esa-sceva/satcom-chunk-collection huggingface dataset:
# The Viable but Non-Culturable (VBNC) State, a Poorly Explored Aspect of Beneficial Bacteria
## 3 The VBNC State in Beneficial Bacteria
### Firmicutes and the VBNC State
It is known that the presence of sulfur dioxide induces the loss of cultivability, and the addition of arginine to the medium allows a return to the cultivable state [102]. According to findings with _O. oeni_, although the bacteria are in the VBNC state, they may still be capable of carrying out malolactic fermentation, although perhaps not as efficiently as their cultivable counterparts. When cells are present in a rich environment and arginine is added, the bacteria return to the cultivable state, increasing the fermentation efficiency. The generation of new knowledge requires changing the paradigm and legislation that require all microorganisms used in food production to be culturable. It is feasible that the use of non-culturable cells could decrease production costs because expensive infrastructure would not be required to preserve the bacteria, since they have all the necessary equipment to preserve themselves. The VBNC state could be an important reservoir of beneficial bacterial species, as this state constitutes a survival strategy in response to harsh environmental conditions. The capability of bacteria to enter the VBNC state in response to stress could have important biotechnological applications. It is possible consider that the VBNC state may reduce negative selection and regulate microbial dominance in soil, as the rhizosphere, where plants could induce a selective bacterial revival by releasing selected organic compounds, directly influences the diversity present in these environments. In the same way, the hosts themselves could be exerting a selection bias by releasing compounds that determine the viability of certain bacteria necessary in a given physiological state.
# Resampling Imbalanced Network Intrusion Datasets to Identify Rare Attacks
## 8 Results and Discussion
### Resampling before and after Splitting
#### 8.2.2 Random Undersampling after Stratified Splitting  
Table 15 presents the classification results for Random Undersampling After Splitting for Worms (UNSW-NB15) for the various oversampling percentages (0.1 to 1.0, at intervals of 0.1). The best results are highlighted in green. Results of [PERSON]'s \(t\)-tests for UNSW-NB15, Worms for Random Undersampling after Splitting (Table 15), are presented in Table 16. Consider the \(t\)-test comparison between 0.1 vs. 0.3 in Table 16. It was found that the \(t\)-values of precision, recall, and macro precision have statistical significance. Since precision and macro precision have positive \(t\)-values, it implies that 0.1 oversampling performed better in these two metrics. However, the recall value was negative, which means that 0.3 had better true positive predictions than 0.1. If the \(p\)-value is higher than 0.1, then there is statistically no significant difference between the two means. This is the case in the comparison of 0.1 vs. 0.2 oversampling in Table 16. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Oversampling \%** & \multicolumn{2}{c}{**Accuracy**} & **Precision** & **Recall** & **F-Score** & **Macro Precision** \\ \hline \multirow{3}{*}{0.1} & Avg & 0.999 & 0.608 & 0.737 & 0.665 & 0.804 \\  & SD & NA & 0.067 & 0.044 & 0.0495 & 0.034 \\  & Avg & 0.999 & 0.601 & 0.712 & 0.646 & 0.8 \\  & SD & NA & 0.108 & 0.089 & 0.083 & 0.054 \\  & Avg & 0.999 & 0.566 & 0.773 & 0.651 & 0.783 \\  & SD & NA & 0.066 & 0.059 & 0.051 & 0.033 \\  & Avg & 0.999 & 0.566 & 0.781 & 0.654 & 0.783 \\  & SD & NA & 0.035 & 0.063 & 0.026 & 0.018 \\  & Avg & 0.999 & 0.581 & 0.738 & 0.65 & 0.791 \\  & SD & NA & 0.078 & 0.082 & 0.079 & 0.039 \\  & Avg & 0.999 & 0.587 & 0.76 & 0.656 & 0.793 \\  & SD & NA & 0.097 & 0.044 & 0.061 & 0.049 \\  & Avg & 0.999 & 0.62 & 0.753 & 0.679 & 0.81 \\  & SD & NA & 0.053 & 0.046 & 0.041 & 0.026 \\  & Avg & 0.999 & 0.54 & 0.719 & 0.614 & 0.77 \\  & SD & NA & 0.081 & 0.036 & 0.041 & 0.018 \\  & Avg & 0.999 & 0.573 & 0.711 & 0.629 & 0.787 \\  & SD & NA & 0.117 & 0.097 & 0.089 & 0.058 \\  & Avg & 0.999 & 0.601 & 0.75 & 0.666 & 0.801 \\  & SD & NA & 0.062 & 0.081 & 0.06 & 0.031 \\  & Averages & 0.999 & 0.5843 & 0.7434 & 0.651 & 0.7922 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: UNSW-NB15: Worms—classification results for random undersampling after splitting. Based on the analysis in Table 16, the best results were obtained at 0.1 oversampling for Worms (highlighted in green in Table 15). Though there are sampling ratios that are statistically equivalent to 0.1 (as shown in Table 16), 0.1 was chosen as the best since it has the smallest amount of oversampled data, thus taking the least computational time. Table 17 presents the classification results for Random Undersampling After Splitting for Shellcode (UNSW-NB15) for the various oversampling percentages (0.1 to 1.0, at intervals of 0.1). The best results are highlighted in green.
# A Model-Based Engineering Approach for Evaluating Software-Defined Radio Architecture
## 3 Analytical Models
### Signal Analysis  
The design point of maximum power or heat dissipation for each FPGA is a crucial factor in ensuring the system's robustness and reliability. Thus, an analysis of power consumption for different output channel types for an FPGA-based system is required. The power dissipation of each channel is calculated based on its output channel bandwidth and data link. This information is used to establish the maximum number of output channels that can be instantiated without exceeding the 30 W limit for maximum power or heat dissipation for each FPGA. The analysis results provide valuable insights into the trade-off between the number of channels and power dissipation in the system.  
For each NB output, which has a BW of less than 1 MHz, the power dissipation (\(PD_{NB}\)) is 6 Watts (W) per channel. This includes 4 W for the Digital Down Converter (DDC) (NB output) and 2 W for the Serializer/Deserializer (SerDes) NB data link, which has a data rate of approximately 100 Mbps. For each wideband (WB) output, which has a BW greater than 1 MHz, the power dissipation (\(PD_{WB}\)) is 12 W per channel. This includes 4 W for the DDC and 8 W for the WB SerDes data link, which has a data rate of approximately 4 Gbps.  
For example, to determine how many NB channels can be instantiated without exceeding the 30 W max, we add the base consumption of 10 W to the consumption of 3 NB channels, which is 18 W. This results in a total consumption of 28 W, leaving a 2 W margin and a maximum of three NB channels per FPGA. To determine how many WB channels can be instantiated without exceeding the 30 W max, we add the base consumption of 10 W to the consumption of 1 WB channel, which is 12 W. This results in a total consumption of 22 W, leaving an 8 W margin and a maximum of one WB channel per FPGA. The SDR-MM heat load is also a factor in determining the number of channels and will be evaluated in the next section.
# A Meta-Analysis on Dual Protocols for Chronic Stroke Motor Recovery: Robotic Training and tDCS
## 3 Results
### Moderator Variable Analysis: Study Designs and Recovery Phase  
The first moderator variable analysis showed no significant short-term effects across different study designs: (a) 12 RCT comparisons from 11 studies (\(SMD\) = \(-\)0.079; \(SE\) = 0.102; variance = 0.010; 95% CI = \(-\)0.279-0.121; \(Z\) = \(-\)0.773; \(p\) = 0.439); (b) 7 crossover comparisons from 3 studies (\(SMD\) = 0.141; \(SE\) = 0.155; variance = 0.024; 95% CI = \(-\)0.162-0.444; \(Z\) = 0.912; \(p\) = 0.362); and (c) 1 non-RCT comparison from one study (\(SMD\) = 0.454; \(SE\) = 0.489; variance = 0.239; 95% CI = \(-\)0.506-1.413; \(Z\) = 0.927; \(p\) = 0.354). These findings indicate that different study designs did not change the transient effects of robotic training combined with tDCS protocols on upper limb rehabilitation post-stroke.  
The second moderator variable analysis revealed no significant short-term effects across different post-stroke recovery phases: (a) 4 subacute comparisons from 3 studies (\(\mathit{SMD}=-0.011\); \(\mathit{SE}=0.174\); variance = 0.030; \(95\%\) CI \(=-0.351\)-0.330; \(Z=-0.061\); \(p=0.951\)); (b) 14 chronic comparisons from 10 studies (\(\mathit{SMD}=0.055\); \(\mathit{SE}=0.108\); variance = 0.012; 95% CI \(=-0.156\)-0.266; \(Z=0.511\); \(p=0.609\)); and (c) 2 subacute and chronic comparisons from 2 studies (\(\mathit{SMD}=-0.008\); \(\mathit{SE}=0.295\); variance = 0.087; 95% CI \(=-0.587\)-0.571; \(Z=-0.028\); \(p=0.978\)). These analyses failed to identify transient motor effects after the augmented combined protocols regardless of the post-stroke recovery phase.
## 2 Materials and Methods
### Equilibrium Dialysis  
The binding was tested using a standardised protocol [24] with sterile-filtered human donor blood plasma. In brief, pooled blood plasma was mixed with PBS with pH 7.2 (dilution 1:2 per volume) containing BP-C1 with concentrations of 500, 250, 125 and 62.5 ug/L; this corresponds to ca. 55.0, 27.5, 13.7 and 6.9 ug Pt/L. Control solutions contained the same dosages of BP-C1 in PBS (pH 7.2). Fifty percent plasma and control solutions were loaded into a Teflon 96-cell dialysis plate with a vertical 8 kDa cut-off dialysis membrane. Rapid Equilibrium Dialysis (RED) Device Single-Use Plate with Inserts, 8K MWCO (cat No. 90006, Thermo Scientific, Rockford, IL, USA) was used for this purpose. The solutions were equilibrated with PBS at pH 7.2 for 4 h at 37 \({}^{\circ}\)C under gentle shaking. The dialysis duration was tested in a preliminary experiment with dialysis tubes in a Float-A-Lyzer G2 Dialysis Device (Repligen, Rancho Dominguez, CA, USA) with 8-10 kDa cut-off, indicating that 2 h is sufficient for stable dialysis; cisplatin was used as a control compound--see Tables S1 and S2 (Supplementary Information). Retained and diffused fractions (0.5 mL) were collected and stored at \(-\)80 \({}^{\circ}\)C until further analysis. For each dosage, plasma and the control buffer were tested in parallel in 6 replicates.
# An Efficient SC-FDM Modulation Technique for a UAV Communication Link
## 5 Experiments and Performance Measures
### Performance Measures  
The input data symbols \(x_{n}\) for \(0\leq n\leq N-1\) are modulated by one of the modulation schemes using an N-point DFT to generate a representation of the frequency domain of the input symbols and then, the SC-FDM output sequence \(X_{k}\) for \(0\leq k\leq N-1\) is given by:  
\[X_{k}=\sum_{n=0}^{N-1}x_{n}e^{-j\frac{2\pi nk}{N}},\ \ \ \ \ 0\leq k\leq N-1. \tag{1}\]  
The SC-FDM symbol \(X[k]\) is a complex number that consists of real and imaginary parts. According to the central limit theorem, as the number of subcarriers \(N\) gets larger, the real and imaginary parts of the spectrum are not captured by the modulation scheme.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline System bandwidth & 10 MHz \\ Frequency & 2.4 GHz \\ Modulation scheme & BPSK, QPSK, 8-PSK, 16-QAM, 64-QAM \\ Number of subcarriers & 1024 \\ Data block size & 32 \\ Cyclic prefix & 64 \\ Pulse shaping filter & Raised-cosine (RC)/Root raised-cosine (RRC) \\ Roll-off factor & 0.3 \\ Oversampling factor & 5 \\ Number of iterations & \(10^{4}\) \\ Subcarrier spacing & 10 kHz \\ Equalization & Zero forcing/Minimum mean square error (MMSE) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 8: Mission Planner ground control station (GCS): (**a**) Radio link status; (**b**) “Flight Data” screen.  
imaginary parts of the SC-FDM symbols follow the normal (Gaussian) distribution and the probability density function (PDF) of \(X[k]\) can be shown as [30]:  
\[f_{X_{k}}(x)=\frac{x}{\sigma^{2}}e^{\frac{-x^{2}}{2\sigma^{2}}}, \tag{2}\]  
where \(\sigma\) is the standard deviation.
# Influence of Stray Current on Fastening System Components in Urban Railway Tracks
## 4 Results  
### Analysis of the Voltage in Fastening System Components  
The average currents detected in each sample in all three test cases are listed in Table 2.  
In the PPE and ZG 3/2 fastening systems, the same voltage was observed for all components (Table 3), which indicates that all components of the fastening systems were in direct contact; no insulating material was present that could stop the flow of current from the rail to the clip and from the clip to other elements of the fastening system. Owing to this direct contact, when dry, the current leaks from the rail through the fastening system to the concrete base and track substructure. The only factor affecting the resistance between the rail and ground in this case is the electrical resistance of the concrete base. Thus, it can be  
Figure 7: Different water levels in which the samples were immersed: (**a**) dry condition, (**b**) water level to the top of the concrete block, and (**c**) immersed condition.  
concluded that the direct fastening system does not exhibit good properties for preventing stray currents.  
For the DEPP and Zagreb 21-CTT indirect fastening systems, a voltage drop between the anchor bolts and the steel plate was observed at the beginning of the test (Table 3). This voltage drop indicates that the bolts are not in direct contact with the steel plate, which will be analyzed in more detail in the following subsection.
# Accurate Modeling, Operation Laws and Commutation Timing Matching for Concrete Pumping Systems
###### Abstract  
The pump system is the heart of the concrete pump truck, and its main function is to deliver high-pressure concrete into the pipeline through the reciprocating motion and cooperation of the main cylinder and the distribution valve driven by the swing cylinder. However, there are currently problems, such as unclear pumping operation rules and mismatched commutation timing, that result in poor pumping efficiency and short life of wear parts such as spectacle plates and cutting rings. To address above problems, based on AMEsim software and system test data, this thesis establishes an accurate simulation model of the pump system with more than 90% precision. Based on this model, the simulation reveals the operating rules of the main cylinder and the swing cylinder during the pumping process; draws the commutation timing diagram of the pumping system; clarifies that the commutation timing has four characteristics: sequential, hysteresis, coupling and cyclic; and proposes an optimization scheme for the commutation timing, i.e., reducing the relief pressure of the main cylinder and shortening the swing time of the distribution valve. The proposed optimization has been proven in the market and significantly improved the life of wearing parts by more than 90%. This study provides technical reference for the structure and parameter optimization of concrete pumping systems.  
concrete pumping system; operation law; commutation timing; erosion wear; pump efficiency Article
## 5 Experimental Results
### Experiment 4: Results Based on the Chest-XRay Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Alg.** & **Model** & **Accuracy** & **BA** & **F1-Score** & **Recall** & **Precision** & **Time** & **No. of Features** \\ \hline \multirow{4}{*}{PSO} & DT & 0.8893 & 0.9048 & 0.89 & 0.8893 & 0.9037 & 0.1484 & \multirow{4}{*}{326} \\  & LDA & 0.9643 & 0.9702 & 0.9643 & 0.9643 & 0.9648 & 0.2309 & \\  & SVM & 0.9643 & 0.9702 & 0.9643 & 0.9643 & 0.9644 & 0.1442 & \\ \hline \multirow{4}{*}{MFO} & DT & 0.9179 & 0.9167 & 0.9185 & 0.9179 & 0.9245 & 0.0813 & \multirow{4}{*}{222} \\  & LDA & 0.9607 & 0.9673 & 0.9607 & 0.9607 & 0.9614 & 0.1393 & \\  & SVM & 0.9643 & 0.9702 & 0.9643 & 0.9643 & 0.9644 & 0.0947 & \\ \hline \multirow{4}{*}{WOA} & DT & 0.8786 & 0.869 & 0.8794 & 0.8786 & 0.8991 & 0.0927 & \multirow{4}{*}{221} \\  & LDA & 0.9679 & 0.9732 & 0.9679 & 0.9679 & 0.9681 & 0.1445 & \\ \cline{1-1}  & SVM & 0.9679 & 0.9732 & 0.9679 & 0.9679 & 0.9679 & 0.0961 & \\ \hline \multirow{4}{*}{AO} & DT & 0.9179 & 0.9226 & 0.9179 & 0.9179 & 0.9261 & 0.0708 & \multirow{4}{*}{159} \\  & LDA & 0.9607 & 0.9673 & 0.9607 & 0.9607 & 0.961 & 0.1135 & \\ \cline{1-1}  & SVM & 0.9643 & 0.9702 & 0.9643 & 0.9643 & 0.9643 & 0.0764 & \\ \hline \multirow{4}{*}{AHA} & DT & 0.925 & 0.9256 & 0.9251 & 0.925 & 0.9293 & 0.0602 & \multirow{4}{*}{141} \\  & LDA & 0.9643 & 0.9702 & 0.9643 & 0.9643 & 0.9643 & 0.1095 & \\ \cline{1-1}  & SVM & 0.9607 & 0.9673 & 0.9607 & 0.9607 & 0.961 & 0.0773 & \\ \hline \multirow{4}{*}{AHA-AO} & DT & 0.9107 & 0.9048 & 0.9115 & 0.9107 & 0.9192 & **0.0482** & \multirow{4}{*}{**107**} \\  & LDA & 0.9571 & 0.9643 & 0.9571 & 0.9571 & 0.9573 & 0.0937 & \\ \cline{1-1}  & SVM & **0.975** & **0.9792** & **0.975** & **0.975** & **0.975** & 0.0523 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results of each feature selection optimization algorithm on the PH2 dataset (bold refers to best value). = 0.8767 for AHA). In this experiment, we noticed that selecting more features was not correlated with an enhancement in the performance, but it sometimes increased the time. For example, the PSO-based DT classifier was based on 79 features and achieved a BA of 0.7487; the MFO-based DT used 91 features and achieved a BA of 0.7641; the WOA-based DT used 98 features and achieved a BA of 0.7778; the AO-based DT achieved a BA of 0.7722 with only 91 features; the AHA-based DT achieved a lower BA of 0.756 with 99 features. Similar behaviors were followed with the LDA and SVM classifiers with all optimizers. As a result, it was not a matter of the number of features, but it depended on the quality of the selected features. PSO selected only 79 features, but they achieved bad results; AHA selected 99 features, but they still achieved bad results. On the other hand, the AHA-AO optimizer selected 96 features, which was less than the AHO and WOA, but achieved the highest results.
# POL-MUX System for Noncoherent Optical Networks
## 4 Proposal of a Wide-Band Passive Optical Network (WDM-PDM-PON)
### Simulation Model  
The simulation model of WDM-PDM-PON is based on the idea described at the beginning of the paper--a combination of a DWDM and a POL-MUX used for multiplexing all WDM channels together. The block scheme is shown in Figure 11.  
The central office (CO) is the main node that contains two OLT (optical line termination) units of the WDM-PON system. Each OLT contains 32 transmitters (TX1 to TX32) in the C-band (@1500 nm) with a 0.8 nm (100 GHz) DWDM grid. The pilot wavelength of the first transmitter is 1 565.49 nm (191.5 THz), and that of the last transmitter (TX32) is 1540.56 nm (194.6 THz). The power level of the lasers was set to 1 mW (0 dBm) with the modulation format OOK-NRZ (on/off keying--no return to zero) and a bitrate of 10 Gbps. The data stream was generated by a pseudo-random bit sequence (PBRS) generator.  
The output multiplexed signals from OLT1 and OLT2 were modified by polarization controllers PC1 and PC2 for optimal multiplexing by polarization splitter PBS1, e.g., the signal from one OLT must be parallel to one of the axes of polarization plane \(x\), and the signal from the second OLT must be parallel with the polarization plane \(y\) of polarization beam splitter PBS2. The spectra of both output OLT signals are identical. The PER of the PBS was set to 25 dB.  
Behind the CO is the first part of the optical distribution network ODN1, which consists of 10 km of G.652.D optical fiber. The parameters were set according to real fibers; the attenuation was 0.2 dB/km, the chromatic dispersion coefficient was 16 ps/(nm\(\cdot\)km) and the coefficient of PMD was 0.1 \(\frac{ps}{\sqrt{km}}\). Before the fiber in ODN1 was placed, an attenuator with 3 dB of attenuation was placed to simulate the attenuation of the optical connectors.  
Behind ODN1 is the first remote node (RN1) with polarization beam splitter PBS2 as a function of the polarization demultiplexer. The PER was set to 25 dB as for PBS1. The distribution network is further divided into two arms, ODN2 and ODN3, with the same fiber length of 5 km. The multiplexed WDM signals that carried 32 channels were demultiplexed in RN2 and RN3. The spectrum of the 17 th demultiplexed channel is shown in Figure 12a, and the spectra of the other channels are shown in Figure 12b. The output demultiplexed signals were further fed via distribution networks ODN4-ODN68 to each optical network unit (ONU). The fiber length was set to 5 km, so the total length of the optical fibers was 20 km, which is standard for PON networks [28]. The ONU units consist of a photodetector and an OOK-NRZ demodulator. Effects of fiber attenuation, chromatic and polarization mode dispersion at 10 Gbps and 20 km long optical link do not influence transmitted signal, so we can focus on the polarization-related degradation.
## 2 Materials and Methods
### Flow Cytometry Analysis  
EpCAM staining: 1 \(\times\) 10\({}^{6}\) cells/sample were stained with anti-EpCAM-PE, according to the manufacturer's instructions (Milentyi Biotec GmBH, Bergisch Gladbach, Germany) or left unstained as a control. The cells were washed twice with 2 mL phosphate-buffered saline (PBS, Thermo Fisher Scientific). Anti-EpCAM-PE (100 \(\mu\)L) with concentrations of 100, 50, and 10 ng/mL was added to the cells, and 100 \(\mu\)L of PBS was used as a negative control. The cells were incubated with EpCAM-PE for 30 min at 4 \({}^{\circ}\)C in the dark and were thereafter washed three times with 2 mL PBS and analyzed using flow cytometry (BD Biosciences, Accuri C6 Flow Cytometry, NJ, USA).  
CD44 staining: 1 \(\times\) 10\({}^{6}\) cells/sample were stained with human anti-CD44 antibody, according to the manufacturer's instructions (R&D, Minneapolis, MN, USA) or left unstained as a control. The cells were washed twice with 2 mL PBS. Anti-CD44 (100 \(\mu\)L) with a concentration of 2.5 \(\mu\)g/mL was added to the cells, and 100 \(\mu\)L of PBS was used as a negative control and incubated for 30 min at 4 \({}^{\circ}\)C in the dark. After incubation, the cells were washed twice with 2 mL PBS. Anti-rat fluorescein isothicaran (FITC, R&D) was used as a secondary antibody. Anti-rat-FITC (100 \(\mu\)L of 1:10 diluted) was added to the cells and to the negative control. The cells were incubated for 30 min at 4 \({}^{\circ}\)C in the dark and were thereafter washed three times with 2 mL PBS and analyzed using flow cytometry.  
MAL I and SNA (Lectin-Biotin): 1 \(\times\) 10\({}^{6}\) cells/sample were stained with Lectin-Biotin, according to the manufacturer's instructions (Vector Laboratories, Burlingame, CA, USA) both for MAL I and SNA or left unstained as a control. The cells were washed twice with 2 mL PBS. Lectin-Biotin (100 \(\mu\)L) with concentrations of 5, 2, and 1 \(\mu\)g/mL was added to the cells, and 100 \(\mu\)L of PBS was used as a negative control. The cells were incubated with Lectin-Biotin for 30 min at 4 \({}^{\circ}\)C and were thereafter washed three times with 2 mL PBS. Thereafter, 100 \(\mu\)L of 1:100 dilutions of streptavidin-FITC (Sigma) was added to the cells and incubated for 20 min at 4 \({}^{\circ}\)C in the dark. After the incubation, the cells were washed three times with 2 mL PBS and analyzed using flow cytometry.  
SA-MIPs: The polymer particles (SA-MIPs) were prepared as previously described by [PERSON] et al. [20]. SA-MIPs equipped with nitrobenzoxadiazole (NBD) fluorescent reporter groups allowing environmentally sensitive fluorescence detection in green fluorescence. Here, 1 \(\times\) 10\({}^{6}\) cells/sample were stained with SA-MIPs or left unstained as a control. The cells were washed twice with 2 mL PBS. SA-MIPs (100 \(\mu\)L) with a concentration of 0.1 mg/mL was added to the cells, and 100 \(\mu\)L of PBS was used as a negative control. The cells were incubated with SA-MIP for 30 min at 4 \({}^{\circ}\)C and were thereafter washed three times with 2 mL PBS and analyzed using flow cytometry.