esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Hardware and Software  
Since the research focused on the steering of the front wheel, the rear wheel was mechanically fixed by mechanical self-locking to eliminate the influence of the free shaking of the rear wheel on the front steering detection. Steering angle detection for the front wheels was carried out using a magnetic sensitive angle sensor (GT-B). Attitude data for the whole machine were collected with the vehicle unit as the core (Figure 2).  
An STM32F103 RC was adopted as the core processor of the onboard unit, and real-time, high-speed data exchange was achieved with an electronic control unit (ECU) through the CAN interface, in order to obtain real-time data from the steering angle sensor of the ECU unit. A remote controller was used for remote operation of the chassis, a data acquisition board performed serial port expansion to collect the nine-axis attitude data of the body, and the data were analyzed using software on a PC.  
The attitude sensor included Witer's intelligent WT901C nine-axis attitude sensor module. The acceleration accuracy of the sensor was 0.01 m/s\({}^{2}\) and the angular velocity accuracy was 0.05\({}^{\circ}\)/s. The differential BeiDou positioning system of UniStrong was used to calibrate the movement trajectory of the center position of the rear axle of the chassis.  
Figure 1: Four-wheel independent-steering paddy field machinery.  
Figure 2: Onboard unit.  
The base station model was BNBASE-010C, the mobile station model was E2020C5, and the positioning data update rate was 10 Hz, and data analysis was based on the MATLAB 2019A(Commercial mathematical software produced by MathWorks, USA) environment.  
Acquisition and Evaluation of Effective Input Factors for a BP Neural Network Based on Sensor Feature Extraction  
During the working process of the steering wheel, there are high-frequency disturbances and spikes in the steering angle data. If all of the signal input sources are used as input for BP neural network prediction, the training complexity will inevitably increase. At the same time, the input factors of the BP neural network not only affect the output, but also the operational efficiency of the system. The MIV algorithm is typically used to analyze the correlation between each input factor and the output. However, MIV is related to the number of iterations and other variables, meaning that there is generally some uncertainty. In this study, by extracting the sensor characteristic parameters, and by analyzing the signal characteristics, the parameters with consistent spectra could be selected as effective factor input criteria. Through comparative experiments, the validity of the above principles was verified, effective input factors were obtained, and actual data were tested to evaluate the validity of the prediction.
## 3 Experimental Results and Discussion
### Effect of Different Laser Pulse Widths on EMPs  
As explained in Section 3.1, femtosecond laser filament interaction with solid targets produced intense EMPs. Furthermore, we examined the effect of different laser pulse widths on the generation of EMPs, while keeping other experimental conditions unchanged (\(E\) = 485 mJ, \(\theta\) = 0\({}^{\circ}\), and \(d\) = 65 cm). We adjusted the laser pulse width to 30, 156, 195, 231, and 300 fs, respectively, and the experimental results showed that the power of the EMPs weakly decreased with increasing laser pulse width, following an approximately linear trend, as shown in Figure 5a. This can be interpreted as follows: the main EMP generation mechanism is believed to be the laser-induced ponderomotive force electromagnetic potential driving the free electrons away from the laser focus in the radial and axial directions [18], and the intensity of the EMPs is mainly determined by the balance between the production rate of the induced stripped free electrons and the annihilation rate of such free electrons. At the same energy, a shorter laser pulse will lead to a higher production rate of free electrons, while the annihilation rate of such electrons in the same experimental setup and experimental space remains constant; as a result, the EMPs will decrease as the laser pulse width is increased at the same energy. In this experiment, the frequencies of EMPs generated at different laser pulse widths were mainly concentrated at approximately 0.19 GHz, and there were also significant EMPs generated at approximately 0.28 GHz, in the 0.3-0.4 GHz frequency band, and at approximately 0.48 GHz (Figure 5b); and the EMP spectrum did not change significantly with the femtosecond laser pulse width. The main reason for the latter is that the main factors affecting the EMPs produced by the interaction between femtosecond laser filament and the target are hot electron emission and electron reflux.  
Figure 4: (**a**) Measured angular distributions of EMPs in the detection plane (\(E\) = 100 mJ, \(t\) = 30 fs, \(d\) = 20 cm, and \(\theta\) ranging from \(-\)120\({}^{\circ}\) to 110\({}^{\circ}\)). (**b**) Measured angular distributions of EMPs in the detection plane (\(E\) = 300 mJ, \(t\) = 30 fs, \(d\) = 30 cm, and \(\theta\) ranging from \(-\)120\({}^{\circ}\) to 0\({}^{\circ}\)). (**c**) Frequency-domain signals for different angles (\(E\) = 300 mJ, the \(t\) = 30 fs, and \(d\) = 30 cm).
## 1 Introduction
We perform a case study to analyze and evaluate the performance of the DSP-based approach, which applies the DSP algorithm to HSR networks. The motivation of this case study is to demonstrate that the DSP-based approach not only provides fault-tolerant communication, but also improves the network traffic performance for wired and wireless networks. In the case study, the DSP-based approach establishes dual paths between source and destination QuadBox pairs in an HSR network. The DSP-based approach then uses the dual paths to forward unicast frames from the source to the destination, instead of doubling and flooding the frames over the whole network, as is done using the standard HSR protocol. Therefore, the DSP approach not only provides seamless redundancy, but also significantly reduces redundant unicast traffic in HSR networks, thus improving the network performance. The remainder of this paper is organized as follows. We describe related works in Section 2. In Section 3, we describe the DSP algorithm in more detail. Then, in Section 4, we introduce the case study, in which the DSP algorithm is applied to HSR networks to provide fault-tolerant communication as well as reducing redundant unicast traffic in the networks. We analyze and evaluate the performance of the proposed DSP-based approach in Section 5, while in Section 6, we describe several simulationsand their results to evaluate and validate the performance analysis. Finally, we conclude the paper in Section 7.
## Formulation of the Rogue Wave Modes  
Under the transformations, \(u=\rho_{1}\exp(-i\omega_{1}z)\frac{\delta_{1}}{f}\), \(v=\rho_{2}\exp(-i\omega_{2}z)\frac{\delta_{2}}{f}\), \(w=\rho_{3}\exp(-i\omega_{3}z)\frac{\delta_{3}}{f}\), Equation (1) can be rewritten in terms of the Hirota bilinear operator [37] as  
\[\Big{[}iD_{z}+i\delta D_{t}+D_{t}^{2}\Big{]}g_{1}\cdot f=0,\]  
\[\Big{[}iD_{z}-i\delta D_{t}+D_{t}^{2}\Big{]}g_{2}\cdot f=0,\]  
\[\Big{(}iD_{z}+D_{t}^{2}\Big{)}g_{3}\cdot f=0,\]  
\[\Big{(}D_{t}^{2}-C\Big{)}f\cdot f=-\sigma\Big{(}\rho_{1}^{2}|g_{1}|^{2}+\rho_ {2}^{2}|g_{2}|^{2}+\rho_{3}^{2}|g_{3}|^{2}\Big{)},\]  
where \(\omega_{1}=\omega_{2}=\omega_{3}=C\), and \(C=\sigma\big{(}\rho_{1}^{2}+\rho_{2}^{2}+\rho_{3}^{2}\big{)}\).  
The methodology in deriving the rogue wave solution is similar to that used in our earlier works [31] and thus the details are omitted here. The amplitude of the plane wave background, \(\varrho\), is taken to be identical for all three waveguides. Basically a breather is first derived using a two-soliton expression with complex conjugate wavenumbers. By taking the small frequency limit of the breather, the rogue wave mode is given by  
\[\begin{split} u&=\rho\exp\bigl{(}-3i\sigma\rho^{2 }z\bigr{)}\left\{1-4\frac{1+i(b-\delta)t+i\bigl{(}a^{2}-b^{2}+b\delta\bigr{)}z }{\Bigl{[}a^{2}+\bigl{(}b-\delta\bigr{)}^{2}\Bigr{]}\,\left[\bigl{(}t-bz\bigr{)} ^{2}+a^{2}z^{2}+\frac{1}{a^{2}}\right]}\right\},\\ v&=\rho\exp\bigl{(}-3i\sigma\rho^{2}z\bigr{)} \left\{1-4\frac{1+i(b+\delta)t+i\bigl{(}a^{2}-b^{2}-b\delta\bigr{)}z}{\Bigl{[} a^{2}+\bigl{(}b+\delta\bigr{)}^{2}\Bigr{]}\,\left[\bigl{(}t-bz\bigr{)}^{2}+a^{2}z^{2}+ \frac{1}{a^{2}}\right]}\right\},\\ w&=\rho\exp\bigl{(}-3i\sigma\rho^{2}z\bigr{)} \left\{1-4\frac{1+ibt+i\bigl{(}a^{2}-b^{2}\bigr{)}\,z}{\bigl{(}a^{2}+b^{2} \bigr{)}\Bigl{[}\bigl{(}t-bz\bigr{)}^{2}+a^{2}z^{2}+\frac{1}{a^{2}}\Bigr{]}} \right\},\end{split} \tag{2}\]  
where \(a\) and \(b\) are the real part and imaginary part of \(\Omega_{0}\): \(\Omega_{0}=a+ib\). The parameter \(\Omega_{0}\) is the leading order term in the asymptotic expansion of the wavenumber, which satisfies the dispersion relation,  
\[\Omega_{0}^{6}+2\Bigl{(}\delta^{2}+3\sigma\rho^{2}\Bigr{)}\Omega_{0}^{4}+\delta ^{4}\Omega_{0}^{2}+2\delta^{4}\sigma\rho^{2}=0. \tag{3}\]  
This cubic polynomial in \(\Omega_{0}^{2}\) will dictate the dynamics and profiles of the rogue waves, to be highlighted in the following section.
## 2 Non-Linear Finite Element Analysis
### Finite Element Modeling  
#### 2.1.1 Geometry  
Pipe rack framings are usually symmetric with uniformly distributed loadings. The braces support any lateral loadings in the longitudinal direction and restrain lateral movements whereby relatively small deflections will be experienced in this direction. This is a reasonable assumption to simplify the model by considering only a single-bay two-storey moment frame in the analysis.  
To provide sufficient space for an access walkway and escape route, a minimum clear space of 1.0m width and 2.1 m height must be included within the structure [22]. Therefore, a column distance of 4 m that represents a typical offshore steel pipe racks framing configuration is considered sufficient. Regarding the requirement for vertical space between beams, the first author found no restrictions as long as the space for pipelines is sufficient in accordance with the piping engineers design, and the structural integrity can fulfil the design criteria. Therefore, a single-bay two-storey frame [15] was adopted and modified. The selected frame configuration is shown in Figure 3. This selected configuration facilitates the final model development after conducting the validation.  
The launch length, a, and angle, \(\theta\), were dimensioned in accordance with the provision given in [PERSON] et al. [13]. The haunch is usually fabricated by cutting a structural beam sections or plates with standard thickness. In this research, plates with a standard thickness of 10 mm were considered for the haunch and stiffener plates. The thickness was selected because it is the maximum standard plate thickness that is less than the beam and column web/flange thicknesses. The haunch flanges are attached to the beam and column flanges by groove welding, and the webs are then fillet welded to the beam and column flanges as described in Figure 4. The haunches are placed only on the lower side of the beams to avoid obstruction for the pipelines on the top of the beams.  
Varying details of beam-column connections were considered in this study. In addition to the joint type based on the configuration shown in Figure 4, a connection without reinforcement and a connection with a haunch slope angle of 45\({}^{\circ}\) were also considered in the analyses, as presented in Figure 5. The latter is a typical connection that is usually used in many offshore frame structures. The FE model of the steel frame was developed using ABAQUS/Explicit. The model geometry was  
Figure 4: The welded triangular haunch configuration.  
Figure 3: The steel frame model description.  
modelled in accordance with the single-bay two-storey frame configuration, as depicted in Figure 3. The cross section of W8 \(\times\) 48 was modelled and assigned to both the beams and columns.
# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 6 Conclusions  
This paper proposes a GPU-accelerated CNN inference architecture for DQN-based routing in dynamic LEO satellite networks. The proposed architecture combines advanced routing algorithms capable of adapting to dynamic environments, such as link disconnections, with GPU-based acceleration techniques to effectively address computational complexity. Experimental results demonstrate that the proposed Dueling DQN-based routing algorithm achieves high exploration performance in dynamic environments with link disconnections. Furthermore, it significantly outperforms existing methodologies, proving its suitability for real-time inference in LEO satellite environments. These results confirm that the proposed architecture meets real-time exploration requirements by enabling faster routing in dynamic environments, including link disconnections. This leads to substantial improvements in onboard system efficiency and performance by reducing energy consumption, enhancing reliability, and providing additional computational capacity. Such technical advantages not only support stable and efficient communication services in LEO satellite networks but also lay a critical foundation for developing real-time routing and optimization solutions and advancing technologies for future onboard applications.  
C.Y., D.K. and H.L.: methodology, software, validation, and writing--original draft preparation. M.H.: conceptualization, formal analysis, and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Agency for Defense Development, funded by the Korean government (Defense Acquisition Program Administration) (UI220033 VD).  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 2 Literature Review
In this study, it is shown that RISs can change user channel gains, therefore the SIC order in NOMA systems can be changed. A three-node cooperative relaying (CRS) system based on spatial modulation (SM)-aided NOMA (CRS-SM-NOMA) system is proposed in [23], where messages for two users are transmitted by two different information-bearing units of SM. In [24], RIS-based two user DL NOMA system is implemented for delay sensitive scenario and its effective capacity is estimated. Combining code-domain NOMA and RIS to implement massive MIMO in a clustered deployment is examined in [25]. By using code-domain NOMA, cluster-wide multi-user transmission is supported. The physical layer security of RIS-aided NOMA system is investigated in [26]. Moreover, a scheme for optimally allocating the power for jamming and transmitting signals is proposed. The following research gaps are identified in the existing literature:  
* The majority of the classic works examined the FNOMA system. * ONOMA, which is anticipated to outperform FNOMA in terms of outage performance for \(SU\), has received very little research attention. * In the majority of works, intelligent transmission schemes are taken into consideration, and it is believed that RIS has perfect channel knowledge for pre- and post-phase compensation. However, because of the increased mobility of vehicles, perfect channel knowledge may not be available in applications like vehicular ad hoc networks. * As far as we are aware, there is no literature that has been proposed for the blind RIS-aided ONOMA system. * The closed-form outage probability and sum capacity expressions for blind RIS-ONOMA are not found in any conventional literature. * The computationally efficient strategy for optimizing the power fraction allocation to maximize the sum capacity is not discussed. * The majority of classic study treats RIS as SR and deploys it in an environment far from the BS and receiver. Longer distances may result in weaker signals being received by RIS, which limits the array and diversity gain benefits. There are very few works that consider both SR and AP scenarios. NOMA may not perform always better than OMA. When the channel gains of different users are same, the conventional OMA is the better option than NOMA. Hence in this work, we tried to incorporate RIS-assisted communication for NOMA to improve sum capacity, throughput, outage, and probability of error performances. In this work, we tried to show that even blind RIS without phase compensation improves sum capacity,throughput, outage, and probability of error performance. The significant contributions of this work are as follows:  
* A novel blind-RIS-assisted ONOMA system model is proposed. * The closed-form outage probability expressions have been derived for SR and AP scenarios of WU and SU. * In order to maximize the DL sum capacity, the expressions for optimal power fraction allocation are derived. * Probability of outage expressions for WU and SU are validated with Monte-Carlo simulations.
## 3 Analysis of Shield Tunneling Parameters
### Unearthed Amount  
The correlation between the unearthed amount and the shield tunnel ring number is illustrated in Figure 9. Table 5 presents the statistical characteristics of the unearthed amount across three distinct stratum conditions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stratum Serial** & **Mean** & **Standard** & **Median** & **Minimum** & **Maximum** & **Skewness** \\
**Number** & **Value** & **Deviation** & **Median** & **Value** & **Value** & **Value** & **Skewness** \\ \hline I & 18,641.8 & 1810.9 & 18,475 & 15,384 & 25,872 & 1.784 \\ II & 19,718.9 & 2088.8 & 19,404 & 15,504 & 32,952 & 2.097 \\ III & 19,429.7 & 2376.3 & 19,152 & 14,784 & 32,475 & 1.871 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical characteristics of different strata with the grouting amount.  
Figure 8: The curve of the grouting amount changing with the ring number.  
Figure 7: Comparison of the mean values of the slurry pressure and slurry pressure.  
Based on the analysis of Figure 9 and Table 5, it is evident that shield tunneling exhibits the highest unearthed amount in stratum I, controlled at approximately 4.157 t/min. This is mainly because stratum I has a shorter mileage, resulting in relatively concentrated statistical data. There is a declining trend in the unearthed amount in stratum II, with most of the unearthed amount concentrated between 0 and 4 t/min, representing a 57.6% decrease compared to stratum I. This decline can be attributed to the stratum's proximity to a residential area. Additionally, as the excavation approaches stratum III, there is a rapid decrease, reaching a minimum of \(-4.811\) t/min. In stratum III, the unearthed amount fluctuates significantly with the ring number, primarily concentrating between \(-6\) and \(-1\) t/min. The average unearthed amount is \(-2.631\) t/min. Due to the stratum's proximity to a residential area and the susceptibility of the tunnel face to instability, the amount of mud sent exceeds the amount discharged, resulting in a negative unearthed amount. This strategy is employed to maintain the tunnel face stability and prevent ground collapse.
# Capacity and Delay Analysis for Large Social-Aware Mobile Ad Hoc Wireless Networks
## 3 Preliminaries  
In this section, we want to demonstrate some models and define the problem to be solved. Some parameters used in following analysis are summarized in Table 1.  
There are \(n\) mobile nodes scattering randomly over an unit square region \(\mathcal{O}\), so each node is independently and uniformly distributed in size of region. We do not focus on edge effect, which is ignored in this paper. As shown in Figure 1, the network area is divided into square cells to avoid interference. We evenly divide the unit region of \(n\) nodes into cells with an area \(b(n)\). Let \(b(n)=\Theta(\log n/n)\) to ensure connectivity in the network.
## 3 Results and Discussions  
### Effect of Nanoparticles on Arterberg Limits  
Table 1 depicts the influence of nanoparticles on the Arterberg limits of the residual soil. Results showed that the liquid limit and plastic limit of residual soils treated with two different nanoparticles decreased; thus, the plasticity index, which is the difference between the liquid limit and plastic limit, also decreased. The changes in the Arterberg limits with the addition of nanoparticles can be related to the diffuse double layer (DDL) theory. After mixing the residual soils with the solutions containing the nanoparticles precipitates, the divalent calcium and magnesium cations were attached to the negatively charged clay surface and displaced other monovalent ions such as the hydrogen ions from the water molecules. In addition, the presence of divalent calcium and magnesium cations increases the electrolyte concentration of pore fluid, leading to a reduction in the DDL thickness. As a result, the Arterberg limits of treated soils decreased due to the reduction in DDL thickness. The reduction in Arterberg limits of treated soils led to the transition of soil plasticity classification from high plasticity clay to intermediate plasticity clay. This transition indicates an improvement in the workability of soil, making the soil easier to compact [12].
# Assessment of Dental Arch Reproduction Quality by Using Traditional and Digital Methods
## 2 Materials and Methods
### Digital Impressions Using Intraoral Scanner  
First, ten impressions of dental arch standard model (phantom) were obtained by digital method using the 3 Shape Trios 3 intraoral scanner in the portable version Pod powered by software version 1.3.4.5. The cleanliness of the optical elements was checked and the scanner calibrated according to the manufacturer's recommendations before scanning began. Models based on digital impressions were printed by a 3D printer (Prodways ProMaker L7000 D) used in dentistry as a standard. The manufacturer claims 3D-printing accuracy at the level of 0.04 mm.
## 2 Background and Related Work
### Background
#### 2.1.1 Cybersecurity Vulnerabilities in Autonomous and Connected Vehicular Systems
Physical manipulation can lead to disruptive attacks, for instance, by placing an electromagnetic actuator to interfere with wheel speed sensors, touching upon vulnerabilities related to physical access to components like ECUs. Replay attacks, which involve intercepting and retransmitting or delaying valid data, are particularly pertinent to theController Area Network (CAN) protocol accessible via OBD ports, given its lack of encryption and digital signatures. Relay attacks, where signals are intercepted and forwarded to a remote receiver, highlight broader communication vulnerabilities in connected car systems. The passive monitoring of sensor transmissions constitutes an eavesdropping attack, posing a significant threat to location privacy. Sybil attacks exploit network layer vulnerabilities by creating multiple fake identities to manipulate the network. Furthermore, specific sensor weaknesses are targeted by blind spot exploitation attacks, which leverage objects of minimal thickness to avoid detection in blind spot regions, and sensor interference attacks, where ultrasonic sensors are positioned to cause signal disruption. Acoustic cancellation attacks transmit inverted-phase signals to neutralize legitimate sensor signals, further emphasizing the susceptibility of sensory systems. Impersonation attacks, by mimicking identities or communication patterns, exploit general trust and identity management issues. Finally, falsified-information attacks involve spreading misleading data to manipulate sensors, while cloaking attacks modify attack signatures to evade detection, underscoring the need for robust cybersecurity measures across all vehicular system layers [24; 25].