esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Microorganism Isolation and Fermented Cell-Free Supernatant (CFS)  
A total of 40 LAB were isolated from different sourdoughs made with wheat flour: wheat flour (TN), whole wheat flour (TI), strength wheat flour (TF), and spelt flour (TE). The sourdoughs were diluted in a 1:10 (_w/w_) ratio with sterile peptone water 0.1% and plated on MRS agar, allowing the isolation of the LAB [24]. The plates were incubated at 37 \({}^{\circ}\)C, and after 24 h of incubation, different colonies were sown on new MRS plates to obtain a pure culture. The plates were incubated under anaerobic conditions using Mikrobiologie Anaerobict A (Merck Millipore, Rahway, NJ, USA). Afterward, the isolates were stored on MRS with 25% glycerol at \(-\)80 \({}^{\circ}\)C. Finally, the LAB were resuspended on MRS broth and incubated at 37 \({}^{\circ}\)C for 48 h before its application.  
In order to obtain a cell-free supernatant (CFS), as described in [PERSON] et al. [25] with modifications, the isolated strains fermented MRS broth, the fermented medium was centrifuged, and the supernatant was lyophilized. After the lyophilization of the metabolites produced by the microorganisms had been prepared, the CFS was obtained.  
Qualitative In Vitro Evaluation of the Antifungal Activity of the lyophilized MRS Medium Fermented with the Isolated Strains  
Once the microorganisms were isolated, a qualitative test was carried out in order to rule out those strains with lower antifungal capacity. The fungi selected for this initial screening were _Aspergillus flavus_ ISPA 8111, _Penicillium expansum_ CECT 2278, _Penicillium verrucosum_ VTT 0184, _Penicillium digitum_ CECT 2954, and _Penicillium commune_ CECT 20767. The screening of bacteria was performed using an agar diffusion assay described by [PERSON] et al. [26] with modifications.  
The agar diffusion method was used to study fermented CFS's antifungal capacity against five fungi of the _Penicillium_ and _Aspergillus_ genera. First, the fungus was sown with a swab in PDA plates under sterile conditions. Then, in the same plates, the agar was perforated with pipette tips, leaving holes in which 100 \(\mu\)L of freeze-dried CFS was introduced in a concentration of 500 g/L. Then, the plates were incubated at 25 \({}^{\circ}\)C for 48 h. Finally, the inhibition halo was measured to determine the CFS antifungal activity.
## 4 Radio Design, Number of Devices Needed and Channel Distribution Plan
On the other hand, let us suppose that there are no more than 3 meters of vegetation from the IP camera and the Access Point (because the IP cameras have to be placed very high to acquire a good view from the forest and the access points should be placed strategically in the line of sight of the sensors and the IP cameras), so every camera covers a radius of 2,940 meters approximately. Taking into account the measurements provided in Figure 3, if we have 24 meters of vegetation, there could be a distance of 80 meters approximately (a tree every 10 meters with a diameter 3 meters) from the sensor to the access point. Figure 4 shows the number of devices that can be placed in our system as a function to the area that is wanted to be covered. We have supposed the worst case: an access point every 6 sensors in order to maximize the area covered. Figure 3: Coverage distance vs. meters of vegetation. Access points are non-root bridge with clients that let the multisensor connect to the access point while it can be connected with root access points. There are access points acting and root bridges that let non-root bridges connect to the infrastructure. The access points that let us configure this type of infrastructure are Cisco Aironet(c) 350 Series Wireless Bridges [27]. Figure 5 shows an example of the topology. There are more sensors than access points and fewer cameras than the other devices (the cameras are the most expensive devices). There is an access point configured as a root and three access points configured as a non-root access points. Figure 4: Number of devices needed per area that is wanted to be covered. Figure 5: Number of devices needed per area that is wanted to be covered. The 802.11g standard (in ETSI countries such as Spain) provides 13 channels inside the Industrial, Scientific and Medical (ISM) band, which belong to 13 frequencies between 2412 MHz and 2472 MHz as it is shown in Table 1.  
However, the spectrum width used by each channel is overlapped by the adjacent channels, causing interferences. These interferences are higher in closer channels. Table 2 shows the level of interference classified in three levels. Concerning Table 2, in order to select the maximum number of simultaneous channels without any interference, channels 1-5-9-13 must be used. However, when there is a slight interference that does not degrade the system, in practice, the use of channels 1-4-7-10-13 is tolerated. This second option provides one usable channel more, five in total. To be able to reuse these channels, we would have to go far away enough to have no interference. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel** & **Frequency (MHz)** & **Channel** & **Frequency (MHz)** \\ \hline
1 & 2412 & 8 & 2447 \\
2 & 2417 & 9 & 2452 \\
3 & 2422 & 10 & 2457 \\
4 & 2427 & 11 & 2462 \\
5 & 2432 & 12 & 2467 \\
6 & 2437 & 13 & 2472 \\
7 & 2442 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Channels inside the ISM band. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Adjacent** & **Adjacent** \\
## 3 Results
### Analysis of Cell Death  
Since MTT tests clearly confirmed the cytotoxicity of the selected compounds, closer examination of the specific mechanisms and pathways associated with cell death was performed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Comp.** & **Ca Ski** & **SiHa** & **HeLa** & **RPE-1** & **HEK 293** \\ \hline Cisplatin & 35.1 \(\pm\) 8.7 & 26.6 \(\pm\) 4.1 & 28.2 \(\pm\) 5.9 & 46.8 \(\pm\) 6.0 & 25.3 \(\pm\) 8.6 \\
1a & 69.4 \(\pm\) 8.6 & 17.1 \(\pm\) 3.4 & 61.5 \(\pm\) 2.9 & 82.8 \(\pm\) 3.7 & \(>\)100 \\
1b & 6.2 \(\pm\) 2.1 & 9.9 \(\pm\) 2.2 & 6.4 \(\pm\) 1.6 & 5.1 \(\pm\) 0.8 & 35.6 \(\pm\) 6.2 \\
1c & 12.5 \(\pm\) 0.6 & 18.2 \(\pm\) 3.1 & 10.6 \(\pm\) 2.5 & 11.0 \(\pm\) 2.4 & 16.7 \(\pm\) 2.0 \\
2a & 8.3 \(\pm\) 2.3 & 14.1 \(\pm\) 3.4 & 7.3 \(\pm\) 1.2 & 4.7 \(\pm\) 0.1 & 6.2 \(\pm\) 1.6 \\
2b & 15.0 \(\pm\) 1.0 & 30.2 \(\pm\) 2.9 & 13.0 \(\pm\) 0.6 & 8.2 \(\pm\) 0.4 & 5.4 \(\pm\) 0.6 \\
3 & 7.6 \(\pm\) 1.3 & 11.1 \(\pm\) 1.5 & 6.5 \(\pm\) 0.4 & 8.5 \(\pm\) 1.1 & 9.5 \(\pm\) 1.0 \\
4 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 \\
5 & \(>\)100 & 91.8 \(\pm\) 9.4 & 7.7 \(\pm\) 1.1 & 7.4 \(\pm\) 2.0 & 3.0 \(\pm\) 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cytotoxic effects of the studied ferrocenes in micromolar concentrations after 24 h against selected cell lines.
## 3 Proposed Method
### Compression Connection Module  
Compression connection integrates semantic information as features extracted from the text for final classification. After receiving the semantic vector \(A^{t}\in R^{Soft}\times E\), \(t\in 0,1,2,\cdots,n\) from the FFCon module, mean pooling is first utilized on these semantic vectors for dimensionality reduction. We can denote the process as follows:  
\[A^{t}_{pooling}=\varphi\big{(}A^{t}\big{)}, \tag{13}\]  
Figure 4: The calculation process of the FFCon module. FFCon is the key component of the connection.  
where \(\varphi\) represents the mean pooling operation, and \(A_{pooling}^{t}\in R^{E}\) represents the overall semantic characteristics of the text after pooling. Thereafter, features from all stages are concatenated into an aggregated semantic vector. The calculation formula is as follows:  
\[\mathbf{A}=\ \Big{[}A_{pooling}^{1},\ A_{pooling}^{2},\ \cdots,A_{pooling}^{n}\ \Big{]}, \tag{14}\]  
where \(\mathbf{A}\) represents the aggregated feature. Thereafter, the feature is fed to the classification head to produce the prediction result \(\overset{\wedge}{y}\). The entire process can be summarized as follows:  
\[\overset{\wedge}{y}=\mathbf{A}\cdot W+b, \tag{15}\]  
where \(\mathbf{A}\) represents the aggregated feature, \(W\) is the linear projection, and \(b\) represents the bias in the linear neural network.  
In this work, a certain amount of text sentences \(X=\{x_{1},x_{2},x_{3},\cdots,x_{r}\}\) and their true category labels \(Y=\{y_{1},\ y_{2},\ y_{3},\cdots,\ y_{r}\}\) are determined for training. The objective is to discover the best parameters \(\theta\) through the maximum likelihood estimation to enable the model to have outstanding classification performance. Meanwhile, the CrossEntropy function is utilized to calculate similarity between the predicted distribution and the actual label. The overall loss computing process is as follows:  
\[\text{Loss}=-\sum_{t=1}^{r}y_{i}\cdot\log\left(\overset{\wedge}{y}_{t}\right) +\varepsilon\|\theta\|^{2}, \tag{16}\]  
where \(\varepsilon\) refers to the parameter of \(L2\) regularization, which has a great contribution to mitigate overfitting. At last, the BERTAdam optimizer is employed to minimize the target loss function.
# Utilizing Trusted Lightweight Ciphers To Support Electronic-Commerce Transaction Cryptography
## 4 Proposed Protocol Methodology for E-Commerce
### Implicated Symmetric and Asymmetric Cryptography  
This section will explain the techniques used in our methodology and how these security measures are used and employed in our protocol. We explain ElGamal, AES and CRT.
## 3 Methodology
### Data Description
#### 3.1.2 CicIDS2017 Dataset (D2)  
The CicIDS2017 dataset (\(D2\)), produced by the Canadian Institute for Cybersecurity (CIC) [48] in 2017, consists of 2,830,743 instances distributed over eight distinct files. Each instance contains 78 attributes along with a label. The \(D2\) dataset comprises 15 classes, including normal traffic and 14 categories of attacks: Web Attack--XSS; Web Attack--SQL Injection; Web Attack--Brute Force; DDoS; FTP--Patator; DoS Hulk; DoS Slowhtptest; DoS slowloris; DoS GoldenEye; Heartbleed; Bot; SSH--Patator; PortScan; and Infiltration. The class distribution of \(D2\) is summarized in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Attack Type** & **Record Count** \\ \hline Normal & 2,273,097 \\ PortScan & 158,930 \\ Bot & 1966 \\ DoS GoldenEye & 10,293 \\ SSH—Patator & 5897 \\ Web Attack—SQL Injection & 21 \\ DoS Slowhtptest & 5499 \\ DDoS & 128,027 \\ Infiltration & 36 \\ FTP—Patator & 7938 \\ Web Attack—Brute Force & 1507 \\ Heartbleed & 11 \\ DoS Hulk & 231,073 \\ Web Attack—XSS & 652 \\ DoS slowloris & 5796 \\ Total attacks & 471,454 \\ Overall total (records) & 2,830,743 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The class distribution in the CicIDS2017 dataset [48].  
Figure 2: Imbalance ratio of categories in \(D1\).  
In dataset \(D2\), 'Heartbleed' is the minority class with 11 records, while 'Benign' is the majority class with 2,273,097 records. Figure 3 illustrates the imbalance ratio across all classes in the \(D2\) dataset, highlighting the substantial class imbalance present. Specifically, the 'Benign' class appears more than 206,649 times as often as the 'Heartbleed' class.  
Utilizing methodologies established by leading authorities on statistical analysis and deep learning [49], we merged the training and testing datasets for both \(D1\) and \(D2\). Afterward, we divided the merged dataset into two distinct, non-overlapping subsets, allocating 80% for training the model and reserving 20% for testing and validation. This approach is particularly suitable for larger datasets, as it avoids the computationally intensive runtime required by k-fold cross-validation. Additionally, it guarantees that the model has enough data for training while still preserving sufficient data for validation and testing. After constructing our model with the training datasets, the subsequent step is to assess its performance in multiclass classification on both \(D1\) and \(D2\).
# Effect of Radial Height of Helical Static Blade on the Cavitation Performance of Inducer
## 4 Results Analysis  
### Comparison of Simulation and Experiment  
In order to verify the reliability of numerical calculation, a water test was conducted for an inducer with and without the helical static blades, whose radial height is \(k=0.125\). Figure 4 shows the test object and the experimental system [28].  
Figure 5 is the comparison of hydraulic performance between calculation results and test results. It can be seen that the variation tendency of the calculated value, whether head or efficiency, is in agreement with the test value. The test value is slightly smaller because the numerical calculation does not take the effects of frictional loss into account. For the separate inducer, the maximum relative errors of head and efficiency between calculated value and test value are 1.7% and 2.8%, while for the inducer with helical static blades, the maximum relative errors are 2.2% and 3.5%, respectively. Therefore, whether helical static blades are equipped or not, the error between the calculated value and the test value is within 5%, which proves the simulated method to be reasonable in the allowable error range.  
Figure 4: Pictures of test object and experimental system. (**a**) Inducer, (**b**) helical static blades, (**c**) experimental system.
## 3 Results  
### Geometrical Analysis  
The scanning electron microscope (SEM) images of the PVDF NF layer, with and without ZnO NWs, are shown in Figure 2; the diameter distributions are shown in Figure 2c,f. The geometry of the PVDF NF layer shows an arbitrary orientation, with a Gaussian distribution of the fiber diameter. On the surface of the PVDF NF layer, the average fiber diameter is \(260.5\pm 148.8\) nm. The NF surface shows a smooth and high aspect ratio without spindle-shaped beads. The ZnO NWs, through hydrothermal synthesis, grew in a vertical direction along the PVDF NF surface, as seen in Figure 2d,e. The ZnO NWs on the PVDF NF surface show a hierarchical structure with an average diameter of \(1158.5\pm 482.2\) nm.  
Figure 1: Schematics of the electrospinningning, hydrothermal synthesis, and electrode fabrication process: (**a**) Preparation for electrospinningning; (**b**) Fabrication of polyvinylidene dilutionide nanofiber layer onto the nylon mesh layer; (**c**) Hydrothermal synthesis for ZnO nanowire fabrication after ZnO nanoparticle seeding on PVDF NF layer; (**d**) Electrode coating on smart filter using Ag paste for electrical properties analysis.  
In Figure 3, the X-ray diffraction (XRD) result shows the identical crystallinity of the PVDF NF layer, regardless of the ZnO NWs fabrication, because the hydrothermal process is not affecting the chemical reaction on the PVDF in low-temperature synthesis. The dominant \(\beta\)-phase crystalline structure (2\(\theta\) = 20.33\({}^{\circ}\)) of PVDF NF was caused by mechanical stretching and electrical poling to molecular chains during the electrospinning process. The other intensity peaks (2\(\theta\) = 31.69\({}^{\circ}\) (100), 34.39\({}^{\circ}\) (002), 36.19\({}^{\circ}\) (101)) in the XRD show the high crystallinity of the ZnO NWs by the wurtzite structure.  
A solar simulator was used as a light source of 1/4 sun intensity (24 mW cm\({}^{-2}\)). The current-voltage (I-V) and current-time (I-t) characteristics of the ZnO NWs-PVDF NF layer under the solar simulator were measured using a Keithley 2400 source meter (Tektronix, Beaverton, OR, USA).
## 2 Materials and Methods
### Experimental Setup and Operational Procedure
air stream entering the system was first pumped through an activated carbon filter, where any residual pollutant of atmospheric air was eliminated. The air was then humidified in a water column, entering afterwards a mixing chamber. In the mixing chamber, the clean and humidified air stream was mixed with a synthetic mixture of H\({}_{2}\)S, toluene and methyl mercaptan in N\({}_{2}\) (purchased to Abello Linde, Spain). The resulting polluted stream was initially divided into three identical streams of 1.25 L min\({}^{-1}\) by means of rotameters. The individual streams were then fed at the bottom of each biofilter and the two-phase biotrickling filter. The clean air exiting the bioreactors was discharged outside the room using an extraction fan. An abiotic test was initially conducted for 15 days at 2 min of GRT. The BFs and 2 PBTF-F were then inoculated as above described and operated at GRTs of 2 min, 1 min, 40 s and 20 s in subsequent operating stages. The biofilters were daily irrigated initially with 40 mL of MSM, increasing the flow rate to 80 mL d\({}^{-1}\), 120 mL d\({}^{-1}\) and 192 mL d\({}^{-1}\) when the BFs were operated at GRTs of 1 min, 40 s and 20 s, respectively. A 0.5-L holding tank magnetically stirred was used as liquid reservoir for the 2 PBTF-F, consisting of a mixture of 70% \(v/v\) of MSM and 30% \(v/v\) of silicone oil 20 cts (Sigma Aldrich, Madrid, Spain). The liquid was continuously recycled through the packed bed at a flowrate of 0.15 L min\({}^{-1}\) countercurrently with the polluted air stream (liquid trickling velocity of 2 m h\({}^{-1}\)). After inoculation, 150 mL of the cultivation medium (prior silicone oil separation) were daily withdrawn for analysis and replaced with fresh MSM in the 2 PBTF-F at GRTs of 2 and 1 min, respectively, while 225 and 360 mL were daily replaced at 40 and 20 s, respectively. Inlet and outlet gas concentrations of H\({}_{2}\)S, methylmercaptan and toluene were daily analysed. Liquid samples from the leachate of the BFs (when available) or from the recycling medium of the BTF were periodically withdrawn in order to determine pH and sulfate concentration. The pressure drop across the packed beds was also periodically measured. Unless otherwise specified, the performance of the bioreactors is reported as the average removal efficiency under steady state for each operating condition (standard deviation <5%).
# Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections
## 3 Model Predictive Intersection Control Design
### Time-Optimal Intersection Control Design
After the conflict zone is free, i.e., \(t>t_{i,ent}+t_{i,wait}\), the autonomous vehicle enters the conflict zone with the predetermined acceleration \(a_{max}\), hence the travel time in the intersection is given as follows:  
\[t_{i,con}=\sqrt{2s_{i,int}/a_{max}} \tag{6}\]  
Finally, the travel time of each autonomous vehicles is the aggregate of the entry and exit times:  
\[t_{i,fin}=t_{i,ent}+t_{i,wait}+t_{i,con} \tag{7}\]  
Founded on the on-board measurements sent by the autonomous vehicles to the intersection coordinator, the computation is evaluated as follows iteratively:  
* First, the maximum vehicle speed \(v_{i,max}\)\(i\in[1...n]\) is defined for each vehicle, calculated based on the planned vehicle trajectory. Given the initial speed and the distance measured from the origin of the intersection, a constant acceleration of \(a_{i}\)\(i\in[1...n]\) is determined based on (1). In addition, if the maximum speed cannot be reached with predefined acceleration thresholds, the maximum speed can be modified by using (2). * The time of entry into the conflict zone \(t_{i,ent}\) and the time of exit \(t_{i,fin}\in[1...n]\) is defined for all vehicles using (4) and (7). Potential conflicts are then determined by analyzing the time overlaps of each vehicle in the conflict zone. If no collision danger is detected, the autonomous vehicles use the previously calculated accelerations to cross the intersection. * In the event of a conflict between two or more vehicles, the algorithm is evaluated as follows: vehicle with the minimum exit time \(t_{i,fin}\)\(i\in[1...n]\) is given priority and does not change its formerly calculated acceleration. The algorithm follows by iteratively reducing the acceleration of other vehicles and recalculating their corresponding entry time \(t_{i,ent}\) using (4) until the entry time exceeds the exit time of the previous vehicle. This iterative calculation process is followed for every conflicting vehicle. The derived accelerations \(a_{i}\)\(i\in[1...n]\) are then used in the entry zone. In case the waiting time \(t_{i,wait}\) is greater than zero, the subject vehicle waits for the calculated time at the beginning of the conflict zone and then applies \(a_{i}=a_{max}\). * Lastly, when a new vehicle enters the intersection, the algorithm follows the vehicle tracking mode until the previous vehicle leaves the intersection conflict zone. If this occurs, the above procedure shall be repeated with the new starting conditions for each vehicle in the intersection control zone.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
## 3 Results and Discussion
### Correlation of PFCs between Cities  
The correlation of PFCs with other cities with similar wastewater characteristics was compared. As shown in Table 2, correlational coefficients between 0.000 and 0.200 were considered to have no relation and are marked as xx, whereas those between 0.210 and 0.400 were considered to be low and are marked as x. A correlational coefficient between 0.410 and 0.600 was considered to have some relation and is marked as \(\triangle\).  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{8}{c}{**Other Cities**} \\ \hline Compounds & \multicolumn{2}{c}{PFOA} & \multicolumn{2}{c}{PFHxA} & \multicolumn{2}{c}{PFNA} & \multicolumn{2}{c}{PFPeA} & \multicolumn{2}{c}{PFHxS} \\ \hline PFOA & 1 & - & 0.050 & xx & 0.013 & xx & 0.141 & xx & 0.486 & \(\triangle\) \\ \hline PFHxA & - & - & 1 & - & 0.516 & \(\triangle\) & 0.583 & \(\triangle\) & 0.090 & xx \\ \hline PFNA & - & - & - & - & 1 & - & 0.160 & xx & 0.019 & xx \\ \hline PFPeA & - & - & - & - & - & - & 1 & - & 0.098 & xx \\ \hline PFHxS & - & - & - & - & - & - & - & - & 1 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of correlation coefficients of PFCs with other cities with similar populations and industries (xx: 0-0.2; x: 0.21-0.4; \(\triangle\): 0.41-0.6; PFOA: perfluorooctanoic acid; PFPeA: perfluoro-n-pentanoic acid; PFHxS: perfluorohexane sulfonate; PFHxA: perfluorohexanoic acid; PFNA: perfluorononanoic acid).  
Figure 2: Flow variations of a wastewater treatment plant in city K.  
The correlation coefficient between PFOA and PFHxS was 0.486, showing some correlation. That between PFHxA and PFNA or PFFeA was 0.516 or 0.583, respectively, showing some correlation. All the other compounds had low or little correlation. The correlation among PFCs showed that PFOA, PFHxA, and PFNA have the -COOH group, so that were highly correlated with PFCs with the same reaction group [3]. [PERSON] et al. also showed that the correlation coefficient between PFO and PFNA was highest at 0.752 [12]. However, [PERSON] et al. reported that whereas PFCs in the -COOH group had a relatively higher correlation, the correlation coefficient between PFOA and PFNA was low at 0.375 [13]. This occurred because there are various emission sources for PFCs, and the outflow water from the wastewater treatment plants are known to be key point pollution sources [3]. Therefore, effluent flow to the water system reflects regional characteristics and determines the correlation of the substances in actual streams, producing these regional differences [3].  
The correlational study among PFCs was significant in determining the concentration level of PFCs through the concentration of some PFCs among those with high correlation. However, those data are still lacking in representability; therefore, further research is necessary.