esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, in most of the prior works [6; 19; 20; 21; 22], the issue of SPO, which significantly affects the transmission performance, was not taken into account sufficiently. Although an interpolation scheme was proposed in [23; 24] to increase the effective sampling points for the synchronization and demodulation, binary fringes whose length is not an integral multiple of the sampling interval would still be obtained after thresholding, which would lead to dislocation sampling. In [25; 26], other modulation schemes were proposed. For PPM (pulse position modulation) or FSK (frequency shift keying), the information is modulated at the pulse position or frequency. However, the brightness of the pixel carries no information in this modulation format, which would increase the redundancy. Considering that on-off keying (OOK) is the mainstream modulation technology of OCC systems, SPO is still an urgent problem to be solved. In this paper, we propose a de-bubble algorithm for RSE-based UOCC to eliminate the interference of bubbles. Then, taking the issue of SPO into account, we propose a novel sampling scheme based on binary fringe correction (BFC), which can effectively alleviate the problem of SPO and further enhance the performance of UOCC. The influence of bubble parameters (size, density, distance, and motion) on UOCC is discussed in detail. The effectiveness of our proposed schemes is experimentally verified in a variety of experimental scenarios. It is demonstrated that a data rate of 7.2 Kbit/s can be achieved in strong bubble environment by applying the proposed two algorithms, which is 8.6 times higher than the baseline [5].
# Signal Separation Based on Knowledge Representation
## 2 Theory
### Application of Knowledge Representations
#### 2.3.2 Application of Physical Knowledge Representation  
In Equation (6), based on the inversion and forward modeling of partial differential equations, a relatively accurate knowledge representation result can be obtained. This representation adheres to the physical mechanisms governing the propagation of the target signal, allowing its application using an L1 norm. The expression for the final application of knowledge is as follows:  
\[M(\mathbf{K}_{p},\mathbf{\tilde{S}}_{p},\mathbf{K}_{s},\mathbf{\tilde{S}}_{s} )=\left|\mathbf{K}_{p}-\mathbf{\tilde{S}}_{p}\right|+\left|\mathbf{K}_{s}- \mathbf{\tilde{S}}_{s}\right| \tag{15}\]  
Here, \(\mathbf{\tilde{S}}_{p}\) and \(\mathbf{\tilde{S}}_{s}\) represent the predicted P-wave and S-wave signals by the neural network, respectively.
## 5 Simulation Results
### Results with Other Traffic Profiles  
In the previous section, results were obtained from several rounds of testing, always with the normal traffic profile, which inserts 566 vehicles per hour in the intersection. However, traffic patterns vary throughout the day; thus, it is important to test the algorithm with traffic profiles with lower and higher vehicles' densities.  
Three other traffic profiles were generated from direct observation of the intersection in key moments: a very low traffic profile (56 vehicles per hour), a low traffic profile (566 vehicles per hour), and a high traffic profile (1679 vehicles per hour). Table 6 shows the results of the simulation with these different traffic profiles. It also contains the results with the normal traffic profile, which had been previously obtained. The passive routing and the smart routing are identified in the titles by the P and S prefixes, respectively.  
Observing the results, some aspects stand out:  
* As expected, as the traffic density increases, all the indicators get worse, both in passive and smart routing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Profile**} & **P-Average** & **S-Average Speed** & **P-Average** & **S-Average** & **P-Average CO2** & **S-Average CO2** \\  & **Speed (km/h)** & **(km/h)** & **Waiting Time (s)** & **Waiting Time (s)** & **(g/km)** & **(g/km)** \\ \hline \multirow{2}{*}{Very low} & 20.9 & 42.0 & 18.3 & 1.0 & 204 & 122 \\  & & (+101\%) & & (\(-95\%\)) & & (\(-40\%\)) \\ \hline \multirow{2}{*}{Low} & 18.5 & 37.2 & 22.2 & 1.9 & 255 & 168 \\  & & (+101\%) & & (\(-92\%\)) & & (\(-34\%\)) \\ \hline \multirow{2}{*}{Normal} & 17.3 & 33.6 & 25.2 & 4.2 & 319 & 216 \\  & & (+94\%) & & (\(-83\%\)) & & (\(-32\%\)) \\ \hline \multirow{2}{*}{High} & 13.3 & 21.3 & 39.2 & 18.3 & 327 & 220 \\  & & (+60\%) & & (\(-53\%\)) & & (\(-33\%\)) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results for all the traffic profiles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **Improvement in** & **Improvement in** & **Improvement in** \\  & **CO2 Emissions** & **Waiting Time** & **Average Speed** \\ \hline Equal cost for both flows & 25.6\% & 78.7\% & 78.4\% \\ Different costs & 28.4\% & 80.0\% & 84.2\% \\ Smart safeguard bypassing & 32.2\% & 83.4\% & 94.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for each algorithm version.  
* The smart algorithm achieves substantial gains in speed, waiting time, and CO2 emissions in all the traffic profiles.
* The lower the traffic density, the higher the gains.
# Real-Time Performance Measurement Application via Bluetooth Signals for Signalized Intersections
## 1 Introduction
The study offers a method to determine the effect of works on signalized intersections on performance. The method developed in this study determines how different operating types at intersections, changes in intersection geometry, and other safety systems installed at intersections affect the performance of the intersection. In the study, a real-time systematic performance measurement is provided for signalized intersections by designing a Bluetooth-based traffic sensor. Monitoring the performance of the systems established for state authorities or local governments responsible for signalized intersections in countries is one of the most significant issues. The study contributes to science in this aspect. Additionally, the other most important science contribution in the study is a feedback system for fully adaptive signaling systems. After the Bluetooth traffic analysis sensor data are sent to the control center, the various results can be used in real time in the fully adaptive signaling system operating at the intersection. In other words, the results obtained from Bluetooth data will increase efficiency if used in a fully adaptive signaling system. The Aimsun Next 20.0.4 traffic simulation software, widely used in traffic engineering and transportation planning, supports traffic modeling on various scales, including micro-, macro-, and meso-simulation [24; 25; 26]. In this study, the intersection where the Bluetooth-based performance measurement system is applied is also modeled using Aimsun. The simulation results are compared with the values obtained from the Bluetooth method to validate the developed approach.
## 6 Case Studies
### Computational Efficiency  
In this paper, the proposed two-stage stochastic mixed-integer linear programming problem is solved on a personal computer equipped with an Intel Core i7 processor (2.60 GHz) with 64 GB RAM, using Gurobi 11.0.1 with its default settings. The termination threshold for Algorithm 1 is set at 0.1%.  
Table 14 compares the computation time between the PHA and directly using Gurobi 11.0.1 (with the MIPGap set to 0.1% for a fair comparison) across varying numbers of scenarios. In the modified IEEE 33-node system, 8 damaged lines are considered with 2 MES devices deployed, while in the modified IEEE 69-node system, 12 damaged lines are considered with 3 MES devices deployed. The results show that as the number of scenarios and problem size increase, the solution time grows for both methods. Notably, in the 69-node system, the computation time for Gurobi increases significantly when the number of scenarios reaches 20. In contrast, the PHA achieves a substantial reduction in solution time compared to using Gurobi directly. By decomposing the main problem into scenario-based subproblems, the PHA effectively reduces the computational load as the number of scenarios increases. However, although a larger number of scenarios improves solution quality, it also increases computation time, making the selection of an appropriate number of scenarios a crucial consideration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Budgets of Damaged Lines** & **Budgets of MES Devices** & **Average CUR** & **Average CUR** \\ \hline
6 & 3 & 46.89\% & 27.42\% \\
7 & 3 & 47.09\% & 30.40\% \\
8 & 3 & 49.03\% & 29.43\% \\
9 & 3 & 50.57\% & 30.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Simulation statistics for average CUR under varying budgets of damaged lines.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Budgets of Damaged Lines** & **Budgets of MES Devices** & **Average CUR** & **Average CUR** \\ \hline
8 & 1 & 49.63\% & 34.44\% \\
8 & 2 & 49.10\% & 32.97\% \\
8 & 3 & 49.03\% & 29.43\% \\
8 & 4 & 46.24\% & 24.39\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Simulation statistics for average CUR under varying budgets of MES devices.
# Investigation of Segmented Halbach Array Design for Passive Magnetorheological Brake System
## 4 Simulation and Analysis  
### Effects of Halbach Configurations  
In a Halbach array, stationary and adjustable segments of permanent magnets are typically configured by arranging them in a sequence where each successive magnet has its magnetic field direction relative to the previous magnet, which enhances the magnetic flux density.  
Figure 6 shows the flux distribution pattern across multiple PMs with color gradients ranging from dark red and orange to blue. It can be seen that darker reddish-orange areas likely indicate regions with higher flux intensity, while the blue areas suggest lower flux distribution. The gradient from red/orange to blue reflects a decrease in flux magnitude across the surfaces. The red arrows signify the direction and intensity of flux flow, with longer arrows in the reddish regions indicating stronger flux movement. This distribution pattern helps visualize how flux transfers across the surface.  
Simulations of a proposed segmented Halbach array typically show a concentrated magnetic flux gradient of the array where the N-pole of adjustable magnets faces the N-pole of stationary magnets. The magnetic flux density is measured at two key locations, such as along the axis of the cylindrical configuration and radially within the magnetorheological fluid at a distance \(R\) from the axis. The axial measurement serves as a baseline, reflecting the uniformity and strength of the field generated by the array, while the radial measurement captures the flux density variations caused by positional adjustments of the segmented magnets that directly impact the shear stress and torque generation. By adjusting the size and position of the permanent magnets, the magnetic field gradient across the array can be controlled, enabling precise spatial distribution of the magnetic field for improved performance in engineering applications. The effects of magnet length, gap width, and position will be further explored.
## 2 Results and Discussion
### Volatile Compounds Analysis
#### 2.2.5 Carbonyls and Phenols  
Three carbonyl compounds (two aldehydes and 1 ketone) were detected in wine samples (Table 2). Although the concentration of carbonyl compounds was relatively low in wine, they could contribute to the overall aroma through a synergetic effect [20; 23]. 6-Methyl-5-hepten-2-one was not detected in wine samples treated with both fining agents, while oct-2-neal and furfural were not identified in MF wine samples. The total content of carbonyl compounds significantly decreased in MF wine while BT and SP wines showed much less impact. Volatile phenols, which formed by the action of hydroxycinnamate carboxylase, originated by the hydrolysis of precursors known as phenolic acids or hydroxycinnamic acids in grapes [27; 28]. Clarification treatments decreased the concentration of volatile phenols significantly, especially the MF treatment, while CF treatment showed the least influence.  
As has been discussed, MF treatment showed the most significant decrease in the total content of different volatile compounds, except for terpenes, which showed the maximum decrease in BT fining wine samples. [PERSON] et al. [24] suggested that the mechanism of aroma losses after bentonite fining may be due to the direct adsorption of the clay. In this experiment, we also confirmed this. On the contrary, SP fining showed the least influence on esters, alcohols, and terpene compounds, while CF and BT wines showed the least influence on fatty acids, phenol, and carbonyl compounds respectively.  
There was no significant difference between SP and CF wine in total terpen. Additionally, the total carbonyls in BT and SP wines also showed no observed difference. The influence of membrane filtration on wine aroma quality could be influenced by the types of filtration, different filter media, filtration parameters and the membrane itself [3; 4]. Recently, researchers have found that cross-flow filtration, a relatively new technique, produced the highest decreases in both total phenol index (TPI) and color intensity (CI) values and removed highly polymerized phenols that are related to astringency in red wines, therefore, could be used in the same way as fining [1; 29]. In this study, MF treatment showed the most significant difference in volatile compounds of 'Italian Riesling' Icewine, but the mechanism still remains to be further studied.
## 6 Blast Performance of Proposed Connection
### Results  
The blast performance of the plate-reinforced large CFT connection was evaluated in comparison to that of the widely used through-diaphragm large CFT connection. Simulation results are presented in Figure 12, which shows von Mises stress distributions and overall deformed shapes of the through-diaphragm and plate-reinforced large CFT connections for the five blast loadings at \(t=20\) ms.  
Figure 11: Normalized moment-drift curves generated by two different finite element codes for the proposed column.  
Figure 12: _Cont._This analysis time was considered sufficient to obtain the maximum responses of the connection. Similarly, the von Mises stress distributions in the vicinity of the connection were generated, as shown in Figure 13. In these contours, the maximum stresses were fixed at 400 MPa for direction comparison of stress distribution between the two connection types. Significant damages for the through-diaphrag connection were observed for \(W=1814\) kg and greater with separation of the beam from the column, whereas the beam separation for the plate-reinforced connection occurred for \(W=4536\) kg and greater. These results indicate that the plate reinforcement is more efficient for connection to resist blast loads.  
Figure 12: Simulation results for large CFT connections at \(t=20\) ms. (**a**) \(W=227\) kg; (**b**) \(W=454\) kg; (**c**) \(W=1814\) kg; (**d**) \(W=4536\) kg; (**e**) \(W=11,000\) kg.  
Figure 13: Von Mises stress distribution in the vicinity of connection of numerical models at \(t=20\) ms. (**a**) \(W=227\) kg; (**b**) \(W=454\) kg; (**c**) \(W=1814\) kg; (**d**) \(W=4536\) kg; (**e**) \(W=11\),000 kg.  
Figures 14 and 15 show deflection histories of beam and column for the through-diaphrag and plate-reinforced large CFT connections, respectively. There were negligible differences in the deflection histories of the column for both connection types, as shown in Figures 14b and 15b, where it is seen that the deflections of the column for both connections continued to increase for \(W=4536\) and 11,000 kg, indicating the column's failure. The maximum deflections of the beam for \(W=227\) and 454 kg were obtained similarly as approximately 27 and 51 mm, respectively, as shown in Figures 14a and 15a. For \(W=1814\) kg, the maximum deflection for the plate-reinforced connection was approximately 269 mm, whereas that for the through-diaphrag connection increased continuously due to the separation of the beam from the column. For \(W=4536\) and 11,000 kg, both connections resulted in beam separation from the column. These numerical results are discussed in more detail in the following section.
# Effect of Aggregate Size on Recycled Aggregate Concrete under Equivalent Mortar Volume Mix Design
## 3 Results and Discussion
### Fresh Properties  
The slump and air contents for each concrete mixture are given in Table 4. The decrease in the slump value was found to increase as the RCA replacement levels were increased. The slump values of RAC1-AP-25 and RAC1-OA-25 using RCA1 with 28% residual mortar content were 23% lower than that of the control mixture (NAC1), and at 50% replacement level, the slump loss was 41-45%. For the RAC2 series using RCA2 with 12% residual mortar content, the slump decreased by 14% than that of the control mixture at 25% RCA replacement level, at 50% replacement, the slump was reduced by up to 24%. Therefore, the use of RCA with a low amount residual mortar can alleviate the slump loss. The lower slump values of RAC compared to NAC are commonly reported in previous studies [52; 53] and can be attributed to the following reasons: (i) high water absorption of RCA; and (ii) reduction in the amount of cement and fine aggregate due to the application of the EMV mix design. At 50% replacement ratio, the slump ranged from 60 to 85 mm, which satisfies the minimum slump requirement of 80 \(\pm\) 25 mm for normal weight concrete with a maximum aggregate size of 20 and 25 mm according to KS F4009 [54] and practically means a medium workability suitable for standard compaction techniques such as vibration. In addition, by optimizing workability for a specific on-site technology, workability can be improved by using plasticizers.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & **Slump** & **Air** & \multirow{2}{*}{**Compressive Strength (MPa)**} & \multirow{2}{*}{**Splitting Tensile Strength (MPa)**} & \multirow{2}{*}{**Elastic Modulus (GPa)**} \\  & **(mm)** & & & & & & & & & \\ \hline  & & & **7-Day** & **28-Day** & **56-Day** & **28-Day** & **56-Day** & **7-Day** & **28-Day** & **56-Day** \\ \hline NAC1 & 110 & 4.3 & 26.6 \(\pm\) 0.8 & 32.7 \(\pm\) 1.0 & 35.4 \(\pm\) 1.4 & 2.90 \(\pm\) 0.14 & 3.20 \(\pm\) 0.10 & 24.2 \(\pm\) 1.1 & 25.8 \(\pm\) 1.7 & 27.4 \(\pm\) 1.4 \\ NAC2 & 105 & 4.5 & 26.8 \(\pm\) 1.0 & 33.0 \(\pm\) 1.3 & 35.8 \(\pm\) 0.7 & 2.88 \(\pm\) 0.22 & 3.17 \(\pm\) 0.15 & 24.4 \(\pm\) 0.9 & 25.9 \(\pm\) 0.9 & 27.0 \(\pm\) 1.0 \\ RAC1- & & 85 & 4.4 & 26.2 \(\pm\) 0.9 & 30.8 \(\pm\) 1.3 & 33.5 \(\pm\) 1.6 & 2.87 \(\pm\) 0.19 & 3.06 \(\pm\) 0.10 & 24.4 \(\pm\) 1.0 & 26.0 \(\pm\) 0.9 & 28.2 \(\pm\) 1.2 \\ RAC1- & & & & & & & & & & \\ AP-50 & 60 & 4.8 & 25.8 \(\pm\) 1.1 & 30.5 \(\pm\) 1.6 & 33.4 \(\pm\) 0.9 & 2.80 \(\pm\) 0.18 & 3.05 \(\pm\) 0.07 & 24.3 \(\pm\) 1.2 & 25.5 \(\pm\) 1.3 & 27.8 \(\
## 3 Problem Formulation
### Artificial Neural Network Models
#### 3.1.2 Multi-Layer Perceptron (MLP)  
One other popular category of neural network structures is the multilayer perceptron (MLP). MLP remains a widely utilized neural network model. Considering this kind of neural network requires a desired outcome as a way to gain knowledge, it is referred to as a guided structure. In order to utilize the model to generate the output whenever what is expected is unknown, this kind of structure aims to build an algorithm that precisely corresponds to the inputs into the output using previous information [14]. Among the input and output layers of an MLP, there may be one or more concealed layers.  
Activation functions (such as sigmoid or rectified linear unit) are used after dots are produced between inputs and weights. Each and every neuron worldwide controls activation. Backpropagation for every layer is commonly used in training. In the field of deep learning, MLPs are frequently employed [4].  
When back propagation as a methodology is used by the MLP and a few other neural networks for learning, the input data continue to enter into the neural network. An error is calculated for each demonstration by comparing the neural network's output regarding the intended output. The neural network thereafter utilizes this error to feed back (back propagate) to its weights, reducing the error with every cycle until the neural model increasingly approaches the desired result [54].  
There are several sets of input parameters and related output variables in the training group. A neural network will only be as effective as the training data that it utilizes. One is unlikely to obtain accurate forecasts if they do not enter all of the variables that impact the outcome into the neural network [54].  
A linear relationship among the data entered and the result is initially identified by the neural network. Neurons that connect the input and output neurons are given weight values. Following the discovery of those linkages, neurons are introduced into the hidden layer to enable the identification of nonlinear connections. Data input into the first layer are transmitted to the next (hidden) layer after being increased by the weights. According to the total of the weighted quantities that are supplied to them, neurons in the hidden layer dismiss or generate outputs. The outcome layer generates the intended outcomes (estimations) based on variables passed from the hidden layer in an identical way. The data groups of parameters are captured by adding more hidden neurons as needed. A robust system of weights eventually develops and, provided this issue is learnable, will yield accurate results for each of the sample decisions or forecasts [54].  
As could be seen in the next paragraphs, RBFs are superior in localized learning and hypersphere-based separation, but MLPs provide flexibility and global learning. Selecting the appropriate architecture for a given problem is facilitated by an understanding of their distinctions.
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
###### Abstract  
Generative adversarial networks (GANs) have recently garnered significant attention for their use in speech enhancement tasks, in which they generally process and reconstruct speech waveforms directly. Existing GANs for speech enhancement rely solely on the convolution operation, which may not accurately characterize the local information of speech signals--particularly high-frequency components. Sinc convolution has been proposed in order to allow the GAN to learn more meaningful filters in the input layer, and has achieved remarkable success in several speech signal processing tasks. Nevertheless, Sinc convolution for speech enhancement is still an under-explored research direction. This paper proposes Sinc-SEGAN, a novel generative adversarial architecture for speech enhancement, which usefully merges two powerful paradigms: Sinc convolution and the speech enhancement GAN (SEGAN). There are two highlights of the proposed system. First, it works in an end-to-end manner, overcoming the distortion caused by imperfect phase estimation. Second, the system derives a customized filter bank, tuned for the desired application compactly and efficiently. We empirically study the influence of different configurations of Sinc convolution, including the placement of the Sinc convolution layer, length of input signals, number of Sinc filters, and kernel size of Sinc convolution. Moreover, we employ a set of data augmentation techniques in the time domain, which further improve the system performance and its generalization abilities. Compared to competitive baseline systems, Sinc-SEGAN overtakes all of them with drastically reduced system parameters, demonstrating its effectiveness for practical usage, e.g., hearing aid design and cochlear implants. Additionally, data augmentation methods further boost Sinc-SEGAN performance across classic objective evaluation criteria for speech enhancement.  
speech enhancement; generative adversarial networks; Sinc convolution; data augmentation; raw samples +
Footnote †: journal: Applied Sciences