esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Cluster-Based Routing Protocol
In addition, building a routing tree between CHs to maintain the energy load balance between sensor nodes was proposed. The EAP proposed in this study has better performance than LEACH according to the simulation results. A previous report proposed a multi-hop energy-neutral clustering (MENC) algorithm [34]. All the CHs in the network transmit data to the BS by a multi-hop communication method and analyze the data rate and transmission energy consumption to impose energy neutrality constraints. Data throughput increases with MENC compared with LEACH. A related study [35] proposed fuzzy logic-based energy efficiency clustering using a fuzzy logic model with residual energy, location suitability, density, compression, and distance from the BS as descriptors. This related study [35] demonstrated improved network lifetime and energy efficiency compared LEACH. However, EAP, MENC, and related algorithms [35] can affect the limited resources of sensor nodes because each sensor node must maintain information about neighboring nodes and residual energy.
## 3 Results and Discussion
### Single-Factor Experiments  
From the results of the orthogonal experiments, it could be seen that the feed pressure and substrate moving speed had greater degrees of influence on the line width. When the print height was below 100 \(\upmu\)m, it was easy to hit the fragile glass nozzle, and when the print height was greater than 600 \(\upmu\)m, the line of conductive paste jumped seriously and had difficulty in attaching to the substrate. On the other hand, there were variations in line width values within approximately 10 \(\upmu\)m in the height range of 100-600 \(\upmu\)m. Therefore, we considered fixing the secondary factor (print height), that is, the subsequent print height H, at 150 \(\upmu\)m while reducing the value of the span between the main factors, and we took three feed pressure gradients and six substrate moving speed gradients to continue the experiment to further explore the impacts of speed and pressure changes on line width. We took four lines in the printing result of each parameter combination, and we took three uniform places on each line to calculate the means and standard deviations of the line widths. The measured line width results are shown in Figure 5.  
As can be seen in Figure 5, the substrate moving speed and the change in the value of the feed pressure had an intuitive effect on the change in line width, and as v increased, the line width decreased, and when P decreased, the line width decreased. It is worth noting that after repeated printing experiments, the results showed that there were critical values for the parameters, and once a parameter was higher or lower than a certain value, it would cause discontinuity and the line width could not be measured. For example, when the feed pressure was less than 0.3 MPa, the pressure was completely unable to push the paste flow in the syringe, and when the print height was greater than 700 \(\upmu\)m, the paste printing became extremely unstable and could not even be completely formed on the substrate. At the same time, the mismatch between the parameters, such as too much feed pressure and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Line Width \(C\)** & \\ \cline{2-4}  & **P** & **H** & **v** \\ \hline \(K_{1}\) & 183.34 & 216.45 & 248.80 \\ \(K_{2}\) & 197.37 & 214.50 & 199.11 \\ \(K_{3}\) & 240.93 & 190.70 & 173.75 \\ \(k_{1}\) & 61.11 & 72.15 & 82.93 \\ \(k_{2}\) & 65.79 & 71.50 & 66.37 \\ \(k_{3}\) & 80.31 & 63.57 & 57.92 less speed or too little feed pressure and too fast, could lead to similar printing failures, as shown in Figure 6 below.
# Impact of a Microbial Cocktail Used as a Starter Culture on Cocoa Fermentation and Chocolate Flavor
## 5 Conclusions  
The inoculation of microorganisms as a starter culture accelerated the fermentation process. The bacterial and yeast communities were different according to each process (PH15 I and PH15 NI), but the bacteria (_Gluconobacter oxydans_, _Lactobacillus plantarum_, _Acetobacter pasteiranus_, _Fructobacillus pseudoficulneus_) and yeast (_Saccharomyces cerevisiae_ and _Pichia kluyveri_) species were found in both processes. Glucose and fructose were consumed faster in the inoculated assay in the first 24 h of fermentation. Different volatile compounds were identified in fermented beans and chocolate produced in the present study. According to the sensory analysis of PH15 I Ch and PH15 NI Ch significant differences on the dominant tastes were observed. The inoculation leads to a chocolate with higher bitter, sweet, and cocoa notes than the chocolate produced by spontaneous fermentation. Bitter was the dominant taste in PH15 NI Ch, whereas bitter, sweet, and cocoa tastes were dominant tastes in PH15 I Ch. These results were corroborated by the analysis of volatile compounds in both chocolate samples. The 2,3-butanediol, which gives flavor to cocoa butter (sweet chocolate), and 2,3-dimethylpyrazine, which gives caramel and cocoa flavors, were detected only in PH15 I Ch. Therefore, that dominant flavor detected in PH15 I Ch was related with these compounds, indicating the inoculation influence in the final product. In this context, the inoculation influenced the fermentation process and the final product. In order to generate a standardized fermentative process and improve the chocolate quality, _Saccharomyces cerevisiae_ UFLA CCMA 0200, _L. Plantarum_ CCMA 0238, and _A. pasteiranus_ CCMA 0241 are, herein, proposed to be used as a cocktail of microorganisms for application in cocoa fermentation.  
**Acknowledgments:** [PERSON] thanks the Universidad de La Frontera (Temuco, Chile) for the partial funding from Project DIUFRO DI16-0135. The authors acknowledge the financial support from the Brazilian Agencies Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG), and Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), and the samples obtained from the Fazendas Reunidas Vale do Juliana (Igrapiunina, Bahia, Brazil).  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] conceived and designed the experiments and made the final revision of the paper; [PERSON] and [PERSON] performed the experiments and wrote the paper; [PERSON], [PERSON], and [PERSON] analyzed the data.  
**Conflicts of Interest:** Authors declare no conflicts of interest.
# Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier
## 4 Experimental Setting and Results
### Performance Parameters and Evaluations
#### 4.2.3 Experiment III: Comparison with Other Classification Algorithms  
In this segment, we compared the recall, precision, and F1-measure over the IPN hand dataset and the Jester dataset. For the classification of hand gestures, we used a decision tree, an artificial neural network, and we associated the consequences with the KATH classifier. Figure 12 shows the results over the IPN hand dataset and Figure 13 shows the results over the Jester dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Class** & **POF** & **PTF** & **COF** & **CTF** & **TU** & **TD** & **TL** & **TR** & **OT** & **DCOF** & **DCTF** & **ZI** & **ZO** \\ \hline POF & 8 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ PTF & 0 & 9 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ COF & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ CTF & 0 & 1 & 0 & 9 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ TU & 1 & 0 & 0 & 9 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ TD & 0 & 0 & 1 & 0 & 0 & 8 & 0 & 0 & 0 & 1 & 0 & 0 \\ TL & 0 & 2 & 0 & 0 & 0 & 0 & 7 & 0 & 0 & 0 & 1 & 0 \\ TR & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 8 & 0 & 0 & 0 & 1 & 0 \\ OT & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 9 & 0 & 1 & 0 & 0 \\ DCOF & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 10 & 0 & 0 & 0 \\ DCTF & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 9 & 0 & 1 \\ ZI & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 10 & 0 \\ ZO & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion Matrix results over IPN Hand dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Gestures** & **SD** & **SL** & **SR** & **SU** & **TD** & **TU** & **ZIF** & **ZOF** & **S** & **RF** & **RB** & **PI** & **SH** \\ \hline SD & 9 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ SL & 0 & 9 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\ SR & 0 & 0 & 9 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ SU & 0 & 0 & 1 & 8 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ TD & 0 & 0 & 0 & 0 & 10 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ TU & 0 & 0 & 0 & 0 & 1 & 8 & 0 & 0 & 1 & 0 & 0 & 0 \\ ZIF & 0 & 0 & 0 & 0 & 0 & 0 & 9 & 0 & 0 & 1 & 0 & 0 & 0 \\ ZOF & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 9 & 0 & 0 & 0 & 0 & 0 \\ S & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 8 & 0 & 0 & 1 & 0 \\ RF & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 9 & 0 & 0 & 0 \\ RB & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 10 & 0 & 0 \\ PI & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 9 & 0 \\ SH & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 1 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion Matrix results over Jester dataset.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 4 Experiment and Evaluation
### Data Pre-Processing
#### 4.2.2 Vowel Distribution Features  
According to [8; 9], benign domain names often have higher vowel numbers than that of automatically generated domain names by botnets, because benign domain names are often built on grammatical features, while botnet automatically generated domain names usually consist of random characters and do not have meanings. Based on this idea, 2 vowel features of each domain name are built, including:  
* \(countnv(d)\) is the number of vowels of the domain name \(d\).
* \(tancy(d)\) is the average number of vowels of the domain name \(d\), calculated by the following formula: \[tancy(d)=countnv(d)/len(d)\] (10)
## 3 Analysis Model
### Hydrodynamic Parameters Calculation  
To obtain the hydrodynamic parameters, a design of the floating buoy was implemented by 3D design software. The geometry parameters of the floating buoy are shown in Figure 2. These parameters are set into ANSYS AQWA software (version number 2021R1). The 3D file, including the geometrical parameters, is added to the workspace in ANSYS AQWA. The mesh can be divided manually or automatically to optimize the process. The mass center and inertia pre-obtained in the 3D model are also set in ANSYS AQWA. The wave boundary conditions, consisting of the wave frequency range and wave direction, are set to calculate the hydrodynamic parameters. In this paper, the wave frequency is set to range from 0.1 rad/s to 4 rad/s to approximate the ocean wave frequency range at 1m water depth. Among the simulation results, there are four main parameters, including the excitation force's coefficient, added mass, the radiation damping coefficient, and the phase angle. These coefficients, noted as \(f_{3}\), \(m_{a}\), \(R_{r}\), and \(\varphi\), were then obtained and plotted in Figure 3.
# Effects of Atmospheric Turbulence on OAM-POL-FDM Hybrid Multiplexing Communication System
## 2 Methods  
### Vortex Beam in Turbulence  
In this paper, the transmission speed of the vortex beam was increased by combining OAM, polarization, and frequency. The vortex beam was generated by a Gaussian beam. The distribution of the complex-amplitude of the beam nested within the cross-section of the Gaussian beam with a center point at (\(x_{0}\), \(y_{0}\)) can be expressed as [21]:  
\[{\rm A}(x,y,z=0)= [(x-x_{0})+{\rm isign}(l)(y-y_{0})]^{\parallel}\exp[-\left(x^{2}+ y^{2}\right)/{r_{0}}^{2}] \tag{1}\]  
The sign of \(l\) determines whether the vortex beam rotates clockwise or counterclockwise, and the magnitude of \(l\) represents the topological charge of the vortex beam. \(r_{0}\) is the beam waist radius of the host Gaussian beam. The amplitude at the center of the vortex beam is 0.  
When multiple vortex beams are multiplexed together, their complex-amplitudes can be expressed as a function [22]:  
\[{\rm A}(x,y,z=0)=\left\{\prod_{n=1}^{N}\left[(x-x_{n})+{\rm isign}(l_{n})(y-y_ {n})\right]^{\parallel}\right\}\exp[-\left(x^{2}+y^{2}\right)/{r_{0}}^{2}] \tag{2}\]  
In this expression, \(N\) is the number of vortex beams that are embedded in the cross-section of the Gaussian beam with radius \(r_{0}\). The order of the n-th vortex beam is \(l_{n}\), and its center is located at point (\(x_{n}\), \(y_{n}\)) in the XY plane.  
A multi-layer phase screen is used to simulate atmospheric turbulence. The refractive index power spectrum \(\phi_{n}(k)\) in the turbulence is the Kolmogorov type [23].  
\[\phi_{n}(k)=0.033{C_{n}^{2}}k^{-11/3} \tag{3}\]  
\({C_{n}}^{2}\) is the refractive index constant, which denotes the turbulence intensity. Different numbers of phase screens indicate different turbulence intensities.  
The original topological charge is changed when the vortex beam passes through the turbulent channel [24]. As shown in Figure 1, the circle denotes the projection of the intensity of the vortex beam and the number of topological charge, \(l\), can be calculated based on the number of times that the phase is changed from 0 to 2\(\pi\) in the whole circle. Mathematically, \(l\) can be defined as the following path integral [25]:  
\[l\ =\ \frac{1}{2\pi}\oint\limits_{C}\
abla\varphi(r)\cdot\mathrm{d}l \tag{4}\]  
where \(C\) denotes the closed integral path and \(\mathrm{d}l\) means an infinitesimal vector unit. By using this method, the topological charge of the distorted beam can be calculated at the receiver.
## 5 Experimental Simulation and Analysis
### Performance of the SLP-MACGT Model
#### 5.2.3 Effect of the Number of Attackers on the Safety Time  
As shown in Figure 7, it typically takes more time for a single attacker to successfully breach or compromise network security. They may need to conduct eavesdropping, analysis, and attack attempts for longer periods of time to identify weaknesses and obtain valuable data. In contrast, multiple attackers can attack different targets at the same time, taking advantage of cooperation to speed up the attack and shorten the time window for a successful attack. A larger network scale complicates the transmission path, and the  
Figure 5: Effect of network side length on performance: (**a**) safety time; (**b**) end-to-end delay; (**c**) packet delivery ratio.  
Figure 6: Effect of the communication radius on performance: (**a**) safety time; (**b**) end-to-end delay; (**c**) packet delivery ratio.  
attacker needs more time to obtain the source location information. In addition, a longer network side length may result in a longer signal transmission path, which increases the time required for the attacker to determine the source location.
## 5 Conclusions  
The dynamic balance control during gait in patients with LS was evaluated through the analysis of COM-COP IA and RCIA, and through a correlational analysis between walking balance and standing balance in the patient group. The patients with LS were found to walk with a conservative and cautious strategy for dynamic balance control in the sagittal plane, maintaining a more posterior COM position relative to the COP with reduced ranges and speed of movement throughout the gait cycle, as well as a slower body weight transfer during DLS. In the frontal plane, the patients moved the COM more radially with greater velocity changes during weight transfer, indicating a less stable transfer of the body weight in the medial-lateral direction. In the sagittal plane, the patients decreased the posterior RCIA with increased posterior IA during DLS, and decreased anterior RCIA and ranges of IA and RCIA during SLS. In the frontal plane, the patients increased medial-lateral RCIA and ranges of RCIA, as well as medial IA during DLS, and increased medial RCIA and ranges of IA during SLS. Standing balance parameters were able to predict IA and RCIA deviations during DLS in the frontal plane, possibly because the standing balance tests were also performed in the double-limb support posture. However, standing balance parameters failed to predict the IA and RCIA deviations during SLS in the frontal plane and the RCIA deviations throughout the gait cycle in the sagittal plane. These correlational results suggest that dynamic balance control deviations during gait in patients with LS could not be deduced solely from data from standing balance tests and, thus, patients should be monitored via dynamic approaches in clinical applications.  
Conceptualization, T.-C.H. and T.-W.L.; Methodology, T.-C.H., H.-P.H., and T.-W.L.; Software, H.-P.H. and T.-W.L.; Validation, H.-P.H., T.-M.W., and T.-W.L.; Formal analysis, T.-C.H. and H.-P.H.; Investigation, T.-C.H., H.-P.H., K.-W.W., J.-L.P., C.-K.C., T.-M.W., and T.-W.L.; Resources, T.-C.H., K.-W.W., T.-M.W., and T.-W.L.; Data curation, T.-C.H., H.-P.H., K.-W.W., T.-M.W., and T.-W.L.; Writing--original draft preparation, T.-C.H., H.-P.H., K.-W.W., and T.-W.L.; Writing--review and editing, T.-C.H., H.-P.H., K.-W.W., J.-L.P., C.-K.C., T.-M.W., and T.-W.L.; Visualization, T.-C.H., H.-P.H., and T.-W.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study protocol was reviewed and approved by the committee of the Institutional Review Board of National Taiwan University Hospital (201702025 RIND).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors express gratitude towards [PERSON] for her assistance during the early stage of data collection.  
The authors declare no conflict of interest.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 6 Discussion
### Analysis of Superior Performance  
ERFO's performance advantage over FedAvg, FedProx, SCAFFOLD, and other baselines arises from the way entropy regularization shapes each client's update. By penalizing overly concentrated parameter changes, ERFO prevents any single client--with a skewed local distribution--from \"pulling\" the global model into a narrow region of the parameter space. Unlike FedProx, which uniformly shrinks every update vector, or SCAFFOLD, which counteracts drift only via additional control variates, ERFO selectively scales back only those coordinates that dominate the update.  
This _selective damping_ produces smoother, more harmonious aggregation. As illustrated in Figure 1, ERFO nearly eliminates the oscillations that FedAvg exhibits under extreme heterogeneity. Likewise, Table 3 shows that ERFO boosts macro-F1 by allocating model capacity more evenly across classes. In effect, ERFO strikes a superior stability-plasticity balance: it curbs damaging drift when necessary yet allows aggressively informative updates when client objectives align.  
In the UNSW-NB15 experiments, FedProx attains strong stabilization and the highest overall accuracy, but ERFO achieves a more balanced per-class performance (higher recall on minority classes) without sacrificing overall accuracy. In the PneumoniaMNIST experiments, FedProx converges more slowly than both FedAvg and ERFO, since its rigid norm constraint is unnecessary in a well-aligned setting and thus mildly impedes plasticity. ERFO, by contrast, permits clients to fully adapt their local models (provided updates retain sufficient entropy), avoiding the slowdown seen with FedProx.  
Overall, ERFO can be viewed as an _adaptive_ regularizer: it allows updates to grow large when they are information-rich, but gently restrains them when they become overly narrow. This mechanism explains ERFO's consistently higher final accuracy and more balanced class performance across heterogeneous FL scenarios.
# Pipe Crack Recognition Based on Eddy Current NDT and 2D Impedance Characteristics
## 2 Operation Principle  
### Principle of Eddy Current Testing  
ECT operates based on electromagnetic induction, where an eddy current is induced on the surface of the conductive material through an electric coil. We move the eddy current probe on the examined surface, and correspondingly, the defects and fluctuation in the size and material of the conductive surface will lead to a change in the eddy current field which, in turn, results in a change of the coil's impedance. Hence, the information on the existence of the defects on the examined surface may be achieved by measuring the variation of the coil impedance [16].
## 3 Results
### Biomarkers Analysis
#### 3.3.5 LDL-Cholesterol Levels
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **LDL-C** & **LDL-C** & **LDL-C** & **LDL-C** & \multicolumn{3}{c}{**95\% Confidence Interval**} & \multicolumn{1}{c}{\multirow{2}{*}{\(p\) Value**}} \\  & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & \multicolumn{1}{c}{**Time**} & **Lower** & **Upper** & **(Group \(\times\) Time)** \\  & **at Baseline (t1)** & **at 1 h (t2)** & **at 2 h (t3)** & **at 4 h (t4)** & **at 4 h (t4)** & **found** & **found** & **found** \\ \hline CTRL & 93.463 \(\pm\) 5.566 & 89.662 \(\pm\) 5.960 & 91.787 \(\pm\) 5.816 & 95.152 \(\pm\) 5.482 & t1 & 81.949 & 104.74 & \\  & & & & t2 & 77.333 & 101.99 & \\  & & & & t3 & 79.755 & 103.82 & \\  & & & & t4 & 83.812 & 106.49 & \\ \hline OLT & 95.379 \(\pm\) 5.360 & 92.216 \(\pm\) 5.696 & 91.583 \(\pm\) 4.880 & 94.414 \(\pm\) 5.093 & t1 & 84.290 & 106.47 & \\  & & & & t2 & 80.434 & 104.00 & \\  & & & & t3 & 81.487 & 101.68 & \\  & & & & t4 & 83.879 & 104.95 & \\ \hline  & & & & t1 & 84.171 & 104.74 & \\  & & & & t2 & 81.157 & 102.45 & \\  & & & & t3 & 84.999 & 106.08 & \\  & & & & t4 & 86.218 & 107.35 & \\ \hline  & & & & & & \\ \end{tabular} Values are presented as mean \(\pm\) standard error (SE). LDL-C: serum low-density lipoprotein cholesterol concentration (mg/dL). t1: baseline, t2: 1 h, t3: 2 h, t4: 4 h postprandial. CTRL: Control meal without olive leaf supplementation. OLT: Real supplemented with olive leaf tea. OLB: Real supplemented with olive leaf-enriched biscuit. Statistical analysis was performed using repeated measures (ANOVA) to assess the interaction effect (group \(\times\) time).  
\end{table} 
Table 8: Postprandial changes in serum LDL-C levels. Figure 6: Postprandial low-density lipoprotein concentrations (Mean \(\pm\) SE) over time (baseline, 1 h, 2 h, 4 h) in control (CTRL), olive leaf tea (OLT), and olive leaf biscuit (OLB) groups.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 3 Mathematical Formulation of Adaptive Channel Allocation Problem
### Assumptions  
A wireless link may interfere with other wireless links due to the broadcast nature of the links, and this may happen if the links are within transmission range of each other. Interfered links can not transfer data at the same instant of time if they use the same channel. The physical interference model [20] is considered in the current formulation. In real implementation, this is called the carrier-sense multiple access (CSMA)-aware interference model introduced in [21] and implemented in many articles, for example [22]. In this model, both the communication range and the interference range are the same. CSMA is a MAC protocol in which a node verifies the absence of other traffic before transmitting on a shared transmission medium. Essentially, in CSMA, multiple nodes send and receive frames in turn on the same medium without collision unless hidden terminals exist. In this study, we assume that the collision between links within carrier-sensing range are avoided due to CSMA, and regard that the two directed links interfere with each other only if they are located in the hidden-terminal position.