esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, the optical transceiver design technology for building a next-generation communication infrastructure requiring large data transmission was proposed. The optical transceiver, in the proposed scheme, is designed with a QSFP28 form-factor type that can achieve twice the transmission efficiency per symbol using the PAM4 scheme. Moreover, it is compact and consumes less power. Optical transceivers using PAM4 require expensive photoreceptor parts for long-distance transmission, but the proposed scheme used low-cost PIN-PD to increase the economic feasibility and to optimize the transmitting optical components to meet the requirements of long-distance transmission. Hardware and firmware design schemes that satisfy the standards were proposed for the QSFP28 optical transceiver. Furthermore, both the receiver sensitivity and the error performance were experimentally measured and compared to existing solutions. The obtained results indicated that data can be transmitted successfully over a distance of up to 40 km and more. In addition, the test could produce results that meet the standard requirements for industrial use at high, low, and normal temperatures. This proves that the scheme is useful for manufacturing industrial optical transceivers.  
Future studies will allow for the data transfer rate to be extended to 200G/400G based on the proposed scheme, while long-distance transmission schemes using PAM4 signals will be used not only for QSFP28 but also for other form-factor types.  
Design and implementation, J.-W.K.; experiment, S.-H.K.; hardware, S.-M.S.; writing--review and editing, D.-S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program and Priority Research Centers through the National Research Foundation (NRF) funded by the Ministry of Education (2019R1I1A1A01063895, 2018R1A6A1A03024003) and the Grand Information Technology Research Center Program through the Institute of Information & Communications Technology and Planning & Evaluation (IITP) funded by the Ministry of Science and ICT (MSIT), Korea (IITP-2020-2020-0-01612).  
Not applicable.  
Data Availability Statement: Data sharing is being prepared.  
The author would like to thank the Co. Coset for the the data provided.  
The authors declare no conflict of interest.
## 3 Model Architecture
### Nonlinear Coupling and Dimensional Interactions  
The numerator of the RC formula, \(P(t)\cdot R^{1.2}(t)+A(t)\cdot L^{0.8}(t)\), incorporates nonlinear coupling mechanisms to reflect the synergistic interactions between defensive (\(P\), \(A\)) and evolutionary (\(R\), \(L\)) capabilities. The superlinear exponent of \(R^{1.2}\) captures the qualitative leap in Destruction Resistance when key thresholds are met. For example, in the P-R-A-L framework, indicators such as \"redundancy robustness\" and \"failure recovery capacity\" (aligned with Dynamic Heterogeneous Redundancy (DHR) principles) demonstrate that once a system achieves sufficient redundancy and fault tolerance, its ability to withstand attacks increases disproportionately, reflecting a significant boost in resilience.  
Conversely, the sublinear exponent of \(L^{0.8}\) models the diminishing marginal returns in Evolutionary Learning. Indicators in the L dimension, such as \"automation in repair processes\" and \"autonomous policy response capabilities\", show rapid improvements in early stages of learning (e.g., initial automation of incident response). However, as the system matures, further gains become incrementally harder to achieve due to complexity and resource constraints, a reality reflected in the sublinear growth.  
These nonlinear factors ensure that the model mirrors empirical observations of system behavior, balancing the impact of investments across dimensions and guiding strategic prioritization (e.g., emphasizing redundancy in \(R\) for immediate resilience gains versus long-term learning in \(L\)).
## 3 Air-to-Air Channel Models
### Theoretical Studies and Simulations for Channel Modeling
The results revealed that the channel's stationarity interval was drastically limited when the velocity escalated (from 0.4 s down to 0.05 s for a correlation level of 0.5), which was expected, because higher speeds entail rapid channel variation. Finally, Table 3 summarizes all the theoretical studies and simulations regarding the A2A channel.
# Sound Field Modeling Method and Key Imaging Technology of an Ultrasonic Phased Array: A Review
## 2 Modelling Method
### Ray-Tracing Method  
The propagation path of sound lines in the workpiece is related to the design of testing technology and the verification of testing ability in the detection process of ultrasonic arrays. The ray-tracing method calculates the propagation path and amplitude attenuation of sound lines in the medium to obtain the propagation state of sound lines in the medium [18]. This method is often used to simulate sound propagation in heterogeneous anisotropic media. The process can be divided into three steps: (1) Solving the equation of elastic wave motion in an anisotropic medium, obtaining the direction of energy velocity and particle displacement. (2) Walking a distance along the direction of energy velocity, the heterogeneous medium is divided into homogeneous regions and the appropriate boundary conditions are given. (3) Calculating the reflected and refracted sound lines on the boundary, and using the boundary conditions to calculate the amplitude of the reflected and refracted sound lines.  
The method moved from theory to research applications in 1985 when [PERSON] [19] first proposed a ray-tracing formulation applied to anisotropic nonhomogeneous media in the field. Subsequently, [PERSON] et al. applied [PERSON]'s Raytraim to the 3-D situation. In the 1990s, [PERSON] et al. [20] verified [PERSON]'s results by comparing them with the simulation results of the finite difference method. The above studies are based on the basic ray tracing pattern shown in Figure 3a [21]. The marching strategy can also be applied to achieve P2P ray-tracing of Figure 3b when combined with the binary searching algorithm. Therefore, P2M ray tracing based on the Dijkstra algorithm is introduced, as shown in Figure 3c. In 2012, [PERSON] et al. [22] presented an adapted 2-D ray-tracing model for evaluating ultrasonic wave fields quantitatively in inhomogeneous anisotropic materials. In 2015, [PERSON] et al. [23] proposed the fast ray-tracing algorithm to calculate the ultrasonic path through the anisotropic distribution of the predetermined weld seam calculation of the scattering response for a given defect using an effective FEM. In 2018, combining the minimizing and the ray-tracing strategies of marching, an efficient ray-tracing algorithm for ultrasonic array imaging was proposed; the proposed algorithm could realize good imaging performance for defect locating [21].  
Several of the above analytical methods can only be used for the simplest geometrical figures. The numerical method can solve the problem of finding a transducer transmitting signal from any shape defect of any shape part. The numerical method is a global discrete approximation method that can solve various complex FEM and FDM problems. Both of these numerical techniques are potential candidates for this task.
## 4 Numerical Results and Analysis
The motion trajectories are shown as blue and red lines in Figure 10a. The measurement equipment consisted of an airborne part and a ground part. The airborne part equipment consisted of a universal software-defined radio peripheral (USRP), an omni-directional antenna, a microcomputer, and a small power supply. The ground part equipment consisted of a USRP, an omni-directional antenna, a computer, and an outdoor power supply. Two USRPs and two omni-directional antennas were used for signal transmission and reception. A high-order pseudo-noise (PN) sequence was transmitted from the Tx end to the Rx end. Figure 10b shows the CDF of the RMS DS, with measurement data, simulation data, and reference model. The measurement result showed that our proposed model can mimic real multi-mobility multi-trajectory channel characteristics. Compared to the WINNER II suburban macro-cell [36] and the 3 rd Generation Partnership Project (3 GPP) [37] RMa, our model fits better to the actual measured data in the field.
## 3 Mathematical Modeling of ProposedDC Architecture
### Mathematical Modeling of Fuel Cell Power  
A different assumption [36] is made, which is described below:  
* Idealized modeling;
* Uniform circulated gases;
* Constant pressure in the flow channel;
* Cell parameters are represented together to form stack parameters;
* The output voltage of the single fuel cell can be represented as \[\begin{array}{l}PH_{2}=\frac{-1}{HR_{2}}\Big{[}PH_{2}+\frac{1}{KH_{2}}\big{(} q_{th}{}^{in}-2K_{r}i_{FC}\big{)}\Big{]}\\ PO_{2}=\frac{-1}{KO_{2}}\Big{[}PO_{2}+\frac{1}{KO_{2}}\big{(}q_{th}{}^{in}-2K_{V} i_{FC}\big{)}\Big{]}\\ PH_{2}O=\frac{-1}{HR_{2}O}\Big{[}PH_{2}O+\frac{1}{KH_{2}O}K_{r}i_{FC}\Big{]} \end{array}\] (25)  
Figure 5: Characteristic curve of wind system (wind speed vs. power).  
Here, \(KH_{2}\) = valve meter constant for hydrogen and \(KO_{2}\) = valve meter constant for oxygen (2.52 \(\times\) 10\({}^{-3}\) kmol/s atm). \(K_{r}\) = constant defined by the rate of reactant hydrogen and fuel cell current. The reactant utilization factor \(U\) is defined as follows:  
\[U_{F}=\frac{q{H_{2}}^{in}\ -\ q{H_{2}}^{out}}{q{H_{2}}^{in}} \tag{26}\]  
\[\begin{array}{l}V_{act}=[E_{1}+E_{2}T+E_{3}Tln(co_{2})+E_{4}ln(i_{FC})]\\ co_{2}=\frac{po_{2}}{5.08\times 10\epsilon\left(\frac{398}{7}\right)}\end{array} \tag{27}\]  
where, \(E_{1}\), \(E_{2}\), \(E_{3}\), \(E_{4}\) is the cell parameter coefficients, \(CO_{2}\) = concentration of oxygen. Figure 6 represents the equivalent circuit of the fuel cell. It consists of cell voltage, actual resistance, concentration resistance, and ohmic resistance [37; 38].  
\[V_{ohm}=i_{FC}(R_{m}+R_{ohm}) \tag{28}\]  
\[R_{m}=\frac{\rho_{m}l}{A} \tag{29}\]  
where, \(\rho_{m}\) = specific resistivity of the membrane for electron flow (\(\Omega\) cm). \(A\) = active cell area (cm\({}^{2}\)) and \(l\) = thickness of the membrane.  
The concentration loss is due to the reactive excess concentration near the catalyst surface.  
\[V_{Con}=\frac{-R_{con}T}{2F}\ln\left(1-\frac{j}{j_{\max}}\right) \tag{30}\]  
The fuel cell current can be determined as  
\[i=i_{o}A\left(e^{\frac{unf}{P_{fc}}}V_{act}-e^{\frac{(1-a)nF_{loc}}{RT\Gamma}}\right) \tag{31}\]  
\(I_{o}\) = exchange current density (A/m\({}^{2}\)), \(A\) = catalyst layer surface (m\({}^{2}\)), and \(i\) = fuel cell current. The Figure 7 shows the single fuel cell characteristics for stack current vs. cell voltage and power. The power of the fuel cell can be obtained from  
\[P_{fuel}=V_{out}i_{C}. \tag{32}\]  
Figure 6: Equivalent circuit of the fuel cell.
## 2 Materials and Methods
### Data Analysis  
To analyze the content validity and reliability of the internal consistency of the instrument, the statistical package SPSS for Windows in its version 21 (IBM Corp., Armonk, NY, USA) was used. For factorial analysis, the program SPSS was used in the exploratory analysis and the program Mplus in its version 8 [36] in the confirmatory analysis. For these analyses, the total sample was randomly divided into two different samples, as recommended by [37], one from 2330 participants for exploratory factor analysis and the other from 2329 participants for confirmatory factor analysis. In the exploratory factor analysis, the principal component analysis was chosen as the extraction method and Varimax was chosen as the rotation method. Likewise, in the confirmatory factor analysis, the estimation method used was the diagonally weighted least squares method (WLSMV). In relation to the values of asymmetry and kurtosis, these are less than 2 and 7, respectively, so following [37; 38], they are considered within normality.
# WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication
## 2 Lossless Watermarking Scheme
[18] adopted a mixed-domain approach, which makes the mechanism resistant to damage from different types of attacks. In addition to using different frequency domain techniques, [PERSON] et al. [19] and [PERSON] et al. [20] also believed that important features of images should be extracted from scattered locations to better resist clipping or tampering attacks in an area of an image. The above studies demonstrate that a good watermarking mechanism should satisfy security, invisibility, and robustness requirements. These characteristics can be satisfied using a lossless watermarking scheme. Robust watermarking mechanisms have higher NC and BER values; thus, during validation, the NC or BER is typically calculated after the watermark extraction to determine whether the image belongs to the corresponding copyright. However, although this is reasonable, it is not entirely objective, and it does not allow for the timely interpretation of highly distorted watermarks. Therefore, this paper presents a WMNet detection model with a lossless watermarking scheme. This model does not require a person to interpret image ownership. It can verify and determine directly whether the copyright of the watermark obtained from an image is correct.
## 3 Results and Discussion
### Influence of Discharge Area
#### 3.2.2 Peak Surface Temperature  
In Figure 5, the peak surface temperatures of large and small atomizers under different HRR conditions are shown. The peak surface temperature indicates the maximum temperature value in the temporal temperature variation measured near the fuel surface. Overall, the peak surface temperatures of HRR at 5.23 kW were higher than those of HRR at 1.17 kW under the only nitrogen discharge and water mist discharge conditions. At the only nitrogen discharge conditions, the increases in nitrogen flow rates reduced the peak surface temperatures. The differences between two HRR conditions appeared to increase with increases in nitrogen flow rates.  
With respect to the large atomizer, as shown in Figure 5a, under the water mist discharge conditions, the increases in the nitrogen flow rates led to decreases in the peak surface temperatures. At the given nitrogen flow rate condition, as the water flow rate increased up to a water flow rate of 85 g/min, the peak surface temperatures gradually decreased. This implies that the fuel surface was cooled by the discharged water mist. With respect to the water flow rate conditions of 160 and 240 g/min, the peak surface temperatures slightly increased under the low nitrogen flow rate condition (e.g., 20 L/min). This can be due to an increase in the droplet size of water mist. However, under the higher nitrogen flow rate conditions, the peak surface temperatures slightly decreased or remained almost constant with the water flow rate. Additionally, the dependency of peak surface temperature variations on water flow rates became weaker than that under the lower nitrogen flow rate conditions.  
In Figure 5b, most of the general trends of the peak surface temperatures for small atomizer are similar to those for large atomizer. The higher HRR case exhibited higher peak surface temperatures than the lower HRR case. The peak surface temperatures decreased with increases in nitrogen and water flow rates.  
In Figure 6, the relation between the extinguishing time and peak surface temperature for large and small atomizers, obtained from Figures 4 and 5, is shown. Increases in extinguishing times resulted in increasing the peak surface temperatures. In addition, the higher HRR case exhibited the shorter extinguishing times and higher peak surface temperatures, as compared with lower HRR case.  
Figure 5: Peak surface temperature for large and small atomizers under different HRR conditions: (**a**) large atomizer and (**b**) small atomizer.  
Figure 6: Extinguishing time vs. peak surface temperature for discharge area effect (note that the scales of \(x\)-axis in both figures are different): (**a**) large atomizer and (**b**) small atomizer.
## 2 Numerical Methods and Model Setup
### Governing Equations of the Continuous Phase  
The continuous phase (exhaust gas) is regarded as an ideal gas, which obeys the conservation of mass, momentum, and energy. The unsteady governing equations of the continuous phase are:  
\[\frac{\partial\rho_{g}}{\partial t}+\
abla\cdot(\rho_{g}\mathbf{u})=S_{m} \tag{1}\]  
\[\frac{\partial}{\partial t}(\rho_{g}\mathbf{u})+\
abla\cdot(\rho_{g}\mathbf{u }\mathbf{u})=-\
abla p+\
abla\cdot(\overset{=}{\tau})+\rho_{g}\mathbf{g}+ \mathbf{S_{f}} \tag{2}\]  
\[\frac{\partial}{\partial t}(\rho_{g}(e+\frac{u^{2}}{2}))+\
abla\cdot(\rho_{g} u(h+\frac{u^{2}}{2}))=\
abla\cdot(k_{e}\
abla T-\sum_{i}h_{i}\overset{\rightharpoonup}{j}_{i}+ \overset{=}{\overline{\tau}}_{e}\cdot\mathbf{u})+S_{h} \tag{3}\]  
with gas density \(\rho_{g}\); velocity vector \(\mathbf{u}\); mass source term \(S_{m}\); static pressure \(p\); stress tensor \(\overset{=}{\tau}\); gravitational acceleration \(\mathbf{g}\); momentum source term \(\mathbf{S_{f}}\); internal energy \(e\); velocity scalar \(u\); enthalpy \(h\); effective conductivity \(k_{e}\); diffusion flux for \(i\)th spiece \(\overset{\rightharpoonup}{j}_{i}\); heat source term \(S_{h}\).
## 4 Discussion
Assessment of static balance provides insights into the ability to regulate body sway, weight shifting, and postural control, all essential for functional recovery [32]. Patients with stroke often show increased postural sway due to muscle weakness, abnormal tone, and impaired movement patterns [40]. Static balance was evaluated using COP parameters--sway area, sway distance, and sway velocity--under eyes-open and eyes-closed conditions. The experimental group showed significant improvements in sway distance under both conditions, with greater improvements under both visual conditions, consistent with previous findings that trunk stabilization education on unstable surfaces reduces sway and improves trunk control [14]. Although both groups showed reduced sway velocity under eyes-open conditions, no significant between-group difference was observed, possibly due to the relatively small sway distances during sitting tasks. Under eyes-closed conditions, both groups exhibited reduced sway distance, with a significantly greater reduction in the experimental group. This may reflect the enhanced trunk control achieved through pre-training trunk stabilization, which could compensate for the lack of visual input and reduce reliance on compensatory strategies such as shoulder elevation or lateral trunk lean [37]. These results highlight the consistent effect of trunk stabilization across both visual conditions, facilitating weight shift toward the paretic side and improving postural symmetry [36]. The observed improvements in postural control and dynamic balance suggest that integrating trunk stabilization into conventional VR-based programs may enhance rehabilitation outcomes, particularly for subacute stroke patients with trunk instability or limited sitting tolerance. While these findings are encouraging, several limitations should be acknowledged. The relatively small sample size may limit the generalizability of the results, underscoring the need for future studies with larger, multicenter populations. Additionally, the short duration of the intervention may not fully reflect long-term postural adaptations, emphasizing the need for long-term follow-up to confirm sustained effects. As the intervention was applied only to subacute patients, its applicability to individuals in the chronic phase remains uncertain. Future studies comparing different recovery stages could help clarify when trunk stabilization is most beneficial. Furthermore, the lack of direct assessment of compensatory trunk strategies limits insight into how patients adjusted their movement patterns. Future studies incorporating biomechanical or neuromuscular data may better explain the mechanisms of improvement. Despite these limitations, this study offers preliminary evidence that trunk-specific stabilization training, when applied prior to VR-based balance rehabilitation, may serve as an effective strategy to enhance postural control and functional recovery in stroke patients.
# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
## 2 Methods
### Machine-Learning-Based Driving Simulation Scenarios  
To evaluate the rollover stability of heavy trucks, we compared three machine learning models in two driving scenarios. This study employed _TruckSim_ and _MATLAB Simulink_ to collect driving data for heavy trucks. The vehicle model used for data acquisition was based on a five-axle truck with the following specifications: overall length of 12,990 mm, overall height of 3995 mm, overall width of 2495 mm, vehicle weight of 12,800 kg, and a center of gravity height of 1000 mm. The vertical load data were extracted from the left and right tires of each axle, while a sensor model was placed at the vehicle's center of gravity to record the vehicle's trajectory coordinates. The driving scenarios were designed based on the double lane change (DLC) and sine with dwell (SWD) test methods.  
Figure 3 illustrates the simulation performed in _TruckSim_ following the DLC and SWD scenarios. The DLC scenario was implemented according to the ISO-3888-1 standard [17], using a test track similar to Figure 4.  
To compare rollover stability at different speeds, three velocity settings were used: a fixed speed of 40 km/h, a variable speed between 50 and 70 km/h, and a high-speed condition of 80.5 km/h. The SWD scenario was based on the \"0.7 Hz Sine Steering Test with Dwell\" method from the automotive stability control system test regulations [18]. The reference steering angle was obtained from the slowly increasing steer (SIS) test, and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vehicle Type** & **SSF** &
\begin{tabular}{c} **Test Evaluation** \\ **Method** \\ \end{tabular} & **ESC** \\ \hline \hline _540s_ & 0.6 & Circle & \\ _Arocs_ & 0.7 & DLC & \\ _NPT_ & 0.8 & SIS & On \\ _Prima_ & 0.9 & J-turn & Off \\ _Xcient_ & 1.0 & SWD & \\  & & Fishhook & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variables for dynamic safety test.  
the input steering model was constructed as shown in Figure 5 by applying 1.5 times the reference steering angle in a 0.7 Hz sine wave pattern. The driving speed for this test followed the regulation standard of 80 \(\pm\) 2 km/h, with the same three-speed conditions as the DLC test.
## 2 Theoretical Aspects  
### Workspace Analysis Using Computer-Aided Design Applied for Parallel Kinematic Machines  
Any additional study concerning the movement behavior of the robot regarding a desired job must first determine the workspace for any moving structure. Geometrical, numerical, and analytical methodologies may all be used to establish the workspace, and each method has its own set of benefits and drawbacks depending on the required workspace parameters [10].  
Each technique for determining the workspace may be achieved in a variety of methods, and depending on the implementation and accuracy aspects, some approaches are easier to achieve than others. The geometrical technique entails creating the workspace volume by intersecting each kinematic chain of the PKM, resulting in the workspace volume of the PKM's whole structure [11]. This method is simple to perform using CAD software and CAD tools, and it generates a model of the entire theoretical workspace [12; 13]. One disadvantage with using this approach in the case of PKM is that some construction factors, such as limit angles for revolute joints, can be difficult to execute, and in most PKM, the moving platform, which closes all the kinematic chains, is difficult to create independently from the kinematic chains [13; 14].