esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved U2 Net-Based Surface Defect Detection Method for Blister Tablets
## 6 Conclusions  
In this paper, the surface defect detection problem of blister tablets was studied, U2 Net was proposed as the basic model, the U2 Net model was improved, and the following conclusions were obtained:  
1. The fusion of U2 Net and the large kernel attention mechanism improves the feature extraction ability of the model, enabling the model to extract defect features completely and enhancing the detection effect, with the highest index among the same type of detection models. The improved U2 Net model can meet the requirements of rapid detection with a detection time of 0.05S; the accuracy rate is 99.8%; the precision rate is 96.3%; and the recall rate is 84.5%.
2. The loss function composed of Gaussian Laplacian and cross entropy can enhance the model's attention to the defects at the edge of the blister sheet, and can reduce the noise, so that the model can segment the defect edges more smoothly.
3. Using OTSU and local average methods can improve the overall accuracy of the model.  
The improved U2 Net model proposed in this paper can detect the size and location of bubble cap defects comprehensively and clearly, and the test time is short. The effectiveness of each part of the method is verified by ablation experiments.  
In this study, improved U2 Net is used to detect blister tablets, but there are still many shortcomings. For example, defect categories are not distinguished in the model, and only the detected binary graph can be marked with a red box in the blister tablet diagram. Therefore, the next step will be to study how to add defect category detection to U2 Net.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, J.H., [PERSON] ([PERSON]) and J.L. ([PERSON]); formal analysis, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]); resources, [PERSON]; data curation, J.H.; writing--original draft preparation, J.H. and J.Z.; writing--review and editing, [PERSON] and J.Z.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### TOPMODEL Model
#### 2.3.4 Model Efficiency Criteria  
Due to the different emphases of daily scale runoff simulation and hourly scale flood simulation, the indexes of the simulation effects of these two scales are also other in this study.  
For daily scale runoff simulations, the first criterion used in the study to evaluate the performance of the model was the Nash-Sutcliffe efficiency (\(NSE\)) index, which is defined by  
\[NSE=1-\frac{\sum_{t}(Q_{t}^{o}-Q_{t}^{s})^{2}}{\sum_{t}\bigl{(}Q_{t}^{o}- \overline{Q}\bigr{)}^{2}} \tag{22}\]  
where \(Q_{t}^{o}\) is the observed streamflow, \(Q_{t}^{s}\) is the simulated streamflow, and \(\overline{Q}\) is the mean observed streamflow. The closer the value of \(NSE\) is to 1, the better the simulation effect of the TOPMODEL model.  
The second efficiency criterion used is the relative error (\(RE\)) between the observed and simulated streamflow series, which is defined as  
\[RE=\frac{\sum_{t}Q_{t}^{o}-\sum_{t}Q_{t}^{s}}{\sum_{t}Q_{t}^{o}}\times 100\% \tag{23}\]  
Figure 2: Distributed topographical indexes of the Chengcun basin.  
The third efficiency criterion used is the root mean square error (\(RMSE\)) between the observed and simulated streamflow series, which is defined as  
\[RMSE=\sqrt{\frac{\sum_{t}(Q_{t}^{o}-Q_{t}^{s})^{2}}{N}} \tag{24}\]  
where \(N\) is the length of the sequence; the closer the value of \(RE\) and \(RMSE\) is to 0, the better the simulation effect of the TOPMODEL model.  
For hourly scale runoff simulations, the evaluation indexes used are peak discharge relative error (PDRE), peak occurrence time error (POTE), runoff depth relative error (RDRE), and \(NSE\) mentioned above. The smaller the value of these errors, the higher the simulation accuracy of the TOPMODEL model.
## 3 Results and Discussion
### Volatile Aroma Compounds
#### 3.2.6 Acids
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Co.** & **Volatile Aroma Compounds** & **ID** & **LRI\({}_{\text{exp}}\)** & **LRI\({}_{\text{lit}}\)** & **F-Ratio** & \multicolumn{2}{c}{**Treatment**} \\ \hline  & & & & & & & _SCE_ & _LEV_ \\ \hline AL16 & 3-Methyl-3-buten-1-ol & MS, LRI & 1245 & 1244 & 13.885 & 0.731 \(\pm\) 0.096  & 0.503 \(\pm\) 0.044  \\ AL17 & 1-Pentanol & MS, LRI & 1245 & 1244 & 12.886 & 12.59 \(\pm\) 1.30  & 9.09 \(\pm\) 1.08  \\ AL18 & _cis_-2-Hexen-1-ol \(\ddagger\) & MS, LRI & 1416 & 1413 & 11.083 & 17.54 \(\pm\) 0.91  & 14.45 \(\pm\) 1.33  \\ AL19 & 1-Dodecanol & MS, LRI & 1968 & 1973 & 9.806 & 1.90 \(\pm\) 0.32  & 1.30 \(\pm\) 0.07  \\ AL20 & 3-Nonanol & MS, LRI & 1492 & 1493 & 9.402 & 0.367 \(\pm\) 0.008  & 0.400 \(\pm\) 0.016  \\ AL21 & 2-Ethyl-2-(hydroxymethyl)- & & MS & 1926 & - & 9.353 & 0.200 \(\pm\) 0.039  & 0.275 \(\pm\) 0.015  \\  & 1,3-propanediol & & & & & & & \\ AL22 & 6-Methyl-5-hepten-2-ol & & S, MS, LRI & 1461 & 1460 & 8.053 & 0.154 \(\pm\) 0.014  & 0.194 \(\pm\) 0.02  \\ AL23 & 1-Undecanol & MS, LRI & 1865 & 1871 & 6.775 & 0.412 \(\pm\) 0.095 & 0.254 \(\pm\) 0.046  \\ AL24 & 3-Octanol & S, MS, LRI & 1392 & 1393 & 6.492 & 1.20 \(\pm\) 0.04 & 1.13 \(\pm\) 0.03  \\ AL25 & 1,4-Butanediod & MS, LRI & 1918 & 1911 & 6.251 & 1.11 \(\pm\) 0.33 & 2.84 \(\pm\) 1.15  & 0.18 \(\pm\) 4.49  \\ AL26 & _trans_-3-Hexen-1-ol \(\ddagger\) & MS, LRI & 1366 & 1361 & 6.183 & 75.45 \(\pm\) 2.49 & 68.08 \(\pm\) 4.49  \\ AL27 & 3,5-Dimethyl-4-heptanol & MS, LRI & 1742 & - & 5.762 & 0.316 \(\pm\) 0.047 & 0.251 \(\pm\) 0.005  \\ AL28 & _trans_-2-Octen-1-ol & S, MS, LRI & 1615 & 1618 & 5.471 & 1.66 \(\pm\) 0.04 & 1.52 \(\pm\) 0.09  \\ AL29 & 2,3-Butanediod isomer & S, MS, LRI & 1573 & 1576 & 4.078 & 383.4 \(\pm\) 33.3 & 339.7 \(\pm\) 17.3  \\ AL30 & 1-Decanol & MS, LRI & 1766 & 1767 & 3.672 & 5.83 \(\pm\) 0.32 & 5.12 \(\pm\) 0.56  \\ AL31 & 2-Ethyl-1-hexanol & MS, LRI & 1487 & 1490 & 2.938 & 12.08 \(\pm\) 2.47 & 19.61 \(\pm\) 7.19  \\ AL32 & 1-Nonanol & S, MS, LRI & 1660 & 1661 & 2.856 & 3.73 \(\pm\) 0.93 & 4.75 \(\pm\) 0.49  \\ AL33 & Methanol (mg/L) & S & \(<\)1000 & 911 & 2.792 & 60.20 \(\pm\) 1.73 & 69.40 \(\pm\) 9.38  \\ AL34 & 3-Ethyl-4-methylpentan-1-ol & MS & 1466 & 1506 & 2.705 & 0.246 \(\pm\) 0.133 & 0.097 \(\pm\) 0.084  \\ AL35 & 1-Hexanol (mg/L) & S, MS, LRI & 1356 & 1357 & 1.706 & 1.53 \(\pm\) 0.044 & 1.46 \(\pm\) 0.08  \\ AL36 & 1,3-Propanediod & MS, LRI & 1785 & 1789 & 1.530 & 0.460 \(\pm\) 0.014 & 0.802 \(\pm\) 0.479  \\ AL37 & _cis_-3-Hexen-1-ol \(\ddagger\) & S, MS, LRI & 1389 & 1389 & 1.448 & 42.77 \(\pm\) 2.01 & 46.16 \(\pm\) 4.451  \\ AL38 & 3-Ethyl-4-methylpentan-1-ol & MS & 1509 & 1506 & 0.977 & 1.62 \(\pm\) 0.10 & 1.54 \(\pm\) 0.07  \\ AL39 & _cis_-4-Decen-1-ol & MS, LRI & 1797 & 1797 & 0.224 & 0.162 \(\pm\) 0.036 & 0.147 \(\pm\) 0.041  \\ AL40 & 2,3-Butanediod isomer & S, MS, LRI & 1587 & 1584 & 0.209 & 4.07 \(\pm\) 7.04 & 2.02 \(\pm\) 3.23  \\ AL41 & 2 -Decanol & MS, LRI & 1616 & 1621 & 0.176 & 0.726 \(\pm\) 0.086 & 0.677 \(\pm\) 0.186  \\ AL42 & 2-Phenoxyethanol & MS, LRI & 2147 & 2144 & 0.034 &
## Appendix A Proof of Theorems 1 and 2
### Proof of Theorem 4  
In this subsection, we need to prove that all achievable rate triples \((R_{1},R_{2},R_{e})\) for the model in Figure 2 with causal channel states are contained in \(\mathcal{R}_{oc}\).  
The conditions in Equations (16) and (18) follow the same as those of Equations (59) and (65). Therefore, we show Equations (17) and (19) as follows.  
To prove the condition in Equation (17), we consider:  
\[R_{1}+R_{2} =\lim\limits_{N\to\infty}\frac{\log(\|\mathcal{M}_{1}\|\cdot\| \mathcal{M}_{2}\|)}{N}\] \[=\lim\limits_{N\to\infty}\frac{H(M_{1},M_{2})}{N}\] \[=\lim\limits_{N\to\infty}\frac{1}{N}[I(M_{1},M_{2};Y^{N})+H(M_{1},M_{2}|Y^{N})]\] \[\leq\lim\limits_{N\to\infty}\frac{1}{N}[I(M_{1},M_{2};Y^{N})+ \delta(P_{e2})] \tag{77}\]\[=\lim_{N\to\infty}\frac{1}{N}[\sum_{i=1}^{N}I(M_{1},M_{2};Y_{i}|Y^{i-1 })+\delta(P_{e2})]\] \[\leq\lim_{N\to\infty}\frac{1}{N}[\sum_{i=1}^{N}I(M_{1},M_{2},Y^{i-1 },S^{i-1};Y_{i})+\delta(P_{e2})],\] \[\leq\lim_{N\to\infty}\frac{1}{N}[\sum_{i=1}^{N}I(U_{i};Y_{i})+ \delta(P_{e2})] \tag{78}\]  
where Equation (77) is based on [PERSON]'s inequality and Equation (78) is from defining \(U_{i}=(M_{1},M_{2},Y^{i-1},S^{i-1})\).  
Before proving the condition in Equation (19), we consider:  
\[I(M_{1},M_{2};Z^{N}) =\sum_{i=1}^{N}I(M_{1},M_{2};Z_{i}|Z^{i-1})\] \[=\sum_{i=1}^{N}I(M_{1},M_{2},Z^{i-1};Z_{i}|Z^{i-1}).\] \[=\sum_{i=1}^{N}I(V_{i};Z_{i}|Q_{i}) \tag{79}\]  
where Equation (79) is from defining \(V_{i}=(M_{1},M_{2},Z^{i-1})\) and \(Q_{i}=Z^{i-1}\).  
Then, utilizing Equations (78) and (79),  
\[H(M_{1},M_{2}|Z^{N}) =H(M_{1},M_{2}|Z^{N})-H(M_{1},M_{2}|Y^{N})+H(M_{1},M_{2}|Y^{N})\] \[=H(M_{1},M_{2}|Z^{N})-H(M_{1},M_{2})+H(M_{1},M_{2})-H(M_{1},M_{2} |Y^{N})+H(M_{1},M_{2}|Y^{N})\] \[=I(M_{1},M_{2};Y^{N})-I(M_{1},M_{2};Z^{N})+H(M_{1},M_{2}|Y^{N})\] \[\leq I(M_{1},M_{2};Y^{N})-I(M_{1},M_{2};Z^{N})+\delta(P_{e2}) \tag{80}\] \[\leq\sum_{i=1}^{N}[I(U_{i};Y_{i})-I(V_{i};Z_{i}|Q_{i})] \tag{81}\]  
where Equation (80) is from Fano's inequality.  
To serve the single-letter characterization, let us introduce a time-sharing random variable, \(J\), independent of all other random variables and uniformly distributed over \(\{1,2,...,N\}\). Set:  
\[U =(U_{J},J),V=(V_{J},J),Q=(Q_{J},J),\] \[A =A_{J},B=B_{J},S=S_{J},X=X_{J},Y=Y_{J},Z=Z_{J}\]  
Then, substituting the above definition into Equations (78) and (81), the conditions in Equations (17) and (19) are verified straightforwardly. From the definition of the auxiliary random variables, the Markov chains \(Q\to V\to U\to Y\to Z\) and \(U\to A\to S\) are easy to be verified. We complete the proof of Theorem 4.
## 2 Materials and Methods
### LC/DAD/MS Analysis  
LC/DAD/MS analysis was carried out using an Agilent Technologies 1260 Infinity II LC system (Agilent Technologies, Inc., Santa Clara, CA, USA), equipped with a quaternary pump, autosampler, multicolumn thermostat, WR Diode Array Detector (DAD), and an Agilent 6546 Quadrupole Time-Of-Flight (QTOF) mass spectrometer. ESI-MS spectra were acquired in negative ion mode ([M\(-\)H]\({}^{-}\)), over an \(m/z\) range of 50 to 1500, with the fragmentor voltage set to 120 V. For MS/MS analyses, collision energies of 10, 20, and 40 V were applied. Chromatographic separation was performed on a Knauer Eurospher II 100-2 C18 column (150 \(\times\) 2 mm, 2 \(\upmu\)m particle size; Knauer Wissenschaftliche Gerate GmbH, Berlin, Germany). The column temperature was maintained at 25 \({}^{\circ}\)C. The autosampler temperature was set to 10 \({}^{\circ}\)C. The mobile phase consisted of eluent A, 0.1% aqueous formic acid (Merck, KGa, Darmstadt, Germany), and eluent B, 0.1% formic acid in acetonitrile (Macron Fine Chemicals(tm); VWR, Radnor, PA, USA). The eluent flow rate was set at 0.2 mL/min, and the injection volume was 2 \(\upmu\)L. The gradient elution program was as follows: 0-50 min, 10% B; 50-62 min, 100% B; and 62-72 min, 10% B. The DAD monitored wavelengths from 190 to 950 nm, with peak detection at 270 nm and 590 nm. Chromatograms were visualized using Agilent MassHunter Qualitative Analysis software, version 10.0. Compound identification was based on the UV absorption profile, the exact mass of the pseudomolecular ion (as referenced in the Agilent METLIN master accurate mass compound database and accurate mass MS/MS spectral library v.B.08.00, Agilent Technologies, Inc., Santa Clara, CA, USA), and external databases such as the Human Metabolome Database (HMDB) ([[http://www.hmdb.ca](http://www.hmdb.ca)]([http://www.hmdb.ca](http://www.hmdb.ca)), accessed on 23 January 2025), MassBank ([[https://massbank.eu/MassBank/](https://massbank.eu/MassBank/)]([https://massbank.eu/MassBank/](https://massbank.eu/MassBank/)), accessed on 23 January 2025), PubChem ([[https://pubchem.ncbi.nlm.nih.gov/](https://pubchem.ncbi.nlm.nih.gov/)]([https://pubchem.ncbi.nlm.nih.gov/](https://pubchem.ncbi.nlm.nih.gov/)), accessed on 21 January 2025) and the MS/MS fragmentation patterns.
## Challenges for Algorithmic Research  
Identifying and preserving digital records remain an open technical challenge. Most of the current tools--checksums, time stamping, and digital signatures, for example--leave open problems. For example, when records must be migrated, bit level changes can mean that the record's authenticity can no longer be verified. Furthermore, digital signatures, because they rely upon certificate authorities, are vulnerable if those authorities cease to exist. New algorithmic research could potentially develop solutions for verifying and authenticating records without reliance upon third parties such as certificate authorities; such algorithms could assure users that the records they entrusted to their CSPs are the records they get back, even if the records must be migrated.  
One area of development for records' privacy and security in the cloud is the blockchain, the distributed ledger technology underlying cryptocurrencies such as Bitcoin and Ethereum. Because they are distributed ledgers, blockchains are essentially recordkeeping systems. Each blockchain relies on an underlying consensus mechanism--an algorithm for verifying transactions--to add transactions to the chain. If blockchain technology is going to fulfill its potential as a recordkeeping technology--suggested use cases include identity records, land records, personal property registration, and records of provenance for the Internet of Things--it will require algorithmic development that accounts for the archival principles that underlie strong recordkeeping systems. Both privacy and security will prove fundamental challenges in this area. Furthermore, many blockchain applications will still require strong support in the way of cloud technology, as the blockchain for many applications will only store pointers to external records and data; one area of development can be seen in [PERSON] and [PERSON]'s work on instantiating the archival bond in blockchain applications [34].  
Ultimately, distributed computing recordkeeping--including cloud and blockchain--is going to require increasingly sophisticated means of identifying, securing, and preserving records, even as they move from system to system. It will also require tools for addressing the transjurisdictional nature of records in the cloud: while this paper looks at two models of privacy (North America and Europe, quite broadly painted), there exist many more legal and regulatory bodies with power over the records in question. The challenges--opportunities--are many.
## 3 Slip-Based Road Friction Monitoring
### Conclusions for Slip-Based Friction Estimation Approaches  
The following points summarise the most relevant conclusions extracted from this discussion on slip-based friction potential monitoring:  
* Overall, tyre model-based approaches dominate the slip-based friction estimation problem. Specifically, the simplified analytical _Brush_ type model has been widely employed in the literature.
* Depending on the methodology employed (longitudinal, lateral or SAT-based) slip-based friction monitoring requires the computation of the tyre forces, tyre SAT and tyre longitudinal or lateral slips:
* Tyre forces might be estimated from a state estimator (e.g., UKF) using a random-walk approach.
* Tyre SAT is often estimated using a linear disturbance observer and EPS current measurements.
* Regarding tyre longitudinal slips, the major difficulty resides on estimating the reference velocity during braking events in which the four wheels present large slips.
* A single-track yaw-body slip observer is often employed to estimate the axle lateral slips.  
Figure 20: Lateral grip margin concept described in [PERSON] et al. [38]. Figure adapted by the authors from [38].  
* Slip-based approaches can accurately predict the friction potential only when a certain level of longitudinal or lateral excitation is present:
* Most promising approaches have been found during the tyre SAT-based survey. In particular, tyre model-less approaches such as the TGM methods are inherently attractive due to its implementation easiness.
* Regarding slip slope approaches, a clearer treatment between loose surfaces and rigid surfaces should be provided. There is still a lack of understanding in what concerns the slip slope changes when the soft material is present in the road-rubber interface.
* Combined approaches based on friction fusion strategies can speed up the friction potential convergence (fusion of information from tyre SAT, braking manoeuvres and limit cornering manoeuvres).
* A friction recognition module relying exclusively on slip-based approaches is seen insufficient for ADAS functions such as ACC or AEB. Additional methodologies capable of sensing the friction potential during free-rolling events (at least capable of providing an initial rough estimate) should be incorporated.
# Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System
## 4 Empirical Study
### Physical Simulation
#### 4.2.2 Physical Simulation Setup  
Here, we describe the implementation of the presented theoretical model on physical testing ground and the simulations setup.  
Three MAVs were used to perform the simulations. The reputation value can be calculated for the MAV when it is located in front of the intersection. At the start of the simulation, the initial value of reputation for each of the MAVs was set as \(R=0.5\). After approaching the intersection, the MAV waited for a confidence interval of \(30\times R\) s. If after this time the MAV did not leave the intersection, we started the \(R\) value calculation.  
First, we needed to calculate the \(Truth\) value. This could be done in two ways. If the MAV was in front of the intersection and the entire intersection was visible, URF could be used to check whether there were other MAVs located at the intersection. An example of such a scenario is represented in Figure 7a. If another MAV was detected at the intersection, then \(Truth=1\), and \(Truth=0\) otherwise. If the MAV was standing in front of the intersection and the whole intersection was not visible (see Figure 7b), then MAV started to interact with other MAVs and \(Truth\) value was calculated based on their \"opinion\" (\(Truth\) evaluation by other MAVs). In this case, \(Truth\) was calculated as \(\frac{\sum\limits_{i=1}^{n-1}Truth_{e_{i}}}{n-1}\), where \(Truth_{e_{i}}=0\) if the \(i\)th MAV opinion was that no one is present at the intersection, and \(Truth_{e_{i}}=1\) if the \(i\)th MAV believed that someone is present at the intersection. Next, we calculated the reputation value according to Equation (8). After that, the intermediate value of reputation was normalized in time.  
Figure 6: Physical model of the urban road network. The numbers indicate the intersections, “I” indicates inaccessible zone for MAVs, and “S” indicates starting point of MAVs.
# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 2 Materials and Methods
### Materials and Samples
#### 2.3.3 Machined Components  
Two samples with machined surfaces have been examined. The first was a large-grained commercially pure titanium sample, which was first prepared and polished for SRAS scanning. Two subsequent milling operations were then performed on the top surface, giving an \(R_{a}\) of 1.6 \(\upmu\)m and 3.2 \(\upmu\)m, respectively. The sample was re-scanned after each milling procedure. The scans of 25 \(\times\) 23 mm of the sample were performed with the Quartet detector using 1000 averages per point and a 100 \(\times\) 100 \(\upmu\)m step size.  
A second sample of a Ti-6 Al-4V disk (260 mm diameter, 13 mm thickness), with a surface roughness of \(\sim\) 0.8 \(\upmu\)m from milling was also scanned. The SKED detector was used to scan the whole surface area with 16 averages per point. The whole disk was scanned with a fixed angular step; it leads to the step size of 100 \(\times\) 200 \(\upmu\)m-100 \(\times\) 400 \(\upmu\)m in the top half from the inner ring to the outer ring and 100 \(\times\) 2 \(\upmu\)m-100 \(\times\) 2180 \(\upmu\)m from the centre of the disk to the outer ring in the bottom half.
## 2 Results  
### Presence of Type II TA Systems in BA1250 Genome  
To assess the presence of type II TA systems in the genome of the hybrid strain BA1250, in silico analyses were conducted using the previously assembled BA1250 genome sequence [23]. The toxin-antitoxin database (TADB) is an integrated database that provides comprehensive information of type II TA systems, and it was used as a platform for TA system identification through TAfinder, concurrently with BLAST alignments.  
Twenty-six different TA loci were detected in BA1250, with highly conserved sequences for most of the identified systems present in the genome of _E. coli_ from different pathotypes, including diarrheagenic and extaintestinal strains (Table 1). The identified systems belonged to types I, II, IV, and V, with the majority (50%) to type II. Among the identified systems, three of them were observed to be present in more than one copy, varying from two to five copies of each gene. To determine if the detected systems were present in the chromosome or in a plasmid, we used two different methods: the sequence contents-aware plasmid peeler (SCAPP), which is a tool able to identify full circular plasmid sequence, and PlasFlow, which uses a neural network to classify the contig sequences into chromosomal, plasmid, and non-classified sequences. According to this methodology, the majority (95.2%) of the described systems were predicted to be located in the BA1250 chromosome except for system SrnC-SmB, which was predicted to be plasmid-encoded (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gene** & **Function** & **Type** & **Number of Copies** & **Location a** \\ \hline _sokC_ & Antitoxin & & 1 & Chromosome \\ _hokC_ & Toxin & & 1 & Chromosome \\ _sokW_ & Antitoxin & & 1 & Chromosome \\ _hokW_ & Toxin & & 1 & Chromosome \\ _srnC_ & Antitoxin & & 1 & Plasmid \\ _srnB_ & Toxin & & 1 & Plasmid \\ _sokX_ & Antitoxin & & 1 & Chromosome \\ _hokX_ & Toxin & & 1 & Chromosome \\ _rdID_ & Antitoxin & & 3 & Chromosome \\ _ldrD_ & Toxin & & 3 & Chromosome \\ _sokA_ & Antitoxin & & 1 & Chromosome \\ _hokA_ & Toxin & & 1 & Chromosome \\ _higA_ & Antitoxin & & 5 & Chromosome \\ _higB_ & Toxin & II & 3 & Chromosome \\ _higA_ & Antitoxin & & 5 & Chromosome \\ _vapC_ & Toxin & II & 1 & Chromosome \\ _tomB_ & Antitoxin & & 1 & Chromosome \\ _Hha_ & Toxin & & 1 & Chromosome \\ _sltpB_ & Antitoxin & & 1 & Chromosome \\ _doc_ & Toxin & II & 2 & Chromosome \\ _ccdA_ & Antitoxin & & 1 & Chromosome \\ _ccdB_ & Toxin & II & 1 & Chromosome \\ _prlF_ & Antitoxin & & 1 & Chromosome \\ _yhaV_ & Toxin & II & 1 & Chromosome \\ _phd_ & Antitoxin & & 1 & Chromosome \\ _doc_ & Toxin & & 2 & Chromosome \\ _yefM_ & Antitoxin & & 1 & Chromosome \\ _yoeB_ & Toxin & & 1 & Chromosome \\ _hipB_ & Antitoxin & & 1 & Chromosome \\ _hipA_ & Toxin & II & 1 & Chromosome \\ _psI_ & Antitoxin & & 1 & Chromosome \\ _pusT_ & Toxin & II & 1 & Chromosome \\ _mazE_ & Antitoxin & & 1 & Chromosome \\ _mazF_ & Toxin & II & 1 & Chromosome \\ \hline \hline \end{tabular}
\end{table}
Table 1: Toxin–antitoxin system components are present in the BA1250 genome.