esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 5 Limitations and Future Work  
The study, like many machine learning studies, was not without its limitations. Based on synthetic data from 20 gas compositions simulated by Aspen HYSYS, the investigation limited the findings and predictions to the conditions of the simulations and not the real-world environment of a pipeline. This reliance on synthetic data restricted the models' proven applicability to operational settings because real pipeline conditions could introduce complexities (e.g., unmodeled impurities) not captured here. The other limitation was that the dataset had limited features; although the proposed models demonstrated good performance in this regard, they may not hold as well for wider or narrower datasets and, therefore, should be used with caution outside this boundary. In addition, the models were highly reliant on the input parameters of flow rate, temperature, pressure, gas specific gravity, and water content, which were optimized for the ranges of this study. However, applying these models outside these ranges (such as extreme temperatures or pressures) may yield erroneous results because they have not been verified for these conditions.  
Future machine learning studies for predicting methanol demand for hydrate control need to include more diverse and detailed datasets, including data from real gas pipelines, to improve the generalizability and strength of the models. Validation using operational pipeline data is essential to confirm the accuracy and practical utility of XGBoost and other models, addressing the current gap between synthetic and real-world performance. Increasing both the diversity of models and the dataset could lead to more generalizable machine learning models. This would enhance their ability to be used more widely across a variety of pipeline conditions and gas compositions. Integrating new machine learning techniques like deep learning may also improve the error levels that KNN was unable to resolve, allowing the modeling of a much wider range of processes involved in hydrate inhibition processes, such as how different input parameters (for example, inhibitor type or pipeline geometry) affect hydrate inhibition behavior.  
Conceptualization, W.L. and M.H.M.; methodology, M.H.M.; software, M.H.M.; validation, W.L.; formal analysis, M.H.M.; investigation, W.L., M.H.M., and G.H.J.; resources, G.H.J.; data curation, M.H.M.; writing--original draft preparation, M.H.M.; writing--review and editing, M.H.M.; visualization, M.H.M.; supervision, W.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
# NMR Analysis of Pulegone in Food Products
## 2 Results and Discussion
### Quantification of Pulegone in Tea, Herbs, and Confectionery Products  
The peppermint tea samples analyzed exhibited a pulegone content ranging from 39.9 to 443 mg/kg. A pulegone content of 639.0 \(\pm\) 2.6 mg/kg was detected in loose peppermint tea. The pulegone concentration of 544 \(\pm\) 97 mg/kg was detected in spearmint tea. As with peppermint tea, this concentration refers to the concentration of pulegone in spearmint tea and not to its concentration in the oil. In the literature, the pulegone content in the oil, but not in the plant, has been stated for spearmint.  
A pulegone content of 275-507 mg/kg was detected in the mint herbs. The packaging of the herbs did not contain a more precise description of the type of mint, only \"mint\". For this reason, it is not clear which type of mint is used. Herbal mint 3 was labeled \"peppermint\". However, in the sample, the signal from carvone was significantly more intense than that from pulegone (see Figure 7). In spearmint oil, the carvone content can be 41%, whereas the pulegone content is only half as high or lower [3; 31]. Therefore, herbal mints 2, 3, and 4 are probably spearmints. Only in herbal mint 1 was carvone not detectable.  
Figure 6: Section of a \({}^{1}\)H NMR spectrum of spearmint essential oil (dark blue line) overlaid with a spectrum spiked with pulegone (thin gray line); both were recorded in MeOD/CDCl\({}_{3}\) (1:1; \(v/v\)).  
A pulegone concentration of 1.50 \(\pm\) 409 mg/kg was detected in penmyroyyal sample 1.  
A pulegone concentration of 20.5-137 mg/kg was detected in the pastilles. The pulegone content in pastille 6 was below the LOD. Pastilles 2, 4, and 5 are below the LOQ.  
A pulegone content of 17.6 \(\pm\) 6.8 mg/kg was detected in dragee 1. However, this concentration was below the LOQ. The pulegone content in the other coated tablet was below the LOD. A pulegone content of 0.45-29.91 mg/kg in mint-flavored confectionery was reported in the literature [10]. The candy investigated is within the range mentioned in the literature.
## 3 Typical Applications of ACE-FHBOC Multiplexing  
For an ACE-FHBOC signal ACE-FHBOC(\(\alpha_{M-1}\):\(d\):\(x_{0}\), \(\mathbf{\beta},\mathbf{p}\)), its flexibility in the frequency-hopping scheme, the number of signal components, and their power allocations make it a promising solution for many situations satisfying the requirements in the GNSS signal design. For the sake of clarity, \(\mathbf{\beta}\) is set to be a scalar \(\beta\), i.e., the four signal components have the same spreading code rate \(f_{c}\) when discussing different types of ACE-FHBOC. This section presents three typical applications of ACE-FHBOC.
## 4 Analysis of the Vibration Suppression Mechanism of the Deflector Plate
### Irrelevance Verification  
In order to ensure the accuracy of the CFD numerical simulation results, it was necessary to verify the irrelevance of the time step and the number of elements. This paper selected four sets of computational mesh for the simulation object: G1, G2, G3, and G4 under the condition of 0\({}^{\circ}\) wind attack angle of the original section model. The relevant parameters and numerical simulation results are shown in Table 5.  
It can be seen from Table 5 that with an increase in the total number of element, the calculation results of the model have little effect, and increasing the total number of elements increases the amount of calculation. In order to significantly reduce the iterative calculation time on the same time step while maintaining accuracy, the G2 mesh scheme was finally determined to be the optimal scheme.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Condition** & **Number of Elements** & \(\triangle\)**t(s)** & \(\mathbf{C_{H}}\) & \(\mathbf{C_{V}}\) & \(\mathbf{C_{M}}\) \\ \hline G1 & & 71,354 & 0.0001 & 0.6392 & 0.2798 & \(-\)0.0134 \\ G2 & & 148,367 & 0.0001 & 0.6453 & 0.2872 & \(-\)0.0140 \\ G3 & & 246,589 & 0.0001 & 0.6477 & 0.2891 & \(-\)0.0143 \\ G4 & & 453,617 & 0.0001 & 0.6485 & 0.2911 & \(-\)0.0146 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the irrelevance verification of the number of elements.  
Figure 22: Mesh of the computational domain.  
The irrelevance verification of the number of elements was checked above, and the G2 mesh was identified. The choice of the time step is also essential to the simulation accuracy. The irrelevance verification of the time step is shown in Table 6.  
It can be seen from Table 6 that as the time step decreases, the calculation results become more and more stable. When the time step is 0.0001 s, further reducing the time step has little effect on the results. Considering accuracy and efficiency, subsequent calculations adopt the time step of 0.0001 s.
## 5 Conclusions  
Aiming at the accuracy and efficiency requirements of defect detection in high-density manufacturing scenarios of printed circuit boards (PCBs), this paper proposes an improved inspection model, YOLO-SUMAS, based on YOLOv8n. By integrating the SCSA attention mechanism, the Unified-IoU loss function, the MobileNetV4 backbone architecture, and the ASF-SDINeck structure, the model achieves a significant improvement in feature extraction, localization optimization, computational efficiency, and multi-scale fusion capability. Compared with traditional methods, the model validates the advantages of single-stage models in industrial real-time inspection with lower parameters and higher FPS while avoiding the high computational overhead of transformer-class methods. In a high-density PCB manufacturing scenario, the model demonstrates robustness in noise interference and uneven exposure environments through data augmentation, and its lightweight feature supports edge device deployment, reducing the hardware dependence and labor cost of traditional inspection means. Future research can focus on noise robustness enhancement (e.g., adversarial training), multimodal data fusion (combined with thermal imaging or electrical inspection), adaptive feature pyramid network optimization, and industrial deployment of model compression techniques (quantization and pruning) to further extend its potential application in cross-domain inspection tasks such as semiconductor packaging.  
The results show that compared with the baseline model YOLOv8n, the YOLO-SUMAS model improves the precision, recall rate, mAP@50, and other key indicators by 1.2%, 9.2%, and 4.7%, respectively, while maintaining a high FPS, which verifies the effectiveness of the relevant improvements. Future work can focus on further optimizing the attention mechanism and exploring more sophisticated feature fusion methods to continue to improve the robustness and generalization ability of the model in noisy interference scenarios and eventually extend its applicability to defect detection tasks in other domains.  
Project administration, Y.T.; software, R.L.; visualization, R.L.; methodology, S.W.; writing--original draft preparation, S.W.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by Sichuan Science and Technology Program, grant number 2022 YFS0531.  
Figure 17: Mobile deployment interface.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank the anonymous reviewers for their constructive comments and suggestions, which greatly improved the quality of this manuscript.  
The authors declare no conflicts of interest.
## 5 Results and Discussions
### CDF of the SINR  
Another important parameter to determine the VLC system quality that we have adopted for underground mining scenarios is determined by the statistics of the received \(\gamma\). Therefore, in this study, the CDFs of the \(\gamma\) received both in a single PD, and in the ADRs with their respective signal combining schemes, are empirically derived to evaluate the performance of the proposed solution. By varying the position of the \(R_{x}\) through all possible positions in the two mining VLC scenarios presented, and estimating the respective attainable \(\gamma\), it is possible to determine the CDF statistically, as illustrated in Figure 14.  
Figure 14a compares the SINR performance of a single PD and the MRC, SBC, and EGC schemes for the hemi-dodecahedron and pyramidal ADRs, in the mining roadway scenario. The ADR that achieves better performance in the SINR with the three signal combination schemes is the ADR hemi-dodecahedron; this is due to its geometric distribution of PDs, although this angular diversity causes it to receive a smaller portion of the desired signal. Nevertheless, as the ADRs and the only PD have the same equivalent FoV, as there are more structurally located PDs, each of these PDs will have a smaller FoV and a smaller coverage area, which means that less interference signal is collected by PDs. For the considered LOS and NLoS channels, they can be divided into regions of high SINR and low SINR. \(R_{x}\) in positions close to the center of the cell are free of ICI, which means that they can achieve a significantly higher SINR than users near the edge of the cell. The best performing scheme is the MRC in both ADRs, although the difference between the MRC in the hemi-dodecahedron ADR and the pyramidal ADR is approximately 20 dB; further, compared to a single PD, they have a difference of 80 dB and 50 dB, respectively. These results are since that the MRC scheme combines with a proportional weight the signals received in the different PDs, which increases the desired signal and attenuates the ICI. As a result, it achieves the best SINR performance. Figure 14b compares the SINR performance of a single PD and the MRC, SBC, and EGC schemes for the hemi-dodecahedron and pyramidal ADRs in the mine working face scenario. Compared to the mine roadway scenario,  
Figure 13: BER as a function of the relationship \(E_{b}/N_{0}\) for the single PD, hemi-dodecahedron and pyramidal ADRs, with their signal combining schemes in the underground mining VLC scenarios. BER curves for the (**a**) mining roadway and (**b**) mine working face scenarios.  
the performance of all the schemes in the two ADRs analyzed decreases. However, it maintains the trend that presents the hemi-dodecahedron ADR as the best performing Rx compared to the pyramidal ADR and the single PD, and the MRC as the scheme that has the largest SINR compared to the other schemes studied.
## 4 Numerical Results and Discussions
### Bit-Error-Rate
#### 4.1.1 Bit-Error-Rate in S\({}_{1}\)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SL\#** & **NU [S\({}_{1}\)]** & **FU [S\({}_{1}\)]** & **D\({}_{1}\)\(\rightarrow\)D\({}_{2}\) [S\({}_{1}\)\& S\({}_{2}\)]** & **NU [S\({}_{2}\)]** & **FU [S\({}_{2}\)]** \\ \hline
1 & A\({}_{\text{BS}}\) = 2, A\({}_{\text{NU}}\) = 2 & \(M_{1}=M_{2}\) = 4, A\({}_{\text{BS}}\) = 2, A\({}_{\text{D}_{1}}\) = 2, A\({}_{\text{FU}}\) = 2 & A\({}_{\text{D}_{1}}\) = 2, A\({}_{\text{NU}}\) = 2 & \(M_{2}=2\), A\({}_{\text{BS}}\) = 2, A\({}_{\text{D}_{1}}\) = 2, A\({}_{\text{FU}}\) = 2 \\ \hline
2 & A\({}_{\text{BS}}\) = 2, A\({}_{\text{NU}}\) = 3 & \(M_{1}=M_{2}\) = 16, A\({}_{\text{BS}}\) = 2, A\({}_{\text{D}_{1}}\) = 2, A\({}_{\text{FU}}\) = 2 & A\({}_{\text{D}_{1}}\) = 2, A\({}_{\text{D}_{2}}\) = 3 & \(M_{1}=16\), A\({}_{\text{BS}}\) = 2, A\({}_{\text{NU}}\) = 2 \\
3 & A\({}_{\text{BS}}\) = 2, A\({}_{\text{NU}}\) = 4 & \(M_{1}=M_{2}\) = 32, A\({}_{\text{BS}}\) = 2, A\({}_{\text{D}_{1}}\) = 2, A\({}_{\text{FU}}\) = 2 & A\({}_{\text{D}_{1}}\) = 2, A\({}_{\text{BU}}\) = 2 & \(M_{2}=2\), A\({}_{\text{BS}}\) = 2, A\({}_{\text{D}_{1}}\) = 2, A\({}_{\text{BU}}\) = 2, A\({}_{\text{BU}}\) = 4 \\ \hline
4 & A\({}_{\text{BS}}\) = 4, A\({}_{\text{NU}}\) = 2 & \(M_{1}=M_{2}\) = 4, A\({}_{\text{BU}}\) = 4, A\({}_{\text{PU}}\) = 4 & A\({}_{\text{D}_{1}}\) = 4, A\({}_{\text{BU}}\) = 4 & \(M_{2}=4\), A\({}_{\text{BU}}\) = 4, A\({}_{\text{BU}}\) = 4 \\ \hline
5 & A\({}_{\text{BS}}\) = 4, A\({}_{\text{NU}}\) = 3 & \(M_{1}=M_{2}\) = 16, A\({}_{\text{BS}}\) = 4, A\({}_{\text{AU}}\) = 4, A\({}_{\text{FU}}\) = 4 & A\({}_{\text{D}_{1}}\) = 4, A\({}_{\text{BU}}\) = 4 & \(M_{1}=16\), A\({}_{\text{BS}}\) = 4, A\({}_{\text{BU}}\) = 4, A\({}_{\text{BU}}\) = 4 \\ \hline
6 & A\({}_{\text{BS}}\) = 4, A\({}_{\text{NU}}\) = 4 & \(M_{1}=M_{2}\) = 32, A\({}_{\text{BS}}\) = 4, A\({}_{\text{D}_{1}}\) = 4, A\({}_{\text{FU}}\) = 4 & A\({}_{\text{D}_{1}}\) = 4, A\({}_{\text{BU}}\) = 4 & \(M_{1}=32\), A\({}_{\text{BS}}\) = 4, A\({}_{\text{BU}}\) = 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation Parameters. equipped with four antennas. Recall that antenna indices are the desired information of NU and D\({}_{2}\) and modulation symbol is the desired information of FU in S\({}_{1}\).
## 4 Experimental Evaluation
### Application
#### 4.2.2 Result
Finally, the last application (_App5_) collects the temperature and sends it to another sensor node as shown in the following:  
```
1 implemention{
2 message_tpckt;msg_t*msg;
3
4 eventvoidBoot.bootooted(){
5 msg=(msg_t*)callPacket.getPayload(&pckt,size(msg_t));
7 callRadioControl.start();
8}
9
10 eventvoidRadioControl.startDone(error_t e){
11 callTimer.startOneShot(100);
12}
13
14 eventvoidTimer.fired(){
15 callTemp.read();
17}
18
19 eventvoidTemp.readDone(error_t e,floatvalue){
20 msg->error=e;
21 msg->value=value;
22 callRadioSender.send(BASE_STATION,&pckt,28);
23}
24
25 eventvoidRadioSender.sendDone(error_t e,message_t p){
26 callTimer.startOneShot(100);
27}
29}
```  
The behaviour of this application consists of turning on the radio in the boot (4-7). When the radio is already started, the application collects a temperature (13-15) after 100 milliseconds (9-11). Next, the application creates a message and sends it to the base station (17-21). This behaviour is repeated each 100 milliseconds (23-25) in a loop. shows the _scheduler_ (see Section 3.3) of _App5_. Figure 16: Power consumption of App4. The results obtained by both methods are depicted in Figure 18. The mean difference between them was of 6.71%. The hypothesis testing returns the p-value equal to 0.083238. Hence, we can assume that, for this experiment, both methods return the same values. The results of the evaluation of these five applications shows that the proposed approach is flexible by allowing the evaluation of either an entire application or just a piece of code (function). It is also possible to observe that the results obtained by measurement and simulation are very close in all cases. Additionally, this test also shows that for more complex codes (which likely consumes more power) and for small applications (which likely consume less power) there is no difference between both methods.
## 3 Experimental Test
### Material Properties  
Tensile coupon tests were also carried out to obtain the material properties including yield stress, ultimate stress, and elongation of the specimens. Three coupon samples for each thickness were cut from the flange of the upright where there was no perforation. A 300 kN capacity MTC Sintech testing machine with a rate of 0.01 mm/s was employed for coupon tests following the AS4600 [32] procedures. Figure 6 shows the stress-strain curves for both 2.5 mm and 1.6 mm sections. The mean value of the ultimate tensile strength (\(\sigma_{\text{u}}\)), yield stress (\(\sigma_{y}\)), and elongation are also presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen Designation** & **Length (mm)** & **Reinforcement Type** & **Thickness (mm)** \\ \hline
1200L-1.6T & & - & \\
1200L-1.6T-400P & 1200 & @ 400 mm & \\
1200L-1.6T-200P & & @ 200 mm & \\ \hline
1800L-1.6T & & - & \\
1800L-1.6T-400P & 1800 & @ 400 mm & \\
1800L-1.6T-200P & & @ 200 mm & \\ \hline
2400L-1.6T & & - & 1.6 \\
2400L-1.6T-400P & 2400 & @ 400 mm & \\
2400L-1.6T-200P & & @ 200 mm & \\ \hline
3000L-1.6T & & - & \\
3000L-1.6T-400P & 3000 & @ 400 mm & \\
3000L-1.6T-200P & & @ 200 mm & \\ \hline
3600L-1.6T & & - & \\
3600L-1.6T-400P & 3600 & @ 400 mm & \\
3600L-1.6T-200P & & @ 200 mm & \\ \hline
1800L-2.5T & & - & \\
1800L-2.5T-400P & 1800 & @ 400 mm & \\
1800L-2.5T-200P & & @ 200 mm & \\ \hline
2400L-2.5T & & - & \\
2400L-2.5T-400P & 2400 & @ 400 mm & \\
2400L-2.5T-200P & & @ 200 mm & \\ \hline
3000L-2.5T & & - & \\
3000L-2.5T-400P & 3000 & @ 400 mm & \\
3000L-2.5T-200P & & @ 200 mm & \\ \hline
3600L-2.5T & & - & \\
3600L-2.5T-400P & 3600 & @ 400 mm & \\
3600L-2.5T-200P & & @ 200 mm & \\ \hline \end{tabular}
\end{table}
Table 1: Specimen details.  
Figure 6: Coupon test results for uprights with 1.6 mm and 2.5 mm thickness.
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 6 Dynamic Simulation Results  
In this section, simulation scenarios on different wind speeds are carried out to investigate the performances of the proposed strategy. As disturbance, SG\({}_{6}\) generating 70.0 MW is tripped at 10.0 s.  
The metrics of the system frequency nadir, second frequency nadir, nadir-based frequency response, captured mechanical power, released rotating kinetic energy (\(\Delta E_{rel}\)), and time for the \(\omega_{r}\) restoration for the proposed strategy are compared in the conventional method and the MPT operation.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **SG1/SG2** & **SG3/SG4** & **SG5/SG6** \\ \hline Nominal Apparent Power (MVA) & 150 & 200 & 100 \\ Inertia Constant (s) & 4.0 & 5.0 & 4.3 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of Synchronous generators.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(K\) & \(K_{1}\) & \(K_{2}\) & \(K_{3}\) & \(T_{1}\) & \(T_{2}\) & \(T_{3}\) \\ \hline
20 & 0.3 & 0.4 & 0.3 & 0.1 & 1.0 & 0.25 \\ \(T_{4}\) & \(T_{5}\) & \(T_{6}\) & \(U_{\text{o}}\) & \(U_{\text{c}}\) & \(P_{\text{max}}\) & \(P_{\text{min}}\) \\
0.3 & 10 & 0.4 & 0.1 & \(-\)0.2 & 1 & 0.4 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the IEEEG1 model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Values** & **Units** \\ \hline Nominal Stator Voltage & 2.3 & kV \\ Nominal Active Power & 5.0 & MW \\ Magnetizing Reactance & 2.9 & p.u. \\ Stator Leakage Reactance & 0.18 & p.u. \\ Stator Resistance & 0.023 & p.u. \\ Rotor Resistance & 0.016 & p.u. \\ Rotor Leakage Reactance & 0.16 & p.u. \\ Inertia Constant & 5.0 & s \\ Stable Operating Range of \(\omega_{r}\) & 0.70–1.25 & p.u. \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of the DFIG.  
Figure 6: IEEEG1 model.  
Among them, the nadir-based frequency response (NBFR), which is one of the system frequency response metrics for assessing the rigidness of an electric power grid, is presented as in [21].  
\[\text{NBFR}=\frac{P_{loss}}{f_{nom}-f_{nadir}}, \tag{15}\]  
where \(P_{loss}\) indicates the loss of power generation and is 70 MW in this study. \(f_{n}\) and \(f_{\text{min}}\) are the nominal frequency and the minimum frequency during a disturbance, respectively.  
According to Equation (15), NBFR signifies that a loss of active power generation required for causing 1.0 Hz frequency decline. Accordingly, the electric power grid with a large NBFR is capable of guaranteeing the frequency stability during a disturbance.  
The released kinetic energy (\(\Delta E_{rel}\)) can be calculated by:  
\[\Delta E_{rel}=H_{\text{DFIG}}\times(\omega_{0}^{2}-\omega_{c}^{2}), \tag{16}\]  
where \(H_{DFIG}\) means the inertia constant of a DFIG, which respectively comprises the inertia constants of an induction generator and a wind turbine.
# A Predefined-Time Control for the Laser Acquisition in Space Gravitational Wave Detection Mission
## 2 Acquisition Scheme
### Uncertainty Cone  
In order to establish the laser links between the satellites, the laser beam signal from the sending satellite must be detected by the acquisition sensors in the receiving telescope. However, because of the long distance, navigation error, attitude accuracy and telescope pointing error, it is almost impossible to establish the links by initial pointing. Therefore, the attitude knowledge supplied by the attitude sensors must be relied upon. Therefore, the scanning process is necessary which means that the telescope on the sending satellite needs control of its pointing direction with a guidance law to cover the uncertainty cone of the receiving satellite until the laser beam signal is detected by the acquisition sensor in the opposite telescope. The acquisition phase is completed when all the telescopes receive the laser beam signal.  
The uncertainty cone is the cone where the receiving spacecraft is expected to be located with respect to the sending spacecraft. The spacecraft attitude accuracy will be the relatively large contribution for the uncertainty cone which is limited by the accuracy of the star tracker. The navigation error is another factor but is associated with the orbit; earth orbit programs have a smaller navigation error than sun orbit programs. The other influencing factors such as calibration error, outgoing beam direction error and telescope pointing error are relatively small. These contributions will determine the size of the uncertainty cone which further affects the acquisition time.