esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Bioaugmentation on Biogas Yields and Kinetics in Anaerobic Digestion of Sewage Sludge
###### Abstract  
Bioaugmentation with a mixture of microorganisms (Bacteria and Archaea) was applied to improve the anaerobic digestion of sewage sludge. The study was performed in reactors operating at a temperature of 35 \({}^{\circ}\)C in semi-flow mode. Three runs with different doses of bioaugmenting mixture were conducted. Bioaugmentation of sewage sludge improved fermentation and allowed satisfactory biogas/methane yields and a biodegradation efficiency of more than 46%, despite the decrease in hydraulic retention time (HRT) from 20 d to 16.7 d. Moreover, in terms of biogas production, the rate constant k increased from 0.071 h\({}^{-1}\) to 0.087 h\({}^{-1}\) as doses of the bioaugmenting mixture were increased, as compared to values of 0.066 h\({}^{-1}\) and 0.069 h\({}^{-1}\) obtained with sewage sludge alone. Next-generation sequencing revealed that _Cytohaga_ sp. predominated among Bacteria in digesters and that the hydrogenotrophic methanogen _Methanoculleus_ sp. was the most abundant genus among Archaea.  
Keywords:anaerobic digestion; wastewater sludge; bioaugmentation; metagenome; NGS +
Footnote †: journal: Journal of Environmental Research and Public Health
# Fluoride and Pineal Gland
## 3 Calcium Accumulation in the Pineal Gland  
Calcium accumulates in the pineal gland in the apatite structure, similar to that found in bones and teeth [63, 85, 86], and as calcium carbonate (calcite) [87]. The process is initiated in childhood [88], and even in newborns [89, 90], so some scientists see it as a physiological phenomenon [64]. It is, however, difficult to agree with such a conviction in the face of ample evidence showing the relationship between pineal calcification and various pathological states. This includes mental illnesses and disorders [91, 92, 93], neurodegenerative disorders [94, 95], primary brain tumors [96], ischemic stroke [97], migraine [98], and sleep disorders [99]. The accumulation of calcium in the pineal gland is also related to aging processes [100] (Figure 4).  
In the study in adolescents and adults, [PERSON] et al. [61] demonstrated an inverse correlation between the degree of pinealocyte calcification and pinealocyte count. Although there was no significant  
Figure 4: Computer tomography (CT) scan through the brain with calcification of the pineal gland (case courtesy of [PERSON], Radiopaedia.org, rID: 11770).  
Figure 3: Biosynthesis of melatonin.  
correlation between gland calcification and plasma melatonin concentration, it was noted that the reduction in pinealocyte count caused by calcification was accompanied by a reduction in melatonin synthesis. Hence, the conclusion that pineal gland calcification has an indirect effect on the production and secretion of this hormone. These observations were confirmed by [PERSON] et al. [101], who, using magnetic resonance imaging, showed a positive correlation between the size of the uncalcified part of the pineal gland and the concentration of melatonin in saliva.  
According to some authors, the concentration of melatonin in the cerebrospinal fluid plays a decisive role in the regulation of circadian rhythms, while plasma hormone concentrations are of little importance in exerting biological effects in this respect [102; 103]. As pineal calcification results in the reduction of melatonin concentration in the cerebrospinal fluid, its relation to diseases of the central nervous system becomes understandable. The pathomechanism of this relationship is to reduce the antioxidant effects of melatonin, which favors neuronal damage by reactive oxygen species (ROS) and, thus, to accelerate the development of neurodegenerative changes [64; 70]. For example, it has been found that the concentration of melatonin in the cerebrospinal fluid in Alzheimer's disease is only 20% of that recorded in healthy individuals [104].
## 4 Simulation Results and Performance Analysis
### Number of Users Accessing the System in a Non-Uniform Scenario  
Under the condition of limited frequency resources, the actual number of users accessing the system is an important indicator for measuring different methods when faced with the massive user access demand that exceeds the upper limit of access. In this section, we simulate the actual user access volume of the MHSTFC-UD as the number of users changes. It is assumed that users are randomly distributed within the beam and that each user only sends a short message once during the satellite's one-time pass. The simulation results are shown in Figure 6 and the maximum user capacity is shown in Table 5.  
As the number of users under the satellite beam increases from 0 to the order of a billion, the BH and UFFR reach the upper threshold limit before the MHSTFC-UD. Using the MHSTFC-UD, the access needs of all users can be met when the number of users does not exceed \(2\times 10^{9}\), and it is not affected by the user distribution. When the number of users exceeds \(2\times 10^{9}\), the MHSTFC-UD cannot meet all user access needs, and the maximum number of user connections that can be accommodated is \(5.8\times 10^{9}\). The degree of fluctuation of the maximum number of users that MHSTFC-UD can access is affected by the user distribution. However, compared with the traditional scheme, the MHSTFC-UD uses a scheme that jointly distinguishes different users from four dimensions, which can increase the number of connected users by about 241 to 1585 times.  
Next, the actual user access volume under the MHSTFC-UD and under the MHSTFC with fixed-band resource allocation and fixed center and edge area division is compared.  
Figure 7 shows a comparison of the MHSTFC-UD and MHSTFC when the number of users in the beam does not exceed \(2\times 10^{9}\) and both can meet the access needs of all users.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MHSTFC-UD** & **MHSTFC** & **UFFR** & **BH** \\ \hline Upper threshold for full user access & \(2\times 10^{9}\) & \(1\times 10^{9}\) & \(4\times 10^{6}\) & \(2\times 10^{7}\) \\ Maximum user capacity & \(6.5\times 10^{9}\) & \(5.4\times 10^{9}\) & \(4.1\times 10^{6}\) & \(2.5\times 10^{7}\) \\ Multiplication factor in maximum user capacity & 1585.37 & 1317.07 & 1 & 6.10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of maximum user throughput in the case of an even user distribution.  
Figure 6: The number of actual users accessing the system changes with the total number of users under the beam.  
The number of users that can access each sub-beam is the same. When the number of users exceeds \(2\times 10^{9}\), both cannot meet the access needs of all users. However, the MHSTFC-UD can dynamically adjust resource allocation based on the user distribution and can meet the access needs of more users than the MHSTFC. Therefore, when the total number of users is \(6\times 10^{9}\), the MHSTFC-UD can meet the access needs of about 27.5% more users than the MHSTFC.
# Quantum Photonic Simulation of Spin-Magnetic Field Coupling and Atom-Optical Field Interaction
## 4 Quantum Geometric Phases
### Optical Measurement of Geometric Phases  
Finally, we present how to measure the geometric phase starting from the measurements of the single-photon detection probability, which can be extracted by a prism-waveguide coupler [10], as shown in Figure 5. We focus on the spin-magnetic field interaction simulation case. By assuming that the input state is a single photon excited in the mode \(e_{0}(x,y)\) and taking into account the relationships given by Equation (52), the following final state is obtained after the two gratings:  
\[|L(z)\rangle=(\cos^{2}\frac{\alpha^{\prime}}{2}+\sin^{2}\frac{\alpha^{\prime} }{2}e^{i\phi_{g}})|1_{0}\rangle+\cos\frac{\alpha^{\prime}}{2}\sin\frac{\alpha^ {\prime}}{2}(1-e^{i\phi_{g}})|1_{1}\rangle \tag{57}\]  
The probability of the detection of a photon in Mode 0, that is \(P_{0}\), or in Mode 1, \(P_{1}\), is a function of the geometric phase, that is,  
\[P_{1}=\frac{\sin^{2}\alpha}{2}\,(1-\cos\Phi_{g}),\ \ \ P_{0}=1-P_{1} \tag{58}\]  
If \(\Phi_{g}=2\pi\), then \(P_{1}=0\) and \(P_{0}=1\), and if \(\Phi_{g}=\pi\), then \(P_{1}=\sin^{2}\alpha\) and \(P_{0}=\cos^{2}\alpha\). Therefore, from the measurement of \(P_{1}\) and \(P_{0}\), the phase \(\Phi_{g}\) is obtained. In Figure 5 it is shown the projective measure of states \(|1_{0}\rangle\) and \(|1_{1}\rangle\) by a prism-waveguide coupler, which projects these state in different spatial directions. Finally, note that by using a coherent state, these probabilities are proportional to the intensity of the light, what can be called a semiclassical optical characterization, or in the most technical way, the geometric phase is also acquired by the classical fields, but would rigorously correspond to the so-called Hannay phase [33].
## 3 Results and Discussion
### Evaluation of Poly (MMA-BA-ST) for Oil-Based Drilling Fluid Plugging Properties
#### 3.5.2 Core Permeability Experiment  
From the mud cake experiment, it can be seen that the plugging agent has the best plugging effect at the dosage of 0.5%. To verify the plugging effect of poly (VS-St-BMA-BA) on real cores, the permeability of the sealer at 105 \({}^{\circ}\)C was tested using a core permeability experiment, and the results are shown in Table 1. From Table 1, it can be seen that compared with pure white oil, the permeability of white oil after adding 0.50% poly (MMA-BA-ST) is 0.41 \(\times\) 10\({}^{-3}\) mD; compared with pure white oil, its permeability decreased by 88.41%. The permeability results indicate that poly (MMA-BA-ST) has good plugging performance and can be used as a plugging agent for oil-based drilling fluids.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Concentration/\%**} & **Permeability after** & **Plugging Rate/\%** \\  & **Plugging/10\({}^{-3}\) mD** & **Plugging Rate/\%** \\ \hline
0.00 & 3.54 & / \\
0.50 & 0.41 & 88.41 \\ \end{tabular}
\end{table}
Table 1: Evaluation of plugging effect of poly (MMA-BA-ST) on artificial cores at 105 \({}^{\circ}\)C.  
Figure 7: Emulsion-breaking voltage change curve after aging of oil-based drilling fluids with the addition of poly (MMA-BA-ST).
## 5 Conclusions  
Wireless smart sensor networks are a viable solution for monitoring the condition, in particular the pressure and hence leaks, of buried water pipelines. Their advantage over other commonly used leak detection methods is that they have a degree of redundancy as individual faulty nodes do not render the whole system obsolete and allow for continuous monitoring without operator intervention. Ultra-low power smart wireless sensor networks allow them to stay operational for extended periods of time without maintenance. This makes them now viable for both existing and novel power supplies. The ultra-low power consumption of 2 \(\upmu\)W was achieved by a careful hardware selection and also adaptation of the firmware.  
A non-invasive (to the pipe) pressure measurement method based on FSR sensors has been presented. This method allows easy installation of the sensor nodes on pipes without jeopardizing the pipes' structural integrity. The FSR sensors detected pressure changes both in the laboratory tests as well as in the field trials. The laboratory data showed that the profile of the pressure drop caused by the leak is different for the measurements before and after the leak. This might be used to determine the location of the leak. The sensor nodes were successfully deployed in field trials and they collected temperature and relative pressure data. Leak tests and daily pressure variations were clearly registered by the nodes showing a response in both the relative pressure sensor (FSR) as well as the temperature sensors. It is postulated that the temperature sensors have the potential to be combined with the FSRdata to identify leaks as opposed to 'normal' pressure drops. This further validates the potential of UWSNs for pipeline monitoring.  
The authors gratefully acknowledge the support by the University of Birmingham and UK Water Industry Research (UKWIR) who jointly funded this project.  
All authors have contributed equally to the paper preparation.  
The authors declare no conflict of interest.
# Aerodynamic Efficiency Improvement on a NACA-8412 Airfoil via Active Flow Control Implementation
## 3 AFC Implementation  
### Jet Location and Mesh Modification  
In order to evaluate the influence of AFC on aerodynamic efficiency, three design variables have been studied: the jet inclination angle \(\theta\), the jet momentum coefficient \(C_{\mu}\) and the non-dimensional forcing frequency \(F_{*}^{+}\). In the present study, the groove width \(h\) and location were kept constant. The design of the AFC synthetic jet begins by studying its location over the airfoil. According to [33] among others, the optimum location is just upstream of the boundary layer separation point, which can clearly be seen in Figures 7 and 8. The separation point is located at \(x/C=0.1045\), considerably close to the LE. This is a consequence of the high AoA used (\(15^{\circ}\)) and of the geometry of the airfoil selected, as it shows a pronounced curvature near the leading edge. At the separation point \(C_{F}\) begins to adopt negative values due to the adverse pressure gradient, leading to flow separation. Based on what it was defined in reference [4], the synthetic jet has been placed at a location of \(0.01C\) upstream of the flow separation point, with a slot width of \(0.01C\) (\(1\%\) of the chord), from \(x/C=0.08\) to \(x/C=0.09\). The next step is to design the AFC velocity signal, which must be time periodic as the jet corresponds to the ZNMF topology. Thus, in the following subsections, the frequency, amplitude and jet inclination angle will need to be evaluated. The mesh was further refined in the slot region due to the quick variation of the flow properties close to the jet. For this reason, the number of partitions at the vicinity of the jet have been increased as it can be seen in Figure 5. The total number of mesh cells increased to \(162\),\(789\) for the cases where AFC was considered. Regarding the instantaneous velocity along the groove it was decided to use a top hat velocity distribution.  
Figure 8: Averaged streamlines of the baseline case.  
Figure 7: Baseline case friction coefficient comparison.
## 3 Results and Discussions  
The results from uniform twist control and segment twist control studies are presented in this section. The mechanism of uniform twist control using single-harmonic inputs and multi-harmonic inputs to reduce the rotor power in forward flights is investigated. The results for optimal segment twist control are discussed and compared with those that were obtained for uniform twist control.
# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 2 Theoretically Background
### Data Preparation  
According to [PERSON] et al. [16], since the beginning of the 1980s data mining has gained increasingly more attention as a means of obtaining knowledge.  
The preprocessing of raw data facilitates the stabilization of the mean and variance and aims to provide a structural, reliable, and integrated data source, and it is a very critical and complex step, which allows guaranteeing reasonable results, whether the analysis is concerned with exploratory data mining, classification, or construction of a good and robust prediction model [17; 18; 19; 20; 21].  
Data cleaning and preparation consume approximately 80% of the total data engineering effort, as it is a process that may require many transformations and be repeated many times [21; 22; 23].  
Through good data cleaning, it is possible to improve performance and quality [17; 24; 25], as well as to isolate characteristics of interest and eliminate elements that \"bother\" the theoretical models [26].
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 3 Joint Cell Anti-Jamming Map Based on Dual Connection
### Maximal Clique Approximate Search Algorithm  
In order to resolve the problem that clusters with the same nodes can be simultaneously scheduled, we propose the heuristic search algorithm for the maximum clique approximate search, and the algorithm failure caused by the vehicular network not arranged according to the cellular network structure can be avoided when the complement graph of the conflict graph composed of the cluster set is searched for the maximal clique. Therefore, firstly, all nodes in the graph are traversed and all nodes connected to the visited nodes are put into the alternative point set _Cd_. The visited nodes are put into the point set _Cm_. After selecting the largest node in _Cd_and checking whether it is connected to all _Cm_nodes, we add it to _Cm_ if so. If not, the node is not added and then deleted. We iterate over and over again until _Cd_is an empty set. The maximum cliques in the graph can be searched out as much as possible by first checking the node with the largest degree, and all nodes are traversed once to ensure the inter-cluster fairness of all cell clusters. The process of the algorithm is detailed by Algorithm 2.  
```
1:Input:\(G(Vn,Em)\), \(Con(C_{V1},\)\(C_{V2},...,C_{Vn})\), \(Ang(A_{V1},\)\(A_{V2},\)\(...,A_{Vn})\)
2:for\(i=1\) to n do
3:\(K=\text{length}(C_{Vi})\)
4:while\(K\geq 1\)do
5:for\(j=1\) to \(K\)do
6:if\(A_{Vi}\) (\(j\))\(>\)\(A_{Vi}\) (\(j+1\)) then
7:temp = \(A_{W}\) (\(j+1\))
8:\(A_{Vi}\) (\(j+1\)) = \(A_{Vi}\) (\(j\))
9:\(A_{Vi}\) (\(j\)) = temp
10:temp = \(C_{Wi}\) (\(j+1\))
11:\(C_{Vi}\) (\(j+1\)) = \(C_{W}\) (\(j\))
12:\(C_{Vi}\) (\(j\)) = temp
13:endif
14:endfor
15:\(K=K-1\)
16:endwhile
17:endfor
18:for\(i=1\) to \(n\)do
19:for\(j=1\) to \(\text{length}(C_{Vi})\)do
20: Put \(\{V_{i},V_{j},V_{j+1}\}\) into _Ci_
21:endfor
22:endfor
23:Delete the repeating items of the _Ci_
24:Output: _Cti_
```  
**Algorithm 2** Clique Algorithm for Interference Graph  
By Algorithm 1, the cell cluster aggregation _Cli_under the interference graph is obtained in a finite time complexity. By Algorithm 2, we can obtain the maximum clique _Cm_of the scheduling conflict graph complement graph. Through _Cli_and _Cm_given in Algorithms 1 and 2, we can further explore how to schedule the base station to maximize the user's performance and maintain the scheduling fairness among users.
## 3 Temperature-Aware Routing Protocols
### TARA (Thermal-Aware Routing Algorithm)  
TARA, or the Thermal-Aware Routing Algorithm [21], is known as the first protocol that introduced temperature as a routing protocol metric. As explained in the previous section, TARA also considers two sources as the major sources of heat: antenna radiation and power dissipation of node circuitry. However, since nodes are small in size and expected to be as simple as possible, it is assumed that there is no temperature sensor inside the node to measure the temperature. Therefore, the temperature is measured by observing sensor activities, from antenna radiation and power dissipation of the node circuitry.  
The general operation of this protocol is as follows. In the setup phase, each node exchanges neighborhood information, creates its own neighbor list and collects the number of hop information, so that every node knows how to reach the sink node. Next, in the data forwarding phase, the nodes having data to send will forward the packet to the next hop, until it reaches the sink node. A node whose temperature exceeds a predefined threshold value will be marked as a hotspot node, and any packet having a hotspot node as its destination node will be buffered until the estimated temperature drops. If the buffered packet period exceeds the timeout period, it will be dropped.  
Figure 2: The taxonomy of temperature-aware routing protocols in WBSNs.  
When the hotspot node is an intermediate node, the packet will be routed through a different path and, thus, avoid the hotspot area. This strategy is called withdrawal strategy, which operates as follows. If the next hop node is a hotspot node, the node will check into the forwarding set of this route whether there is any other available next hop node to send the packet to. Should there be no more available next hop nodes, the packet will be forwarded back to the previous node. This previous node will try to forward the packet using an alternative path or might again forward it back to its previous node. The information about hotspot nodes is carried by the packet when the withdrawal strategy is used. A sample of how TARA works is shown in Figure 3.  
Basically, TARA tries to avoid the hotspot area by observing the neighboring nodes' temperature and detour the packet using the withdrawal strategy. This strategy causes high delay and low network lifetime, since the packet will be roaming around the network for some considerable amount of time. However, this withdrawal strategy, which avoids the hotspot nodes, will balance the load in the network, since hotspot nodes are considered as high load nodes, _i.e._, nodes that already served a lot of packets.
## 1 Introduction
In addition, it is hard to establish precise observation noise models in underwater integrated navigation models because the sensor performance changes with the environment. Therefore, the variational estimation (VE) method was introduced. The method is simple and easy, and the approximation degree increases in each iteration [27]. Unlike other optimization algorithms, there is no need for the VE method to calculate Jacobian matrices, regardless of calculating the joint probability density function (PDF) [28]. Therefore, the VE-based method is effective for obtaining accurate measurement noise. A few VE-based algorithms have been proposed, such as neural network-assisted variational Bayesian adaptive CKF [29] and the variational Bayesian adaptive cubature information filter [30]. However, in these algorithms, the influence of inaccurate process noise is ignored, and the information filter performs poorly when the state dimension is low. Although various solutions have been proposed for underwater navigation in recent years, and great progress has been made [31; 32; 33], the measurement information loss in underwater environments is still an open issue. In this paper, a switched variational estimation filter (SVEF) algorithm is proposed, which combines the merits of the Gaussian filter and VE method. The Gaussian filter is used to obtain a more accurate state vector information estimation, and the VE method can help with computing the optimal solution of measurement noise and with predicting the error covariance matrix and measurement loss information. The proposed SVEF algorithm has high estimation accuracy and good robustness, especially when working in ocean and marine environments for long periods of time. The major contributions are as follows:  
1. A new method searching for the optimal solution is designed, in which the initial loss parameter makes a valuable contribution toward state estimation;2. The problem of measurement information loss in a continuous discrete underwater navigation model (i.e., the state model is continuous and the measurement model is discrete) is effectively solved by the proposed SVEF algorithm;
3. In the VE method, linear or nonlinear filtering is employed to effectively determine the state vector; moreover, the measurement error covariance matrix and predicted error covariance matrix are accurately calculated. The remainder of the article is presented as follows: The underwater navigation system and the preliminaries of the measurement information loss are given in Section 2. Section 3 proposes an SVEF algorithm based on the VE method, which is a novel hybrid algorithm for time-varying noise matrices. The proposed SVEF is tested and evaluated in Section 4 by using the raw data collected in real underwater experiments. Section 5 concludes the paper.