esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Results and Discussion
#### 4.2.3 Evaluation of End-to-End Prediction Model  
In order to alleviate the bias caused by the imbalanced training dataset and improve the prediction accuracy of the model for non-major grades, we resampled the training dataset before training. We used an oversampling method that can make full use of the information in the training dataset. This method can synthesize a new record based on the existing records to make the dataset roughly balanced. As can be seen from Figure 7, the latent features generated by the autoencoder of students with the same grades have a closer distance in the feature space, so we used the distance-based algorithm KMeansSMOTE [41] to oversample the imbalanced training dataset. KMeansSMOTE uses K-MEANS [42] for clustering and SMOTE [43] for oversampling, including three steps, clustering, filtering, and oversampling, which avoids the generation of noise and effectively overcomes the imbalance between and within classes. The end-to-end prediction model composed of the pretrained autoencoder and the classifier trained with the resampled dataset is shown in Figure 5, and the results of its evaluation are shown in Figure 9.  
Figure 8: Influence of different sizes of latent features on prediction results.  
It can be seen from Figure 9 that the new prediction model we designed has a better prediction performance than the original model, especially in the three metrics of Pre, Rec, and F1, which increased by more than 10%, reflecting that the end-to-end prediction model has a better prediction accuracy for non-major grades. In particular, when code_size is 18, the new model has the best performance. Clearly, the size of the latent features extracted by the autoencoder has a significant impact on the prediction performance, and how to set the size of the latent features is a problem that needs further discussion.  
We compared the performance of our model with five selected baseline models, and the results are shown in Table 5. From Table 5, we can see that our model has obvious advantages compared to the five baseline models, especially for non-major academic grades.
# Advance on the Capitalization of Grape Peels By-Product in Common Wheat Pasta
## 3 Results
### Uncooked and Cooked Pasta Texture
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Cooking Behavior**} & \multicolumn{2}{c}{**Color**} \\ \cline{2-5}  & **Water Absorption (\%)** & **Cooking Loss (\%)** & \(L^{*}\) & \(a^{*}\) & \(b^{*}\) \\ \hline Control & \(161.14\pm 5.33\) \({}^{\text{a}}\) & 5.22 \(\pm\) 0.12 \({}^{\text{de}}\) & 72.54 \(\pm\) 0.17 \({}^{\text{a}}\) & \(-2.31\) \(\pm\) 0.20 \({}^{\text{e}}\) & 21.61 \(\pm\) 0.24 \({}^{\text{a}}\) \\ GP1\% & \(129.46\pm 3.55\) \({}^{\text{c}}\) & 4.55 \(\pm\) 0.30 \({}^{\text{e}}\) & 68.37 \(\pm\) 0.09 \({}^{\text{b}}\) & \(-0.56\) \(\pm\) 0.02 \({}^{\text{d}}\) & 21.45 \(\pm\) 0.64 \({}^{\text{a}}\) \\ GP2\% & \(139.38\pm 1.37\)\({}^{\text{bc}}\) & 5.43 \(\pm\) 0.18 \({}^{\text{cd}}\) & 65.97 \(\pm\) 1.11 \({}^{\text{c}}\) & 0.34 \(\pm\) 0.06 \({}^{\text{c}}\) & 20.94 \(\pm\) 0.48 \({}^{\text{a}}\) \\ GP3\% & \(145.76\pm 4.54\)\({}^{\text{abc}}\) & 5.72 \(\pm\) 0.17 \({}^{\text{cd}}\) & 62.88 \(\pm\) 0.52 \({}^{\text{d}}\) & 0.56 \(\pm\) 0.02 \({}^{\text{d}}\) & 20.57 \(\pm\) 0.49 \({}^{\text{ab}}\) \\ GP4\% & \(147.02\pm 12.62\)\({}^{\text{ab}}\) & 6.09 \(\pm\) 0.31 \({}^{\text{c}}\) & 58.32 \(\pm\) 0.28 \({}^{\text{e}}\) & 0.99 \(\pm\) 0.07 \({}^{\text{b}}\) & 19.62 \(\pm\) 0.26 \({}^{\text{bc}}\) \\ GP5\% & \(149.46\pm 1.08\)\({}^{\text{ab}}\) & 7.28 \(\pm\) 0.36 \({}^{\text{b}}\) & 55.20 \(\pm\) 0.50 \({}^{\text{d}}\) & 1.40 \(\pm\) 0.04 \({}^{\text{a}}\) & 18.73 \(\pm\) 0.23 \({}^{\text{cd}}\) \\ GP6\% & \(155.91\pm 5.04\)\({}^{\text{ab}}\) & 7.99 \(\pm\) 0.14 \({}^{\text{a}}\) & 52.57 \(\pm\) 0.31 \({}^{\text{a}}\) & 1.45 \(\pm\) 0.26 \({}^{\text{a}}\) & 18.48 \(\pm\) 0.23 \({}^{\text{d}}\) \\ \hline \hline \end{tabular} Each experiment was carried out in triplicate and data was reported as means \(\pm\) standard deviation. Means in the same column with different letters are significantly different (\(p<0.05\)).  
\end{table} 
Table 3: Effects of grape peel flour on pasta cooking behavior and color parameters. Cooked pasta elasticity did not differ significantly (\(p<0.05\)) among samples, a slight increase with the addition level of grape peel flour being noticed (Table 4). The chewiness property ranged between 48.79 N for the control and 34.11 N for GP6%, a decreasing trend being obtained as the amount was higher. Similar reduction tendency was observed in the case of firmness values which varied from 74.05 to 51.67 N.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variables** & & **Control** & **Group A** & **Group B** & \
\begin{tabular}{c} **Group** \\ **Comparison** \\ \end{tabular} &
\begin{tabular}{c} **Effect Size** \\ **\(\mathbf{\eta^{2}}\)** \\ \end{tabular} \\ \hline EF MVC (Kg) & M1 (Mean \(\pm\) sd) & 10.21 \(\pm\) 1.57 & 13.83 \(\pm\) 4.36 & 10.64 \(\pm\) 2.02 & \textless{}0.001 \({}^{\dagger}\) & 0.37 \\  & M2 (Mean \(\pm\) sd) & 9.48 \(\pm\) 2.08 & 13.68 \(\pm\) 4.46 & 10.17 \(\pm\) 2.17 & \textless{}0.001 \({}^{\dagger}\) & 0.39 \\  & M3 (Mean \(\pm\) sd) & 9.13 \(\pm\) 2.06 & 14.49 \(\pm\) 4.39 & 10.80 \(\pm\) 2.43 & \textless{}0.001 \({}^{\dagger}\) & 0.46 \\  & \(\Delta\)1 (M2-M1) & \(-\)0.73 \(\pm\) 1.27 & \(-\)0.15 \(\pm\) 1.63 & \textless{}0.07 \(\pm\) & 0.301 & 0.10 \\  & \(\Delta\)2 (M3-M1) & \(-\)1.08 \(\pm\) 1.25 & \(-\)0.66 \(\pm\) 1.17 & 0.16 \(\pm\) 1.36 & \textless{}0.001 \({}^{\ddagger}\) & 0.38 \\  & \(p\) Repeat Means & \textless{}0.001 \({}^{\star}\) & 0.012 \({}^{\star}\) & 0.026 \({}^{\star}\) & & \\  & Effect Size & 0.32 & 0.14 & 0.11 & & & \\ \hline HGS (Kg) & M1 (Mean \(\pm\) sd) & 26.34 \(\pm\) 6.98 & 31.73 \(\pm\) 8.88 & 24.84 \(\pm\) 6.24 & 0.001 \({}^{\dagger}\) & 0.27 \\  & M2 (Mean \(\pm\) sd) & 26.28 \(\pm\) 7.11 & 31.83 \(\pm\) 8.69 & 25.75 \(\pm\) 5.41 & 0.002 \({}^{\dagger}\) & 0.26 \\  & M3 (Mean \(\pm\) sd) & 25.90 \(\pm\) 7.05 & 32.50 \(\pm\) 8.92 & 26.22 \(\pm\) 5.88 & \textless{}0.001 \({}^{\dagger}\) & 0.27 \\  & \(\Delta\)1 (M2-M1) & \(-\)0.11 \(\pm\) 0.59 & 0.27 \(\pm\) 2.27 & 0.18 \(\pm\) 1.08 & 0.089 & 0.15 \\  & \(\Delta\)2 (M3-M1) & \(-\)0.46 \(\pm\) 0.83 & 1.34 \(\pm\) 2.92 & 1.28 \(\pm\) 1.93 & \textless{}0.001 \({}^{\ddagger}\) & 0.33 \\  & \(p\) Repeat Means & 0.001 & 0.062 & \textless{}0.001 & & & \\  & Effect Size & 0.25 & 0.10 & 0.25 & & & \\ \hline \hline \end{tabular}  
Notes: \({}^{\star}=p<0.05\) based on ANOVA for repeated measures between moments; \({}^{\ddagger}=p<0.05\) for comparisons between the control group and Group A; \({}^{\ddagger}=p<0.05\) for comparisons between the control group and Group B; \({}^{\ddagger}=p<0.05\) for comparisons between Group A and Group B; M1 refers to the first moment of evaluation, corresponding to the baseline level; M2 refers to the second moment of evaluation; M3 refers to the third moment of evaluation, which occurred at the end of the study.  
\end{table} 
Table 2: Description of elbow flexors maximum voluntary isometric contraction and hand grip strength in each group for the three moments of the program. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**95\% CI for B**} & & \\ \cline{3-10}  & **Variables** & **B** & & **LL** & **UL** & **SE** & \(\beta\) & **R\({}^{2}\)** & **\(\Delta\)R\({}^{2}\)** & **F** & **Sig. ** \\ \hline \multirow{10}{*}{KE MVC (Kg)} & \(\Delta\)1 & & & & & & 0.15 & 0.12 & 5.10 & 0.003 \({}^{\star}\) \\  & Model & & & & & & & & & \\  & Constant & \(-\)1.67 & \(-\)8.84 & 5.50 & 3.61 & & & & & \\  & Group A & 2.14 \({}^{\star}\) & 0.84 & 3.45 & 0.66 & 0.38 \({}^{\star}\) & & & & \\  & Group B & 2.29 \({}^{\star}\) & 0.99 & 3.59 & 0
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 3 Optical Camera Communication Link Model
### Noise Sources  
The most important noise sources in OCC are shot noise, Johnson noise, and Fixed-Pattern Noise (FPN). Shot noise has a quantum nature and is related to the fluctuations that occur during optoelectronic conversion. This kind of noise comprises photo-generated noise (\(N_{px}\)), whose variance linearly increases with the photo-generated current, readout noise (\(N_{ro}\)), and dark current noise (\(N_{d}\)). Readout noise occurs when the pixel value is delivered by the CMOS circuitry and does not depend on the input signal level. Although shot noise is Poisson distributed, it is usually approximated by a Gaussian assuming sufficient input power (high number of incident photons). Johnson noise (\(N_{th}\)) occurs due to the thermal agitation of the receiver's molecules and is dominant under low-light conditions. Finally, FPN is related to the nonuniformity of the sensor's pixel response, although it has a residual impact on communications systems. As it can be observed in Equation (5), all the noise contributions can be summed up into an overall noise variance \(\sigma_{N}^{2}\).  
\[\sigma_{N}^{2}=\left(N_{d}+N_{px}\right)\cdot T_{exp}+N_{ro}^{2}+N_{th}^{2} \tag{5}\]  
\(N_{px}\) (photo-generated shot noise variance) is equal to \(S_{px}^{(i,j)}\), according to the Poisson nature of shot noise. All the above terms are referred to Root Mean Square (RMS) electrons. Combining Equation (3) with Equation (5) and considering several interfering contributions (other channels), the Signal-to-Interference-plus-Noise Ratio (SINR) formula can be easily obtained (Equation (6)). \(S_{px}^{(i,j)}\) defines the rate at which electrons are converted into the device (i-th emitter to j-th receiver). Therefore, the amount of electrons in a given time window (after a row scanning in the CMOS sensor) is obtained by integrating this rate during the camera's exposure time \(T_{exp}\).  
\[SINR_{j}=\frac{\left(S_{px}^{(j,j)}T_{exp}\right)^{2}}{\left(N_{d}+\sum_{j=1}^ {n}S_{px}^{(i,j)}\right)\cdot T_{exp}+N_{ro}^{2}+N_{th}^{2}+\sum_{j\
eq i} \left(S_{px}^{(i,j)}T_{exp}\right)^{2}} \tag{6}\]
## 2 System and Channel Model
### RF Sub-Link  
Compared with the Rayleigh distribution and Rician distribution, the Nakagami-\(m\) distribution is more consistent with the experimental data. Therefore, the Nakagami-\(m\) distribution is selected as the RF fading channel in this section. The PDF of the output instantaneous SNR \(\gamma_{x,y}^{RF}\) of the RF sub-link (Nakagami-\(m\) channel) in any one-hop hybrid link is [16]:  
\[\begin{split} f_{\gamma_{x,y}^{RF}}(\gamma_{x,y}^{RF})& =\left(\frac{m_{x,y}}{\overline{\gamma}_{x,y}^{RF}}\right)^{m_{ i,j}}\frac{\gamma^{m_{x,y}-1}}{\Gamma(m_{x,y})}\exp\left(-\frac{m_{x,y} \gamma_{x,y}^{RF}}{\overline{\gamma}_{x,y}^{RF}}\right)\\ &=\left(\frac{m_{x,y}}{\overline{\gamma}_{x,y}^{RF}}\right)^{m_{ x,y}}\frac{\gamma^{m_{x,y}-1}}{\Gamma(m_{x,y})}G_{01}^{1,0}\left[\frac{m_{x,y} \gamma_{x,y}^{RF}}{\overline{\gamma}_{x,y}^{RF}}\left|\begin{array}{c}-\\ -\\ 0\end{array}\right.\right]\end{split} \tag{5}\]  
where \(\overline{\gamma}_{x,y}^{RF}\) is the average SNR of one-hop RF link, \(m_{x,y}\) (\(m_{x,y}\geq 0.5\)) is the fading parameter of one-hop RF link. The CDF of the SNR \(\gamma_{x,y}^{RF}\) can be obtained by integration,  
\[F_{\gamma_{x,y}^{RF}}(\gamma_{x,y}^{RF})=\frac{1}{\Gamma(m_{x,y})}G_{12}^{1,1 }\left(\frac{m_{x,y}\gamma_{x,y}^{RF}}{\overline{\gamma}_{x,y}^{RF}}\left| \begin{array}{c}1\\ m_{x,y},0\end{array}\right.\right) \tag{6}\]
## 5 Theoretical Analysis
### Energy Consumption  
We define the unit of energy consumption as the energy cost for transmitting one packet in one hop. Similar to the analysis of the search delay, we only consider the scenario that the actor is discovered in the chase phase. The energy consumption of TS strategy consists of two parts: energy cost in the search phase and that in the chase phase. In the search phase, for every host inside the ring forwards the packet one hop each, we have the energy cost in the search phase,  
\[E_{s}=(2S-1)^{2} \tag{17}\]  
In the chase phase, with the help of TS-R scheme, there is only one packet moves along the trail to chase the actor. Thus the energy consumption in the chase phase is related to the number of hops needed to discover the actor. According to Equation (12), we have the energy consumption in the chase phase,  
\[E_{c}=\frac{\varepsilon\cdot\tau_{P}}{\tau_{A}-\tau_{P}}\cdot\frac{1}{\tau_{P} }=\frac{\varepsilon}{\tau_{A}-\tau_{P}} \tag{18}\]  
The energy consumption of TS strategy is  
\[E_{TS}=E_{s}+E_{c}=(2S-1)^{2}+\frac{\varepsilon}{\tau_{A}-\tau_{P}} \tag{19}\]  
In ERS strategy, the energy consumption consists of the first search attempt and the flooding all over the network. Then we have the energy consumption of ERS strategy,  
\[E_{ERS}=(2S-1)^{2}+N_{s} \tag{20}\]  
where \(N_{s}\) is the number of sensor nodes in the network.  
The difference between ERS and TS can be derived as,  
\[E_{ERS}-E_{TS}=N_{s}-\frac{\varepsilon}{\tau_{A}-\tau_{P}} \tag{21}\]  
According to Equation (21), when the following condition is satisfied, the energy consumption of TS is smaller than that of ERS,  
\[N_{s}>\frac{\varepsilon}{\tau_{A}-\tau_{P}} \tag{22}\]  
Similar to the search delay analysis in Section 5.2, this condition is easier to achieve with reasonable setting of \(\varepsilon\). Thus the energy efficiency of TS is guaranteed in most scenarios.
## 3 Post-Layout Simulation and Experimental Results
As it can be seen, the circuit consumes as low as 8.5 \(\upmu\)W from the power supply of 1.8 V at the data rate of 20 Mbps, and the highest deviation of power consumption from its typical value is almost 30%. For verifying the functionality of the presented scheme in the circuit level, a proof-of-concept prototype was developed via TTL logic gates, a D flip-flop, an inductive coupling coil, and a diode rectification bridge, a photograph of which is shown in Figure 15. In this case, a 1 MHz, 1 Mbps BPSK signal with an amplitude of 5 V was employed as the input signal. Figure 16 shows an oscilloscope screen picture, presenting the important waveforms, which supports the proposed idea. Performance comparisons between the proposed BPSK demodulator and other reported designs in the 0.18 \(\upmu\)m CMOS process working at 1.8 V power supply are presented in Table 3. Note that the presented circuit is also simulated in different frequencies for making better comparisons. Figure 14: Monte Carlo simulation of power consumption with 100 iterations. Figure 12: Monte Carlo simulations of demodulated data with 500 iterations. Figure 13: Monte Carlo simulation of the recovered clock with 500 iterations. The simulation shows that static power dissipation is dominant at low operating frequencies. It can be seen that the designed circuit works with extremely low power consumption and within a small occupied active area, with a DRCF ratio of as high as 100%. Figure 16: Experimental waveforms that resulted from the proof-of-concept prototype. Figure 15: The tested proof-of-concept prototype. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & [7] & [8] & [10] & [11] & [14] & [15] & [16]* & [22] & [23] & [24] & & **This Work** \\ \hline
**Frequency (MHz)** & 10 & 13.56 & 10 & 16 & 4 & 10 & 1 & 8 & 10 & 10 & 2 & 10 & 20 \\
**Data rate (Mbps)** & 10 & 1.12 & 10 & 16 & 0.8 & 10 & 1 & 8 & 10 & 10 & 2 & 10 & 20 \\
**DRCF (\%)** & 100 & 8.25 & 100 & 100 & 20 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\
**Chip Area (sum2)** & - & \(19\times 10^{4}\) & - & - & 4300 & 3500 & - & - & - & 1120 & 459 & 459 & 459 \\
**Power Consumption** & 119 & 610 & 232 & 27 & 59 & 77.9 & 88.2 & 148 & 27.2 & 14 & 5.6 & 6.7 & 8.5 \\
**ROM** & & & & & & & & & & & & & & \\ ((PREF(w)) \(\times\) 10 \({}^{-3}\)) & 8.4 & 0.14 & 4.3 & 37 & 3.4 & 12.8 & 11.3 & 6.7 & 36.7 & 71.4 & 160 & 144 & 113 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance comparisons with other similar works.
## 2 Materials and Methods
### Sensor Faults  
Lack of sensor functioning can be caused by a variety of factors, including a signal of doubtful quality [59], a bad electrical connection or sensor failure [60]. The diagnosis of different DO sensor malfunction types is of critical importance for the overall WWTP operation. The sensor defects investigated in the present work are:  
* Bias--caused by the miscalibrated sensor, is a change in the sensor-generated signal, such as the supplied value being altered typically by a constant value in addition to the actual sensor signal [59].
* Drift--characterized by the continuous time-varying divergence of the sensor signal from the genuine one [59].
* Wrong gain--happens when the sensor slope is affected by an inappropriate gain factor, frequently determined incorrectly during the calibration process [61].
* Loss of accuracy--occurs when the signal of the sensor changes randomly, and its value is imprecise when it is compared to the genuine value [59].
* Fixed value--the sensor displays the same value all the time [61].
* Complete failure (with two cases, minimum and maximum)--characterized by a measured value that is equal or close to the sensor minimum calibration limit or is equal or close to the maximum calibration value [61].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equipment** & **Parameter** & **Value** & **Measurement Unit** \\ \hline Primary settler & Area & 2125 & m\({}^{2}\) \\  & Height & 3.5 & m \\ Anaerobic bioreactor & Volume & 9015 & m\({}^{3}\) \\ Anoxic bioreactor & Volume & 12,678 & m\({}^{3}\) \\  & Volume & 33,066 & m\({}^{3}\) \\  & Area & 6012 & m\({}^{2}\) \\ Secondary settler & Area & 11,304 & m\({}^{2}\) \\  & Height & 3 & m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Municipal WWTP main equipment parameters.  
Figure 1: WWTP general structure.  
The majority of the simulated fault types originate from the WWTP operating practice, and they were chosen to follow the typical behavioral pattern. For the loss of accuracy type of fault, the random scenario used for the faulty sensor signal was chosen to roughly reflect an irregular change in the true signal. For other magnitudes of the bias, drift, wrong gain and loss of accuracy types of faults, where fault samples are not directly available from the actual WWTP measurements, the use of the calibrated dynamic model can be used for generating data to be embedded in the FDA fault identification methodology. As new faults and magnitudes show up, the database of faults behavior can be completed by the plant measurements, and the diagnosing methodology becomes adaptive.
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
## 4 Experimental Validation  
### Experimental Data and Processing  
The five reference stations identified as SHQA, HRSZ, MDI, JMTY, and HRFS in the CORS network were composed of four reference station networks, namely A, B, C, and D. The ambiguity fixing performance of the four reference station networks was studied. Among them, the reference station network C contained the user station \"User\", which was used for testing the positioning performance of the user station under the correction of reference station error. The observation span was 28 April 2020, and the data sampling rate was 1 Hz. The distribution of reference stations and user stations is shown in Figure 3, and Table 2 provides baseline length and longitude and latitude information for the reference stations.  
It can be seen from Table 2 that the distance between reference stations was more than 100 km, so the atmospheric error between reference stations could not be eliminated by observations difference [37] and had to be estimated as random walk parameters. By adjusting the PSD in the random walk, the ambiguity fixing performance can be significantly improved.  
After determining the DD ambiguity of the reference station network using the LAMBDA method, the correctness of the DD ambiguity was first determined using the ratio value of the FFRatio test ([PERSON] et al., 2013) [38]. At the same time, the correctness of the DD ambiguity at each frequency was determined using an ambiguity closure error of 0, satisfying the relationship:  
\[\Delta N_{j,AB}+\Delta N_{j,BC}+\Delta N_{j,CA}=0 \tag{46}\]  
While ensuring a fast ambiguity fixing time, it is also necessary to ensure a high ambiguity fixing success rate. Therefore, this study also analyzed statistics on the fixing success rate under atmospheric time-varying parameter constraints. The ambiguity fixing success rate was defined as the ratio of the epochs through the FFRatio test and the closure error test to the total epochs [39].
# Effect of Pseudomonas Fluorescens on Isofetamid Dissipation and Soil Microbial Activity
###### Abstract  
The aim of this study was to assess the effect of _Pseudomonas fluorescens_ (P) application on isofetamid (IS) dissipation; the number of specific genes of archaea, bacteria and ammonia-oxidizing bacteria (AOB); and the activity of \(\beta\)-Glucosidase, phosphomonoesterase, N-acetyl-glucosamineaminidase and arylsulfatase. It was observed that the IS concentration was lower in the P+IS than in IS throughout the entire study period, which indicates the potential of \(P\), _fluorescens_ to decompose isofetamid faster. IS+P application significantly influenced N-acetyl-glucosamineaminidase, arylsulfatase and phosphomonoesterase activity in soil compared to the control by approximately 29%, 72% and 6.5%, respectively. Moreover, it was observed that on day 21 in IS+P, the number of bacterial genes was significantly higher than in the control and IS and than on day 1, by 10% and 20%, respectively. On day 21, the number of archaea was significantly higher in all variants and ranged from 3.61 (control) to 6.88 log\({}_{10}\) gene copies/g dm (IS+P). Correlation analysis showed a positive correlation between IS and TOC, while there was a negative correlation between IS and \(\beta\)-Glu and the number of archaea and AOB genes. The tested strain has the potential to be a biofetlizer and an agent in the bioremediation of contaminated soils.  
isofetamid; soil enzymes; _Pseudomonas fluorescens_; functional genes +
Footnote †: journal: Applied sciences