esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Case 3: PMSMs Have Different Speeds with the Same Load Torque  
In this situation, PMSMs with different speeds of 1000 rpm and 3000 rpm are examined with the same load torque of 9 Nm. Without the proposed method, the RMS value of the  
Figure 10: The dc-link capacitor current for varying load torques of PMSM-2.  
Figure 9: The dc-link capacitor current (**a**) and frequency spectrum of it (**b**): (red) zero rad phase difference; (blue) \(\pi\)/2 rad phase difference.  
dc-link capacitor current is 70.4 A, while with the proposed method, it is reduced to 23.2 A. The peak-to-peak ripple current of the dc-link capacitor is reduced with the proposed technique, as shown in Figure 11a. Figure 11b shows the frequency spectrum of the dc-link capacitor current, and the second, sixth, and tenth harmonics are eliminated with the proposed technique as in the other cases.  
In addition, the dc-link capacitor current is simulated for a scenario where one PMSM (PMSM-1) has a constant speed of 2000 rpm and a constant load torque of 9 Nm, and the other PMSM (PMSM-2) has a varying speed with a constant load torque of 9 Nm, as shown in Figure 12. The simulation results show that the dc-link capacitor current increases when the PMSM-2 starts to operate if there is no phase difference between the carrier waves. The closer the coefficients of the same harmonics of the inverters are, the more effectively they cancel each other out. The most unfavorable condition for the dc-link capacitor current of the dual-axis system with the proposed technique occurs when one motor is operating while the other is not, because only the harmonic of one motor is present, so there is no complete cancellation of the harmonic content of the dc-link capacitor current.
## 3 Finite Element Modeling
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter** & **Inclusion** & **Matrix** & \begin{tabular}{c} **CIE-** \\ **Interface** \\ \end{tabular} & \
\begin{tabular}{c} **CIE-** \\ **Matrix** \\ \end{tabular} &
\begin{tabular}{c} **CIE-** \\ **Inclusion** \\ \end{tabular} \\ \hline Elastic Modulus, \(E\) (GPa) & 72 & 28 & - & - & - \\ Poisson’s ratio, \(\
u\) & 0.16 & 0.2 & - & - & - \\ Elastic Stiffness, \(K_{n}\) (MPa/mm) & - & - & 10\({}^{6}\) & 10\({}^{6}\) & 10\({}^{6}\) \\ Maximum normal stress, \(\sigma_{n}^{0}\) (MPa) & - & - & 2.4 & 4 & - \\ Maximum shear stress, \(\sigma_{s}^{0}\) (MPa) & - & - & 10 & 30 & - \\ Normal mode fracture energy, \(G_{I}\) (N/mm) & - & - & 0.02 & 0.06 & - \\ Shear mode fracture energy, \(G_{II}\) (N/mm) & - & - & 0.4 & 1.2 & - \\ B-K criterion material parameter, \(\eta\) & - & - & 1.2 & 1.2 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Material properties for CZM simulations. The properties of inclusion and matrix, including Young’s modulus, Poisson’s ratio, and the \(\eta\) parameter, are derived from [56]. The elastic stiffness of CIEs is referenced from [57]. The input parameters for the TSLs in relation to CIEs are obtained from [52]. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Young’s Modulus** & **Poisson’s Ratio** & **Fracture Energy** & **Failure Strength** \\
**Phase** & \(E\) (GPa)** & \(\
u\) & \(G_{c}\) (N/mm) & \(\sigma_{u}\) (MPa) \\ \hline Inclusion & 72 & 0.16 & 0.2 & 20 \\ Matrix & 28 & 0.2 & 0.06 & 4 \\ Interface & 21.9 & 0.2 & 0.02 & 2.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Material properties for phase-field simulations. The Young’s modulus of the interface zone is obtained from [58].
# Study on Noise Reduction with Paving Different Low Noise Pavement Materials
## 2 Experimental Setup of Testing Lanes  
### Pavement Mixtures in Experimental Road Section  
The experimental section is located on the Yanjing highway (G1515) in Jiangsu Province, China, which is a two-way, four-lane road. The stake number of this section ranges from K161+300 to K159+100, with a total length of 2.2 km. The original pavement layer is 2.5 cm easy compressed asphalt (ECA-10) dense pavement. The 4.0 cm porous asphalt concert (PAC-13) pavement struck at both ends of this experimental section was laid more than 1 km to avoid the blockage of the porous road surface caused by the boundary of dense road surface and the porous road, which may affect the measurement results [22]. According to the requirements of rut detection and road maintenance in this experimental section, the experimental section has different mixture ratios of PAC-13 and porous ultra-thin wearing course (PUC-10), which can be used for porous low noise asphalt pavement. The sieving and compose gradations of PAC-13 and PUC-10 are summarized in Table 1.  
By designing the target mixture ratio of asphalt mixture with PAC-13 on the upper layer, the relevant volume parameters of asphalt mixture can be determined, which are summarized in Table 2. This table also presents the relevant volume parameters of asphalt mixture for PUC-10.  
Note that all the requirements of engineering standards need to be met, which include the leakage loss, the flying loss, the residual stability, the freeze-thaw splitting strength ratio, the dynamic stability, and the water permeability coefficient of the asphalt mixture, with both PAC-13 and PUC-10 on the upper layers [23].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Particle Size (mm)** & **PAC-13** & **PUC-10** \\ \hline
16 & 100.0 & \\ \hline
13.2 & 91.0 & 100 \\ \hline
9.5 & 62.1 & 99.2 \\ \hline
4.75 & 18.1 & 25.3 \\ \hline
2.36 & 10.8 & 12.3 \\ \hline
1.18 & 8.7 & 10.4 \\ \hline
0.6 & 7.5 & 7.9 \\ \hline
0.3 & 6.6 & 6.6 \\ \hline
0.15 & 6.1 & 5.6 \\ \hline
0.075 & 5.4 & 4.8 \\ \hline Else & \
\begin{tabular}{c} 4.8\% oil-stone ratio \\ 0.1\% polyester doped \\ \end{tabular} &
\begin{tabular}{c} 5.0\% oil-stone ratio \\ 0.1\% polyester doped \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gradations of pavement mixture.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Relevant Volume Parameters** & **PAC-13** & **PUC-10** \\ \hline Maximum theoretical relative density (g/cm\({}^{3}\)) & 2.674 & 2.655 \\ \hline Gross volume relative density with volume method (g/cm\({}^{3}\)) & 2.067 & 2.077 \\ \hline Void ratio with volume method (\%) & 22.7 & 21.8 \\ \hline Relative density of gross volume with vacuum method (g/cm\({}^{3}\)) & 2.142 & 2.105 \\ \hline Void ratio with vacuum method (\%) & 19.9 & 20.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relevant volume parameters of asphalt mixture for PAC-13 and PUC-10.
## 1 Introduction
Measurements were conducted using the closed chamber method (CCM), i.e., an experimental technique used to measure the radon exhalation rate from a material by placing it inside a sealed container (chamber), in combination with the Durridge Rad7 instrument [46; 47; 48]. Specifically, the first analytical approach utilizes data from a 10-day measurement period, whereas the second relies on results from just the first 24 h [49]. A statistical comparison of the two methods was carried out using the u-test. To further assess the implications for indoor air quality, the resulting exhalation rates were applied to the Markkanen room model, i.e., a mathematical model used to estimate indoor radon concentration based on the radon exhalation rate from surfaces (such as walls and floors), the room's volume, the ventilation rate, and the decay constant of radon, thus enabling an estimation of potential indoor radon concentrations. Unlike prior studies, this paper (i) directly compares two standard analytical protocols using the same instrumentation; (ii) introduces a fast yet validated method for time-efficient assessments; and (iii) incorporates results into a radiological hazard framework using the Markkanen room model. The novelty also lies in focusing on heritage stones from southern Italy, rarely analyzed in terms of radon emissions.
## 2 Methods  
### Laser Forming Experiments  
American Iron and Steel Institute (AISI) 316 stainless-steel sheets with dimensions of \(18\times 22\times 0.5\) mm were used in this study. This material was extensively used at the macro- and micro level in various industries. The specific dimensions were chosen to not hinder the mechanical handling. The source of radiation was an ytterbium fiber laser (IPG YLR-200, IPG Photonics, Germany), with a wavelength of 1.07 \(\upmu\)m and a maximum power of 200 W at 6 A.  
A raster scanning pattern was applied for the laser forming, which involved several sequential scans, as shown in Figure 1a. The applied scanning approach and desired shape are illustrated in Figure 1b,c, respectively. Based on the preliminary experiments, the laser power and scanning velocity were set at 150 W and 3.82 mm/s, respectively, while the dwell time between each irradiations was 2 s [12]. The studied laser bending parameters are illustrated in Figure 2, where \(h_{s}\) is the hatch spacing, \(D_{b}\) is the beam diameter, and OR is the overlap ratio (OR \(=1-\frac{h_{s}}{D_{b}}\)) [12]. Table 1 summarizes the laser forming parameters used in this study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Case** & \(A\) & \(B\) & \(C\) & \(D\) & \(E\) & \(a\) & \(b\) & \(c\) & \(d\) & \(e\) \\ \hline \(h_{s}\) (mm) & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ \(D_{b}\) (mm) & 0.25 & 0.5 & 0.7 & 1.0 & 1.3 & 0.25 & 0.5 & 0.7 & 1.0 & 1.3 \\ OR & –3.0 & –1.0 & –0.43 & 0.0 & 0.23 & –1.0 & 0.0 & 0.30 & 0.50 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Process parameters.  
The plastic deformation at the center point of the deformed sample was considered to be the total deformation of the formed sample. A Mitutoyo micrometer was used for the measurements. After the laser forming, cross-section micrographs were acquired. For optical microscopy and scanning electron microscopy (SEM; ZEISS GEMINI SUPRAT; 40 VP) observations, the polished AISI 316 samples were etched by an aqua regia solution, consisting of 30 mL of distilled water, 20 mL of HCl, and 15 mL of HNO3, at a room temperature of approximately 120 s.  
The thermal cycle in the irradiation pass is of importance for the evaluation of the suitability of the determined process parameters with respect to metallurgical properties [13]. Hence, the sample was laterally sectioned at the middle part and a microhardness test was performed along a specific scan pass. The Vickers microhardness test with a square-based pyramid diamond was performed, based on the American Society for Testing and Materials E 384-16 standard.  
Figure 1: (**a**) Raster scanning strategy; (**b**) applied raster scanning strategy; and (**c**) depiction of the desired shape.  
Figure 2: Schematics of the studied laser forming parameters.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 4 Mobile LiDAR Sensing Circuit and Chip Design
### System Architecture  
The mobile LiDAR sensing architecture can be divided into four parts, namely the Main Controller, CORDIC Vertical Angle Calculator, CORDIC Horizontal Angle Calculator, and Coordinate Conversion Calculation (Figure 7).  
The main controller is primarily used to classify and decode input data. The Main Controller receives 8-bit LiDAR input signals, and outputs 2-bit packets, 4-bit blocks, 8-bit reflection information, and 16-bit distance information to the coordinate conversion calculator. The vertical angle and horizontal angle calculators are trigonometric operations.  
The CORDIC algorithm is used to approximate the values to be obtained, the horizontal and vertical angles of SIN and COS are calculated, and the final output 17 bits is transmitted to the coordinate conversion calculator.  
The final module coordinate conversion calculator is to receive the 16-bit distance information, the 17-bit SIN and COS values of the vertical angle, and the 17-bit SIN and COS values of the horizontal angle. The final XYZ coordinates are obtained by the formula operation.
## 4 Cybersafety Analysis
### Generate Loss Scenarios  
Next, we determine causal factors that enable the issuance of the earlier identified unsafe/insecure control actions; we want to understand 'why', in the context of the larger system, an unsafe/insecure control action may be issued by a controller. According to [PERSON] [14], two types of causal scenarios should be considered:  
1. [label=()]
2. Scenarios that lead to the issuance of unsafe control actions; these could be a result of (1) unsafe controller behavior or (2) inadequate/malformed feedback.
3. Scenarios in which safe control actions are improperly executed or not executed altogether; these could be a result of issues along the (1) control path or the (1) controlled process itself.  
For illustration purposes, we zoom into the functional control structure for the chiller controller from Figure 3 and superimpose it with guidewords from [30], signifying sample attack scenarios; the simplified control structure is presented in Figure 4. By going around the control loop and hypothesizing why a controller may issue a hazardous control action while considering the actions and motivations of malicious actors, we can generate a list of causal factors for loss scenarios. Two example loss scenarios, UCA-5 and UCA-3S, along with potential causal factors and associated safety/security constraints are presented in Table 4 which is followed by a discussion of some of the key findings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Chiller** & **UCA-5** \\ \hline \hline \multicolumn{2}{c}{Chiller controller toggles compressor speed between upper and lower limits repeatedly causing it to pass through resonant speeds \(\rightarrow\)[H.5]—(Cooling Capacity Control Loop)} \\ \hline \hline Scenarios & Associated Causal Factors & Safety/Security Constraints \\ \hline  & 1. & Process/Mental Model Flaw \\  & & Controller intrinsically trusts the sensor \\  & & values—it believes the compressor needs to be \\  & & speed up or slowed down to meet cooling load \\  & & demand without verification \\  & &
## 4 Results and Discussion
### Discussion
As mentioned above, future work will focus on extending the system to multiclass attack classification, incorporating additional datasets to improve generalization across environments, and enabling online learning capabilities to adapt to evolving threat patterns. Furthermore, integrating the IDS with cloud-based security platforms or enterprise SIEM systems will make the solution more viable for deployment in real-world infrastructures, including critical sectors such as smart agriculture, military, healthcare, and government networks. These enhancements will strengthen the model's utility as a robust, scalable, and trustworthy tool for proactive network defense.
## 2 Preliminaries on the 3 GPP NR Physical Layer Specification  
In the NR specification for 5G, AVs with eMBB and URLLC traffic should coexist efficiently in a single carrier bandwidth (CBW). Accordingly, the standardization activities of 3 GPP aim to support these AVs with different requirements. Among the various improvements that can be made to achieve efficient coexistence, we focused on the physical layer enhancements standardized in 3 GPP until the recent Release 16 specification in this study.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 4 Modeling the Performance of Bluetooth Low Energy for Opportunistic Sensor Data Collection
### Connection-Based Approach
#### 4.2.1 Sensor Node Current Consumption
Therefore, we can calculate \(T_{com}\) as follows:  
Figure 7:1  
where \(cSTO\) denotes _connSupervisionTimeout_ and \(I_{comn}\) can be determined by using the next equation:  
Figure 7:2  
where \(T_{adv\_inter}\) and \(I_{adv\_inter}\) refer to the average duration of the interrupted, last advertising event before connection establishment, and the average current consumed during that time interval. We assume that the initiator may hear any of the \(N\) advertisements in that last advertising event with the same probability. Therefore, \(T_{adv\_inter}\) and \(I_{adv\_inter}\) can be computed as shown in the following equations:  
Figure 7:3Figure 7:4  
By plugging Equations (9) and (16)-(22) into Equation (7), the average current consumption of the sensor node in the connection-based approach can be obtained. The sensor node lifetime can be calculated by using Equation (5), but replacing \(I_{\mathit{{agg\_adv}}}\) by \(I_{\mathit{{agg\_com}}}\).
## 2 Related Works
### Temporal Properties  
Currently, both domestic and international studies employ various performance metrics to evaluate fieldbus systems like Modbus, such as network utilization rate \(P_{util}\), packet loss rate \(P_{loss}\), network efficiency \(P_{eff}\), and throughput \(Q_{thr}\).  
The transmission delay \(T_{delay}\) of a fieldbus message is defined as follows: when the source node starts to send a request frame at the timestamp \(T_{S}\), the destination node receives the frame at \(T_{D1}\) and immediately generates a response to send it after processing, and the source node ultimately receives the response successfully at \(T_{D2}\), and the transmission delay \(T_{delay}\) is the time interval from \(T_{S}\) to \(T_{D2}\). It mainly consists of node's total transmission delay \(T_{SSt}\), channel transmission delay \(T_{trans}\), node reception delay \(T_{drr}\), and application layer processing delay \(T_{processing}\). Here, \(T_{SSt}\) includes the waiting delay \(T_{wait}\) and the frame transmission delay \(T_{frame}\), where \(T_{wait}\) comprises the queuing delay at source node \(T_{queue}\) and blocking delay \(T_{block}\) (which occurs when the transmission channel is occupied by other messages). The transmission delay model is illustrated in Figure 2 above (the upper table for each delay represents the corresponding node or direction).  
Based on the transmission delay model listed in Figure 2, the following mathematical model can be listed:  
\[T_{\text{delay}}=T_{\text{D1}}-T_{\text{S}}+T_{\text{D2}}-T_{\text{D1}}=T_{ \text{D2}}-T_{\text{S}}=T_{\text{SSt}}+T_{\text{trans}}+T_{\text{drv}}+T_{ \text{processing}} \tag{1}\]  
In the above equation, \(T_{\text{SSt}}=T_{\text{queue}}^{\text{S}}+T_{\text{block}}^{\text{S}}+T_{ \text{frame}}^{\text{S}}+T_{\text{queue}}^{\text{D}}+T_{\text{block}}^{ \text{D}}+T_{\text{frame}}^{\text{D}}\), \(T_{\text{trans}}=T_{\text{trans}}^{\text{S}\to D}+T_{\text{trans}}^{ \text{D}\to S}\), \(T_{\text{drv}}=T_{\text{drv}}^{\text{D}}+T_{\text{drv}}^{\text{S}}\), \(T_{\text{processing}}=T_{\text{processing}}^{\text{D}}+T_{\text{processing}}^{ \text{S}}\). The total delay in message transmission for \(N\) nodes of the system to complete a periodic communication is  
\[\sum_{i\in N_{\text{nodes}}}\sum_{j=1}^{M(i)}T_{\text{delay}}^{(i,j)} \tag{2}\]  
Figure 1: Modbus RTU, Modbus TCP, and Modbus UDP frame structure.  
Let \(N\) be the number of nodes, \(N_{node}\) the node set, and \(M(i)\) the message count per node. Compared to Modbus TCP, Modbus UDP omits the three-way handshake and four-way termination, reducing the total transmission delay \(T_{delay}^{cum}\) in periodic N-node communication. The elimination of redundant control frames increases throughput \(Q_{thr}\) and network efficiency \(P_{eff}\), while avoiding TCP state machine design accelerates node response and data processing.  
The above analysis indicates that Modbus UDP exhibits lower latency than Modbus TCP, thereby achieving enhanced real-time capabilities in the Modbus UDP protocol.
# Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization
## 2 Mathematical Model  
### Stress Velocity Formulation of 2D Elastic Wave Equation  
The stress velocity formulation of the elastodynamic equation, as given by [12] and substituted for two-dimensional in-plane wave motion (P-SV wavefield), can be written as in Equations (1)-(5).  
\[\rho\frac{\partial v_{x}}{\partial t}=\frac{\partial\sigma_{xx}}{\partial x}+ \frac{\partial\sigma_{xy}}{\partial y}+f_{x}, \tag{1}\]  
\[\rho\frac{\partial v_{y}}{\partial t}=\frac{\partial\sigma_{xy}}{\partial x}+ \frac{\partial\sigma_{yy}}{\partial y}+f_{y}, \tag{2}\]  
\[\frac{\partial\sigma_{xx}}{\partial t}=(\lambda+2\mu)\frac{\partial v_{x}}{ \partial x}+\lambda\frac{\partial v_{y}}{\partial y}+\frac{\partial\sigma_{ xx0}}{\partial t}, \tag{3}\]  
\[\frac{\partial\sigma_{yy}}{\partial t}=\lambda\frac{\partial v_{x}}{\partial x }+(\lambda+2\mu)\frac{\partial v_{y}}{\partial y}+\frac{\partial\sigma_{yy0}} {\partial t}, \tag{4}\]  
\[\frac{\partial\sigma_{xy}}{\partial t}=\frac{1}{2}\Big{(}\frac{\partial v_{x} }{\partial y}+\frac{\partial v_{y}}{\partial x}\Big{)}+\frac{\partial\sigma_{ xy0}}{\partial t} \tag{5}\]  
where \(x\) and \(y\) are two-dimensional spatial variables, \(t\) is time variable, \(v_{x}\) and \(v_{y}\) are particle velocities along \(x\) and \(y\) directions, \(\partial\sigma_{xx}\), \(\partial\sigma_{yy}\) and \(\partial\sigma_{xy}\) are principle and shear stress variables, \(\partial\sigma_{xx0}\), \(\partial\sigma_{yy0}\) and \(\partial\sigma_{xy0}\) are body stresses and, \(\frac{\partial}{\partial t}\), \(\frac{\partial}{\partial x}\) and \(\frac{\partial}{\partial y}\) are partial differential operators to the respective variables.
## 3 Proposed Methodology
### Estimation of Link Delay
#### 3.3.5 Predicting Exact One-Hop Channel Delay Using Machine Learning
Thus, there can be a large difference between the predicted delay and the practical one-hop delay that occurs in the network. However, in low-contention scenarios (\(\mathcal{C}<0.5\)), the error between the value predicted by the neural network and the real one-hop delay that occurs in the practical network can be expected to be lower.
## 4 Discussion  
This study aimed to examine the potential of deep learning as a precise COVID-19 diagnostic tool and for suggesting a patient-tailored therapeutic strategy. The results of this scientific investigation imply that these objectives have been achieved.  
The first step involved the collection and preparation of CT medical images from various patients with different conditions. A considerable number of diverse scans were obtained to ensure that the tested models would reach a high level of accuracy.  
Three different models based on convolutional neural network architectures were constructed and tested for diagnostic purposes. They provided satisfactory results, with accuracy, sensitivity, and specificity that were well above the 50% threshold. The overall performance of the models was compared, and the third custom-made model delivered the best outcomes. This model was then used in the development of a prototype window application.  
The next phase involved predicting the condition's severity and suggesting a therapeutic strategy. A U-Net-based model was trained on lung and infection masks, which enabled it to extract these masks from a CT slice and then calculate the percentage of pathologically altered tissue. This measure was then matched with the COVID-19 severity score, and a patient-tailored therapeutic approach was proposed accordingly. This functionality was also incorporated into the prototype window application.  
The study results provide a functioning deep learning-based approach that may prove advantageous in experimental medical and/or scientific settings. The quality of the results ensures reliability, and the approach constitutes a simple clinical decision-support tool.