esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Monitoring System for IoT Healthcare Data in the Cloud
## 5 Proposed System
### System's Algorithm Approach
(3) Regarding the generate encryption key, an encryption key is generated using the Fernet symmetric encryption scheme from the _\"cryptography\"_ library. (4) Regarding the encrypt IoT data, the IoT data is converted to a string, encoded, and then encrypted using the generated encryption key ('cipher_suite'). The result is 'encrypted_data'. (5) Regarding the secure communication setup, an SSL/TLS secure communication context (context) is created. The purpose is set to \"_ssl.Purpose.related-client_\" (which might be a typo and could be \"_ssl.Purpose.SERVER_AUTH_\"), and certain verification checks are disabled to simplify the example. (6) Regarding the connect to cloud server securely, a secure socket is established ('_cloud_socket_') with the cloud server at a specified address ('_cloud_server_') and port ('_cloud_port_'). The SSL/TLS wrap_socket method is used to secure the communication. (7) Regarding the send encrypted data to cloud server, the encrypted IoT data is sent to the cloud server over the secure connection. (8) Regarding the receive response from cloud server, the script waits to receive a response from the cloud server. (9) Regarding the decrypt and validate response, the received response is decrypted using the same encryption key. Moreover, the integrity of the response is then validated by comparing the MD5 hash of the original response and the decrypted response. If the hashes match, the response integrity is considered verified; otherwise, a tampering detection message is printed. (10) Regarding closing the connection, the secure connection to the cloud server is closed. Particularly, this algorithm is a basic illustration of secure communication practices, including data encryption and integrity verification, commonly used in IoT applications where maintaining the confidentiality and integrity of the transmitted data is crucial. The algorithm demonstrated in this Python source code scenario is a simplified example crafted for illustrative purposes, aiming to cover essential security considerations of a real-world implementation. For a comprehensive solution, additional measures such as robust error handling, advanced authentication mechanisms, secure storage practices, and compliance with relevant standards and regulations should be implemented. This proposed source code algorithm illustrates the encryption of IoT data using the _cryptography_ library and the utilization of SSL/TLS to establish a secure connection with the cloud server. Additionally, it incorporates a basic integrity check using the _hashlib_ library to verify the response received from the server. Table 1 presents the capabilities of our proposed system compared to other related systems. As it is clearly shown, our system can detect not only COVID-19 and other infectious diseases but also air pollution levels in different environments, e.g., rooms, halls, etc. Furthermore, our monitoring system provides security and privacy for its users since it integrates many security features compared to other proposed schemes.
## 3 Results  
### Limitations of the Lamb Wave Application  
Based on the presented dispersion curves (Figure 5a,b) and the waveform of the signals at different distances (Figure 7b-d), two main limitations can be distinguished, which significantly aggravate the application of the Lamb waves.  
* Dispersion. As the signals of the dispersing modes consist of different frequency components that propagate with different velocities depending on the distance, it influences the waveform distortion: the signal is elongated, and the peak amplitude is decreased (Figure 7b-d). This phenomenon complicates the analysis of the received signals and detection of defects [56, 57, 58]. The halving effect of the signal amplitude (at a level of 0.5 or \(-6\) dB) is one of the main parameters in estimating the location and size of the defect in ultrasonic NDT. Due to the effect of dispersion, the signal waveform is changed (Figure 7b-d), and the application of the conventional criterion is not appropriate. In spite of that, the Lamb wave velocity variation can be used to indicate a defect [53]. Taking that into account, the specific SPMs for the reconstruction of the dispersion curves and new criteria for the evaluation of the velocity variations should be developed, investigated, and proposed.
* An infinite number of modes. As presented in Figure 5a,b, the Lamb waves possess an infinite number of dispersive modes. Depending on the thickness of the object, the material under investigation, and the frequency, other higher-order symmetric and asymmetric modes emerge [59, 60], and each of them is described by two velocities: phase and group. Even at a low frequency of 50 kHz, higher-order dispersive modes appear (Figure 5a,b). In the higher frequency range (Figure 5a,b), there is a forest of them with visible effects of converging, interlacing, and splitting. So, using higher-order modes in the application, separating signals of the different modes, extracting a useful signal, and obtaining the required information in the whole trail of the signals is a difficult task [59, 61]. Thus, in many cases, only fundamental A\({}_{0}\) and S\({}_{0}\) modes of the Lamb waves are used in various application cases. The fact that the wavelength is directly proportional to the velocity and inversely proportional to the frequency [47] must be emphasised. This fact is crucial for the application of higher-order modes, as by using different wavelengths, defects of different spatial dimensions can be detected. Thus, the algorithms that could identify the different modes, separate them, and obtain the required information need to be developed.  
The two main unusual properties of Lamb waves named here indicate that the specific SPMs need to be used.
## 3 Results
### Porosity Prediction
#### 3.4.2 Inversion Result Validation  
Figure 12 compares the proposed EEI porosity predictions (in blue) with the well's porosity log curve (in red). Well three (Figure 12b) is used as a control or calibration; Well two (Figure 12c) and Well three (Figure 12a) are blind. Both predicted, and log porosity is plotted with a five point moving average smoothed filter. The quality of this prediction is acceptably good, with the predicted porosity curve lying closely with the well porosity log.  
The cross plot of the estimated EEI porosity against the targeted porosity log for the three wells is shown in Figure 13. The cross plot for Well one (Figure 13b) gives an R-squared factor (R\({}^{2}\)) of 0.81, while both the cross plot for blind Well three (Figure 13a) and Well two (Figure 13c) give R\({}^{2}\) of, respectively, 0.67 and 0.72. The R\({}^{2}\) values inform that more than half of the predicted porosity in this inversion can explain the targeted or measured porosity [26]. The R\({}^{2}\) of 0.81, 0.72 and 0.67 indicate that the porosity estimation is performing well at both control and blind well location [25].  
Figure 11: Porosity inversions at Well 1 location. (**a**) porosity log, (**b**) near seismic trace, (**c**) Far seismic trace, (**d**) traditional inverted EEI porosity, (**e**) porosity log (red) overlaid by proposed inverted EEI porosity (blue). “SC3”, “SC3A”, consecutively, locate the anomalous thick and thin gas reservoir sand and “Water sand” locates the deeper water sand.  
Figure 12: Porosity estimation at well location, (**a**) Well 3 (blind), (**b**) Well 1 (control) and (**c**) Well 2 (blind).
# UAV Image Multi-Labeling with Data-Efficient Transformers
## 5 Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Sp** & **Re** & **Pr** & **F1** & **F2** & **Avg** & **mAP** & **HL** & **RL** \\ \hline Full-ML-CRF [9] & 90.80 & 75.90 & & & 83.40 & & \\ \hline HCB [31] & 91.90 & 61.40 & & & 76.60 & & \\ \hline GoogLeNet(MLR) & 92.90 & 58.70 & & & 75.80 & & \\ \hline AlexNet(MLR) & 94.00 & 58.00 & & & 76.00 & & \\ \hline GoogLeNet-Logistic regression [11] & 92.90 & 58.70 & & & & \\ \hline AlexNet-Logistic regression [11] & 94.00 & 58.00 & & & & \\ \hline GoogLeNet-RBFNN [11] & 96.10 & 58.70 & & & 77.40 & & \\ \hline AlexNet-RBFNN [11] & 95.40 & 58.30 & & 76.90 & & \\ \hline GoogLeNet-SVM (linear) [11] & 96.10 & 58.10 & & 77.10 & & \\ \hline GoogLeNet-SVM(RBF) [11] & 95.10 & 53.60 & & 74.40 & & \\ \hline AlexNet-SVM (linear) [11] & 95.20 & 52.30 & & 73.70 & & \\ \hline AlexNet-SVM(RBF) [11] & 95.20 & 52.30 & & 73.80 & & \\ \hline GoogLeNet-RBFNN(multi-label layer) [11] & 92.60 & 68.60 & & 80.60 & & \\ \hline AlexNet-RBFNN(multi-label layer) [11] & 93.20 & 66.10 & & 79.70 & & \\ \hline CNN-MaxMargin loss [35] & 95.70 & 70.79 & 74.71 & 83.33 & 57.44 & 8.53 & 26.51 \\ \hline CNN-Softmax loss & 95.22 & 70.37 & 72.52 & & 82.79 & 55.53 & 8.54 & 27.31 \\ \hline CNN-LSTM & 94.60 & 69.40 & 69.72 & & 82.00 & 53.03 & 9.22 & 28.93 \\ \hline LSTM+CNN +Spatial Attention & 96.78 & 69.51 & 79.45 & & 83.14 & 59.86 & 7.35 & 27.11 \\ \hline Deep Attention CNN-LSTM [14] & 95.56 & 78.30 & 75.94 & & 86.93 & 62.76 & 7.06 & 21.18 \\ \hline Full-ML-CRF [9] & 90.80 & 75.90 & & & 83.40 & & \\ \hline SSSVM [12] & 91.57 & 76.69 & & & 83.40 & & \\ \hline Our approach & 97.91 & 76.77 & 86.43 & 81.32 & 84.31 & 87.34 & 69.79 & 5.20 & 15.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of the Civezzano multi-label dataset.
## 3 Results
### Classification Results of Different Models
Tables 1 and 2 show the confusion matrices of SVM and CNN models using 3000 training samples of each rice seed variety. The performances of KNN models were much worse than SVM models and CNN models, so the confusion matrices are not presented. As shown in Tables 1 and 2, rice seeds of variety 1 (Xiushui 134) and 2 (Zhejing 99) were more likely to be misclassified as each other, while rice seeds of variety 3 (Zhongjiazao 17) and 4 (Zhongzao 39) showed the similar phenomenon. This might be caused by the difference in rice types. Rice seeds of variety 1 and 2 are japonica rice, while variety 3 and 4 are indica rice. Compared with SVM, CNN produces less misclassified samples in each variety. In both spectral ranges, CNN outperformed SVM in overall accuracy and per-class accuracy. The results indicated that CNN was effective for spectral data analysis and could improve the general classification accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \hline \multirow{4}{*}{SVM} & 1 & 2281 & 606 & 74 & 39 & 76.0 & 1849 & 693 & 80 & 42 & 69.4 \\  & 2 & 461 & 2350 & 145 & 44 & 78.3 & 467 & 1735 & 120 & 72 & 72.5 \\  & 3 & 42 & 139 & 2578 & 241 & 85.9 & 36 & 127 & 1582 & 188 & 81.8 \\  & 4 & 20 & 75 & 290 & 2615 & 87.2 & 46 & 62 & 192 & 1616 & 84.3 \\  & total & & & & & & & & & & 76.1 \\ \hline \multirow{4}{*}{CNN} & 1 & 2530 & 462 & 8 & 0 & 84.3 & 1979 & 643 & 33 & 9 & 74.3 \\  & 2 & 347 & 2631 & 16 & 6 & 87.7 & 455 & 1841 & 68 & 30 & 76.9 \\ \cline{1-1}  & 3 & 11 & 21 & 2804 & 164 & 93.5 & 12 & 80 & 1673 & 168 & 86.5 \\ \cline{1-1}  & 4 & 0 & 0 & 166 & 2826 & 94.2 & 12 & 21 & 169 & 1714 & 89.5 \\ \cline{1-1}  & total & & & & & & & & & 80.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Confusion matrices of SVM and CNN models using 3000 training samples of each rice seed variety in the Spectral range 1. Figure 5: Test performance of KNN, SVM and CNN on the fixed hold-out test set. (**a**) Results of models using Spectral range 1; (**b**) Results of models using Spectral range 2.
## 4 Results
### Analysis of Movements Based on Coordinate Paths  
Figure 5 represents, in a unified way (all periods), the graphic representation of the coordinates of the tourist visits. It is remembered that the coordinates of the X and Y axes correspond to the latitude and longitude of each visit made, and the stroke between points represents the movement or moment of jump between each site, according to the instant of time (date and time) in which it took place. There is a greater concentration in the coordinates of what is called the historical centre and a specific range of the routes, the volumes of visits can be reaffirmed with the results of Figure 3, previously presented, which indicate the counts of visits of each site according to its ID for all periods.  
In Figure 6, all periods plotted on a map are shown, as follows hybrid, showing the total routes developed for all periods. In the upper right corner, you will find the nomenclature of each period, being red for 2011, green for 2012, blue for 2013 and violet for 2015. In this it can be noted that only for 2012 there is a point far from the city, being for the other periods in an area of greater concentration, eminently related to the historic centre and surroundings. This central zone can be revised in an enlarged way in Figure 7.  
Figure 5: Visual map of visits by coordinates - All periods. Source: Own elaboration.  
\begin{table}
\begin{tabular}{c c} \hline
**FROM** & **TO** \\ \hline
13 & 14 23 27 29 30 36 55 RP \\
14 & 13 23 26 29 30 36 55 57 6 RP \\
23 & 13 26 27 29 30 32 55 57 6 RP \\
24 & 29 30 32 55 57 6 RP E \\
26 & 23 24 29 30 32 55 57 RP \\
27 & 28 32 RP E \\
28 & 13 29 30 31 5 55 RP \\
29 & 23 28 30 31 32 38 42 5 55 57 RP \\
3 & 14 30 5 6 RP \\
30 & 14 26 29 30 31 32 42 55 57 6 RP \\
31 & 13 14 5 55 RP E \\
32 & 23 24 28 29 31 42 48 5 55 57 6 RP \\
36 & 23 24 57 RP \\
38 & 48 RP \\
4 & 6 RP \\
42 & 55 7 RP \\
48 & RP \\
5 & 13 29 30 32 38 55 57 6 RP \\
55 & 14 24 29 30 32 5 57 6 RP \\
6 & 13 14 23 24 26 29 30 32 42 55 57 6 RP \\ \hline \end{tabular}
\end{table}
Table 5: Transition alternatives between states simplified. Source: Own elaboration.  
Figure 8 shows a representation of the magnitude of the visits, presenting a greater radius, the sites with greater visits. It can be noted that the area of greatest activity is the centre, confirming the raised with the Markov Chains, and then by way of expansion is Figure 9.  
Figure 6: Display map of all periods on hybrid map. Source: Own elaboration.  
Figure 7: Extended map display of all periods on street map. Source: Own elaboration.  
The purpose of these representations, diagrams and maps generated is to confirm and provide visualization support to the analysis of patterns previously carried out, given that they are their representation in space, which also allows evidence of some other elements or patterns that mathematically cannot be evidenced.
# Effect of End-Effector Compliance on Collisions in Robotic Teleoperation
## 4 Effects of Tool Compliance
### Simulated Results
The comparison between Figures 16 and 18 highlights that the reduction in the coefficient of restitution inverts the effect of the contact stiffness. A coefficient of restitution near to zero means that the tool and the object move almost synchronously after the collision, because the final relative velocity is only a small fraction of the initial relative velocity. There are two contributions to the final velocity of the object: the momentum transferred during the collision and the momentum transferred during the transient after the impact, when the robot brakes. Figure 19a,b represent the velocities of the robot, tool and object. Figure 19c,d shows the contact force and the total force generated on the tool by the decoupling elements. All simulations were carried out with \(k_{t}=150\ \frac{N}{m}\), \(\zeta=1\), \(e=0.1\). The contact stiffness was set to \(k_{c}=10^{5}\ \frac{N}{m!-5}\) in Figure 19a,c and to \(k_{c}=10^{3}\ \frac{N}{m!-5}\) in Figure 19b,d, respectively. Furthermore, in this case, the increase in contact stiffness increases the deceleration of the tool. When the contact stiffness is low (Figure 19b), even if the deceleration of the tool is reduced, the duration of the total momentum transfer is made longer, and the robot is therefore able to markedly reduce its velocity during the contact between the tool and the object. An inelastic impact makes  
Figure 18: Effects of the impulses of the damping and elastic forces on the final velocity of the object with \(e=0.1\). the dynamics of the object strictly dependent on the robot control system. Thus, the robot control system can be exploited to reduce the final velocity of the object. Figure 19c,d shows that the total force applied on the tool by the decoupling elements is positive during the contact with the object, since the decoupling system is compressed and the velocity of the tool is lower than the robot velocity. Figure 20 compares the total impulse generated by the decoupling system during the contact in the two cases analyzed in Figure 19.  
It is worth noting that the larger impulse corresponds to a larger final velocity of the object, in agreement with Equation (22). Since the impulse generated is equal to the integral of the force applied during the contact time between the tool and the object, the force profile generated by the end-effector during the contact represents a key factor in the optimization of the decoupling system.
## 4. SOP and Energy Storage Optimization Configuration Model
### Constraint Condition
#### 4.2.4 Energy Storage Operation Constraints  
In (41)-(46), \(P_{t}^{ES,c}\) and \(P_{t}^{ES,d}\) are the charging power and the discharging power of energy storage at time \(t\), respectively. \(u_{t}^{ES,c}\) and \(u_{t}^{ES,d}\) represent the charging and discharging states of energy storage at time \(t\), respectively. \(P^{ES,\text{max}}\) is the maximum charging and discharging power of energy storage. \(SOC_{t}^{ES}\) is the state of charge of energy storage at time \(t\). \(\eta_{c}\) and \(\eta_{d}\) are the charging efficiency and discharging efficiency of energy storage, respectively. \(SOC^{ES,\text{min}}\) and \(SOC^{ES,\text{max}}\) are the minimum and maximum values of the energy storage state of charge, respectively.  
\[P_{t}^{ES}=P_{t}^{ES,c}-P_{t}^{ES,d} \tag{41}\]  
\[0\leq u_{t}^{ES,c}+u_{t}^{ES,d}\leq 1 \tag{42}\]  
\[0\leq P_{t}^{ES,c}\leq u_{t}^{ES,c}P^{ES,\text{max}} \tag{43}\]  
\[0\leq P_{t}^{ES,d}\leq u_{t}^{ES,d}P^{ES,\text{max}} \tag{44}\]  
\[SOC_{t+1}^{ES}=SOC_{t}^{ES}+\Delta tP_{t}^{ES,c}\eta_{c}-\frac{\Delta tP_{t} ^{ES,d}}{\eta_{d}} \tag{45}\]  
\[SOC^{ES,\text{min}}\leq SOC_{t}^{ES}\leq SOC^{ES,\text{max}} \tag{46}\]
## 3 Results
### Assessment of Relations through a Clustering Approach
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Quality Parameter**} & \multicolumn{5}{c}{**Alentejo**} & \multicolumn{5}{c}{**Dão**} & \multicolumn{5}{c}{**Douro-C**} \\ \cline{2-10}  & **Jun** & **Jul** & **Aug** & **Jun** & **Jul** & **Aug** & **Jun** & **Jul** & **Aug** \\ \hline \multirow{3}{*}{Berry weight} & \(\Delta\)TX & \(-2.0\)\({}^{**}\) & \(-3.0\)\({}^{***}\) & & \(-1.5\)\({}^{**}\) & & \(-1.8\)\({}^{**}\) & \(-1.8\)\({}^{**}\) & \(-1.5\)\({}^{***}\) \\  & \(\Delta\)TG & \(-1.0\)\({}^{*}\) & \(-1.3\)\({}^{***}\) & & \(-1.3\)\({}^{*}\) & & \(-1.2\)\({}^{**}\) & \(-1.0\)\({}^{*}\) & \\  & \(\Delta\)TN & \(-1.0\)\({}^{**}\) & & \(-0.8\)\({}^{*}\) & \(-1.0\)\({}^{**}\) & & \(-1.0\)\({}^{**}\) & & \\ \hline \multirow{3}{*}{Titratable acidity} & \(\Delta\)TX & \(-1.4\)\({}^{*}\) & & & \(-2.1\)\({}^{***}\) & \(-2.0\)\({}^{***}\) & & \(-1.5\)\({}^{***}\) & \\  & \(\Delta\)TG & \(-1.3\)\({}^{***}\) & & & \(-1.9\)\({}^{***}\) & \(-1.8\)\({}^{***}\) & \(-0.9\)\({}^{*}\) & \(-1.8\)\({}^{***}\) & \(-0.9\)\({}^{**}\) & \\  & \(\Delta\)TN & & \(-0.8\)\({}^{*}\) & \(-1.0\)\({}^{**}\) & & \(-1.5\)\({}^{***}\) & \(-1.4\)\({}^{***}\) & \(-1.0\)\({}^{**}\) & \(-1.9\)\({}^{***}\) & \(-0.9\)\({}^{**}\) \\ \hline \multirow{3}{*}{pH} & \(\Delta\)TX & \(2.7\)\({}^{***}\) & \(3.5\)\({}^{***}\) & \(2.0\)\({}^{***}\) & & \(1.6\)\({}^{***}\) & \(2.6\)\({}^{***}\) & \(1.2\)\({}^{*}\) & & \(1.2\)\({}^{***}\) \\  & \(\Delta\)TG & \(1.7\)\({}^{**}\) & \(1.8\)\({}^{**}\) & \(0.8\)\({}^{*}\) & & \(1.4\)\({}^{***}\) & \(1.9\)\({}^{***}\) & \(1.2\)\({}^{**}\) & \(1.2\)\({}^{**}\) & \(1.1\)\({}^{***}\) \\  & \(\Delta\)TN & \(1.1\)\({}^{*}\) & \(1.0\)\({}^{**}\) & \(0.8\)\({}^{*}\) & & \(0.9\)\({}^{*}\) & \(1.6\)\({}^{***}\) & \(1.7\)\({}^{***}\) & \(1.2\)\({}^{***}\) & \(1.1\)\({}^{**}\) \\ \hline \multirow{3}{*}{Potential alcohol} & \(\Delta\)TX & \(2.7\)\({}^{***}\) & \(3.5\)\({}^{***}\) & \(2.0\)\({}^{***}\) & & & \(1.1\)\({}^{**}\) & \(3.3\)\({}^{***}\) & \(2.1\)\({}^{***}\) \\  & \(\Delta\)TG & \(1.7\)\({}^{**}\) & \(1.8\)\({}^{***}\) & \(0.8\)\({}^{*}\) & & & \(1.0\)\({}^Figure 5: Grape berry quality parameters at maturity for TON (left panel) and ARA (right panel). The sites are listed from left to right according to average GST (April to October) in decreasing order. The bars represent the cluster-mean, the whiskers the standard deviation and statistically significant differences between clusters are indicated by asterisks (\({}^{*}\,p\leq 0.05\), \({}^{**}\,p\leq 0.01\), \({}^{***}\,p\leq 0.001\)). Different letters indicate statistically significant differences between regions (for cluster 1).
# Risk Assessment of Soil Heavy Metals in the Jiahe River Basin of Yantai City, China
## 3 Results
### Assessment Using Pollution Indices
#### 3.3.3 Enrichment Factor (EF)  
The enrichment factor (EF) [32; 33; 34] is presented here to evaluate the degree of enrichment of a heavy metal relative to a selected reference element through the ratio of their concentrations in the soil. The calculation equation is as follows:  
\[\rm EF=(\frac{C_{\rm i}}{C_{\rm ie}})_{S}/(\frac{C_{\rm i}}{C_{\rm ie}})_{RS} \tag{4}\]where C\({}_{i}\) represents the concentration of element i, and C\({}_{ie}\) denotes the concentration of an immobile element. S indicates the analyzed sample, and RS refers to the selected reference sample. Here, the reference sample was the virtual Chinese soil represented by the elemental abundances (Table 1). The immobile elements that are commonly used are Al [35; 36], Zr [37], Ti [38], Fe [39], and Mn [40]. Here, Mn was used as the immobile element. EF < 2, 2 \(\leq\) EF < 5, 5 \(\leq\) EF < 20, 20 \(\leq\) EF < 40, and EF > 40 correspond to the levels of depletion to normal, moderate enrichment, significant enrichment, very high enrichment, and extremely high enrichment, respectively (Table 2). The EF values of heavy metals except for Mn in the 213 soil samples were calculated, and their counts on each level are listed in Table 2.  
For Cr, Ni, Co, and V, the majority of the samples were at the level of depletion to normal, with a few at moderate enrichment level. Regarding Pb, Cu, Zn, Cd, Hg, and As, most samples were at the level of depletion to normal, with some at the moderate enrichment level and few at the significant enrichment level.  
The enrichment level of Mn is not discussed here because it was used as the immobile element.
## 2 Materials and Methods
### Antioxidant Effect of N-Acetylcysteine (NAC) on Metal-Induced Persisters  
Cells were grown overnight in NBY medium until turbidity of \(\sim\)0.8 to 1.0, and 25 mL was centrifuged; the pellet resuspended in 25 mL fresh NBY medium supplemented with NAC (1 \(\times\) MIC) and copper (1 \(\times\) MIC) and incubated for 30 min. Cells were harvested, resuspended in fresh NBY supplemented with ciprofloxacin (100 \(\times\) MIC) and incubated for6 h. Afterward, aliquots were taken for serial dilution and CFU counting. Control cultures followed the same procedure but were pretreated only with copper (1 \(\times\) MIC, positive control) or without pretreatment (negative control).