esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis and Discussion
### Discussion  
#### 3.5.1 Relationship Between Conductivity, pH, and Chemical Dissolution Rate  
The chemical dissolution rates of carbonate rocks are significantly influenced by external environmental conditions. In static dissolution experiments conducted with phosphogypsum leachate at an initial pH of 2, post-reaction analyses quantified Ca\({}^{2+}\) and Mg\({}^{2+}\) concentrations in the leachate and calculated chemical dissolution rates for each rock specimen (Table 10). [PERSON] et al. [48] established a linear correlation between solution electrical conductivity (EC) and calcium ion content. Subsequent analysis of post-static dissolution leachates revealed a strong dependence of electrical conductivity on \(C\)(Ca\({}^{2+}\)), as evidenced by the positive correlation between these parameters in Figure 9. Conversely, an inverse relationship was observed between electrical conductivity and \(C\)(Mg\({}^{2+}\)).  
Throughout the rock dissolution process, the continuous ionization of H\({}^{+}\) from phosphogypsum leachate drives chemical reactions with carbonate minerals, consuming sub  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rock Specim ID** & **Conductivity** & & & **Chemical** \\
**Co** & **[uS/cm]** & **Ca\({}^{2+}\) [mg/L]** & **Mg\({}^{2+}\) [mg/L]** & **Dissolution Rate** \\  & & & & **[mg/(L-d)]** \\ \hline HY03 & 966 & 460.1 & 86.8 & 48.402 \\ HY04 & 992 & 458.2 & 87.8 & 48.585 \\ HY05 & 954 & 457.8 & 92.7 & 48.998 \\ HY06 & 929 & 420.3 & 109.8 & 52.127 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Electrical conductivity and chemical dissolution rate.  
Figure 9: Electrical conductivity, \(C\)(Ca\({}^{2+}\)) and \(C\)(Mg\({}^{2+}\)) concentration. Specimens HY03-HY06 under static dissolution conditions: identical pH (2.0) with varying fracture apertures.  
stantial H\({}^{+}\) ions while releasing newly generated ions into the solution. These dynamic ionic exchanges critically modulate the leachate's electrical conductivity, thereby regulating the dissolution kinetics between phosphogypsum leachate and carbonate rocks. Linear regression analysis of electrical conductivity versus chemical dissolution rates across four experimental groups revealed a positive correlation, with dissolution rates increasing proportionally to conductivity (slope = 0.08323). The linear fitting results are presented in Figure 10a.
# Intra Prediction-Based Hologram Phase Component Coding Using Modified Phase Unwrapping
## 4 Experiment and Result
### Unwrapping Result  
In this section, some unwrapping results are shown. Figure 11 shows the result of full unwrapping for the hologram used in the experiment and the result of unwrapping using the proposed method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segment Size** & **CU Size** & **Dynamic Range of Unwrapping** & **Codec** \\ \hline \multirow{3}{*}{\(64\times 64\)} & 64 & \multirow{3}{*}{\(32\)} & \multirow{3}{*}{\(4\pi\), \(8\pi\), \(16\pi\), \(32\pi\)} & \\ \cline{2-3}  & 16 & & \\ \hline \multirow{3}{*}{\(32\times 32\)} & 32 & \multirow{3}{*}{\(4\pi\), \(8\pi\), \(16\pi\), \(32\pi\)} & HEVC Intra, JPEG2000 \\ \cline{2-3}  & 16 & & \\ \hline \(16\times 16\) & 16 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coding conditions for the experiment.  
Figure 11: Result of the proposed phase unwrapping: (**a**) original phase, (**b**) full unwrapping, (**c**) proposed vertical phase unwrapping.  
Figure 10: Proposed modified unwrapping methods considering HEVC Intra prediction for (**a**) vertical, (**b**) horizontal, and (**c**) diagonal prediction.  
Figure 12 shows the unwrapping results for each direction. In each figure, it corresponds to the vertical, horizontal, and diagonal unwrapping in order from the top. Each figure is the result of different dynamic range. If the dynamic range is large, the linearity of the phase increases, and the error rate per bit increases as the amount of information per bit increases after normalization.
## 3 Results and Discussion
### VPM and XFOIL Simulations--DU40 Airfoil
Sudden drops of the mean suction can be observed in \(0.4<x/C<0.6\) on both the upper and lower surfaces. At \(\alpha=0^{\circ}\), XFOIL simulations indicate that pressure recoveries occur at \(x/C=0.6\) and 0.5 on the upper and lower surfaces, respectively, which agree with the instantaneous pressures obtained from VPM. For the higher angle of attack, \(\alpha=10^{\circ}\), both XFOIL and VPM show that the upper surface pressure recovery point moves upstream to about \(x/C=0.42\) but disagree on the lower surface recovery point (VPM gives about \(x/C=0.5\) while XFOIL gives \(x/C=0.62\)). For both angles of attack, VPM simulations give higher upper surface sections than the XFOIL results on the surface upstream to the pressure recovery point. However, after the pressure recovery, VPM gives lower sections. Figure 14: Instantaneous distribution of surface pressure coefficient of DU40 airfoil. For \(\alpha=10^{\circ}\), \(R_{C}=4000\) and \(Ut/C=\) (**a**) 59.315, (**b**) 59.58, (**c**) 60.035, and (**d**) 60.42. For \(\alpha=0^{\circ}\), \(R_{C}=60\),000 and \(Ut/C=\) (**e**) 3.048, (**f**) 3.146, (**g**) 3.36, and (**h**) 3.556. Note that the blue-colored symbols represent the upper surface while the red represents the lower surface.
## 2 Relevant Research
The above independent multipath routing algorithms are based on the maximum transmission distance per hop, without considering the balance of delay and energy consumption. In WMSNs applications with lax delay requirements, they can reduce the distance per hop and save energy consumption, in this section, delay and energy balance independent multipath routing algorithm is studied; When multiple source nodes send data to sink nodes through multiple paths at the same time, in order to avoid interference caused by path crossover, multiple source nodes need to coordinate transmission paths with each other. At present, there is little research on multi-source and multi-path routing. In this paper, a new multi-source and multi-path routing algorithm is proposed by applying the idea of software definition and controlling the routes of multiple source nodes centrally through sink nodes.
## 5 Autonomous Monitoring Networks  
The increase in the exploitation of marine resources enforces the necessity to develop new methods of environmental monitoring which, with the integration of new technologies, make the reaching of new frontiers possible in the field of biological features, namely for environmental, physical, and chemical parameters and sampling surveys [85]. In fact, in recent years, several projects had the goal of identifying new tools for the optimization of monitoring and sampling techniques for the improved assessment of an environmental status, which is the basis of several international management policies [86; 87]. The conception of new models of structures for data collection is necessary to cope with the different types of marine environments in which the survey is carried out to increase the operational range either in time or space [64]. While multiparametric cabled bases are a well-proven solution for the remote and continuous monitoring of marine environments [84], the implementation of more autonomous solutions is an important future prospect to ideally allow data collection at any depth and distance from the coast. In this light, network complementation with surface or aerial (radio frequency transmission) and underwater (acoustic) video monitoring may represent the smartest solution.  
Video monitoring, in fact, can also be realized by taking advantage of a fixed-point cabled camera installed over a platform [88] or a mobile underwater television (UWTV) consisting of a towed camera sled. The sled is positioned on the seabed and dragged along  
Figure 3: Front (left) and rear (right) sides of SCW adopted to produce a smart ASV for coastal monitoring (see Figure 2).  
a transport. Care must be taken to try to keep the vessel speed stable, as it is affected by the surface conditions [89]. The advantages of the UWTV solution lay in the fact that if used properly, it allows for obtaining a relatively constant measurement while being more accurate and less invasive than trawling surveys. For example, the Scottish government and Joint Nature Conservation Committee [90] considered the use of UWTVs an excellent solution to identifying any new areas potentially eligible to become MPAs [91].  
Upon the set-up of various autonomous monitoring vehicles (AUVs), a network composed of AUVs moving around a single buoy may produce timely maps of the marine areas under control (Figure 4). The network should contain a master buoy equipped with a wireless link receiving data from the AUVs and, eventually, satellite communication to an inshorc station in order to raise warning signals to the station as well as to check the real-time evolution of pollution events.
## 2 Materials and Methods
### Model Evaluation Metrics  
Four evaluating metrics were applied to validate the model's prediction accuracy: RMSE, MAE, PCC, and KTC. Specifically, RMSE measures the average difference between values predicted by a model and the actual values, MAE measures the average absolute differences between the predicted values and the actual target values, PCC measures the linear correlation with a number between \(-1\) and \(1\) to determine the strength and direction of the relationship between two variables, and KTC measures the ordinal association between two variables and a nonparametric measure of strength and direction of association.  
The equations are shown in the following:  
\[RMSE=\sqrt{\sum_{i=1}^{N}\frac{\left(\hat{y_{i}}-y_{i}\right)^{2}}{N}} \tag{1}\]  
\[MAE=\frac{1}{N}{\sum_{i=1}^{N}}{\left|\hat{y_{i}}-y_{i}\right|} \tag{2}\]  
\[PCC=1-\frac{\sum_{i=1}^{N}\left(y_{i}-\overline{y}\right)\left(\hat{y_{i}}- \overline{y}\right)}{\sum_{i=1}^{N}\left(y_{i}-\overline{y}\right)^{2}\sum_{i= 1}^{N}\left(\hat{y_{i}}-\overline{y}\right)^{2}} \tag{3}\]  
where \(y_{i}\) represents the ground truth value of regressive models; \(\hat{y_{i}}\) denotes the \(i\)th sample of prediction values of the regression model; and N is the sample number.  
\[KTC=\frac{P-Q}{\sqrt{(P+Q+T)(P+Q+U)}} \tag{4}\]  
where \(P\) is the number of concordant pairs, \(Q\) is the number of discordant pairs, \(T\) is the number of ties only in \(x\), and \(U\) is the number of ties only in \(y\).
## 2 SAS Research Activity  
The SAS is placed in Livingston Island (\(62.7\,^{\circ}\)S, \(299.6\,^{\circ}\)E; geomagnetic latitude \(52.6\,^{\circ}\)S) in the South Shetlands archipelago. It was inaugurated in 1988 and it is managed by the Spanish National Research Council (CSIC), which belongs to the Spanish Ministry of Science and Innovation (MICINN). Spain endorsed the Antarctic Treaty System in 1982, by which only scientific and technological issues can be developed in the Antarctica. It is MICINN who is in charge of the scientific research developed at the SAS and awards research groups grants to undertake research activities there.  
The Spanish research activities in Antarctica are focused on the study of the biological and geological environment as well as the physical geography in Livingston and Deception islands and some places in the Antarctic Peninsula. Some examples of this research activities include: (i) tracking the changes in the temperature of permafrost to model the weather behaviour; (ii) study the geomorphology and movement of Johnsons-Hurd glaciers, which show a large sensitivity to temperature changes as they are very close to the melting point; (iii) study the variation of the magnetic field at Livingston Island to complete the map of the Earth's magnetic field and (iv) study the layers of the ionosphere and their relationship with the Sun activity. Many of these research projects collect data of temperature, position, magnetic field, height, _etc._ that are stored in data loggers. Also some of the data are transmitted to research laboratories in Spain.  
In this project, we work on the design of a system for oblique ionospheric sounding and to transmit the data from remote sensors located at the SAS to Spain. The situation faced in this research project is similar to other situations where communication is scarcely possible due to economic or coverage problems. Therefore, the solutions and conclusions presented in this work may be adopted in other situations such as communications in developing countries or communications in the middle of the ocean.
## 3 Results and Discussions
### Chemical Characterization of Pyrolytic Oil
#### 3.3.3 GC-MS
In addition, the HHV values in the range of 46.9 to 47.0 MJ/kg were obtained using the components of each oil sample shown in Figure 3. These results confirm those determined from the elemental analysis of the oil samples (Table 3). Figure 4: Hydrocarbon type distribution of the pyrolytic oil samples using different zeolites and without catalyst. The catalytic conversion and product yield of the liquid fraction from pyrolytic oil obtained by thermal pyrolysis shown in Figure 6 reflect that the three catalysts are gas-selective but provide important amounts of useful or valuable liquid cuts (jet fuel and diesel) that have to be added for the consideration of activity when liquid fuels are the target. In this way, HY zeolite is the most active, followed by HBeA and HUSY zeolites. Two types of acid sites are present in studied zeolites: Bronsted acid sites and Lewis acid sites. In general, an increase in the Si/Al molar ratio typically decreases the acidity of this type of material [48,49]. Figure 7 shows the catalytic conversion as a function of the Br\(\O\)nsted/Lewis acid site ratio. These results show that the HUSY catalyst (the zeolite with the highest Si/Al molar ratio and lowest Br\(\O\)nsted/Lewis acid site ratio) had lower activity than HY and HBeta catalysts. Thus, the fraction of light products in pyrolytic oil from catalytic pyrolysis with the HUSY catalyst was relatively low. In summary, it can be appreciated that conversion decreases when the Br\(\O\)nsted/Lewis acid site ratio also decreases, while the non-cracked bottom yield increases. Generally, the bottom fraction is composed of a large amount of paraffin  
Figure 5: Liquid product yield of the pyrolytic oil samples from thermal and in situ catalytic pyrolysis. Figure 6: Product yield of catalytic conversion of the liquid fraction obtained by thermal pyrolysis. [PERSON] et al. [50] studied the catalytic pyrolysis of LDPE and found differences in the catalytic activity of the materials used mainly due to differences in their acid properties. [PERSON] et al. [51], while studying the co-pyrolysis of poplar wood sawdust and HDPE with acid-modified ZSM-5 as a catalyst, found that acid treatment affects the catalytic activity of ZSM-5 by changing the number of acid sites. On the other hand, since mesopores in the zeolites seem to present a selective catalytic behavior [48], a study of the conventional conversion, which considers gases and naphtha, and the liquid yield in the function of mesopores area was also performed. Figure 8 shows the decrease in the conversion and yield of the liquid fraction as the mesopore area increases. In this sense, the zeolite with the lowest mesopore area (HY zeolite, 78 m\({}^{2}\)/g) presented the highest conversion (68%) and amount of liquid fraction (37%). Conversely, the pyrolysis performed with HBeA zeolite, the zeolite with the highest mesopore area (218 m\({}^{2}\)/g), showed the biggest conversion (71%) and liquid content (43%).
## 4 Experimental Studies
### Simulation of the DWTA Problem
#### 4.2.3 Setting of Experimental Parameters  
To verify the optimization performance of the IMOPSO algorithm in solving multiobjective DWTA problem, this section also uses IMOPSO, MOPSO, NSGA-II and MOEA/D for comparative analysis. Three different combat scenarios, i.e., small-, medium- and  
Figure 4: Encoding scheme of DWTA problem.  
Figure 3: Decision-making process of DWTA problem.  
large-scaled cases are randomly generated in line with the number of weapons and targets. For each case, we generate the killing probability matrix \(P(s)\), ammunition consumption matrix \(U(s)\), feasibility matrix \(F(s)\) and fire transferring feasibility matrix \(FTF(s)\), and the elements in the matrix are randomly generated. The related parameters of DWTA problems are as follows: the number of weapons/targets/stages are \(5/8/3\), \(15/24/5\) and \(30/50/8\) in small/medium/large-scale cases, respectively. In all three cases, \(n_{i}\) is 1, \(N_{i}\) is 2. \(m_{j}\) is 1 in small-scale case, and \(m_{j}\) is 2 in medium-scale and large-scale case. The value vector of targets comes from Ref [40]. The number of iterations of all algorithms in the three scales are 100,150,200, respectively. The crossover probability and mutation probability of NSGA-II and MOEA/D are 0.9 and 0.1, respectively. Other parameters of the four algorithms are the same as those of solving the benchmark functions.
## 3 Results and Discussion
### Effects of Operational Parameters Photo-Fenton like Catalyst
#### 3.3.3 Effect of Initial \(\text{S}_{2}\text{O}_{8}{}^{2-}\) Concentration  
The effect of \(\text{S}_{2}\text{O}_{8}{}^{2-}\) concentration on RR198 and COD removal was investigated at a constant catalyst dosage of 0.3 g/L, reaction time 25 min, and at pH 6.5 (Figure 7).  
According to Figure 7, the highest RR198 and COD removal efficiencies were 99% and 95% respectively. With the increase of PS concentration to 5 mM/L, the removal efficiency increased significantly. However, PS would be generating more \(\text{SO}_{4}{}^{\bullet-}\) at higher PS concentration. Hence, radical reactions may be occurring prior to the radical-organic reactions under these reaction conditions, when the dosage exceeded 5 mM/L, the removal efficiency decreased. In addition, excess sulfate radicals  
Figure 7: Effect of PS dose on RR198 and COD removal efficiencies, optimum conditions: [RR198 = 25 mg/L, catalyst dosage = 0.3 g/L, reaction time: 25 min and pH = 6.5].  
are known to obstruct CFO e\({}^{-}\) of the catalyst, which could induce consumption of SO\({}_{4}\)\({}^{-\bullet}\) at higher concentration of the oxidant (Equations (14) and (15)) [46]. Therefore, the overall removal efficiency is reduced when PS concentration is over 5 mM/L.  
\[\mathrm{SO_{4}}^{-\bullet}+\mathrm{S_{2}O_{8}}^{2-}\rightarrow\mathrm{SO_{4}}^ {2-}+\mathrm{S_{2}O_{8}}^{-\bullet} \tag{14}\]  
\[\mathrm{SO_{4}}^{-\bullet}+\mathrm{CFO\,e^{-}}\rightarrow\mathrm{SO_{4}}^{2-}+ \mathrm{CFO}. \tag{15}\]  
The high efficiency of dye removal in PF like catalyst may be ascribed to active radicals (SO\({}_{4}\)\({}^{-\bullet}\)) oxidation.
## 2 System and Channel Model
### Log-Normal Tracking Channel  
The centroid accuracy of detectors including image sensors or position-sensitive detectors can be significantly degraded by beacon intensity fluctuations due to atmospheric turbulence over the tracking channel. Over the years, a number of statistical models have been introduced to describe intensity fluctuations of a propagated beam in terms of probability density function. The log-normal model is restricted to weak turbulence conditions with scintillation indexes of less than 1.0. Taking into account the exposure time, the average number of received beacon photoelectrons can be calculated as  
\[K_{s}=\frac{\eta_{Q}\lambda\langle P_{R}\rangle}{\hbar c}T_{s}, \tag{8}\]  
where \(\eta_{Q}\) is the quantum efficiency of the detector; \(T_{s}\) is the exposure time in seconds; and \(\hbar\) and \(c\) are the Plank's constant and light speed, respectively. Without loss of generality, \(K_{s}\) can also be considered a log-normal distributed random variable instead of the received intensity. Therefore, the probability density function of the log-normal model is defined as [31]  
\[f(K_{s})=\frac{1}{\sqrt{2\pi\sigma_{k}^{2}}K_{s}}\exp\left(-\frac{(\ln K_{s}-m _{k})^{2}}{2\sigma_{k}^{2}}\right), \tag{9}\]  
where  
\[m_{k}=\ln(E[K_{s}])-\frac{\sigma_{k}^{2}}{2}, \tag{10}\]  
\[\sigma_{k}^{2}=\ln\Bigl{(}\sigma_{si}^{2}+1\Bigr{)}. \tag{11}\]  
Here, \(m_{k}\) and \(\sigma_{k}^{2}\) denote the mean and variance of \(\ln(K_{s})\), respectively, and \(\sigma_{si}^{2}\) is the scintillation index to describe the intensity fluctuation. The scintillation index is expressed by various formulas depending on turbulence strength, wave types, and wave propagation path such as uplink, downlink, and horizontal path. In the case of both weak turbulence condition and uplink, \(\sigma_{si}^{2}\) can be written for a spherical wave as [12]:  
\[\sigma_{si}^{2}=2.24k^{7/6}\sec^{11/6}(\theta_{z})\int_{h_{s}}^{h_{u}+H}C_{n} ^{2}(h)(H+h_{0}-h)^{5/6}\biggl{(}1-\frac{H+h_{s}-h}{H}\biggr{)}^{5/6}dh. \tag{12}\]
## 3 SRB Impact on Environments and Humans
### Research in the Field of SRB Bacteriophages
As generally, it is thought that all or at least most bacteria types possess at least one associated phage, it should be assumed that most or all SRB also have associated phages. A variety of methods exist toward phage discovery, the most traditional of which is simply isolation as plaques against a given bacterial host [118]. Generally, this isolation is most conveniently and therefore most typically takes place when working with bacteria that are able to readily form lawns on agar surfaces. For bacteria requiring anaerobic environments for growth, phages are still readily isolated and propagated, e.g., as within anaerobic chambers. Generally, many more phages have been isolated, and their role in bacterial infection has been studied. Here, we provide a comprehensive overview of SRB-infecting phages isolated from specific SRB hosts. Previous studies induced bacteriophages from cultures of _Desulfovibrio vulgaris_ NCIMB 8303 and _Desulfovibrio desulfuricans_ ATCC 13541 by UV light. The UV effect during 9 to 10 h resulted in the phage maximum yield. Nucleic acids of phages from both cultures (_D. vulgaris_ NCIMB 8303 and _D. desulfuricans_ ATCC 13541) were cut by restriction endonucleases (specific for double-stranded DNA). DNAs of phages from these two cultures showed different restriction enzyme cleavage patterns. The homology was not noticed between a 25 kb phage DNA of _D. vulgaris_ and _D. desulfuricans_. The protein profiles of isolated phages from these two cultures were also analyzed, and it was found that the _D. vulgaris_ phage contained two major bands (Mr values of 37,000 and 56,000) and the _D. desulfuricans_ phage contained only one major band (Mr 38,000) [125]. Bacteriophage isolated from _D. vulgaris_ was defined through establishing a preliminary endonuclease restriction map. The mapping succeeded in linking four BamHI fragments into two DNA segments, though not linking was detected between the two segments. The obtained results from the authors lead to the conclusion that two phages were induced from _D. vulgaris_ culture. The results in the study were supported by the size approximation of restriction enzyme fragments, electron micrographs, and density gradients [126].