esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Fungistatic Effect of VOCs Emitted by Naganishia_ sp. _on B. cinerea_  
The effect of VOCs emitted by _Naganishia_ sp. on the viability of _B. cinerea_ hyphae was analyzed using the MTT assay, which measures mitochondrial activity by reducing MTT into formazan [32]. Results showed no significant differences between VOC-treated _B. cinerea_ and the negative control (Figure 10), suggesting that VOCs emitted by _Naganishia_ sp. do not cause cell death in _B. cinerea_; however, VOCs might have a fungistatic effect. To confirm this, _B. cinerea_ was grown in the presence of VOCs for three days and then re-inoculated in fresh PDA without VOCs (Figure 10). The growth of the previously VOC-treated mycelium was similar to untreated mycelium.  
On the other hand, the effect of the most abundant volatile compound emitted by the endophytic yeast, 2-phenylethylanol, was tested, pure 2-phenylethylanol inhibited the mycelial growth of _B. cinerea_ and, the antifungal effect was dose-dependent (Figure 11) with an IC\({}_{50}\) value of 0.21 \(\mu\)L mL\({}^{-1}\). It was observed that this compound, at all analyzed concentrations, had a fungistatic effect, since the growth of the _B. cinerea_ mycelia can be recovered when the mycelia treated with this compound were re-inoculated in fresh medium  
Figure 9: Lipid peroxidation in _B. cinerea_ treated with VOCs emitted by _Naganishia_ sp. after 16, 24, and 48 h of treatment, represented by malondialdehydedehyde (MDA) content. The bars represent the mean of three biological replicates \(\pm\) SD. For statistical analysis, Mann–Whitney test was carried out. Asterisks indicate statistically significant differences. ns: no significant differences. \({}^{**}=p<0.01\)), \({}^{***}=p<0.001\).  
Figure 11: Effect of 2-phenylethylanol in the mycelial growth of _B. cinerea_. Different concentrations of 2-phenylethylanol were added to the sandwiched Petri plates assay, and the mycelial growth of _B. cinerea_ was determined after 48 h of exposition. Values represent the mean of three biological replicates \(\pm\) SD.  
Figure 10: Viability and growth of _B. cinerea_ with and without VOCs emitted by _Naganishia_ sp. (**A**) Viability of _B. cinerea_ determined by MTT assay after treatment with VOCs. Mycelium treated with 70% v/v ethanol was used as a positive control. (**B**) Re-inoculation of _B. cinerea_ treated with VOCs in a fresh, VOC-free medium. Circles represent untreated control, and squares represent VOCs pre-treated mycelium. Values represent the mean \(\pm\) SD. ns: there are no significant differences. The bars represent the mean of three biological replicates \(\pm\) SD. For statistical analysis: ANOVA was performed using Tukey’s test post-test. Columns with different letters are significantly different (\(p<0.05\)).
## 3 Results and Discussion
### IEC and Water Uptake  
The ion exchange capacity (_IEC_) and the water uptake of the sPEEK-based composite membranes have been measured and are shown in Figure 3, as well as a function of the filler amount. The membrane's _IEC_ grows by the addition of the filler because it enhances the number of organo-phosphoric functionalities that are grafted onto the PSLM lamellae surface. The highest _IEC_ value, 1.98 meg g\({}^{-1}\), is achieved with 3 wt% of filler loading. By further increasing the loadings (5 wt%), not only did _IEC_ not improve, but a reduction was observed. This effect can be rationalized by considering (i) the formation  
Figure 2: (**a**) Storage modulus (E’) and (**b**) \(\tan\delta\) of the sPEEK-based membranes as a function of temperature.  
of agglomerations between the nanoparticles, with the formation of clusters, and (ii) the only partial exfoliation of the siliceous layered nano-particles in the polymer. In both cases, a decrease of the number of hydrophilic sites available for proton exchange is achieved. In parallel, such features clearly affect the water absorption capacity of the electrolyte membranes [37]. The water uptake varies between 40 wt% of the pristine sPEEK and the 50 wt% of the sPEEK-PSLM\({}_{3}\). Therefore, the introduction of PSLM platelets increases the hydrophilicity of the PEMs, however no excessive membrane swelling is produced by the filler addition.  
Further insights concerning the membrane swelling behavior can be achieved under variable temperatures, as reported in Figure 4, where both water uptake and \(\lambda\) (i.e., number of water molecules per mole of acid group have been evaluated in the temperature range 20-80 \({}^{\circ}\)C. The swelling clearly increases by heating, however, the composite at 3% filler loading shows a lower swelling just at higher temperatures, with respect to both pristine SPEEK and the composites at 1 and 5% of filler.  
The \(\lambda\) behavior (Figure 4b) gives information on the membrane's microstructure. We observed that at 20 \({}^{\circ}\)C, \(\lambda\) is almost invariable (about 12) among the various samples, meaning that the presence of PSLM nanoparticles within the polymer matrix does not actually change the microstructure and size of the ionic clusters, but it does affect the distribution of water molecules within these hydrophilic regions to some extent. In particular, the SPEEK-PLSM\({}_{3}\) sample shows a slightly different trend from the other membranes, i.e., above 50 \({}^{\circ}\)C, there is no linear increase in water uptake as expected. This means that for this membrane, the structural organization of the exfoliated filler does not allow for an  
Figure 4: Temperature evolution of (**a**) the water uptake and (**b**) \(\lambda\) for pristine sPEEK and sPEEK-PSLM composite membranes.  
Figure 3: Ion exchange capacity and water uptakes of sPEEK/PSLM composite membranes as a function of the filler loadings.  
excessive swelling of the hydrophilic pores, but provides the right distribution of water molecules between the acidic sites of the polymer and the nanoparticles.
# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 5 Experimental Results
### No-Obstacle Scenario  
An experiment was performed to test the no-obstacle, soft-object, and hard-object detection capabilities of the system. Initially, the no-obstacle case was evaluated by closing the door without placing any object between the doors. As shown in Figure 14b, the motor starting current initially peaked at 7.9 A. A slight ripple observed in the motor current, visible in the figure, was attributed to the vibration of the test bench. The ripple diminished as the motor reached its steady state, indicating a stable operational condition. During door closure, the motor consumed 1.3 A of continuous current to overcome Coulomb friction.  
Figure 14: Results of no-obstacle detection experiment. (**a**) W9 and peak amplitudes during motor startup and steady-state operation, showing compliance with threshold ranges; (**b**) motor current signal indicating the starting current; ripple was due to test bench vibration.  
Figure 13: Assembled test bench.  
Furthermore, Figure 14a highlights the transient behavior of motor current. The W9 and peak amplitudes started at high values of 116 and 32.1, respectively, during motor startup. These amplitudes gradually reduced to 22 and 5.4 during steady-state operation, as indicated in the figure. These were in accordance with the threshold ranges specified in the function, validating its behavior under the no-obstacle scenario.
## 4 Experiments
### Experiments on Registration Effectiveness
#### 4.3.3 Settings of Experimental Parameters  
Table 1 lists all parameters used in our experiments. \(\varepsilon_{\vartheta}\) is a threshold for a surface variation index, and a point is defined as a keypoint when its surface variation index is greater than \(\varepsilon_{\vartheta}\); \(L_{th}\) is a threshold for the maximum difference between adjacent included angles, and a point is defined as a boundary point when its maximum difference is greater than \(L_{th}\). \(d_{min}\) is a threshold for the nearest distance between a keypoint and a boundary point, and a keypoint is regarded as a boundary point when its distance to its nearest boundary point is lower than \(d_{min}\). \(r_{1},\dots,r_{s}\) is the supported radius used to generate a multi-scale feature descriptor proposed in our paper. \(\varepsilon_{r}\) is a threshold for the distance ratio between a pair of the first nearest descriptor and a pair of the second nearest descriptor; a pair of descriptors can be regarded as a matched pair of descriptors when the distance ratio between a pair of the first nearest descriptor and a pair of the second nearest descriptor is less than \(\varepsilon_{r}\). \(\kappa\) is the number of clusters in coarse transformer estimation, and we set it to seven in this paper; \(\varepsilon_{d}\) is a threshold for determining whether a point is an inlier, and a transformed point by a transformer matrix in a source point cloud is regarded as an inlier when its distance to its nearest point in the target point cloud is less than \(\varepsilon_{d}\).
## 5 Evaluation
### Sink Orientation to Calculate Radius and Mean Connectivity  
In order to measure the reachability between any sink node and a sensor after execution of our algorithm, we introduce a parameter, _mean connectivity_, which is denoted as \(\gamma\). Mean connectivity is defined as the percentage of the total sensor nodes covered by any sink node in the network. In this section, four schemes described above will be proposed: original MSCWSNs-PC, considering MSBSRC, considering BCP, and considering both of MSBSRC and BCP. For these simulations, two modes of axis ordering orientation are employed simultaneously. In these experiments, generally all results are the statistical average of 1,000 rounds. Figure 12 shows the mean connectivity using the original MSCWSNs-PC at variety sink number. With an increasing number of sinks, the mean connectivity of the network becomes nearly 100%. For the modes of axis ordering orientation, anticlockwise mode brings higher mean connectivity compared to axis mode, with a low sink density of (less than six), but with an increase of the number of sinks the gap between the two lines gradually narrows.  
Figure 11: _Cont._  
Figure 13 shows the relationship between the number of sinks and the mean connectivity of sensor nodes, when only considering MSBSRC. It can be seen that by employing MSBSRC a great improvement in network coverage is achieved. Unlike in original MSCWSNs-PC, axis mode provides higher mean connectivity compared to anticlockwise mode.  
Figure 14 shows the relationship between the number of sinks and the mean connectivity of sensor nodes, when only considering BCP. Although BCP results in an obvious increase of mean connectivity compared to the original algorithm, the degree of improvement is smaller compared to the condition only considering MSBSRC. It is noteworthy that with the increase of sinks neither mode of axis ordering orientation is always dominant. The demarcation point is when the number of sinks reaches six, before  
Figure 12: Mean connectivity of MSCWSNs-PC at variety sink number with considering neither MSBSRC nor BCP (mean of 1,000 rounds).  
Figure 13: Mean connectivity of MSCWSNs-PC at variety sink number with considering MSBSRC (mean of 1,000 rounds).  
which anticlockwise mode shows its advantages, while the axis mode is better under other conditions.  
Figure 15 shows the mean connectivity result when considering both BCP and MSBSRC schemes. In this condition, the MSCWSNs-PC combines the advantages of the BCP and MSBSRC schemes, and as a result the simulation result is very attractive. Like in Figure 12, axis mode results in higher mean connectivity compared to anticlockwise mode.  
Figure 14: Mean connectivity of MSCWSNs-PC at variety sink number with considering BCP (mean of 1,000 rounds).  
Figure 15: Mean connectivity of MSCWSNs-PC at variety sink number with considering BCP (mean of 1,000 rounds).
# Depth Image Completion through Iterative Low-Pass Filtering
## 3 Experimental Results
### Rate of the Maximum Defected Area  
In this section, we assess the maximum continuous defected area and denoising capability of the Depth Image Completion Process (DICP) for a real-scene depth camera. Figure 4 illustrates a real-scene cylinder captured as a raw depth matrix using the Kinect for Xbox One at an observation distance of 1.5 m. The detected depth data inherently carry noise, distributed normally with a standard deviation of 1.5 mm. Subsequently, we systematically removed depth data and applied the DICP. By progressively increasing the continuous defected area along the vertical direction, we created different scenarios. Figure 4c presents the accuracy error and precision error corresponding to various defected areas. Notably, when the defected area constituted 80.22% of the cylinder's detection area, the precision was 10.28 mm.
## 3 Results
### Comparison of Performance Results with Difference Models  
Different models were applied to investigate tomato leaf disease classification, such as AlexNet, GoogleNet, and ResNet50. Most network architectures gave relatively good  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Learning Rate** & **0.00001** & **0.0001** & **0.001** \\ \hline _ACC_ & 99.72 & 99.34 & 99.02 \\ _SEN_ & 99.69 & 99.36 & 98.48 \\ _SPE_ & 99.90 & 99.77 & 99.66 \\ _PPV_ & 99.49 & 99.23 & 98.75 \\ _F1S_ & 99.59 & 99.29 & 99.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification evaluation results with the confusion matrix parameters and three different learning rates (in unit %).  
Figure 10: The representation of the loss function values during training the network using 300 epochs.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Epochs** & **50** & **100** & **200** & **300** & **400** \\ \hline _ACC_ & 98.50 & 99.13 & 99.53 & 99.72 & 99.66 \\ _SEN_ & 98.61 & 99.18 & 99.54 & 99.69 & 99.59 \\ _SPE_ & 99.32 & 99.66 & 99.83 & 99.90 & 99.89 \\ _PPV_ & 98.16 & 98.75 & 99.38 & 99.49 & 99.43 \\ _F1S_ & 98.39 & 98.97 & 99.46 & 99.59 & 99.51 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification evaluation results with confusion matrix parameters and five different epochs (in unit %).  
results, as shown in Figure 11. In this experiment, we used the tomato leaf image dataset divided into the sub-datasets for training and testing, as shown in Section 3.2. With the same parameters, the AlexNet has an accuracy of 99.16%, and the ResNet has an accuracy of 99.19%. The GoogleNet shows an accuracy slightly higher than the two above models, at 99.38%. Meanwhile, our model achieved the highest accuracy at 99.72% with VGG-19.
###### Abstract  
Wireless resource utilizations are the focus of future communication, which are used constantly to alleviate the communication quality problem caused by the explosive interference with increasing users, especially the inter-cell interference in the multi-cell multi-user systems. To tackle this interference and improve the resource utilization rate, we proposed a joint-priority-based reinforcement learning (JPRL) approach to jointly optimize the bandwidth and transmit power allocation. This method aims to maximize the average throughput of the system while suppressing the co-channel interference and guaranteeing the quality of service (QoS) constraint. Specifically, we de-coupled the joint problem into two sub-problems, i.e., the bandwidth assignment and power allocation sub-problems. The multi-agent double deep Q network (MADDQN) was developed to solve the bandwidth allocation sub-problem for each user and the prioritized multi-agent deep deterministic policy gradient (P-MADDPG) algorithm by deploying a prioritized replay buffer that is designed to handle the transmit power allocation sub-problem. Numerical results show that the proposed JPRL method could accelerate model training and outperform the alternative methods in terms of throughput. For example, the average throughput was approximately 10.4-15.5% better than the homogeneous-learning-based benchmarks, and about 17.3% higher than the genetic algorithm.  
uplink; multi-cell multi-user system; joint-priority-based reinforcement learning (JPRL); prioritized replay buffer; throughput +
Footnote †: journal: _Sensors_**2023**, 23, 6822. [[https://doi.org/10.3390/s23156822](https://doi.org/10.3390/s23156822)]([https://doi.org/10.3390/s23156822](https://doi.org/10.3390/s23156822))
# Substrate-Integrated Waveguide Microwave Sensor for Water-in-Diesel Fuel Applications
## 3 Applicator Design
### Cross Slot SIW Applicator in Air
As the slot length \(L_{slot}\) decreases, the impedance matching increases, and the bandwidth becomes larger. The impedance matching with \(|S_{11}|=-19\) dB at the frequency of \(f=9.6\) GHz is simulated for \(L_{slot}=4.9\) mm. After all the optimizations, the optimal geometrical dimensions for the cross-slot SIW device are reported in Table 5. These values ensure a good impedance matching and the wide bandwidth in the 9-10 GHz range. The device is also compact and low-profile. Figure 11: Modulus of the simulated scattering parameters \(S_{11}\) (solid curves) and \(S_{21}\) (dotted curves) as a function of the frequency \(f\) for different values of \(W_{gap}\). Figure 10: Equivalent circuit of the cross slot SIW applicator.
# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 4 Experimental Setup and Datasets
### Statistical Tests
#### 4.4.2 Post Hoc Tests  
A multiple test procedure is recommended while comparing more than two methods. When the null hypothesis of equivalent performances becomes rejected for multiple methods, post hoc tests are performed to find the significantly different methods. In this study, two post hoc tests are performed. They are discussed as follows:  
**Nemenyi Test**  
Nemenyi test is a post hoc test performed after Freidman test. Nemenyi test is applied for relative comparison of all classifiers evaluated in the study [9]. The performance differences of various classifiers are checked against the value of Critical distance obtained using Equation (8)  
\[CD=q_{a}\sqrt{\frac{m(m+1)}{6n}} \tag{8}\]  
where \(m\) is the number of classifiers, \(n\) is the number of datasets and the value of \(q_{a}\) is based on studentized range statistics of the Nemenyi test.  
The Nemenyi test can be well understood with the help of critical distance diagrams presented in Section 5.2.  
**Wilcoxon Test**  
It is also recommended to perform the pairwise comparison of classifiers based on values of evaluation metrics obtained from experiments [55]. Wilcoxon test is a non-parametric test useful for this purpose. The null hypothesis _HW0_ of the Wilcoxon test is that the median difference between pairs of experimental methods is zero. The term significance level \(\alpha\) in hypothesis testing is referred to as the probability of rejecting a true null hypothesis. \(\alpha\) is generally set to 0.05 in empirical studies [11]. The observed significance level is called as \(p\)-value. The null hypothesis can be rejected if the \(p\)-value is less than or equal to \(\alpha\), leading to the conclusion that a given pair of deep learning methods is significantly different in performance.
## 3 Results and Discussion
### Effect of Interface Performance  
The interface of the composite material exists between the region where the properties of the reinforcing agent change and the region where the resin base body is consistent. Its state and strength have a very important effect on the properties of the composite material. Due to the small proportion of the interface relative to the whole composite material, it is difficult to measure separately. In this paper, the NOL ring test was used for macroscopic characterization, and the interface properties were studied by analyzing the fracture morphology of NOL ring samples. As can be seen in Figure 11, the annularly wound NOL ring is torn, the fibers at the fracture have explosive damage characteristics, and there is obvious delamination in the fracture area. This indicates that the microcrack extends along the fiber interface direction and the composite cracks along the winding direction. NOL interlayer shear ring cracks have no obvious interfacial debonding, fiber pulling out, or brittle fracture. In conclusion, the resin content is appropriate, and the interfacial properties are good during the torus winding. The fracture energy of the composite material is large, and it has certain toughness. After the fracture of the 45\({}^{\circ}\) wound  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Number** & **Curing Times** & **Heating Rate** & \
\begin{tabular}{c} **Tensile Strength** \\ **(MPa)** \\ \end{tabular} & **Tensile Dispersion** &
\begin{tabular}{c} **Shear Strength** \\ **(MPa)** \\ \end{tabular} & **Shear Dispersion** \\ \hline
1 & 1 & 1 min/\({}^{\circ}\)C & 1766 & 0.132 & 59.2 & 0.043 \\
2 & 2 & 1 min/\({}^{\circ}\)C & 1867 & 0.066 & 58.2 & 0.101 \\
3 & 3 & 1 min/\({}^{\circ}\)C & 1698 & 0.101 & 61.0 & 0.132 \\
4 & 1 & 2 min/\({}^{\circ}\)C & 1758 & 0.13 NOL ring sample, the fiber crack has a zigzag path and expands in other directions, and the fiber tear failure decreases while the brittle fracture increases. Therefore, it can be inferred that the 45\({}^{\circ}\) wound NOL ring sample has a local brittle fracture. The bond strength of the composite material is low, and energy dissipation occurs at the bond. Debonding occurs, forming layered damage. The resin matrix is damaged by shear stress, the material shows irregular damage, and the distribution of broken fibers will be randomly distributed [29]. In addition, fiber fracture will occur during the processing of the 45\({}^{\circ}\) wound NOL ring sample prepared by the ring cutting method, resulting in weak local links. Therefore, the composite property of the 45\({}^{\circ}\) wound NOL ring sample is weaker than that of the circumferentially wound sample.  
Figure 11: Macromorphology of NOL ring sample before and after damage. (**a**) Circumerentially wound NOL ring shear failure; (**b**) circumferentially wound NOL ring tensile failure; (**c**) 45\({}^{\circ}\) wound NOL ring shear failure; and (**d**) 45\({}^{\circ}\) wound NOL ring tensile failure.