-demand\" only the routes for the UAVs waiting for the messages.
## 4 Protocol Description
### Routing Reconfiguration
For sensor \(f\), it reports to sink by route path: {_f_, \(b\), \(i\), \(j\), _sink_}. After working for a while, sensor \(i\) (colored red) senses the fire occurrence. Then sensor \(i\) broadcasts a STATE (IN-FIRE) message to notify its communication neighbors (colored yellow): \(a\), \(b\), \(d\), \(j\), and \(c\). When these nodes receive the message, they will enter into the \"_lowsafe_\" state. For the state change of sensor \(i\), then _timeout_ (_i_) is updated as \(t_{unsafe}\). Accordingly, sensor \(i\) will update the timeout of its upstream and downstream link, _i.e., link_ (_b_, _i_) and _link_ (_i_, _j_). As our designed condition for reconfiguration, when _timeout_ (_link_ (_b_, _i_)) and _timeout_ (_link_ (_i_, _j_)) is lower than a predetermined threshold, the routing reconfiguration is invoked by the upstream node whose timeout is no less than the link timeout. Then sensor \(b\) will broadcast a RTR message to find a new relay to the sink, _i.e.,_ route path {_f_, \(b\), \(c\), \(e\), _sink_}. When it comes to path link (_i_, _j_), sensor \(i\) is the upstream node of the link with the lower valid time. It will still work on this path (to forward data from sensor \(i\) to the sink) until sensor \(i\) becomes \"_unsafe_\". It is assumed that data packet acknowledgements are sent at the link layer (not end-to-end). When a node does not receive an acknowledgement after a given time, we say the downstream link becomes invalid and then reconfigure routing.
## 3 Results
### Spatial Patterns in SMAP/CYGNSS \(\beta\) Values
#### 3.3.1 Relationships between Mean Annual Precipitation, NSSM Variability and \(\beta\) Values  
SMAP emissivity and CYGNSS reflectivity are often poorly correlated in dry areas (Figure 7a,d,e). This is because dry regions have little NSSM variability, and therefore little-to-no temporal variation in emissivity or reflectivity (Figure 4f).  
The impact of precipitation and NSSM variability on SMAP/CYGNSS \(\beta\) values can be illustrated by examining the seasonal cycle of \(\beta\) for a single 33 km box (Figure 8). \(\beta\) is most negative (and correlation best) during the wet season, when NSSM is fluctuating due to rainfall events. In contrast, periods with no precipitation and low NSSM variability produce near-zero \(\beta\) values and poor correlations. We calculated seasonal \(\beta\) values using SMAP/CYGNSS data pairs that were temporally collocated using \(\pm\) half the time between successive SMAP observations. The 45-day SMAP/CYGNSS \(\beta\) value (Figure 8b), calculated using the methodology described in Section 2.2.2, is closer to the seasonal \(\beta\) values generated during periods with higher precipitation and higher NSSM variability. This indicates that our 45-day \(\beta\) values provide an adequate estimate of the annual range of coarse-scale NSSM variability.  
The relationship between SMAP/CYGNSS \(\beta\) values and NSSM variability is shown in Figure 9a. We represented NSSM variability by calculating the variance through time of SMAP NSSM for each 9 km grid cell during 2020. The relationship between SMAP/CYGNSS \(\beta\) values and FLDAS mean annual precipitation (described in Section 2.3) is shown in Figure 9b. As expected, regions with very low NSSM variance (<0.0025) and very low mean annual precipitation (<0.25 m) have near-zero and positive \(\beta\) values. In contrast, the most negative \(\beta\) values are found in regions with moderate NSSM variance (\(-\)0.01-0.0325) and low-to-moderate mean annual precipitation (\(\sim\)0.25-1.5 m). The near-zero \(\beta\) values found in regions with high mean annual precipitation might be due to a lack of NSSM variability, if the soils are always wet, or could be due to increasing vegetation density, which is discussed below.
## 5 Experiments and Results
### Dataset Description and Pre-Processing  
In our previous work, we evaluated our approach by adopting the dataset of 8000 tweets on earthquakes, hurricanes, floods, and wildfires. In this work, we used the same database to perform our experiments with machine-learning and deep-learning models. Since each tweet in the dataset contains the annotations performed by using [PERSON]'s approach [14]. The statistics of each category, \"Eyewitness\", \"Non-Eyewitness\", and \"Unknown\", are illustrated in Table 4 in Section 3.1.  
Since the dataset is already utilized and updated by [14; 15], it contained only a few tweets that required the pre-processing steps. As the same database was used in our previous experiments on rules-based classification, we already have pre-processed data. A total of 2942 such tweets exist with \" \" \" or comma as a character that might disturb our rules. Such tweets were handled through the pre-processing phase. The annotated results were added to the same database against each tweet. In this work, we used the same dataset as the benchmark to compare the results of the proposed LR-TED with the static dictionary-based approach [14].
# New Trends in Separation Techniques of Lithium Isotopes: A Review of Chemical Separation Methods
## 1 Introduction
### Challenges in Development of Separation Methods for Lithium Isotopes Using Electrochemical
#### 2.3.1 Inorganic Microstructure Methods  
There are just a few studies involving separation of lithium isotopes using displacement chromatography based on inorganic microstructures. Separation of lithium was investigated using an ion exchange column filled with MnO\({}_{2}\) and the exchange capacity of MnO\({}_{2}\) oxide was calculated to 0.5 meq/g [7]. In this study, the MnO\({}_{2}\) phase was enriched in \({}^{6}\)Li and the enriched fraction was eluted using CH\({}_{3}\)COONa with concentration of 2 M. The separation factor (\(\alpha\)) of \({}^{6}\)Li was 1.026 [7], a value which is lower compared with the values obtained using equilibrium phase exchange with crown ethers. More recently, [PERSON] et al., 2017 [31] measured isotopic ratios \({}^{7}\)Li/\({}^{6}\)Li for effluent fractions from a biphasic zeolite column ([Li\({}_{0.08}\)(NH\({}_{4}\))\({}_{0.92}\)]A and [Li\({}_{0.33}\)(NH\({}_{4}\))\({}_{0.67}\)]A hydrates). The study shown the accumulation of 6 Li in the zeolite proceeded by a mechanism of differential elution of \({}^{7}\)Li from the biphasic zeolite.
These researches mainly focus on large-scale natural disaster such as hurricane, earthquake. They used a highway or expressway network to simulate and evaluate the efficiency of emergency rescue, emergency evacuation and evacuation effectivity [10; 12]. However, disasters in urban areas have become more frequent in recent years. These disasters demonstrated as happening in local positions, regional affected and required rapidly emergency response, such as sudden fire disaster, gas explosion, and road subsidence during metro construction. Due to the spatial-temporal uncertainty and multiplicities of urban small-scale disaster, the literature focusing on traffic flow analysis in urban high-density areas under sudden disasters is limited [13]. In addition, it is not conducive to make a scientific and effective emergency traffic management and control strategy to guarantee the disaster prevention, mitigation and relief work under sudden disaster in urban high-density areas. Most urban high-density areas in China are in the old town of inner cities, the electrical and gas systems are older, degradation and prone to trigger sudden fire disaster. The reported sudden fire disasters were mainly caused by the ageing electrical and gas systems in the old towns of inner cities. Furthermore, with the aim of understanding the aggregation, propagation and distribution of traffic flow in urban high-density area under sudden fire disaster, providing a theoretical basis method for traffic agents to manage and control regional traffic flow in disaster-affected area, and preventing the regional traffic flow being caught into disorder and congestion, tThis paper attempts to identify the catastrophe border of traffic flow in urban high-density area under sudden fire disaster. This study focuses on:  
(1) Using catastrophe theory to analysis the traffic flow characteristics in urban high-density area under sudden fire disaster, modeling a regional traffic flow model based on Cusp Catastrophe Theory (CCT) to describe the changing of traffic flow and to identify the catastrophe border of traffic flow. (2) For the aim of regional traffic flow control, using VISSIM simulator data with different data collection intervals to identify the catastrophe border, and analysis the suitable data collection interval for adaptive and automatic traffic control in sudden fire disaster. The rest of the paper is organized as follows: Section 2 introduces the state-of-the-art research under fire disaster. Section 3 presents the proposed methods for analyzing traffic flow under sudden fire disaster. Simulation and calibration are presented in Sections 4 and 5 concludes the paper with contributions and limitations, as well as the perspectives on future work.
###### Abstract  
Injecting CO\({}_{2}\), a greenhouse gas, into the reservoir could be beneficial economically, by extracting remaining oil, and environmentally, by storing CO\({}_{2}\) in the reservoir. CO\({}_{2}\) captured from various sources always contains various impurities that affect the gas-oil system in the reservoir, changing oil productivity and CO\({}_{2}\) geological storage performance. Therefore, it is necessary to examine the effect of impurities on both enhanced oil recovery (EOR) and carbon capture and storage (CCS) performance. For Canada Weyburn W3 fluid, a 2D compositional simulation of water-alternating-gas (WAG) injection was conducted to analyze the effect of impure CO\({}_{2}\) on EOR and CCS performance. Most components in the CO\({}_{2}\) stream such as CH\({}_{4}\), H\({}_{2}\), N\({}_{2}\), O\({}_{2}\), and Ar can unfavorably increase the MMP between the oil and gas mixture, while H\({}_{2}\)S decreased the MMP. MMP changed according to the type and concentration of impurity in the CO\({}_{2}\) stream. Impurities in the CO\({}_{2}\) stream also decreased both sweep efficiency and displacement efficiency, increased the IFT between gas and reservoir fluid, and hindered oil density reduction. The viscous gravity number increased by 59.6%, resulting in a decrease in vertical sweep efficiency. In the case of carbon storage, impurities decreased the performance of residual trapping by 4.1% and solubility trapping by 5.6% compared with pure CO\({}_{2}\) WAG. As a result, impurities in CO\({}_{2}\) reduced oil recovery by 9.2% and total CCS performance by 4.3%.  
CCUS; CO\({}_{2}\)-EOR; carbon capture and storage; CCS-EOR; impure CO\({}_{2}\); water alternating gas (WAG)  
3 August 2021  
25 August 2021  
27 August 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Key words:** CO\({}_{2}\)-EOR; carbon capture and storage; CCS-EOR; impure CO\({}_{2}\); water alternating gas (WAG)