esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Plant Sesquiterpense Diversity by Generating Chemical Networks
## 2 Results
### Protonation-Dependent Diphosphate Cleavage  
Natural class I terpene synthases precisely pre-orient their acyclic diphosphate substrates in their active site pockets in a reaction-ready conformation. Then, the complex reaction cascade is initiated by cleaving off the diphosphate group from the pre-oriented substrate molecule, a \(pH\)-dependent [4] process that requires divalent metal ions as cofactor [28; 29]. It has been shown that this ionization step of the allylic diphosphate is the rate-limiting step in a class I terpene synthetase catalysis [30]. Then, the enzyme guides the resulting highly reactive arbocation via rearrangements of the carbon skeleton towards various polycyclic product molecules. The highly reactive arbocation intermediates can scavenge nucleophiles, present in the enzyme's active site, or can saturate the positive charge by deprotonation, terminating the rearrangement cascade in an early stage. A fair example of a rearrangement cascade is the isomerization of FPP to Nerolidyl Diphosphate (NPP) [31], where the diphosphate anion terminates the reaction cascade by reattaching to the carbocation intermediate immediately after an allylic rearrangement has occurred. The reachability of NPP from FPP is trivial to ascertain in a constraint-independent simulation (Figure 1) using only the cleavage of the diphosphate group and the addition of a nucleophile to a cation in the reaction set (Figure 7).  
Synthesis of \(\beta\)-Caryophyllene, \(\alpha\)-Humulene, \(\beta\)-Farnesene, and their Side Products  
(E)-\(\beta\)-caryophyllene is produced from FPP and is emitted by different plant tissues, often in response to a herbivore attack [16; 32; 33]. There is ample evidence that TPS catalysis produces a mixture of sesquiterpenes rather than a single sesquiterpene product [2; 34; 35]. The same mechanism that potentially produces \(\beta\)-caryophyllene, \(\alpha\)-humulene, and \(\beta\)-farnesene also can produce additional compounds. Simulation 02 shows the computational reachability of these compounds (Figure 2) together with \(P0,0\) as an intermediate compound, and the predicted side compounds \(P0,1\) and \(P0,2\). For this simulation, the applied set of rules are presented in Figure 8.  
Figure 1: The plotted result of Simulation 01 for OPP (Diphosphate) cleavage from FPP (Farnesyl Diphosphate).
## 3 Experimental Study on Vibrated Grout-Enriched CSGR Material of Tangba Protection Dam
### The Mix Proportion of CSGR  
According to the principle of 'double gradation and double strength' proposed by [PERSON], the mixing material is prepared and controlled [2]. In this process, the maximum particle size of coarse aggregate is set at 150 mm, with a cementitious material content of 80 kg/m\({}^{3}\). The vibrating-compacted (VC) value at the mixer's exit is regulated between 2 to 8 s without any admixture. The specific details are outlined in Table 5, where the schemes A1 and A2 represent \(M1\) aggregate (the finest gradation, sand ratio is 28.1%, extra-large stones: large stones: medium stones: small stones = 27.5: 35.6: 23.3: 13.6) and \(M2\) aggregate (the average gradation, sand ratio is 18.8%, extra-large stones: large stones: medium stones: small stones = 22.9: 33.0: 25.2: 18.9), respectively, for producing CSGR materials with the lowest strength, denoted as C\({}_{180}\)4.  
Figure 4: Particle size distribution curve of aggregate (Cumulative Sieve Residue).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{\begin{tabular}{c} **Sand Ratio** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **W/Cm** \\ **Ratio** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **VC** \\ **Value(s)** \\ \end{tabular} } & \multicolumn{4}{c}{**Quantity for 1 m\({}^{3}\) CSGR (kg)**} & \multicolumn{4}{c}{**Compressive Strength**} \\ \cline{5-10}  & & & & **Vc** & & & & **(MPa)** & \\ \cline{5-10}  & & & & **Water** & **Cement** & **Fly Ash** & **Coarse** & **Fine** & **Aggregate** & **28 d** & **90 d** & **180 d** \\ \hline \(A1\) & 28.1 & 1.43 & 3.5 & 114 & 40 & 40 & 1649.39 & 644.61 & 2.7 & 5.5 & 6.5 \\ \(A2\) & 18.8 & 1.06 & 6.0 & 85 & 40 & 40 & 1942.30 & 449.70 & 6.0 & 11.2 & 12.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mix proportion of CSGR.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 3 Proposed Architecture for Real-Time Flood Monitoring
### Phase 3: Crosshair Averaging  
The frame passed onto the depth extraction process in this module was the average of 15 processed frames over a period of 15 min. The 15 min averaging period was to match the on-site sensor measurement update rate which was used as the water level reading benchmark. As the sensor updated the data every 15 min, the values were averaged over that period to smoothen out any possible noise. To mimic the sensor averaging period, the pipeline for the RealSense depth extraction followed a similar principle. Each frame was captured and pushed into the frameset every 60 s. Once 15 frames had been queued up, the entire postprocessing filters pipeline was activated, and the resulting frame was the processed frame.  
The downstream reference point was determined by the depth distance reading from the camera, which was used as the offset to add or subtract the excess values to determine the actual water level from. From the processed frame, an average depth reading of several pixels was taken in the formation of a crosshair to minimise the slight fluctuations across the measurement area. The crosshair was 10 pixels down and 9 pixels across, with the centre pixel covered by the vertical down pass, as shown in Figure 4. The figure is the processed frame after being passed through the postprocessing pipeline as it has been smoothed out. The colour map going from dark blue to dark red displays the transition from the extremely close pixels to the furthest-away pixels, respectively.  
Figure 4: The crosshair which is averaged from the processed depth frame to obtain the final depth reading.
# A Time-History Contact Force Model of the Dynamic Load of AERORail Structures
## 2 Data Fitting and Bezier Curves
The expression of the Bezier curve can be determined by the parameter \(t\) and the coordinates of \(n+1\) control points (Equation (3)):  
\[P(t)=\sum_{i=0}^{n}P_{i}B_{i,n}(t),\ t\in[0,1] \tag{3}\]  
where \(P(t)\) is the expression of the Bezier curve, \(P_{i}\) are the coordinates of the control points, \(t\) is the independent variable parameter of the curve, and \(B_{i,n}(t)\) is the Bernstein basis function of degree \(n\) (Equation (4)):  
\[B_{i,n}(t)=C_{n}^{i}t^{i}(1-t)^{n-i},\ i=0,1,2\cdots,n \tag{4}\]  
If the above equation is rewritten in matrix form, it can be expressed as (Equation (5)):  
\[\mathbf{B}=\left\{ \begin{array}{cccc}C_{n}^{0}C_{n}^{0}&C_{n}^{0}C_{n}^{1}& \cdots&C_{n}^{0}C_{n}^{n-1}&C_{n}^{0}C_{n}^{n}\\ 0&C_{n}^{1}C_{n-1}^{0}&\cdots&C_{n}^{1}C_{n}^{n-2}&C_{n}^{1}C_{n-1}^{n-1}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ 0&0&\cdots&C_{n}^{n-1}C_{1}^{0}&C_{n}^{n-1}C_{1}^{1}\\ 0&0&\cdots&0&C_{n}^{n}\end{array} \right\}\left\{ \begin{array}{c}t^{0}\\ t^{1}\\ \vdots\\ t^{n-1}\\ t^{n}\end{array} \right\} \tag{5}\]  
Substituting Equation (5) into Equation (3), we obtain Equation (6):  
\[P(t)=\left\{ \begin{array}{c}x\\ y\end{array} \right\}=\mathbf{P}\mathbf{B},\ \mathbf{P}=\left\{ \begin{array}{c}\mathbf{P}_{x}^{T} \\ \mathbf{P}_{y}^{T}\end{array} \right\}=\left\{ \begin{array}{c}P_{0,x}&P_{1,x}& \cdots&P_{n,x}\\ P_{1,y}&P_{1,y}&\cdots&P_{n,y}\end{array} \right\} \tag{6}\]  
Taking four control points as (0, 0), (10, 10), (20, \(-10\)), and (30, 0), the Bezier curve can be drawn as shown in Figure 1.  
Figure 1: Bezier curve illustration. Substituting the expression of the Bezier curve from Equation (6) into Equations (1) and (2), we can obtain the least squares fitting Equation (7) based on the Bezier curve basis function:  
\[e=\sum_{i=1}^{N}\|y(x_{i})-\mathbf{P}\mathbf{B}\|^{2},\ e\to 0 \tag{7}\]  
Using Equation (7) to fit the contact force curve of the AERORail structure from [PERSON] et al. [2], different time-history force models can be obtained.
## 2 Materials and Methods
Additionally, the flow of each gas component was measured using Coriolis--(for CO\({}_{2}\)) and conductivity-type (for CH\({}_{4}\) and H\({}_{2}\)) mass flow meters. The exhaust gas composition measured directly from the engine outlet without any aftertreatment system was measured by using a CAPELEC gas analyzer type CAP 3201. The gas analyzer parameters are listed in Table 4.  
The in-cylinder pressure was measured with a type 6117 BF17 piezoelectric pressure sensor, integrated in the spark plug and manufactured by KISTLER. The signal from the pressure transducer was amplified by using a type 5039A212 charge amplifier. Since piezoelectric pressure transducers only allow the measurement of pressure to increase, it was also necessary to measure the absolute pressure in the engine intake manifold in order to relate the in-cylinder pressure to a known absolute pressure value. For this purpose, a type 4045A1 KISTLER piezoresistive transducer, connected with a type 4618A0 amplifier, was used. The voltage pressure signals from the in-cylinder and the intake pressure transducers were sampled and processed by a measuring system containing two type PXI-6133 and PXI-6602 cards from NATIONAL INSTRUMENTS. Measurements were performed with a resolution of 1024 measurement points per crankshaft revolution. In addition, the encoder was fitted with a position marker to indicate the position of the piston in the cylinder. Each set of measurement consisted of 100 consecutive engine cycles. The collected air and fuel consumption data and in-cylinder pressure were used to analyze the combustion parameters with utility BURN in AVL BOOST software. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Measuring Range** & **Accuracy** \\ \hline CO & \(0\div 10\),5\%, by vol. & \(\pm 3\)\% \\ CO\({}_{2}\) & \(0\div 21\)\%, by vol. & \(\pm 4\)\% \\ HC \({}^{1}\) & \(0\div 20\) 000 ppm, & \\  & (equivalent of C\({}_{6}\)H\({}_{4}\)), by vol. & \(\pm 5\)\% \\ NOx & \(0\div 10\) 000 ppm, by vol. & \(\pm 1\)\% \\ O\({}_{2}\) & \(0\div 21\)\%, by vol. & \(\pm 3\)\% \\ \hline \hline \end{tabular} \({}^{1}\) HC—hydrocarbons.  
\end{table} 
Table 4: Measurement range and accuracy of the exhaust gas analyzer.
## 3 Results and Discussion
### Nitrogen Removal Effect of Reed Carbon Source Constructed Wetland
#### 3.1.2 Removal Effect of NO\({}_{3}\)\({}^{-}\)-N  
From Figure 3, it can be seen that the influent NO\({}_{3}\)\({}^{-}\)-N concentration of RW, CW and N-RW was 2.06 to 2.71 mg/L and the NO\({}_{3}\)\({}^{-}\)-N concentration of the effluent was 0.20 to 0.49, 2.72 to 3.55 and 0.25 to 0.59 mg/L. The corresponding average removal rates of NO\({}_{3}\)\({}^{-}\)-N were 84.69 \(\pm\) 3.35%, \(-\)31.29 \(\pm\) 12.63% and 83.76 \(\pm\) 3.22%, respectively. The comparison between RW and CW showed that the NO\({}_{3}\)\({}^{-}\)-N concentration in the RW effluent was low and stable, and the NO\({}_{3}\)\({}^{-}\)-N removal rate was increased by 115.98% compared to CW. The addition of a need carbon source could significantly improve the NO\({}_{3}\)\({}^{-}\)-N removal effect of constructed wetlands (\(p\) < 0.05). The decomposition of the need carbon source in RW created an anoxic/anaerobic environment with a high concentration of organic matter in the middle and lower layers of the substrate, which greatly enhanced the denitrification process in the system. At the same time, the abundant pore structure on the surface of the need carbon source could also be used as a carrier for the growth and attachment of denitrifying bacteria, which together promoted the removal of NO\({}_{3}\)\({}^{-}\)N from wetlands [25]. The NO\({}_{3}\)\({}^{-}\)-N concentration in the CW effluent was higher than in the influent mainly because the denitrification rate was low due to the lack of organic carbon. In addition, the high nitrification rate in CW promoted the continuous conversion of NH\({}_{4}\)\({}^{+}\)-N into NO\({}_{3}\)\({}^{-}\)-N in the influent, which accumulated a large amount of NO\({}_{3}\)\({}^{-}\)-N in CW.  
The comparison between RW and N-RW showed that the NO\({}_{3}\)\({}^{-}\)-N removal rate of RW was only 0.93% higher than that of N-RW, and need planting had no significant impact on NO\({}_{3}\)\({}^{-}\)-N removal in constructed wetlands (\(p\) > 0.05). This is because only five reeds were planted in RW and the state of reed development does not allow a higher role for nitrogen uptake and nitrogen removal. The removal of nitrogen forms in the wetland were mainly associated to nitrification, denitrification and some non-conventional pathways, as strongly observed by [PERSON] et al. [26].
# Bell-X, An Opportunistic Time Synchronization Mechanism for Scheduled Wireless Sensor Networks
## 6 Results
### Maximum Time for Achieving Network Connection  
Figure 15 shows the maximum value, the minimum value, the average value, and the coverage for three standard deviation of the time needed by a new node to join effectively the RPL network so it can start transmitting data. All of the statistical measurements have been obtained from the average of the 15 simulations for each of the configurations.  
The first thing to consider when looking at these results involves the M4 configuration, which shows the best values in connection time by far in comparison with the rest of configurations, but only because simulation where nodes did not connect were not computed in the calculation. When the node connects, it connects fast, but on 20% of the occasions, the node will never connect which is probably unacceptable in most situations, so this M4s configuration is likely to be discarded in favour of other solutions. M32 also has a better average than Bell-65, but with a greater dispersion of connection times, reaching very high maximum values without guaranteed connection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Synchronization Success** & **Connection Success** \\ \hline Bell-32 & 100\% & 100\% \\ Bell-65 & 100\% & 100\% \\ M4s & 100\% & 80\% \\ M16s & 100\% & 93\% \\ M32s & 100\% & 93\% \\ Trickle Timer & 100\% & 93\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Probability of synchronization/connection success.  
Figure 15: Statistical comparison of the different simulated configurations.  
The results show that the configurations Bell-32 and Bell-65, which are implementations of the proposed mechanism, achieve the best results in terms of average and dispersion in connection time while being the only configurations that are able to achieve 100% connection success.
## 4 Results  
The purpose of the experiments was to test the performance of the fuzzy control based self-adaptive system regarding the energy consumption and the communications reliability. The energy consumption is directly related to the battery discharge rate, and the communications reliability is measured by the error rate. In the following subsections we will discuss the results for both.
###### Abstract  
This study evaluated ratings of vocal strain and perceived listening effort by normal hearing participants while listening to speech samples produced by talkers with adductor spasmodic dysphonia (AdSD). In addition, objective listening effort was measured through concurrent pupillometry to determine whether listening to disordered voices changed arousal as a result of emotional state or cognitive load. Recordings of the second sentence of the \"Rainbow Passage\" produced by talkers with varying degrees of AdSD served as speech stimuli. Twenty naive young adult listeners perceptually evaluated these stimuli on the dimensions of vocal strain and listening effort using two separate visual analogue scales. While making the auditory-perceptual judgments, listeners' pupil characteristics were objectively measured in synchrony with the presentation of each voice stimulus. Data analyses revealed moderate-to-high inter- and intra-rater reliability. A significant positive correlation was found between the ratings of vocal strain and listening effort. In addition, listeners displayed greater peak pupil dilation (PPD) when listening to more strained and effortful voice samples. Findings from this study suggest that when combined with an auditory-perceptual task, non-volitional physiologic changes in pupil response may serve as an indicator of listening and cognitive effort or arousal.  
auditory-perceptual ratings; voice disorders; adductor spasmodic dysphonia; vocal strain; Listening effort; pupillometry 1
## 2 Materials and Methods
### C3 TR Module and SPPF  
The C3 TR module is an enhanced C3 module that integrates transformer blocks to improve its performance [22], as shown in Figure 3. The C3 module consists of multiple convolutional layers and shortcut connections. The convolutional layers are good at capturing local features and spatial hierarchies of bad traffic sign images while the shortcut connections help mitigate the problem of vanishing gradients. In the C3 TR module, the traditional bottleneck layer is replaced with a transformer block, which incorporates a self-attention mechanism. The self-attention mechanism allows the model to weigh the importance of different parts of the input bad traffic sign image and allows the model to capture long-term dependencies. It is represented in Equation (1).  
\[\text{Attention}(\text{Q},\text{ K},\text{V})=\text{softmax}(\frac{\text{QK}^{ \text{T}}}{\sqrt{\text{d}_{\text{k}}}})\text{V}, \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter (Millions)** & **GFLOP (Billion)** \\ \hline Yolov5n & 1.9 & 4.5 \\ Yolov5s & 7.2 & 16.5 \\ Yolov5m & 21.2 & 49.0 \\ Yolov5l & 46.5 & 109.1 \\ Yolov5m & 86.7 & 205.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variants of the Yolov5 model showing parameters and GLOP.  
Figure 1: Sample images representing all the classes in the dataset: (**a**) occluded; (**b**) displaced; (**c**) faded; (**d**) perforated; (**e**) good; (**f**) rusted; (**g**) defaced.  
where Q denotes the query, K is the key, and V represents the value. And \(\rm{d_{k}}\) is the dimension of the key vector, while the softmax ensures that the attention weight is summed to \(1\).  
Spatial Pyramid Pooling-Fast (SPPF) module [23] is an optimized version of the traditional Spatial Pyramid Pooling (SPP) layer [24]. The module extracts features at multiple spatial scales by applying pooling operations with different kernel sizes, which provides a rich representation that is further processed by the network to detect bad traffic signs of varying sizes and conditions.  
In the proposed model, as shown in Table 2, placing SPPF at the beginning of the head and C3 TR modules before the detection head has significantly improved the performance of Yolov5n, Yolov5s, and Yolov5m models.  
Figure 3: Structures of C3 and C3 TR modules.  
Figure 2: Structure of the Yolov5 model.
## 5 Conclusion  
Since EMPP provides several transformer models and a large amount of information exists in the transformer test data, it is difficult for the power system operator to simulate the transformer inrush current directly. Therefore, this paper establishes the simulation process of the transformer inrush current using EMPP so that any power system operator can easily perform it. The method presented in this paper consists of three steps: (1) Estimation of current-flux values, (2) input of current-flux values, and (3) power system modeling. In this paper, the methods of inputting current-flux values are explained in detail in each step. An example of transformer inrush current simulation using actual 154 kV transformer test report data is described by applying these methods. Thus, both Method 1 and 2 in this paper can be utilized to simulate the transformer inrush current. However, it is recommended to apply Method 2 if the actual transformer contains a large amount of residual magnetic flux; otherwise, either Method 1 or Method 2 may be applied.  
When the mal-operation of the protection relay occurs in actual power systems due to a very large inrush current, the power system operator will attempt to identify this problem and provide solutions. Since the inrush current of the transformer cannot be actually tested, the problem can be solved using computer simulation. If the power system operator has no experience with inrush current simulations and does not understand the well-organized simulation method, it will take a long time to provide a solution. Therefore, the simulation process described in this paper can aid the power system operator in analyzing the problem using inrush current simulation in a short time and developing a solution to achieve stable power system operation.  
[PERSON] conceived, designed, and performed the experiments. [PERSON] and [PERSON] analyzed the data and wrote the paper.  
This work was supported by a 2019 Yonam Institute of Technology grant.  
The authors declare no conflict of interest.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 2 Spectrum-Sensing Methods
### Single-Node Spectrum Sensing
#### 2.2.3 Matched Filter Detection
Complexity of real environments: The spectrum sensing range of a single node is generally physically limited, as it may be influenced by propagation distance and large obstacles such as plateaus and trees. This limits the range and accuracy of the spectrum resources that nodes can perceive. In wide area communication systems, multiple nodes may need to be deployed to extend the sensing range. 3. Spectrum resource competition: A single node performing spectrum sensing may compete with other devices for the same spectrum resources. This can lead to a decrease in the accuracy of the spectrum sensing and may require additional coordination mechanisms to manage the allocation of spectrum resources.