esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### XRD Analysis  
The XRD spectra obtained after the biosorption stage did not show any peaks characteristic of the presence of CuO-NPs. This could be due to the fact that the crystalline precipitates were present in very low concentrations compared to the total biomass and went unnoticed. Nevertheless, as described in Section 2.3, nanoparticle synthesis tests  
Figure 1: FT-IR spectra obtained from the biomass of _Staphylococcus epidermidis_ CECT 4183 before (blue) and after (red) the Cu(II) biosorption step.  
were carried out from the cell extract of _Staphylococcus epidermidis_ CECT 4183. Figure 2 shows the XRD spectra corresponding to the powder obtained after the synthesis stage and clearly shows the presence of the characteristic peaks of this type of nanoparticle [26]. It shows the peaks identified by the 20 angle and their corresponding Miller indices, which identify the crystallographic planes. Figure 2 shows the spectrum before (blue) and after (red) the calcination step, demonstrating that, despite the presence of some impurities of organic origin, the last step could be omitted if necessary. However, calcination allowed us to obtain nanoparticles of high purity and crystallinity, as indicated by the narrow and intense peaks obtained in the spectrum. Finally, a theoretical average crystal size of 16 nm was calculated from the Debye-Scherrer equation.
# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 1 Introduction
The rest of this paper is organized as follows: Section 2 provides a general overview of the study, the data used, and the analysis area and methodology. The detailed analytical results are presented in Section 3. Section 4 compares the results with those of a previous study that extracts Korea's maritime traffic routes through a density analysis. Section 5 concludes the study, describes the limitation of the study, and suggests primary directions for future research.
## 3 Results and Discussion
### Correlation between BEPS-Simulated Crop NPP and Ground Measurements  
The R\({}^{2}\) between the simulated NPP and AGB for all the plots is 0.83 (Figure 5); individual R\({}^{2}\) ranges from 0.56 to 0.95 for the six crop types (Figure 6). There are minor improvements in R\({}^{2}\) compared to GPP-derived AGB, except for black bean for which the R\({}^{2}\) is already very high, and for soybean. Similar to GPP-derived AGB, a non-linearity between simulation and measurements is still observed for corn. Since we used the same biomass respiration rates for different crops in BEPS simulations, the minor differences between GPP-derived and NPP-derived AGB are expected. Overall, the RMSE and mean absolute error (MAE) for AGB estimation from NPP are 115.7 and 85.8 g m\({}^{-2}\), respectively.  
Figure 5: The correlation between BEPS-simulated crop net primary production (NPP) and ground-measured above-ground biomass for six crop types (oat, soybean, wheat, corn, canola and black bean) for SMAPVEX16-MB.  
Figure 6: The relationship between BEPS-simulated crop NPP and ground-measured above-ground biomass for each crop type for SMAPVEX16-MB.
## 2 Reliability Assessment Method Based on Fusion of Simulation and Test Data
### Reliability Assessment Method for Fusion of Simulation and Test Data  
#### 2.2.1 Lifetime Reliability Simulation Test Method  
The correctness of the simulation results must be judged before they are used. If the simulation is incorrect, the use of simulation data will exacerbate the deviation of the evaluation results from the true results. In this study, according to the concept of the confidence distribution of parent eigenvalues and confidence test theory [4,28], we established a reliability simulation test method that can evaluate whether the product reliability life is correct at a high confidence level.  
Let the random variable T be the product life and \(t_{R}\) be the life percentile value for a given reliability \(R\) (\(R\geq 50\%\)). The reliable life \(t_{S,R}\) of the product with reliability \(R\) is obtained through simulation, and because \(t_{R}\) is unknown, it is not yet possible to directly determine whether \(t_{S,R}\) and \(t_{R}\) are equal.  
Let \(t_{N,RU}\) and \(t_{N,RL}\) be the upper and lower one-sided confidence limits, respectively, for the confidence level of \(\gamma\) (\(\gamma\geq 50\%\)) for the reliable life \(t_{R}\) obtained via the test. The confidence limits of \(t_{R}\) can be obtained using the test results. Reliable-life simulation tests were performed using statistical methods, as shown in Table 1.  
Here, the precision \(\delta\) is manually determined. In the case where \(t_{N,RL}\leq t_{S,R}\leq t_{N,RU}\), our test method is equivalent to the hypothesis test where the simulated reliable life \(t_{S,R}\) is not rejected at the significance level of \(\alpha=2(1-\gamma)\) as equal to the actual reliable product life \(t_{R}\), i.e., the hypothesis that \(t_{S,R}=t_{R}\).
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 5 Reinforcement Learning
### Policy Selection  
Q-learning is a type of off-policy reinforcement learning algorithm, which allows for the creation of a nearly optimal policy even if actions are selected based on a random exploratory policy. As a result, a nearly greedy action selection policy is adopted, consisting of two modes.  
* Exploration: In the beginning, when the agent is unaware of the most effective action, it tries different actions randomly to determine an effective action \(a_{t}\)
* Exploitation: Once the agent learns the various actions, it then chooses an action based on this knowledge to maximise the state-action value function \(Q_{\pi}(s,a;\varphi_{t})\). According to this policy, where \(\epsilon:0<\epsilon<1\) is a hyperparameter, regarding the trade-off between exploration and exploitation: 1. the agent carries out exploration with a probability \(\epsilon\). 2. \(1-\epsilon\) probability is used for exploitation. The trade-off results in this policy being referred to as \(\epsilon\)-greedy action selection policy. This policy has a linear regret in t. In the system model adopted in this work, it is assumed that UAVs move at speed v and the agent selects \(a_{t}\) from its \(s_{t}\) and receives \(w_{s^{\prime},a}[t;q]\) and finally moves to \(s^{\prime}:=s_{t+1}\). An episode is the time period in which the agent and environment interact. If the target is achieved within the episode, then it is said to have converged. In the proposed DQN implementation, the UAV coordinates are particularly kept track of. These coordinates are reported to the network and used for decision making to improve the network performance. Thus, UAV coordinates are part of the DRL framework.
# Optimized Combination of Local Beams for Wireless Sensor Networks
## 1 Introduction
Unlike the node-selection and excitation-adjustment methods presented in [28; 29; 30; 31], we utilize the beam-perturbation scheme which changes the steering angles of the beams by controlling phase shifters. Due to the strong directivity of the mmWave channel [11; 12] and the intimate relation between the average INR and the sidelobe levels [28; 29], it is expected that reducing the average INR, collected from the undesired APs, leads to the decrease of the sidelobe levels in the finite-discrete directions. To solve the problem in the efficient manner, we here introduce a meta-heuristic method such as the simulated annealing (SA) having the advantages of low memory capacity and scalability [32; 33]. For the performance verification of our algorithm, we conduct numerical simulations in terms of the SNR, average INR, and complexity and show that the proposed algorithm remarkably reduces the average INR with the relatively low complexity. In summary, the main contribution of our paper is verifying that the CB is useful to increase the transmission range from the nodes being equipped with the ULAs to the desired AP and providing the optimization algorithm to lower the interference in the undesired APs under the mmWave channel. Figure 1: System model.
## 3 Materials and Methods
### Measurements in Sound Transmission Suites  
Sound transmission loss predictions have been validated by reverberation rooms tests according to ISO 10140-2:2010, performed on a panel cut from the same production batch as the sandwich beams described in the previous section (see Table 3). The panel under test is placed in an opening obtained in the wall dividing two reverberation rooms, a \"source room\" and a \"receiving room\". An omnidirectional sound source is placed in the source room, and the test is repeated for two source positions. With the source turned on, the sound pressure levels are measured in six positions of the source room and in six positions of the receiving room. Finally, the sound pressure levels in the source room, \(L_{1}\), and in the receiving room, \(L_{2}\), are evaluated as the energetic average of the measurements performed in the source room and in the receiving room respectively. The sound transmission loss _STL_ is given by:  
\[STL_{\text{ISO 10140}}=\ L_{1}-L_{2}+10\log\biggl{(}\frac{S}{A_{2}}\biggr{)} \tag{14}\]  
where \(S\) is the area of the free test opening, and \(A_{2}\) is the equivalent sound absorption area in the receiving room.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & **Homogeneous** & **Sandwich Beam,** & **Sandwich Beam,** \\  & **Beam** & **x Direction** & **y Direction** \\ \hline Length \(L\) (m) & 1.176 & 1.166 & 1.666 \\ Core thickness \(h_{c}\) (m) & 0.017 & 0.017 & 0.017 \\ Laminate thickness \(h_{l}\) (m) & - & 0.0015 & 0.0015 \\ Width \(b\) (m) & 0.05 & 0.05 & 0.05 \\ Mass per unit area \(\mu\) (kg m\({}^{-2}\)) & 3.02 & 8.38 & 8.45 \\ Young’s modulus of the & - & 9000 & 9000 \\ laminates \(E_{l}\) (MPa) & - & 9000 & 9000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the beams used for the dynamic tests.  
Figure 4: Outline of the beams cut from a sandwich panel in orthogonal directions.  
The panel under test consists of three sub panels (parts). In Figure 5, the boundary of each part is marked with a yellow line. The two slices placed in the upper part have a height of 600 mm, while the slice placed at the bottom of the panel is 290-mm high. The different parts are coupled to each other with a layer of plastic-reinforced glass (PRG).  
The air gap between the panel and the wall dividing the two rooms has been filled with silicone and backed up with wooden studs.
# Passwordless Authentication Using a Combination of Cryptography, Steganography, and Biometrics
## 3 Methods
### Implementation  
#### 3.2.1 Pre-Installation  
To prevent an adversary-in-the-middle attack, SSL/TLS certificates are generated from an open-source certificate authority--letsencrypt.org [19] on the BE, and the certificate's public key is hardcoded on the FE. This ensures that the communication between the FE and BE is always authenticated. The BE then generates a separate 4096-bit RSA key pair using the native Nodejs crypto module [20] to be used for encrypted communication.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Distance of the Positive Pressure Ventilator from the Door Opening (3 m)**} \\ \hline \hline \multicolumn{10}{c}{**x (mm)**} \\ \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**91**} & \multicolumn{2}{c}{**273**} & \multicolumn{2}{c}{**455**} & \multicolumn{2}{c}{**637**} & \multicolumn{2}{c}{**819**} \\ \cline{3-10}  & & **AVG** & **SD** & **AVG** & **SD** & **AVG** & **SD** & **AVG** & **SD** & **AVG** & **SD** \\ \hline \multirow{3}{*}{**101.5**} & EXP & 10.63 & 0.93 & 15.31 & 0.28 & 18.00 & 0.35 & 14.44 & 0.34 & 8.74 & 0.49 \\ \cline{2-10}  & CFD & 4.47 & - & 11.71 & - & 11.99 & - & 11.99 & - & 3.39 & - \\ \hline \multirow{3}{*}{**304.5**} & EXP & 7.21 & 0.77 & 14.95 & 0.77 & 18.48 & 0.62 & 14.36 & 0.63 & 6.67 & 0.55 \\ \cline{2-10}  & CFD & 5.06 & - & 14.06 & - & 15.68 & - & 12.11 & - & 3.47 & - \\ \hline \multirow{3}{*}{**507.5**} & EXP & 4.96 & 0.70 & 9.59 & 0.61 & 12.58 & 0.76 & 10.12 & 0.59 & 5.48 & 0.70 \\ \cline{2-10}  & CFD & 7.63 & - & 15.30 & - & 16.47 & - & 15.66 & - & 6.20 & - \\ \hline \multirow{3}{*}{**710.5**} & EXP & 2.12 & 0.52 & 3.88 & 0.94 & 4.19 & 0.96 & 4.31 & 0.45 & 1.78 & 0.43 \\ \cline{2-10}  & CFD & 3.23 & - & 8.60 & - & 8.05 & - & 8.14 & - & 4.34 & - \\ \hline \multirow{3}{*}{**913.5**} & EXP & 1.13 & 0.49 & 0.90 & 0.53 & 0.88 & 0.49 & 0.84 & 0.58 & 0.94 & 0.36 \\ \cline{2-10}  & CFD & 0.76 & - & 1.64 & - & 1.61 & - & 1.48 & - & 0.87 & - \\ \hline \multirow{3}{*}{**116.5**} & EXP & 0.75 & 0.22 & 0.62 & 0.20 & 1.20 & 0.57 & 0.78 & 0.53 & 0.93 & 0.43 \\ \cline{2-10}  & CFD & 0.52 & - & 0.62 & - & 0.56 & - & 0.59 & - & 0.59 & - \\ \hline \multirow{3}{*}{**1319.5**} & EXP & 0.37 & 0.20 & 1.02 & 0.43 & 0.70 & 0.13 & 0.72 & 0.62 & 0.57 & 0.07 \\ \cline{2-10}  & CFD & 0.35 & - & 0.40 & - & 0.42 & - & 0.35 & - & 0.33 & - \\ \hline \multirow{3}{*}{**1522.5**} & EXP & 0.40 & 0.35 & 0.83 & 0.30 & 0.93 & 0.70 & 0.79 & 0.39 & 0.50 & 0.12 \\ \cline{2-10}  & CFD & 0.25 & - & 0.31 & - & 0.33 & - & 0.32 & - & 0.28 & - \\ \hline \multirow{3}{*}{**1725.5**} & EXP & 0.37 & 0.26 & 0.31 & 0.10 & 0.43 & 0.39 & 0.46 & 0.27 & 0.42 & 0.30 \\ \cline{2-10}  & CFD & 0.24 & - & 0.24 & - & 0.25 & - & 0.15 & - & 0.24 & - \\ \hline \multirow{3}{*}{**1928.5**} & EXP & 0.40 & 0.33 & 0.29 & 0.06 & 0.27 & 0.47 & 0.23 & 0.21 & 0.25 & 0.14 \\ \cline{2-10}  & CFD & 0.09 & - & 0.11 & - & 0.11 & - & 0.11 & - & 0.13 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Airflow velocity in the door opening for experimental studies and CFD simulation (distance 1 m), where x—position of the measuring point in relation to the \(x\)-axis; y—position of the measuring point in relation to the \(y\)-axis; AVG—arithmetic mean of the flow rate; SD—standard deviation taken as the measurement error.
# Intra-Day Variability Observations of Two Dozens of Blazars at 4.8 GHz
## 1 Introduction  
The intra-day variability (IDV) of active galactic nuclei (AGNs) at centimeter wavelengths was first discovered in 1980s [1; 2]. Since then, more observations have been carried out, and it turns out that the IDV occurs only in flat spectrum radio sources (FSRSs) which are core-dominated AGNs, with a detection rate of 25-50% in the FSRSs [3; 4] and with a higher rate of \(\sim\)60% in bright _Fermi_ blazars [5].  
For a long time, it is controversial if the origin of the radio IDV at centimeter wavelengths, is AGN-intrinsic variations or is due to scattering-induced variations of AGN signals by ionized interstellar medium in our Galaxy [6; 7]. If the IDV is AGN-intrinsic, the timescale of a day or less will imply very high brightness temperatures of the jet-emitting region, several magnitudes (typically \(10^{3-5}\) times) greater than the inverse-Compton limit of \(T_{\rm IC}\sim 10^{11.5}\) K [8] or the equipartition temperature of \(T_{\rm eq}=5\times 10^{10}=10^{10.7}\) K [9]. On the other hand, the brightness temperatures (\(T_{b}\)) measured from the RadioAstron (RA) space VLBI (SVLBI) observations, are 10-100 times larger than the IC limit, in a significant fraction of blazars [10; 11; 12; 13]. The \(T_{b}\) can be boosted by the Doppler beaming effect when the jet of blazar points closely to our line of sight, i.e., \(T_{b}^{\rm obs}=\delta T_{b}^{int}\), where \(T_{b}^{\rm obs}\) is the observed brightness temperature and \(T_{b}^{int}\) is brightness temperature in source frame. However, the Doppler factor \(\delta\) is \(<\)60 for blazars observed from ground-based VLBIobservations [14; 15; 16] and mostly \(\delta\leq 10\)[11], which is not high enough to account for the \(T_{b}\) excess in the space VLBI observations, implying that the IC limit might be violated in some of the blazars. Nevertheless, the apparent 'brightness temperature' implied from the IDV, as mentioned above, is much higher than the \(T_{b}\) of blazars measured with the RA space baselines.  
Today, it becomes clear that the IDV at centimeter wavelengths is not AGN-intrinsic, but caused by the interstellar scintillation (ISS) effect in our Galaxy [17]. The evidence is from the annual modulations of IDV timescales discovered in a couple of IDV sources that had been monitored for years, which can be well fitted by the Earth orbiting around the Sun and annually across the scintillation pattern (e.g., [18; 19; 20], see a summary on this in [21]).  
Furthermore, the flux fluctuations caused by the ISS-induced IDV, may have contributed partly to the RA SVLBI observed \(T_{b}\) excess (over the IC limit) of blazars [22]. So it is important to find and study the IDV from the blazars in the RA SVLBI observations [23]. In order to search for IDV in the RA sources in parallel with the RA SVLBI sessions, we carried out the IDV observations of the RA sources (plus a few blazars from other samples) with the Urumqi 25 m radio telescope in Xinjiang Astronomical Observatory. In this paper, we report the data reduction and the first results of the observations performed in search of IDV.
# Influence of Ozone on the Biochemical Composition of Birch Sap
## 3 Results
### Microbiological Research
Comparing the number of yeast colony-forming units (Tukey HSD test, \(p<0.05\)) recorded in sap (day 1), there was no significant difference between untreated and treated for 5 min (No. 2), and between treated for 15 min, 20 min, 25 min, and 30 min. After seven days of storage at 2 \({}^{\circ}\)C, the number of yeast colony-forming units was lower in all ozonated samples of the sap, which was statistically significant. Data from day 5 (20 \({}^{\circ}\)C) showed no significant difference between the groups. Comparing the control group (Dunnett's tests, \(p<0.05\)) immediately after ozonation, significant differences were found for groups 3-7 on day 7 with all ozonation groups and on day 5 for groups 5-7. In summary, ozonation at an ozone concentration of 0.99 \(\pm\) 0.09 mg L\({}^{-1}\) affected the number of microorganisms. However, it depends on the duration of ozone treatment: the longer the sap is exposed to ozone, the more effective the inactivation of microorganisms.
# Water Pipeline Leakage Detection Based on Coherent \(\varphi\)-OTDR and Deep Learning Technology
## 4 Deep Learning Training
### Multi-Leakage Point Detection  
In order to validate and compare the two models described above, we further performed experiments with multiple leak points. In this experiment, three leakage points were set at about 2070 m, 2100 m, and 2150 m from the start of the fiber. The two leak points at 2070 m and 2150 m were simulated by ball valves, and the maximum opening diameter was 20 mm. The leak point at 2100 m was a fixed-size hole with a diameter of about 5 mm. Then, using the phase-sensitive OTDR system described above to collect the experimental data, a total of 100 groups of raw phase data were collected, and each group contained 767 phase data, that is, corresponding to the phase data of all sampling points on the entire FUT. Then, these 100 groups of data were input to the previously trained CNN and ResNet18 models for prediction and to perform statistical analyses of the prediction results, as shown in Figure 12(a),b. Experimental results showed that Resnet18 could effectively locate the locations of three leakage points while CNN could only identify leakage events at 2100 m, and the performance of the ResNet18 model at other non-leakage points was also significantly better than that of the CNN model.  
Figure 12: Training results of the ResNet18 model: (**a**) training curves; (**b**) confusion matrix.  
Figure 13: Multi-leakage points: predicted results of the (**a**) CNN model and (**b**) ResNet18 model.  
In summary, the CNN and ResNet18 models trained on Mel spectrograms, which contain more informative features, demonstrated good validation accuracy in the classification. However, although, in the previous training and testing phases, the CNN model and the ResNet18 model had similar accuracy, in the multi-leakage point detection experiment, the ResNet18 model performed better, which indicates that Resnet18 has better generalization ability compared to the CNN model when processing new input data. ResNet18 is also the most complex in terms of architecture, computational requirements, and training time.
## 3 Results
### Top Scored Enzyme-Associated Species Obtained from CRC Dataset  
The human gut microbiome is considered to have a role in disease formation and prognosis [62]. Therefore, it is important to understand the microbiome-disease associations, including microorganisms, enzymes, and pathways. It is worth noting that the enzymes that have been identified in metagenomic studies may be synthesized by different microorganisms. To enlighten these relations, for the top 100 scoring enzymes that have been identified by the EC-nomenclature-based G-S-M in the CRC-associated metagenomic data, the organisms that synthesize these enzymes have been obtained from Uniprot. We eliminated the species if they were not included in the CRC-associated metagenomic data at the taxonomic level. In total, 268 species have been identified for 100 enzymes, and their relations are visualized in Figure 13. As shown in Figure 13, most of the top 100 identified enzymes are synthesized by _Escherichia coli_, _Salmonella enterica_, _Klebsiella pneumoniae_, _Staphylococcus aureus_, _Streptococcus pneumoniae_, and _Clostriidioides difficile_.  
Glycine dehydrogenase (EC: 1.4.4.2) and glycine C-acetyltransferase activity (EC: 2.3.1.29) have been commonly identified by both XGB and SKB. In addition to that, the methylaspartate mutase activity (EC: 5.4.99.1) enzyme has been commonly selected by the EC-nomenclature-based G-S-M approach, XGB, and SKB (as shown in Figures 4 and 7).  
Similarly, for the top 10 scoring enzymes that were identified by each of XGB and SKB feature selection methods (glycine dehydrogenase (EC: 1.4.4.2), glycine C-acetyltransferase activity (EC: 2.3.1.29), citrate CoA-transferase (EC: 2.8.3.10), methylaspartate mutase activity (EC: 5.4.99.1), aminomethyltransferase (EC: 2.1.2.10), glycine C-acetyltransferase activity (EC:2.3.1.29), and the related species were obtained from Uniprot. We eliminated species if they were not included in CRC-associated metagenomic data at the taxonomic  
Figure 13: Network of top 100 enzymes that have been identified by the EC-nomenclature-based G-S-M on the CRC-associated metagenomic data and associated organisms. A total of 268 species that synthesize the top scoring 100 enzymes are presented. Node size is associated with betweenness centrality.  
level. In total, 85 species have been detected as being associated with the top 16 enzymes (4 enzymes have been found common in XGB and SKB), as reported by the XGB and SKB FS methods. As shown in Figure 14, _Eschericia coli_, _Salmonella enterica_, _Klebsiella pneumoniae_, and _Citrobacter koseri_ have been found to be important. As one can notice in Figure 14, _Eschericia coli_ is the most significant species by having the highest betweenness centrality.