esa-sceva/satcom-chunk-collection huggingface dataset:
# Biodegradation of Picolinic Acid by _Rhodococcus_ sp. PA18
## 3 Results
### Activity of the Enzyme in the Cell Extract  
As shown in Figure 6A, a new absorption peak at 310 nm appeared, the intensity of which increased significantly, when PMS acted as an electron acceptor. This corresponded to the absorption maximum of the product 6 HPA. However, no 6 HPA was produced without the electron acceptor or when the electron acceptor was MB or DCIP (data not shown). These results indicate that the cell extract was able to convert PA into 6 HPA, with PMS as the electron acceptor. The Km of the crude enzyme for PA at pH 7.0 and 30\({}^{\circ}\)C was 68.86 \(\pm\) 14.85 \(\upmu\)M, and the specific activity was 154.17 U/mg. To verify the quantitative relationship between the PA and PMS, the amount of PA was changed continuously from 0.0 to 0.5 mM, whereas that of PMS was maintained at a constant of 0.1 mM, as shown in Figure 6B. The amount of product 6 HPA increased with the concentration of PA, and the PA:6 HPA ratio was 1:1. Subsequently, the amount of 6 HPA formed did not change, even when the substrate (PA) concentration was increased, indicating that the optimal molar ratio of PA:PMS was 1:1. The hydroxyles of aromatic or N-heterocyclic aromatic compounds that incorporate oxygen into the product are Rieske non-heme iron aromatic ring-hydroxylating oxygenase or cytochrome P450s [29; 36]. In strain _Pseudomonas putida_ KT2440, the nicotinic acid can be converted to 6-hydroxynicotinic acid by a two-component hydroxylase (NicAB), whose electron transport chain to the molecular oxygen includes a cytochrome c domain [37]. Interestingly, unlike other studies, the hydroxylase in the strain PA18 cell extract that can convert PA to 6 HPA uses PMS as an electron acceptor.  
Figure 6: Measurement of the enzyme activities in cell-free extracts. (**A**) Spectrophotometric changes during the transformation of PA. The spectra were recorded every 1 min. The arrow indicates the directions of the spectral changes. (**B**) Relationship between PA and 6 HPA while the concentration of PMS was constant. PA concentration was increased from 0.00 to 0.50 mM whereas the phenazine methosulfate (PMS), concentration was constant at 0.10 mM. The data were calculated from three independent replicates, and error bars indicate standard deviations.
## 3 Results
### Statistical Patterns of Bedform Features
#### 3.1.1 Dune Length Variation Characteristics  
The probability density distribution of dune length, as fitted by the program, is depicted in Figure 6. Panels (a) through (d) of Figure 6 illustrate the distribution of the dune lengths (\(Lc\), \(Lt\), \(Lupcr\), and \(Ldowncr\)) for the 2D bedforms, while panels (e) through (h) display the distribution for the 3D bedforms. The results indicate that the four dune lengths (\(Lc\), \(Lt\), \(Lupcr\), and \(Ldowncr\)) for both the 2D and 3D bedforms adhere to a lognormal distribution. The parameters of the lognormal distribution for the dune length of the 2D and 3D bedforms are listed in Table 2. According to Table 2, the mean log standard deviations for the dune length of the 2D and 3D bedforms are 0.58 and 0.45, respectively. The mean log standard deviation for the 2D bedforms is marginally higher than that for the 3D bedforms by 0.13, which reflects a disparity in the distribution of dune lengths, as depicted in Figure 6a-d.  
Figure 6: Probability density distribution of dune length. (**a–d**) probability density distributions of dune length of 2D bedforms, and (**e–h**) probability density distributions of dune length of 3D bedforms.
## 3 Results
### Effect of Combined Magnetic Fields on ROS Generation by Neutrophils
#### 3.2.2 Water-Mediated Effect of Magnetic Fields on Neutroph
We hypothesized that activating effect on neutrophils mediated by water pre-exposed to the CMF may be exerted not only at a specific frequency (12.6 Hz) but also after water exposure to the spectrum of frequencies including the 12.6 Hz. To test the hypothesis of spectral effect of magnetic fields on water samples, the signal of human encephalogram was used where all the channels of multichannel EEG were averaged by each time point until a single averaged channel was obtained (Figure 2a in \"Materials and Methods: Generation of the CMF with a spectral range regime\"). The most pronounced part of spectrum of EEG signal is approximately between the frequencies from near zero to 20-25 Hz, which also include the 12.6 Hz band. This band, according to the results of the experiments shown in Tables 3 and 4, has stimulating activity on the cell chemiluminescence. The results describing the effect of water samples pre-exposed to CMF with static component of 60 \(\mu\)T and alternating component formed proportionally to EEG signal with an amplitude of approximately 100 nT are presented in Table 6. Unlike harmonic signal where the amplitude may be defined, EEG signal is variable and is characterized by a specific distribution histogram presented in Figure 2c in the section \"Materials and Methods: Generation of the CMF with a spectral range regime\"). Moreover, 100 nT value approximately corresponds to the amplitude of the distribution half-width (Figure 2c). The results in Table 6 demonstrate that addition to neutrophil suspension of water samples incubated under the CMF conditions with alternating component formed with a random signal (EEG signal) similarly to the previously described experiments, increased chemiluminescence (\(p\) = 0.02597 vs. Control). In other words, obtained values of \"Experimental\" samples in Tables 3, 4 and 6 are comparable and equal to 66%, 52%, and 56%, respectively. Meanwhile, the results presented in Tables 4 and 6 show that the addition of the CMF pre-exposed samples as well as same samples but after serial dilution under conditions similarly decreased the maximum neutrophil chemiluminescence by approximately 16% and 23%, respectively. Therefore, the CMF, which were formed using a signal proportional to electroencephalogram recording, as well as single-frequency 12.6 Hz band signal, may result in biological cell response: rapid increase in ROS generation.
## 4 Security Attacks
### Contract Layer
#### 4.4.2 Overflow Attacks  
Overflow attacks exploit arithmetic vulnerabilities in smart contracts [28; 48]. These occur when a variable exceeds its maximum or falls below its minimum value, causing it to \"wrap around\". For example, a 32-bit integer can only represent values from \(-\)2,147,483,648 to 2,147,483,647. In a smart contract for online betting, if a user sends an amount that exceeds this limit, the resulting value may wrap around to 0, leading to unexpected behavior. A notable case of an overflow attack occurred in 2018 on the Beauty Chain smart contract, where an attacker exploited this vulnerability to bypass verification checks and withdraw tokens exceeding the contract balance [49]. Employing safe arithmetic libraries and adhering to secure programming practices in Solidity can help prevent such attacks.
# Disguised Full-Duplex Covert Communications
## 4 Proposed Solutions  
We first note that the covert rate in (17a) is an increasing function of \(P_{D}\) while the upper limits of \(r_{P}\) in (17b) and (17c) are decreasing functions of \(P_{D}\). This means that the covert rate cannot exceed a value at which one of the upper limits becomes \(r_{P}\), i.e., \(r_{P}=\min(r_{P,R},r_{P,D})\). Therefore, it is optimal for \(r_{P}\) to be as low as possible for the maximum covert rate as  
\[r_{P}^{\star}=\bar{r}_{P}. \tag{18}\]  
We now simplify (P1) using the monotonicity of logarithms as  
\[\text{(P1.1):}\max_{P_{D}} P_{D},\] (19a) subject to: \[P_{D}\leq\frac{1}{|h_{DR}|^{2}}\Bigg{(}\frac{|h_{SR}|^{2}P_{S}}{ 2^{\bar{r}_{P}}-1}-\sigma_{R}^{2}\Bigg{)}, \tag{19b}\] \[P_{D}\leq\frac{1}{\left|\bar{h}_{DD}\right|^{2}}\Bigg{(}\frac{|h _{SD}|^{2}P_{S}}{2^{\bar{r}_{P}}-1}-\sigma_{D}^{2}\Bigg{)},\] (19c) \[P_{D}\leq\left(\zeta^{(1-4\epsilon)}-\frac{1}{\zeta}\right) \frac{\sigma_{W}^{2}}{|h_{DW}|^{2}},\] (19d) \[P_{D}\leq\left(\zeta-\frac{1}{\zeta}\right)\frac{\sigma_{W}^{2} }{|h_{DW}|^{2}},\] (19e) \[0\leq P_{D}\leq\bar{P}_{D}. \tag{19f}\]The right-hand side of (19d) is larger than or equal to that of (19e) for \(0\leq\varepsilon\leq 0.5\), implying that satisfying (19d) automatically fulfills (19e). Therefore, the optimal transmit power can be obtained by taking the minimum of the upper bounds from (19b)-(19d) and (19f) as  
\[P_{D}^{\star}\!\!=\!\!\min\!\left\{\frac{1}{\left|h_{\text{\tiny{D} \text{N}}}\right|^{2}}\!\left(\frac{\left|h_{\text{\tiny{S}\text{N}}}\right|^{2}P _{\text{\tiny{S}}}}{2^{P_{\text{\tiny{P}}}}-1}\!-\!\sigma_{\text{\tiny{R}}}^{2} \right),\frac{1}{\left|\tilde{h}_{\text{\tiny{D}\text{D}}}\right|^{2}}\!\left( \frac{\left|h_{\text{\tiny{S}\text{D}}}\right|^{2}P_{\text{\tiny{S}}}}{2^{P_{ \text{\tiny{P}}}}-1}\!-\!\sigma_{\text{\tiny{D}}}^{2}\right),\left(\zeta^{(1-4 \varepsilon)}\!-\!\frac{1}{\zeta}\right)\!\frac{\sigma_{\text{\tiny{W}}}^{2}}{ \left|h_{\text{\tiny{D}\text{N}}}\right|^{2}},P_{\text{\tiny{D}}}\right\}. \tag{20}\]  
**Remark 1**: _When the \(D\)-R link is extremely strong, i.e., \(\left|h_{\text{\tiny{D}\text{N}}}\right|^{2}\to\infty\), we have \(P_{D}^{\star}\to 0\) since the hidden receiver cannot eliminate a source message in the presence of a dominant covert message, which is a prerequisite. When there exists excessive FD self-interference, i.e., \(\left|\tilde{h}_{\text{\tiny{DD}}}\right|^{2}\to\infty\), we also have \(P_{D}^{\star}\to 0\) for the public data rate, which cannot reach the given threshold \(\tilde{r}_{\text{\tiny{P}}}\). Lastly, when the channel gain of the \(D\)-W link is exceptionally high, i.e., \(\left|h_{\text{\tiny{DW}}}\right|^{2}\to\infty\), we have \(P_{D}^{\star}\to 0\) as well, since the absence and existence of covert transmission will cause a large difference in the received power at the warden, making it easier to detect any covert transmissions._
## 3 System Model
### Simulations Results
#### 4.3.6 Average Energy Consumption with Different Node Density  
In this work, the change in energy with different simulations and changing the number of nodes was also tested. The results are shown in Figure 10a,b represents the two graphs at two different times 300 s and 1000 s along with the changing in the number of nodes from 5 to 13. The result indicates that energy consumed during 300 s for various simulations and an increasing number of nodes was less (0.075 j to 0.124 j) for two presented protocols while MRRP increased (0.084 j to 0.334 j). Similarly, this behavior was investigated by changing the time from 300 s to 1000 s. The results showed the variation in the average energy of the various simulations by changing the number of nodes at 1000 s. It can be observed that even with more nodes, the maximum average energy consumption for ATEAR and Enhanced ATEAR was 0.71 j and 0.59 j, respectively. The MRRP consumed more energy with increasing nodes, i.e., 1.173 j and at time 1000 s. The proposed protocols performed better than the MRRP.  
Figure 9: Average temperature rise consumption with different node density. (**a**) Simulation time = 300 s; (**b**) simulation time = 1000 s.
## 3 Datasets  
### Gridded Precipitation Products (GPPs)  
Six widely used and recent GPPs, including five satellite precipitation datasets and one reanalysis dataset, were considered to identify the most accurate GPPs over Iran. It is worth noting that all mentioned data were downloaded from Google's cloud computing platform, Google Earth Engine [66; 67]. A brief description of each GPP is provided in the succeeding subsections.
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 5 Interference Reduction
### Proposed Pilot-to-Data Interference Reduction
In cases when the amount of data to transmit is not very high, some of the carriers of the DD grid could be excluded from data transmission. It makes sense to use these data-free columns to concentrate ICI on them. This can be conducted by increasing \(K\) and choosing appropriate \(i\) if the nonzero pilot slices, present in the DD grid, should be shifted. The pilot repetition is particularly important in scenarios where multiple mobile terminals (MTs) transmit data over the same DD grid, by sharing different parts of the grid, e.g., different columns of the DD matrix. Each MT uses its own pilots and data symbols, while the data occupy only a portion of the grid (as illustrated in Figure 12). Since, in general, MTs are located in different positions, each of them experiences a distinct channel characterized by unique delays and Doppler shifts. As discussed previously, a mismatch between the channel and the grid causes oscillations. If, for instance, the channel of one MT (1) is not aligned with the grid of another MT (2), pilots of MT-1 can induce oscillations within the grid region of MT-2. Repetition of the pilots redistributes the power of the interference and allows these oscillations to be concentrated in specific regions of the grid while suppressing them in some other, more critical regions. Moreover, due to the random phase of the complex channel coefficients, the resulting interference adds up incoherently, which further improves the signal-to-interference power ratio. This makes multiple MT transmissions more reliable. The figure below illustrates the case of interference between MT-1 and three other mobile terminals (MT-2, MT-3, MT-4), where the same channel is used in all cases. It can be seen that the use of repeating pilot patterns significantly reduces the level of interference. Figure 12: Different scheduling MT scenarios with different pilot repetition parameters, but the same channel. Without the pilot repeating feature, the interferences form pilots has the highest level (**a**). Only MT-2 and MT-3 have significant interferences (**b**). In case of 3 MTs, two options could be used to reduce interference between (**c**,**d**). The interferences are combined incoherently.
## 1 Introduction
[17] summarized various water quality measurements and introduced quantification indexes for water quality assessment, such as the Langelier saturation index (LSI) and Ryznar stability index (RSI). Moreover, [PERSON] et al. [18] demonstrated various water quality indicators by spatial, temporal, and hotspot analysis using Moran's index (correlation) and Getis-Ord Gi (spatial clustering). Summarizing the previous research, the majority of indexes focused on the hydraulic outcomes of WDNs and few studies introduced water quality aspect. None of them seek multiple aspects of WDNs services (e.g., social, water quality, and economic, etc. ), rather focus on a single item. In addition, the aforementioned metrics are based on a system-based analysis to measure the functional capability of the entire network. By applying these metrics, however, no information on the location of the vulnerable part can be obtained. To overcome the drawbacks of the conventional metrics, we propose an element-based analysis in which individual components are evaluated using several performance quantification metrics to identify the critical elements in WDNs. By locating and prioritizing the critical elements of the network, actions can then be accordingly planned to improve the network resiliency against potential disturbances. The objective of this study is to identify critical pipes in a WDN. To that end, first, several criticality indexes are developed to measure the network performance loss during a disturbance event. The indexes are then applied to several networks to analyze the responses. The responses from the separate indexes are then combined to determine the overall criticality of individual pipes. Finally, a verification of the proposed criticality indexes is conducted by comparing them with the resilience index (RI).
## 3 Research Progress on Adsorption and Separation of Hydrocarbon Molecules
### Aromatic Hydrocarbons
#### 3.2.2 Silicon-Based Materials
With the increase of initial concentration, polycyclic aromatic hydrocarbons are first desorbed, while monocyclic aromatic hydrocarbons (benzene, toluene, xylene) are largely retained in the aerogel gel, which is consistent with the BET results, that is, in the process of adsorption, small aromatic molecules on the surface of aerogel gel firstly and following the multi-layer adsorption mechanism of polycyclic aromatic hydrocarbons. In the process of desorption, the \(\pi\)-\(\pi\) stacking effect of inner monocyclic aromatic hydrocarbons is stronger than that of outer polycyclic aromatic hydrocarbons, so it is more difficult to desorption monocyclic aromatic hydrocarbons macroscopically, to achieve the effect of separation. Figure 11: Dependence of adsorption capacity of MTMS-aerogel on the initial concentration of different mixtures [58]. Ordered mesoporous silica has also been widely used as an adsorbent for arenes and phenols due to its uniform pore structure, tunable pore size, high specific surface area, and high mass transfer efficiency. Moreover, its performance for specific hydrocarbons can be optimized by metal modification. For example, SBA-15 with framework Fe (III) can be applied to the removal of pyrene and Cu (II) in water [61]. Protonated Fe (III) acts as an adsorption site, while the bridge bond between pyrene and Cu (II) further promotes adsorption. [PERSON]. [62] reported the adsorption of various organic pollutants (chloroform, ethylene glycol, acetonitrile, ethyl acetate, xylene, acetone, toluene, dimethyl sulfoxide, 2-butoxyethanol) with mesoporous silica colloids (pore size \(\sim\)3 nm, specific surface area \(>\)1100 m\({}^{2}\)/g). The adsorption efficiency displays a negative correlation with the polarity of organic components, which is similar to classical adsorbents. Furthermore, enhancing the hydrophobicity of the adsorbent with organilanes promotes the adsorption capacity and the regeneration of diffusion-assisted adsorbent molecules. In addition, the molecular sieve can also be used for the adsorption and desorption of hydrocarbon molecules in petroleum. [PERSON] [63] used Cu\({}^{+}\)Y molecular sieves to adsorb hydrocarbons by \(\pi\) complexation with N-containing compounds. During the experiment. They also found its good adsorption potential for S-containing compounds. However, its low maximum adsorption capacity (3 mg/g of nitrogen-containing compounds) means that finding materials with high adsorption performance is still a serious challenge.
## Priority-Based Protocol Extension Method
### Data Priority Delineation
\begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Priority** & **Type** & **Pipeline** & **Data** & **Output Delay** \\ \hline
1 & Data Frame & Data &
\begin{tabular}{l} Phasor estimates, frequency offset, \\ frequency change rate (PHASORS, \\ FREQ, DFREQ) and analog quantities \\ requiring time synchronization \\ \end{tabular} & \(\leq\)50 ms \\ \hline
2 & Application extension frame-2 & Command &
\begin{tabular}{l} Remote confirmation, execution \\ confirmation, execution \\ completion confirmation \\ \end{tabular} & \(\leq\)90 ms \\ \hline
3 & Application extension frame-3 & Command &
\begin{tabular}{l} Telemetry and telesignalization \\ summoned by the main station, \\ changed data, SOE, \\ \end{tabular} & \(\leq\)200 ms \\ \hline
4 & Application extension frame-4 & Command &
\begin{tabular}{l} Periodic upload digital quantities and \\ analog quantities that do not require \\ time synchronization \\ \end{tabular} & \(\leq\)400 ms \\ \hline
5 & Application extension frame-5, \\ file transfer frame, head frame, CFG, command frame & Command, File &
\begin{tabular}{l} Various historical data files, \\ configuration files, header files \\ \end{tabular} & \(\leq\)800 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data priority. The priority 4 (P4) data includes periodic upload digital quantities and analog quantities that do not require time synchronization, the frame rate is 0.1-1 frames/s, and the output delay is within 400 ms. The priority 5 (P5) data includes various historical data files, configuration files, and header files, and the output delay is within 800 ms. Application extension frame-2, frame-3, frame-4, and frame-5 simply indicates that the application extension frame carries different data, and their frame structure is not different. In fact, the data priority and output delay should also be set according to the actual application. The level of data priority can be increased or decreased, and the output delay can also be adjusted. Table data are used as reference values. In order to guarantee the data output delay is within the specified milliseconds, this paper uses the timer and the priority queue method to ensure the data will be sent out before the set time expires, so that the data output does not time out.
## 3 PAZ Description
### PAZ Antenna  
The PAZ antenna is an X-band active-phase planar array implemented in printed-radiator technology. Together with a low profile, low-mass, flexibility and easiness of manufacturing, this technology allows the development of flexible beamforming networks (BFN).  
The PAZ planar array is composed of 12 panels (4.8 m \(\times\) 0.7 m). Each panel is composed of 32 subarrays, and each subarray is composed of 16 microstrip patches. Each subarray is a single radiating element, which includes a dedicated transmit-receive module (TRM) adjustable in amplitude and phase by applying complex excitation coefficients. This enables beam steering, adaptive beamforming in the azimuth and elevation and the generation of more than ten thousand beams. An accurate antenna model (AMOR, Antenna MOdelleR ) was developed for generating the antenna patterns for improving radar image quality and calculating TRM settings to ensure the performance further on, even in the case of drifting and/or failed modules during the spacecraft lifetime, among other objectives [29]. Figure 13 shows a qualification model composed of nine subarrays [8].  
The array gain is a key element in the feasibility study, so an estimated value was calculated using the available information [30]:  
* Subarray directivity higher than 20.1 dB for vertical and horizontal polarizations.
* Losses lower than 1 dB.
* Peak power equal to 2.26 kW.  
Figure 13: PAZ panel subarrays [8].  
Assuming that all of the elements of the array are equal and unaffected by the position in the array, thus ignoring some possible edge effects, the array gain can be estimated as follows [31]:  
\[G_{array}\left(\theta,\phi\right)=N\cdot D_{element}\left(\theta,\phi\right) \cdot\epsilon_{element}\cdot L_{scan} \tag{7}\]  
where:  
* \(\epsilon_{element}\) is the element efficiency term due to possible mismatching effects.
* \(L_{scan}\) represents the scan loss effect, which is typically modeled as a function of \(cos(\theta_{0})\), being \(\theta_{0}\) the main beam pointing direction with respect to the array broadside direction.
* \(D_{element}\left(\theta,\phi\right)\) represents the directivity of each single radiating element.
* \(N\) is the number of single radiating elements in the array.  
Given the similarities between PAZ and TerraSAR-X, the angle between the array panel inclination and nadir direction of TerraSAR-X was considered for the study, this value being equal to \(33.8^{\circ}\).  
Finally, the Equivalent Isotropic Radiated Power (EIRP) was estimated for \(\alpha\) ranging from \(15^{\circ}\) to \(65^{\circ}\). For \(\alpha=15^{\circ}\) and \(\alpha=65^{\circ}\). EIRPs of \(65.4\) dBm and \(63.4\) dBm, were obtained, respectively.