esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle of Ultrasonic Waves and FEM Simulation
### Simulation of Ultrasonic Wave Propagation  
In this study, the critical angle of the ultrasonic transducer was determined in order to generate Rayleigh waves on the piston rods. For this purpose, a finite element method (FEM) was performed based on 2D simulation, and the propagation of ultrasonic waves was analyzed on the surfaces of the piston rods. This study also used FEM tools to visualize the ultrasonic waves in order to analyze the propagation of the ultrasonic waves on the surfaces of the piston rods. As shown in Figure 2, a finite element (FE) model was built for simulation under the assumption of an infinite plane in the water region, and the plane wave radiation was set as the boundary condition, but not for the interface with steel. The reflection of the ultrasonic waves was minimized by setting a low reflecting boundary everywhere in the steel region, except for at the interface with water to simulate ultrasonic  
Figure 1: Diagram showing reflection field at Rayleigh angle.  
waves from the transducer as shown in Equation (2), and the propagation of Rayleigh waves was confirmed [10,11].  
\[p(t)=\left[1-\cos\left(\frac{2\pi\times f\times t}{2}\right)\right]\cos(2\pi \times f\times t) \tag{2}\]  
Here, \(f\) and \(t\) denote frequency and time, respectively; the center frequency was set to 10 MHz, the mesh of the water was set to 0.1 mm, the mesh of the sample was set to 0.02 mm, and the depth of the Circum-type defect was set to 700 \(\upmu\)m by considering the specifications of the FEM simulation workstation. Here, the minimum detectable size was approximately 152 \(\upmu\)m. Additionally, the resolution of ultrasonic waves is strongly related to the frequency, and for use of bulk waves, the equation of the wave length is usually considered to be \(\lambda/2\), but for surface micro-defects, the wave length of Rayleigh waves can be obtained to be approximately 50 \(\upmu\)m through previous studies [11].  
Figure 3 shows the propagation of the Rayleigh waves over time. Here red dotted circles are mainly directions in wave propagations. Figure 3a shows a simulation of the ultrasonic waves at the Rayleigh angle, and Figure 3b shows the moment when the ultrasonic waves are converted into Rayleigh waves at the interface between the water and steel, where reflection, transmission, and scattering occurred due to the defects. Figure 3c shows the behavior of the transmitted Rayleigh waves. Here, it was confirmed whether the Rayleigh angle value obtained through Equation (1) is an actual value. In addition, in order to obtain the optimal values of the Rayleigh angle, FEM simulation was implemented with the changing of several parameters, and the behavior of the ultrasonic waves was visualized as shown in Figure 3.
# Study of High-Frequency Pulsating Flow Control Valves
## 3 Mathematical Modeling
### Mathematical Model of Sinusoidal Flow Valve  
The operating principle of the sinusoidal flow valve is that the servo motor drives the eccentric wheel shaft to rotate, and the eccentric wheel shaft drives the spool to move. The spool displacement of the sinusoidal flow valve is related to the rotational speed of the servo motor:  
\[x_{\
u 2}=x_{0}+x_{0}sin\frac{\pi}{30}n_{r}t \tag{12}\]  
where \(n_{r}\) is the servo motor speed.  
The flow equation through a sinusoidal flow valve is:  
\[q_{3}=q_{4}=C_{d}W_{2}x_{\
u 2}\sqrt{\frac{2}{\rho}(p_{2}-p_{3})} \tag{13}\]  
where \(q_{4}\) is the outlet flow rate of the sinusoidal flow valve.  
At node 1 shown in Figure 1, Equation (8) can be obtained using the flow continuity equation.  
\[q_{2}=q_{3}+q_{t1}+\frac{V_{3}}{\beta_{e}}\frac{dp_{2}}{dt} \tag{14}\]  
where \(V_{3}\) is the total volume from the outlet of the constant difference pressure-reducing valve to the inlet of the sinusoidal flow valve and the unsprung chamber damping orifice.  
The flow relationship at the outlet of the flow control valve is:  
\[q_{5}=q_{4}+q_{t2}-\frac{V_{4}}{\beta_{e}}\frac{dP_{3}}{dt}=C_{d}A_{3}\sqrt{ \frac{2}{\rho}P_{3}} \tag{15}\]  
where \(q_{5}\) is the flow rate at the outlet of the flow control valve; \(A_{3}\) is the size of the damping orifice at the outlet; and \(V_{4}\) is the total volume from the outlet of the flow control valve to the outlets of the sinusoidal flow valve, and the damping orifice of the left spring chamber.  
From the conservation of mass, it can be seen that the inlet and outlet flow rates of the two-way flow control valve are equal, so Equation (9) can be obtained.  
\[q_{1}=q_{5} \tag{16}\]  
The flow rate generated from this flow control valve also needs to be calibrated using testing equipment, and since the dynamic cylinder has good dynamic performance, the experimental system shown in Figure 2 was constructed. The oil flowing from the valve pushes the piston to move, so the flow rate can be obtained by collecting the speed of the piston.  
The Equations (2)-(5) and (13)-(16) are subjected to Raschel transformations, in which a block diagram of this flow control valve can be obtained according to Equation (11):  
In Figure 3, \(K_{\text{q1}}\), \(K_{\text{q2}}\) for \(Q_{1}\), \(Q_{3}\) are the flow gain, \(K_{\text{c1}}\), \(K_{\text{c2}}\), \(K_{\text{c3}}\), \(K_{\text{c4}}\), \(K_{\text{c5}}\) for \(Q_{1}\), \(Q_{3}\), \(Q_{\text{f1}}\), \(Q_{\text{f2}}\), \(Q_{5}\) are the flow--pressure coefficient, \(A_{4}\) is the effective area of the hydraulic cylinder piston, \(V_{p}\) is the piston speed, and \(K_{D}\), \(K_{\text{c}}\) represent the servo motor input voltage and speed of rotation, as well as the speed and sinusoidal flow valve spool displacement of the relationship between them. From the figure, it is easy to understand that by controlling different input voltage signals, the corresponding frequency of the output flow can be obtained.  
Figure 3: Flow control valve block diagram.  
Figure 2: Test system.
## 4 Experimental Results
### Ablation Study
#### 4.3.1 Environmental Perception Attention (EPA)
The smaller r indicates that the high contribution pixels are concentrated around the central area, while the contribution of the external points is very low, indicating that the ERF is limited. In Table 3, under different experimental hyperparameter settings of k, the high contribution area (ERF) covering a specific threshold t does not continue to increase, especially when k is larger; in fact, the growth of the receptive field slows down or stagnates. This shows that continuously increasing the size of the convolution kernel does not linearly increase the network's focus area on the target. Too large a convolution kernel may even increase the area ratio of the low contribution area, thereby reducing the detection accuracy. Therefore, choosing an appropriate one-dimensional convolution kernel size is important for environmental perception attention. A reasonable convolution kernel size can help the model better capture the contextual information around the target, thereby improving the robustness of the object detection model. Experiments show that our environmental perception attention can effectively improve the receptive field of the baseline model. At the same time, by properly adjusting the size of the one-dimensional convolution kernel, it can help the model further obtain long-range information and obtain a larger effective receptive field. Previous work has improved the receptive field by deepening the network and using large-scale convolution kernels to extract features, but this will bring problems such as network optimization and an excessive number of parameters and calculations. Environmental perception attention provides another simple and lightweight way to improve the effective receptive field and avoid these side effects.
## 1 Introduction
### Auxiliary Frequency Regulation Architecture of EV Charging Station  
In general, various operations are undertaken in the power grid to balance the dynamic power disturbance, such as the load-damping and generator inertia, primary frequency control, and secondary frequency control. Particularly, the first two operations generally execute within a second, while AGC usually refers to the secondary frequency control with the time cycle from 1 to 16 seconds [35]. Besides, the load frequency control (LFC) consists of the primary frequency control and the secondary frequency control. The framework of AGC power dynamic allocation with EV charging stations is demonstrated in Figure 1, which mainly consists of a dispatching center, EV centralized control center, EV charging station, and EV batteries.  
The main responsibilities of the dispatching center are to collect the frequency deviation and tie-line power deviation of power systems, calculate the total power generation command \(\Delta P_{\Sigma}\), and assign \(\Delta P_{\Sigma}\) to AGC power supply units and EV centralized control centers via some certain algorithms.  
The centralized EV control center acts as the interaction bridge, which ensures the two-way energy flow between EVs and the power grid [36]. Basically, it uploads the information of each EV charging station, for example, the price of unit-adjusted electricity and real-time adjustable capacity of each EV charging station, to the dispatching center. Besides, it assigns the total power instruction \(\Delta P_{\mathrm{EV}}\) issued by the dispatching center to each EV charging power station. However, consider that the EV charging stations under the electricity market belong to different agents, and the price of unit-adjusted electricity is different when participating in the power system frequency regulation. Hence, on the basis of power grid company benefits, some AGC power allocation strategies should be adopted among different EV charging stations to minimize the regulation costs of the power grid company.  
An EV charging station can be regarded as an EV agent, which can implement cluster dispatching to large-scale EV batteries to meet the demand of power grid frequency regulation capacity. Under such circumstances, an EV will be preferable to participate in AGC ancillary service if its charging demand can be completely satisfied after the ancillary service period [32]. Hence, each EV should upload its maximum charging power, initial battery state of charge (SOC), expected SOC, connecting time, expected exit time, charging efficiency, and energy capability to the control center of EV charging stations. On the other hand, the control center of the EV charging station allocates the power instructions issued by the centralized control center of EV to each EV battery based on the information of EV batteries such as the charged state and adjustable capacity.
## 3 Results and Discussion
### Effect of DB and Heat Treatment on the Microstructure of 304 SS  
#### 3.2.1 Initial State of the Microstructures  
The phase analysis results are shown in Figure 6. A shift of the maxima to smaller angles is observed because of the crystal lattice deformation caused by the dispersed chromium and manganese. The X-ray diffraction pattern also shows the presence of residual \(\delta\)-ferrite in the as-received state.  
The microstructure in the as-received state was characterised by structural inhomogeneity (Figure 7). Zones with sliding stripes, zones with equiaxed austenitic grains with an average size of 35 \(\upmu\)m, twin grains, and sliding zones in the grains themselves were observed. Residual \(\delta\)-ferrite was observed in and around the sliding zones. These areas were likely to have an increased local chromium content. The presence of M23C6 carbide was also observed in these areas. The metal component M probably contained manganese  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Specimen** & **Young’s** & **Yield** & **Tensile** & **Percentage** & **Hardness, HB** \\
**Group** & **Modulus, GPa** & **Limit, MPa** & **Strength, MPa** & **Elongation, \%** & **Reduction of Area, \%** & **Hardness, HB** \\ \hline (1) as received & 200 & 432 & 734 & 41 & 68.8 & 245 \\ (2) heat treated & 195 & 258 & 595 & 59 & 76.6 & 158 \\ \hline \end{tabular}
\end{table}
Table 2: Main mechanical properties of the studied AISI 304 steel at room temperature (initial state).  
Figure 5: Nominal stress–nominal strain diagram.  
Figure 6: Phase analysis results for 304 SS (initial states).  
and chromium. The presence of \(\delta\)-ferrite reduced the corrosion resistance of the steel [1]. Notably, the resulting wells in the structure were a consequence of the acid treatment.  
After the initial heat treatment, a significant homogenisation of the structure and an increase in the size of the grains, with an average value of up to 50 \(\upmu\)m, was observed (Figure 8). Residual inhomogeneity obtained in the manufacturing process of the bar,twinnings, and \(\delta\)-ferrite decay (\(\delta\)-ferrite \(\rightarrow\delta+\gamma\)) were observed only in the core of the sample (Figure 8b). The decomposition of \(\delta\)-ferrite may have been the reason for the increase in corrosion resistance. To clarify the type of pits after treatment with an etching acid solution, observations were made under polarised light. Some of the particles glowed, indicating the presence of SiC or NbC (Figure 8c). The resulting pits in the structure were a consequence of the acid treatment. After the passivation of the surface layer, the development of pits was blocked.
# Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves
###### Abstract
Despite the success of PDA in monitoring changes in BP using a finger PPG, the degree of uncertainty in the fit parameters determined from literature PDA models is high (up to 30%) [12; 13; 14], and a potentially large number of heartbeats are required to obtain acceptable signal-to-noise blood pressure estimates. As an alternative Gaussian PDA, the use of secant hyperbolic waves (sech) for the PDA of PPG signals was considered in [14]. Sech may provide a better PDA for blood pressure estimation because it represents a possible solution to the Moens-Korteweg equation describing a pressure pulse in an elastic tube. Indeed, in [14], their results suggest that the hyperbolic secant wave function performs better for three-wave PDA than previous functions in the PDA literature, as section reduces pulse-to-pulse parameter noises in the reflected waves P\({}_{2}\) and P\({}_{3}\). In the present study, we compared sech waves PDA for blood pressure estimation with the corresponding Gaussian waves PDA. We experimentally acquired PPG data and simultaneously acquired near-real-time continuous blood pressure using a Finapres finger  
Figure 1: Pulse decomposition into three constituent waves (adapted from [14; 15]). cuff. The sech waves' PDA parameters were used for multiple regression against the acquired blood pressure compared with the corresponding values of the Gaussian waves' PDA. The results indicated that sech PDA correlated better with blood pressure than Gaussian PDA. Thus, sech should be considered when building PDA models.
## 2 A Collaborative System Design
### Data Acquisition  
In order to create the geometric infrastructure of the e-Rota, first of all, sidewalks, ramps, pedestrian crossings, and bus stations in the study area were measured with the help of the Garmin eTrex Vista GPS receiver, which is manufactured by Garmin Ltd. in Olathe, KS, USA, and thus a set of points with known geographic coordinates in the WGS84 datum were obtained. After these points were positioned in the Universal Transversal Mercator (UTM) projection (Zone: 37) in the NetCAD 8.5 environment, a cartography software, point and line features representing sidewalks, ramps, pedestrian crossings, and bus stations were created. The point and line features were topologically structured in the ArcGIS 10.6 environment, a GIS software, and exported in the KML format for inclusion in Google Maps, the base map for e-Rota. The KML file was included in Google Maps, and red exclamation symbols were manually placed at the endpoints of the sidewalk, where there were no ramps.  
The study area was Ataturk University's main campus in Erzurum, Turkiye. The campus surface area is approximately 6.5 km\({}^{2}\). In addition to faculties, social facilities, a library, etc., there is also a hospital on campus. Therefore, thousands of people from all age groups come to the campus together with students every day. Some of them are people with disabilities who use wheelchairs. Studies on people with disabilities were carried out by the Disabled, Elderly and Veterans Research and Application and Research Center of Excellence affiliated with the Rectorate. The study area and aforementioned data are presented in Figure 2.  
Figure 2: The study area and data.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 2 Literature Review
### Traditional Econometric Models  
The Nelson-Siegel (NS) model, introduced by [PERSON] and [PERSON] [5], has been one of the most influential tools for modeling the term structure of interest rates. Its extension by [PERSON] and [PERSON] [1] introduced a dynamic component that improved its forecasting capabilities. These models typically represent the entire yield curve using a small number of parameters that capture the level, slope, and curvature of interest rates. While effective for curve fitting and macro-finance interpretation, these models are less suited for short-term prediction of a single yield like the 10-year bond.  
Linear regression (LR) models have also been widely used to understand the relationship between macroeconomic indicators and bond yields. Studies such as [4; 8] examined the predictive power of variables such as inflation, short-term interest rates, and GDP growth on long-term yields. Although LR models are interpretable and statistically tractable, they are limited in capturing nonlinear dynamics present in real-world financial data.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 1 Introduction
### From Neural Networks Layer to Hardware Layer  
ANNs were introduced as an approach to reduce the design complexity by looking at the overall communication system (or major components of it) as one big block, instead of optimizing internal blocks individually [11; 12; 13]. While different proposed solutions can meet and overcome the performance of traditional communication algorithms [14; 15], whenever dealing with ANN-based methods, the high memory demand and computational requirements raise the implementation complexity. This leads to high power and energy consumption, which is critical, especially for embedded communication systems. This is one reason why ANN solutions had no fundamental impact on the practical implementation of baseband processing systems so far.  
In this context, three major challenges are of high importance:  
* Low-latency, which is essential for delay-sensitive applications;
* Low-power, which is mandatory to meet the constraints of embedded devices;
* Adaptability, which is of high relevance due to the varying channel conditions.  
General-purpose processors such as graphics processor units (GPUs) or central processing units (CPUs) are not able to satisfy all those constraints at once. In contrast, FPGAs provide a platform that can meet the requirements of our intended application. They offer a flexible memory hierarchy, arbitrary precision datatypes, custom datapaths, as well as huge bit-level parallelism, which allows to implement ANNs in an extremely energy-efficient manner [16; 17; 18].  
The majority of FPGA-based ANN architectures target the inference only, by training the network in software and exporting the trained parameters to optimize inference engines. This approach is not sufficient for the AE application proposed in this work because retraining to adapt to channel fluctuations is of high importance. Therefore, we tackle the challenges of designing a low-latency, low-power architecture for inference and training of the AE on the edge device. To efficiently exploit the capabilities of FPGAs, we integrate our architecture into a framework that automatically selects the DOP for the inference and training module to satisfy the application requirements and optimize for power or latency.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 3 Distributed DL Communication: Libraries and Architectures Compared
### Linux Shell Environment
#### 3.1.2 Ring All-Reduce Method  
As depicted in Figure 7, the ring all-reduce method involves the creation of data in GPU memory, with data circularly shared among each worker. CudaMemcpy replicates data to the CPU memory of each worker, and communication between CPU memories is then transmitted clockwise to each node via MPI. Once all transfers are completed and data from each worker are gathered, they are copied back from the CPU memory to the GPU memory (cudaMemcpy). In the case of CUDA-aware MPI and NCCL, they support direct communication between GPUs. However, for multi-node communication, data need to be transmitted via CPU memory. Therefore, CUDA-aware MPI requires the cudaMemcpy operation, as shown in Figure 7b. This means that for intra-node communication, data must first be copied from the GPU to CPU memory and then transmitted to other nodes.
# Electrodegradation of Selected Water Contaminants: Efficacy and Transformation Products
###### Abstract  
The electrooxidation (EO) of three important environmental contaminants, anticorrosive \(1H\)-benzotriazole (BTA), plasticizer dibutyl phthalate (DBP), and non-ionic surfactant Triton X-100 (_tert_-octylphenoxyoxy[poly(ethoxy)] ethanol, t-OPPE), was studied as a possible means to improve their elimination from wastewaters, which are an important emission source. EO was performed in a batch reactor with a boron-doped diamond (BDD) anode and a stainless steel cathode. Different supporting electrolytes were tested: NaCl, H\({}_{2}\)SO\({}_{4}\), and Na\({}_{2}\)SO\({}_{4}\). Results were analysed from the point of their efficacy in terms of degradation rate, kinetics, energy consumption, and transformation products. The highest degradation rate, shortest half-life, and lowest energy consumption was observed in the electrolyte H\({}_{2}\)SO\({}_{4}\), followed by Na\({}_{2}\)SO\({}_{4}\) with only slightly less favourable characteristics. In both cases, degradation was probably due to the formation of persulphate or sulphate radicals. Transformation products (TPs) were studied mainly in the sulphate media and several oxidation products were identified with all three contaminants, while some evidence of progressive degradation, e.g., ring-opening products, was observed only with t-OPPE. The possible reasons for the lack of further degradation in BTA and DBP are too short of an EO treatment time and perhaps a lack of detection due to unsuitable analytical methods for more polar TPs. Results demonstrate that BDD-based EO is a robust method for the efficient removal of structurally diverse organic contaminants, making it a promising candidate for advanced water treatment technologies.  
1H-benzotriazole; dibutyl phthalate; Triton X-100; electrooxidation; boron-doped diamond electrode; degradation rate; kinetics; energy consumption; transformation products; LC-MS/MS +
Footnote †: journal: Applied Sciences