esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### SMA Mix Design  
Stone mastic asphalt was first developed in Germany in the 1960s and it was soon used in the rest of Europe, the United States, Canada and Australia due to its good performance. The combination of selected aggregates according to a gap-graded gradation and the adoption of modified bitumen allows for a high resistance to plastic deformation, making SMA suitable for high- and heavy-traffic roads. Furthermore, the use of high quality aggregates ensures remarkable skid-resistance properties.  
In the present research, the adopted mix design for the SMA followed the District of Bologna technical specification [32]. The grading distribution is presented in Figure 2 and Table 2, while the rheological properties of the used bitumen are reported in Table 3.  
Based on previous studies and laboratory tests on the same SMA mix design, the optimum binder content was fixed at 6% on the weight of aggregates, while the amount of filler was 10% on the weight of aggregates [38].  
Following the technical specification of the commercial fibers and the optimum bitumen content, their ratio was 0.3% of the weight of the aggregates for the reference mix (SMA0). As for the SMAF, as shown in the following paragraph, specific drain-down tests (ASTM D6390-11 [39]) were carried out in order to evaluate the absorption properties of the shredded CFs and to define their optimum dosage within the bituminous mixture.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sieve (mm)** & **Retained Material (\%)** & **Passing Material (\%)** \\ \hline
**14** & 0.00 & 100.00 \\
**12** & 0.68 & 99.32 \\
**10** & 19.10 & 80.22 \\
**8** & 15.01 & 65.21 \\
6.3 & 13.65 & 51.57 \\
**4** & 10.14 & 41.42 \\
**2** & 14.62 & 26.80 \\
**1** & 7.36 & 19.45 \\
**0.5** & 3.96 & 15.49 \\
**0.25** & 2.88 & 12.61 \\
**0.125** & 1.64 & 10.97 \\
**0.063** & 1.16 & 9.81 \\
**<0.063** & 9.81 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: SMA gradation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Unit** & **Characteristic Value** & **Standard** \\ \hline Penetration @ 25 \({}^{\circ}\)C & dmm & 50–70 & EN 1426 [33] \\ Softening Point & \({}^{\circ}\)C & 46–54 & EN 1427 [34] \\ Penetration Index & - & 1.05–0.70 & EN 12591 [35] \\ Dynamic Viscosity @ & Pa-s & 145 & EN 12596 [36] \\
60 \({}^{\circ}\)C & Ductility & \% & 80 & ASTM D 113 [37] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Conventional properties of the adopted bitumen.  
Figure 2: SMA grading distribution.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 4 Results and Analysis
### Estimation of Winter Wheat Yield  
In this study, the winter wheat NPP was used to estimate the winter wheat economic yield. First, the improved CASA model was applied to estimate the winter wheat NPP in the Tongzhou and Shunyi districts of Beijing for the 2008-2009 growing season. Next, the NPP-yield conversion model [Equation (19)] was used to convert the winter wheat NPP into the winter wheat economic yield.  
Figure 8 shows the spatial distribution of the estimated winter wheat yield in the study area, which ranged from 3 to 8 t ha\({}^{-1}\) over more than 90% of the total study area. The output of the entire study area was about 50% for the range 3-5 t ha\({}^{-1}\) and 35% for the range 5-7 t ha\({}^{-1}\). The results show that, during the 2008-2009 growing season, the Tongzhou and Shunyi districts of Beijing had similar winter wheat growth trends, however, the winter wheat yield in the central part of the Tongzhou District and in the western part of the Shunyi District was higher than that in the northern part of the Tongzhou District and in the western part of the Shunyi District. Overall, the growth of winter wheat in the study area was ideal.
## 5 Results  
The results were collected automatically by the simulation environment and imported for statistic analysis in a spreadsheet software. Firstly, they were analyzed without any classification and consequently with classification in specific categories. These categories were constructed according to the discrimination test and the _BL_ mix mode. More specifically, the ones specified by the discrimination test were: (a) beginners, (b) experienced, and (c) experts, and the ones formulated according to the _BL_ mix mode were; (a) low mix mode, (b) mid mix mode, and (c) high mix mode.  
The number of participants in each group was:  
* Total number of participants: 78;
* Naive: 3, beginners: 27, experienced: 26, experts: 22;
* Low _BL_ mix: 26, mid _BL_ mix: 26, low _BL_ mix: 26.  
In each category, a \(T\)-test between the mean of the _BL_ virtual mix and the means of other groups determines if there is a significant difference between the respective two groups. Tables 6-8 present the \(T\)-test results.  
Generally, in Table 6, the \(BL\) mix results seem to outperform the alternative ARA mixes. Furthermore, the box plots of each analyzed group indicate that the \(BL\) ARA mix has a different distribution, approximately 3 to 5, than the other mixes.  
Besides the distribution analysis (see Figure 16), the \(T\)-test method was applied on pairs of the \(BL\) virtual source and each of its other alternatives. The hypothesis of the \(T\)-test examines the relation of the \(BL\) adaptation to the unfiltered virtual signal through a comparison in the MUSHRA test. The subjects compared each ARA mix result to evaluate the quality of each mix.  
Testing hypothesis:  
Null hypothesis: the \(BL\) adaptation does not affect the ARA mix;  
Alternative hypothesis: the \(BL\) adaptation improves the ARA mix;  
Question: evaluate the mix adaptation of the virtual source in the real environment.  
According to the \(T\)-test results, the null hypothesis is rejected (\(p<0.05\)), except for the case of the six phons mix (BL high mix mode), which seems to have caused confusion  
Figure 16: MUSHRA results—distribution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Virtual Source** & **Beginners AVG** & \(p\)**-Value < 0.05** & **Experienced AVG** & \(p\)**-Value < 0.05** & **Experts AVG** & \(p\)**-Value < 0.05** \\ \hline Unfiltered & 2.89 & 0.0004 & 3.20 & 0.02 & 4.05 & 0.0045 \\ \(BL\) Mix & 3.92 & & 3.96 & & 4.15 & \\ LPF 3.5 kHz & 3.16 & 0.0171 & 3.17 & 0.01 & 2.95 & 0.0041 \\ LPF 7 kHz & 3.32 & 0.0730 & 3.00 & 0.004 & 3.15 & 0.015 \\ LPF 10 kHz & 2.84 & 0.001 & 3.30 & 0.09 & 3.20 & 0.003 \\ \hline \hline \end{tabular}
\end{table}
Table 8: \(T\)-test results—experience.  
(Table 7). Moreover, in the beginner and experienced groups, the \(T\)-tests indicate that the virtual source with lower bandwidth 7 kHz, and 10 kHz, respectively, does not make a significant difference in the ARA mix (Table 8).
## 2 State of the Art
### Prior Comparisons of FDM and SLA
#### 2.1.3 Cost and Throughput  
Cost analyses of FDM and SLA repeatedly highlight that FDM is less expensive on a per-part basis, with more economical material inputs (filament spools) and minimal post-processing equipment [18]. SLA resin costs can be two to three times higher than filament, and ancillary tools (e.g., alcohol wash stations, UV-curing units) add to the overall investment [7]. Nonetheless, some SLA machines leverage automated workflows and advanced resin formulations, suggesting the cost gap may shrink under certain production scales. Detailed comparative data for identical geometries, accounting for labor hours and energy use, remain limited.  
Environmental and Reliability FactorsEnvironmental aspects and reliability are less frequently studied but are increasingly relevant to practitioners. Although PLA (a popular FDM material) is biodegradable, many SLA resins contain hazardous components and require special disposal procedures [19]. FDM may suffer from filament jams or warping under suboptimal temperature control, while SLA can experience partial curing and failed prints if the resin is not handled properly [20; 21]. Few published works integrate these dimensions--waste generation, recyclability, energy consumption, and failure rates--into comprehensive FDM vs. SLA comparisons [22; 23].
## 3 Performance Analysis
### Bob's Average Bit Error Probability
As a result, it should take influence of CIM into consideration jointly while calculating the upper bound of ABER. Against this scenario, the influence of CIM should be considered in the analysis of ABEP. Noticing that the mapped covert information will not affect the Hamming distance and PEP between \(\mathbf{x}_{\mathcal{I}_{\mathcal{I}_{\mathcal{I}_{\mathcal{I}_{\mathcal{I}}}}} }^{s}\) and \(\mathbf{x}_{\mathcal{I}_{\mathcal{I}_{\mathcal{I}_{\mathcal{I}_{\mathcal{I}_{ \mathcal{I}_{\mathcal{I}_{\mathcal{I}}}}}}}}}^{n}\), but may influence mapping of the \(k_{1}\) bits in which covert information embeds. According to (17), if the former \(k_{1}\) bit is determined wrongly, the latter one is likely to be misjudged. In other words, the accuracy of the latter \(k_{1}\) bit is depended on the former one in some extent. Therefore, the average error probability of the bit for receiver group selection can be approximated by:  
\[P_{r}=P_{B_{k-1}}\times\big{(}1-P_{B_{k}}\big{)}+\big{(}1-P_{B_{k-1}}\big{)} \times P_{B_{k^{\prime}}}, \tag{24}\]  
which illustrates that the misjudgment of the original \(k_{1}\) bit is caused by the misjudgment of the receiver group at the \((k-1)\)-th or \(k\)-th slot, even though the remaining data bits is correctly obtained. It can be used that \(P_{B_{k-1}}=P_{B_{k^{\prime}}}\), (24) can be arranged as  
\[P_{r}=2P_{B_{k}}\big{(}1-P_{B_{k}}\big{)}. \tag{25}\]  
Furthermore, the ABEP should be adjusted as the influence of the mapped covert information discussed aforementioned, which is considered the \((\mathrm{k}-1)\)-th slot and the \(k\)-slot jointly and can be detailed as  
\[ \begin{split}\bar{P}_{B}&=\frac{1}{R_{p}}\times P_{r}+ \frac{\big{(}R_{p}-1\big{)}}{R_{p}}\times P_{B_{k}}\\ &=\frac{\big{(}R_{p}+1-2P_{B_{k}}\big{)}}{R_{p}}.\end{split}  \tag{26}\]  
In a nutshell, the upper bound of ABEP at Bob is obtained at last.
# Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy
## 4 Predictive Control Algorithm for IACC  
### Addressing Computing Infeasibility  
During the process of the receding horizon control, a key issue is the problem of computing infeasibility. There will be no optimal solution in case constraints, shown as Equations (21)-(23), are not satisfied. To avoid such issues, a constraint softening method, as illustrated in Figure 5, is employed to relax the hard I/O constraints [5,21].  
Figure 5: Sketch of system IO constraints. 0—steady driving zone, 1—computing feasible sets, and 2—collision boundary.  
As mentioned above, Equations (21) and (22) are so strict that they cannot be softened. Therefore, we just soften Equation (23) with a slack variable vector, and the corresponding soft constraint is expressed as  
\[\left\{\begin{array}{l}\mathbf{u}_{\min}+\varepsilon_{1}\mathbf{v}_{\min}^{u}\leq\mathbf{u }\leq\mathbf{u}_{\max}+\varepsilon_{1}\mathbf{v}_{\max}^{u}\\ \Delta d_{\min}+\varepsilon_{2}\mathbf{v}_{\min}^{\Delta d}\leq\Delta d\leq\Delta d _{\max}+\varepsilon_{2}\mathbf{v}_{\max}^{\Delta d}\\ \Delta v_{\min}+\varepsilon_{3}\mathbf{v}_{\min}^{\Delta v}\leq\Delta v\leq\Delta v _{\max}+\varepsilon_{3}\mathbf{v}_{\max}^{\Delta v}\\ a_{x,\min}+\varepsilon_{4}\mathbf{v}_{\min}^{a_{x}}\leq a_{x,\max}+ \varepsilon_{4}\mathbf{v}_{\max}^{a_{x}}\\ j_{\min}+\varepsilon_{5}\mathbf{v}_{\min}^{j}\leq j\leq j_{\max}+\varepsilon_{5} \mathbf{v}_{\max}^{j}\end{array}\right. \tag{25}\]  
where \(\varepsilon_{1}\geq 0\), \(\varepsilon_{2}\geq 0\), \(\varepsilon_{3}\geq 0\), \(\varepsilon_{4}\geq 0\), \(\varepsilon_{5}\geq 0\) are the corresponding slack variables, \(\mathbf{v}_{\min}^{u}\leq 0\), and \(\mathbf{v}_{\min}^{\Delta d}\leq 0\), \(\mathbf{v}_{\min}^{\Delta v}\leq 0\), \(\mathbf{v}_{\min}^{a_{x}}\leq 0\), \(\mathbf{v}_{\max}^{j}\leq 0\), \(\mathbf{v}_{\max}^{u}\geq 0\), \(\mathbf{v}_{\max}^{\Delta d}\geq 0\), \(\mathbf{v}_{\max}^{\Delta v}\geq 0\), \(\mathbf{v}_{\max}^{a_{x}}\geq 0\), \(\mathbf{v}_{\max}^{j}\geq 0\) are the corresponding slack coefficients.  
Moreover, when the boundaries of the hard constraints are not exceeded, the slack variables equal 0. Otherwise, the slack variables will increase automatically so as to guarantee the existence of a solution.
## 3 Model Structure
### Tcn-Lstm
_tanh_ is the hyperbolic tangent function, one of the activation functions, which compresses the input into the range [\(-\)1, 1]. _Wc_ is the weight matrix connecting the input \(x_{t}\) to the candidate cell state ct, _ULc_ is the weight matrix connecting the hidden state \(h_{t-1}\) from the previous moment, \(\widetilde{c}_{t}\), \(h_{t-1}\) is the hidden state of the previous moment, and \(b_{c}\) is the bias vector. \(f_{t}\) is the forgetting gate, which determines which parts of the cell state should be forgotten or retained. Its output value is within [0, 1]. \(c_{t-1}\) is the cell state at the previous moment, and \(i_{t}\) is the input gate that determines how much of the current moment's input is written to the cell state. Its output value is within [0, 1]. The output gate and the cell state jointly determine the hidden state unit \(h_{t}\) of the LSTM \(c_{t}\). The formulation for generating \(h_{t}\) is as follows in Equation (10):  
\[h_{t}=o_{t}\odot\tanh(c_{t}) \tag{10}\]  
In LSTM, a connection exists not only between the hidden units \(h_{t-1}\) and \(h_{t}\) but also in a linear self-loop between cell states \(c_{t-1}\) and \(c_{t}\). This linear self-loop mechanism, functioning as an information sliding process, retains past information when the gating unit is activated and discards it upon deactivation. It enables LSTM to address long-term dependency issues in RNNs. Considering the synergistic combination of TCN and LSTM, leveraging their respective strengths, processed time-series data are initially input into the TCN and subsequently passed through LSTM via convolution and residual block processing, with the final output derived from a fully connected layer. The TCN-LSTM model amalgamation aims to serve as a potent instrument for time-series prediction, with the TCN layer tasked with feature extraction and the LSTM layer dedicated to sequence modeling. This hybrid methodology enhances the model's capacity to learn from data embodying complex temporal dynamics [37]. The overall network architecture of the TCN-LSTM model is shown in Figure 5 below:  
Figure 5: Overall architecture of the TCN-LSTM model.
## 4 Results
### Performance on Different MRI Modalities and Planes
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Plane** & **Metric** & **FLAIR** & ** DWI** & **T1** & **T2** \\ \hline \multirow{4}{*}{Axial} & IoU & 0.8596 \(\pm\) 0.1598 & 0.8524 \(\pm\) 0.1562 & 0.8355 \(\pm\) 0.1926 & 0.8441 \(\pm\) 0.1959 \\  & F1-Score & 0.9129 \(\pm\) 0.1362 & 0.9096 \(\pm\) 0.1290 & 0.8916 \(\pm\) 0.1783 & 0.8966 \(\pm\) 0.1809 \\  & HD & 4.09 \(\pm\) 7.67 & 4.13 \(\pm\) 6.94 & 5.12 \(\pm\) 9.24 & 4.98 \(\pm\) 8.96 \\  & N. S. & 4.10 \(\pm\) 2.70 & 2.70 \(\pm\) 2.05 & 11.20 \(\pm\) 6.35 & 10.70 \(\pm\) 4.05 \\ \hline \multirow{4}{*}{Coronal} & IoU & 0.8550 \(\pm\) 0.1658 & 0.8491 \(\pm\) 0.1660 & 0.8447 \(\pm\) 0.1846 & 0.8532 \(\pm\) 0.1973 \\  & F1-Score & 0.9094 \(\pm\) 0.1425 & 0.9058 \(\pm\) 0.1418 & 0.8995 \(\pm\) 0.1651 & 0.9017 \(\pm\) 0.1816 \\  & HD & 4.74 \(\pm\) 6.88 & 5.58 \(\pm\) 9.34 & 5.95 \(\pm\) 9.67 & 5.20 \(\pm\) 8.13 \\  & N. S. & 5.00 \(\pm\) 2.94 & 5.00 \(\pm\) 2.16 & 7.67 \(\pm\) 2.49 & 12.33 \(\pm\) 2.49 \\ \hline \multirow{4}{*}{Sagittal} & IoU & 0.8350 \(\pm\) 0.2087 & 0.8238 \(\pm\) 0.1978 & 0.8040 \(\pm\) 0.2206 & 0.8038 \(\pm\) 0.2366 \\  & F1-Score & 0.8885 \(\pm\) 0.1933 & 0.8847 \(\pm\) 0.1752 & 0.8667 \(\pm\) 0.2030 & 0.8623 \(\pm\) 0.2235 \\ \cline{1-1}  & HD & 8.20 \(\pm\) 14.44 & 8.63 \(\pm\) 14.02 & 10.38 \(\pm\) 17.49 & 7.60 \(\pm\) 12.10 \\ \cline{1-1}  & N. S. & 8.33 \(\pm\) 0.94 & 4.33 \(\pm\) 0.47 & 8.33 \(\pm\) 2.87 & 13.33 \(\pm\) 4.19 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance of the proposed model in different MRI modalities and planes using the ISLES 2015 dataset. Figure 8: Overall performance of the proposed model on different MRI modalities using the ISLES 2015 dataset (dot and white line represent the median and the mean values): (**a**) IoU scores in the coronal plane, (**b**) IoU scores in the sagittal plane. Figure 6: Violin plot of the proposed model’s results on FLAIR images using the axial view, where the dot localizes the median, and the white line represents the mean: (**a**) IoU scores by mask’s size category, (**b**) F1-Scores by mask’s size category. Figure 7: Performance of the proposed model in segmenting small lesions on different MRI modalities using the ISLES 2015 dataset (dot and white line represent the median and the mean values): (**a**) IoU scores by MRI modality, (**b**) F1-Scores by MRI modality.
## 2 Materials and Methods
### Description of the Proposed Control Strategies
#### 2.1.2 Economic-Oriented Model Predictive Controllers
This assumption is analogous to the selection of a control horizon of 1 in a typical discrete MPC, because the control horizon is defined as the number of discrete changes allowed for the control signal during the prediction horizon before stabilizing. In any case, although the consideration of control horizons greater than 1 is straightforward through considering more decision variables in the optimizationproblem (representing step changes in the control signal during the prediction horizon time), it has not been implemented because it increases the computational times, and the improvement of the results is not remarkable. As for predictions and other magnitudes in the cost function [17], although a continuous model is considered, a discrete update of the manipulated variable is performed because the optimization problem is solved at each sampling time \(\tau_{s}\). More precisely, the problem is solved at the time instants given by the sequence \(\{\tau_{k}=k\tau_{s}\}\), where \(k=0,1,2,\ldots\), and \(\tau_{k}\) is the sampling time instance of the continuous time model. Therefore, each time the optimization problem (2) is solved, the increments of the manipulated variable are obtained considering \(\textbf{u}(\tau_{k-1})\) as starting value. Finally, note that due to the use of a reduced model of the process for predictions, there is a plant-model mismatch that is tackled here by applying a bias correction [23]. More precisely, it consists of modifying the set point and output constraints using the difference between the measured output and predicted output at each sampling time. This correction is equivalent to a Dynamic Matrix Control (DMC) scheme that has been proved to be effective in these cases [23].
# Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention
## 6 Hypergraph Construction
### Simple Hypergraphs  
Using the algorithm described above, we constructed four hypergraphs, denoted as \(\mathcal{H}_{1-4}\). Two of these hypergraphs (\(\mathcal{H}_{1,2}\)) correspond to attention directed toward the left-cube orientation, while the other two (\(\mathcal{H}_{3,4}\)) correspond to attention directed toward the right-cube orientation. Hypergraphs \(\mathcal{H}_{1}\) and \(\mathcal{H}_{3}\) are constructed for frequency region \(f_{1}\), while \(\mathcal{H}_{2}\) and \(\mathcal{H}_{4}\) are constructed for \(f_{2}\).  
Each hypergraph contains six hyperedges (\(m=6\)): \(e_{1}\) and \(e_{2}\) represent coherence and anticoherence, \(e_{3}\) and \(e_{4}\) represent correlation and anticorrelation, and \(e_{5}\) and \(e_{6}\) represent mutual information and information loss, respectively.  
The constructed hypergraphs \(\mathcal{H}_{1-4}\) are visualized in Figures 11-14 in three distinct forms: as a bipartite graph (left panels), a Venn diagram (middle panels), and a brain map (right panels). The correspondence between node numbers and EEG channels is provided in Table 4.  
A hypergraph size is the number of hyperedges (in our case \(m=6\)), and the order of a hypergraph is the number of vertices. The degrees of hyperedges and the orders of the hypergraphs are summarized in Table 5.  
Figure 12: Hypergraph \(\mathcal{H}_{2}\) associated with left-cube attention for \(f_{2}\), presented as a bipartite graph (**left**), a Venn diagram (**middle**), and a brain map (**right**). The right Venn diagram (\(e_{2}\), \(e_{4}\), \(e_{6}\)) indicates deactivation of left frontal and right prefrontal lobes, while the left one (\(e_{1}\), \(e_{3}\), \(e_{5}\)) depicts activation of parietal, right anterior, and left prefrontal lobes.  
Figure 13: Hypergraph \(\mathcal{H}_{3}\) associated with right-cube attention for \(f_{1}\), presented as a bipartite graph (**left**), a Venn diagram (**middle**), and a brain map (**right**). The left Venn diagram (\(e_{2}\), \(e_{4}\), \(e_{6}\)) indicates deactivation of frontal cortex, while the right one (\(e_{1}\), \(e_{3}\), \(e_{5}\)) depicts activation of parietal area.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measure** & **Hyperedge** & \(\mathcal{H}_{1}\) **(Left \(f_{1}\))** & \(\mathcal{H}_{2}\) **(Left \(f_{2}\))** & \(\mathcal{H}_{3}\) **(Right \(f_{1}\))** & \(\mathcal{H}_{4}\) **(Right \(f_{2}\))** \\ \hline Coherence & \(e_{1}\) & 3 & 1 & 3 & 4 \\ Anticoherence & \(e_{2}\) & 1 & 3 & 2 & 4 \\ Correlation & \(e_{3}\) & 2 & 2 & 3 & 4 \\ Anticorrelation & \(e_{4}\) & 1 & 2 & 1 & 3 \\ Mut. Inform. & \(e_{5}\) & 4 & 3 & 1 & 3 \\ Inform. Loss & \(e_{6}\) & 2 & 3 & 2 & 2 \\ \hline Order & & 6 & 9 & 7 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Degrees of hyperedges and orders of hypergraphs.
## 4 Monitoring System Testing and Analysis
### Communication Testing  
The monitoring terminal transmits data to the monitoring host through the wireless module a total of 30 times. If no reply is received or there is a check error, etc., which makes the communication fail, the monitoring terminal will resend the data after 5 s. Each time the data is sent, the sending indicator will flash once. The data are transmitted by the monitoring terminal at different distances, and the success of communication is judged by the number of blinks of the indicator. Table 7 shows the test results of the communication test. The results prove that the communication distance is within 200 m, the communication reliability is large, and the error rate is zero. The communication error rate increases when the communication distance exceeds 200 m; however, it is approx. 7%, which is within the acceptable range of error.
## 6 Empirical Research on New Heterogeneous Traffic Control Mechanisms
### Testing and Validation of Control Mechanisms in Simulation Environments  
Simulation environments provide a safe and controllable platform for the development and validation of control mechanisms, allowing researchers to systematically test the performance of different strategies under various scenarios. In recent years, simulation research for heterogeneous traffic environments has primarily focused on two directions: traffic flow stability control and intersection collaborative optimization.