esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, the three-point bending experiment and the numerical simulation analysis of Al/CFRP fiber metal laminates were conducted, and the relevant results were obtained. The failure behavior and damage mechanism of CARALLs were investigated by utilizing the Linde and Hashin failure criterion, and the feasibility of the failure criterion was discussed. The preliminary conclusions were as follows:  
(1) The experimental results indicated that fiber fracture and matrix failure occurred in the CFRP layer above the neutral layer of the four structures of the laminates. Among the four stacking structures of laminates, the flexural modulus of FMLs of the patch-type was relatively higher because the CFRP layer directly contacted the loading head. Furthermore, the bending strength and modulus of laminate increase with the increase of fiber volume fraction, suggesting that the carbon fiber-reinforced hybrid layer played an important role in the bending property of laminates. The good consistency between the finite element simulation with the experimental results also demonstrated that the model is effective in predicting the performance of CARALLs.  
(2) The macroscopic performance of the specimens and the finite element simulation both revealed that the CFRP layer in the compression region played an essential role in the bending property of the laminate. The main failure mechanism of the laminates was fiber compression fracture and matrix cracking of the CFRP layer. The tensile strength of fibers was much greater than the compression strength. The Linde and Hashin models implemented with the UMAT accurately predicted the failure behavior of the CARALLs.  
(3) Some future work is still needed with the FE models involving failure criteria of hybrid materials being addressed. When analyzing the large deformations and damage evolution, implicit analysis is difficult to converge. More accurate material models in explicit analysis needs further exploring. Additionally, the effectiveness of the FE model proposed in this paper should be verified in future work.  
Methodology, J.X. and W.H.; software, S.G.; investigation, J.X. and S.G.; data curation, W.H. and S.G.; writing--original draft preparation, L.S. and S.G.; supervision, W.H.; project administration, L.S.; funding acquisition, L.S. and W.H. All authors have read and agreed to the published version of the manuscript.  
This work was sponsored by the Fundamental Research Funds for the Central Universities (DUT21)C28) and the National Natural Science Foundation of China (No U21A20165, 52175216) and the Research Funds of the State Key Laboratory of Structural Analysis for Industrial Equipment (S22308).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to on-going relevant study.  
The authors declare no conflict of interest.
# Trajectory Tracking of Autonomous Vehicle Using Clothoid Curve
###### Abstract  
This paper proposes a clothoid-curve-based trajectory tracking control method for autonomous vehicles to solve the problem of tracking errors caused by the discontinuous curvature of the control curve calculated by the pure pursuit tracking algorithm. Firstly, based on the Ackerman steering model, the motion model is constructed for vehicle trajectory tracking, Then, the position of the vehicle after the communication delay of the control system is predicted as the starting point of the clothoid control curve, and the optimization interval of the curve end point is determined. The clothoid control curves are calculated, and their parameters are verified by the vehicle motion and safety constraints, so as to obtain the optimal clothoid control curve satisfying the constraints. Finally, considering the servo system response delay time of the steering system, the steering angle target control value is obtained by previewing the curvature of the clothoid control curve. The field experiment is conducted on the test road, which consists of straight, right-angle turns and lane-change elements under three sets of speed limitations, and the test results show that the proposed clothoid-curve-based trajectory tracking control method greatly improved the tracking accuracy compared with the pure pursuit method; in particular, the yaw deviation is improved by more than 50%.  
trajectory tracking; clothoid control curve; curvature continuity +
Footnote †: journal: Applied  
0023
## 2 Model Construction for an SCCNMT
### Minimal Capacity Vectors and Demand Reliability  
Let the set of capacity vectors that satisfy the demand vector \(D=(d^{1},\,d^{2},\,\ldots,\,d^{k})\) within time constraints be denoted as \(G\). The demand reliability \(R_{D}\) is defined as the probability that an SCCNMT can satisfy the demand under the maximum time constraint. Thus, \(R_{D}\) can be represented using Formula (5) as follows,  
\[R_{D}=\sum\text{Pr}\{\,X|X\in\boldsymbol{\Omega}\}, \tag{5}\]  
where \(\text{Pr}[X]=\text{Pr}\{x_{1}\}\times\text{Pr}\{x_{2}\}\times\ldots\times \text{Pr}\{x_{q}\}\) with assumption 4.  
However, when the network size is very large, it is inefficient to calculate network reliability by enumerating all \(X\) in the set \(\boldsymbol{\Omega}\) and summing their probabilities. Instead, we can identify the minimal capacity vector from those capacity vectors \(X\) that satisfy the demand within the max time constraint and regard them as MCV. If there are \(\gamma\) MCVs, the formulation of demand reliability can be revised. In order to obtain the MCVs, we need to go through the following steps and present in Table 1 how to obtain the process of \(\boldsymbol{\Omega}_{\text{min}}\). The demand reliability can be evaluated using the recursive sum of disjoint products (RSDP) algorithm [17; 18]. Therefore, we can apply the RSDP algorithm to calculate demand reliability, as shown in Formula (6),  
\[R_{D}=\text{Pr}\bigg{\{}\mathop{\cup}\limits_{i=1}^{\gamma}\big{\{}X|X\geq X_{ i}\big{\}}\bigg{\}}. \tag{6}\]  
\begin{table}
\begin{tabular}{l l} \hline Line 1: & Suppose there are \(\gamma\) delivery vector \(X\) in \(\boldsymbol{\Omega}\) \\  & Set \(l=\boldsymbol{\Omega}_{\text{min}}=\varnothing\) (\(l\) is stack, which stores the MCV index) \\ Line 2: & For \(i=10\;\tau\) with \(i\
otin I\) \\ Line 3: & For \(j=i+1\) to \(\gamma\) with \(i\
otin I\) \\ Line 4: & If \(X_{i}\leq X_{j}\), \(X_{i}\) is not a MCV and belongs to \(\boldsymbol{\Omega}_{\text{min}}\). \\ Line 5: & Else \(X_{i}\) belongs to \(\boldsymbol{\Omega}_{\text{min}}\), \(l\gets I\cup\{i\}\) and go to Line 6. \\ Line 6: & End \\ Line 7: & \(\boldsymbol{\Omega}_{\text{min}}\leftarrow\boldsymbol{\Omega}_{\text{min}} \cup X_{i}\) \\ Line 8: & End \\ \hline \end{tabular}
\end{table}
Table 1: The process of obtaining the \(\boldsymbol{\Omega}_{\text{min}}\).  
Various methods can be used to calculate the probability of a union set, including the inclusion-exclusion principle [17], state space decomposition method [18], sum of disjoint products [19], and recursive sum of disjoint products [17]. In this study, we employ the RSDP to obtain the reliability, which represents the probability of meeting time constraints.
# A New DGNSS Positioning Infrastructure for Android Smartphones
## 3 DGNSS Positioning Infrastructure
### Assumptions on DGNSS Based on NMEA Messages  
The DGNSS based on NMEA messages works on some essential assumptions regarding the GNSS chipsets embedded in smartphones:  
* The GNSS chipsets apply the standard Klobuchar model to estimate the ionospheric delay.
* There are some other methods to solve for unknowns, such as the closed-form method and the Kalman filtering method. The proposed DGNSS assumes that the conventional iterative least squares method is applied.
* The satellite measurements are weighted based on the satellite elevation.  
The performance of the proposed DGNSS is thus dependent on the model and the algorithms used in the chipsets. In the proposed DGNSS, it is assumed that the most conventional model is applied. This also indicates that the direct cooperation with chipset manufacturers is required in the future to understand the method and the model used in the chipsets.
## 2 Materials and Methods
### Pre-Impact Fall Detection During E-Scooter Riding
#### 2.2.2 Supervised Model  
A sliding-window technique was applied to prepare input data for the supervised deep learning model, with a window size of 1 s and a 20% overlap [18]. All normal riding movements were labeled as non-fall. For fall movements, the period started from 0.2 s before ground impact was labeled as the fall onset. If a window contained more than 20% of fall onset data, the window was labeled as a fall. For model training, the data were split into a 2:1 ratio for training and testing, with 20% of the training data set aside for validation during the training process. The non-fall data were undersampled to match the number of fall windows due to the significant imbalance between the two. The number of fall windows used for training was about 400, while the number of non-fall windows was approximately 9000. To adjust the ratio between the two datasets, 320 non-fall windows were randomly selected. The slightly higher number of fall windows was intended to emphasize falls during training.  
The model was built based on the ResNet-18 architecture, a residual network that consists of 18 layers and leverages skip connections to overcome the vanishing gradient problem, allowing for more efficient training of deep networks. The ResNet model is typically used for image-related problems, but it has recently demonstrated strong performance in the time series domain as well [18]. The input data size for the model was determined to be 6 \(\times\) 60, based on the 6-axis inertial sensor data and the window size. This architecture is well suited for extracting complex features from data while maintaining a relatively lightweight model. For training and testing, the softmax function was used, and windows with a probability value of 0.65 or higher were classified as falls [17]. To prevent overfitting, a max-pooling layer was applied to the ResNet-18 model, and early  
Figure 3: Threshold-based pre-impact fall-detection algorithm flow chart.  
stopping was applied during training with a validation ratio of 20%, stopping the process if validation accuracy did not improve for 20 consecutive epochs. To enhance the algorithm's robustness, a fall was confirmed when two consecutive fall windows were detected. The last frame of the second fall window was determined as the fall detection frame, and the lead time was calculated as the difference between this frame and the impact frame. Accuracy, sensitivity, precision, and F1 score were calculated following the same approach as described in Equations (3)-(7).
## 2 System Working Principle
### Alignment Principle
In addition, the two-dimensional mirror has a large dynamic adjustment range, which can quickly execute the scan capture procedure, saving capture time compared with traditional alignment methods. Figure 3 shows the workflow of the airborne laser communication non-common-view axis alignment system. Figure 3a,b are the workflows of the ground tracking system and the airborne receiver system, respectively. The ground transmitter uses the camera to extract the image features of the UAV and drives the gimbal to deflect according to the change of the image feature information of the UAV, to keep the image of the UAV in the center of the camera's field of view. After alignment, the laser can cover the surface of the UAV. The alignment process of the airborne end is the process of grating scanning by using a two-dimensional mirror until a light spot appears on the surface of the four-quadrant detector, and adjusting the motor angle to control the outgoing beam to pass through the central through hole of the four-quadrant detector. Figure 2: Beam alignment schematic diagram. (**a**) Common-view axis beam alignment; (**b**) Non-common-view axis beam alignment.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 3 ConvLSTM-DSConvGRU Hybrid Prediction Model
### ConvLSTM  
ConvLSTM is a hybrid variant model based on the LSTM network that is used for spatiotemporal prediction prob-lems such as predicting rainfall intensity in local areas [27]. Compared with the LSTM neural network, ConvLSTM replaces the fully connected operations between the input of the network to state and state to state with convolution operations, allowing ConvLSTM not only to address time series modelling problems but  
Figure 6: Cross-sectional wind speed from CFD simulation results. (**a**) Y = wind speed at the 2800 m cross-section; (**b**) X = wind speed at the 11,000 m cross-section; (**c**) Z = wind speed at the 50 m cross-section; (**d**) Z = wind speed at the 200 m cross-section.  
also to introduce multiple layers of convolution to better capture local spatial features. The operation of the ConvLSTM model is illustrated in Equations (1)-(5):  
\[i_{t}=\sigma(W_{xi}*x_{t}+W_{hi}*h_{t-1}+W_{ci}\circ C_{t-1}+b_{i}) \tag{1}\]  
\[f_{t}=\sigma(W_{xf}*x_{t}+W_{hf}*h_{t-1}+W_{cf}\circ C_{t-1}+b_{f}) \tag{2}\]  
\[o_{t}=\sigma(W_{xo}*x_{t}+W_{ho}*h_{t-1}+W_{co}\circ C_{t}+b_{o}) \tag{3}\]  
\[C_{t}=f_{t}\circ C_{t-1}+i_{t}\circ\tanh(W_{xc}*x_{t}+W_{hc}*h_{t-1}+b_{c}) \tag{4}\]  
\[h_{t}=o_{t}\circ\tanh(C_{t}) \tag{5}\]  
where \(i_{t}\), \(f_{t}\), and \(o_{t}\) represent the input gate, forget gate, and output gate at time \(t\), while \(c_{t}\) and \(h_{t}\) are the unit state and hidden state at time \(t\), respectively; \(\sigma\) and \(\tanh\) denote, respectively, the Sigmoid activation function and hyperbolic tangent activation function; \(W\) and \(b\) are the trainable weight matrix and bias term, respectively; and \"\(\circ\)\" and \"\(\ast\)\" represent the Hadamard product operation and convolution operation, respectively.
###### Abstract  
This study explores the integration of a custom-designed pneumatic spring into a car-seat cushion and its interaction with a simplified human body model using the Finite Element Method (FEM). A 3D half-symmetry FEM framework, developed from experimental data, ensured computational efficiency and convergence. This research bridged experimental and numerical approaches by analyzing the contact pressure distributions between a seat cushion and a volunteer with representative biometric characteristics. The model incorporated two material groups: (1) human body components (bones and muscles) and (2) seat cushion materials (polyurethane foam, latex, and fabric tape). Mechanical properties were obtained from both the literature and experiments, and simulations were conducted using MSC.Marc software under realistic boundary and initial conditions. The simulation results exhibited strong agreement with experimental data, validating the model's reliability in predicting contact pressure distribution and optimizing seat cushion designs. Contrary to the conventional notion that uniformly distributed contact pressure inherently enhances comfort, this study emphasizes that the precise localization of pressure plays a crucial role in static and long-term seating ergonomics. Both experimental and simulation results demonstrated that modulating the pneumatic spring's internal pressure from 0 kPa to 25 kPa altered peak contact pressure by approximately 3.5 kPa (around 20%), significantly influencing pressure redistribution and mitigating high-pressure zones. By validating this FEM-based approach, this study reduces dependence on physical prototyping, lowering design costs, and accelerating the development of ergonomically optimized seating solutions. The findings contribute to a deeper understanding of human-seat interactions, offering a foundation for next-generation automotive seating innovations that enhance comfort, fatigue reduction, and adaptive pressure control.  
pneumatic spring; contact pressure distribution; finite element method (FEM); human-seat interaction; ergonomic automotive seating 2025
## 4 Results and Discussion  
Validation using the Cameleon-1 helmet was carried out through several experimental tests. Preliminary results have shown that implementing an ergonomically optimized  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Characteristics** & **Specs** \\ \hline Power & 3–6 V \\ Microcontroller & PIC32 MX250F128B \\ Analog Front End & ADS1299 \\ Accelerometer & Lis3 DH \\ Voltage Regulation & (3.3 V, +2.5 V, \(-\)2.5 V) \\ Board Dimensions & 2.41\({}^{\prime\prime}\)\(\times\) 2.41\({}^{\prime\prime}\) \\ Input Voltage Range & +2.5 to \(-\)2.5 V \\ Signal–Noise Ratio & 121 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features of the openBCI cyton card.  
design increases the reliability of the acquired signals, especially in prolonged monitoring applications.  
This study analyzes cognitive activity using F3 and F4 electrodes, which are widely used in neurophysiological research for their effectiveness in EEG signal acquisition and the assessment of executive functions, attention, and cognitive processing.  
To validate the helmet's effectiveness, brain oscillations in the delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), and gamma (>30 Hz) bands were analyzed, as they serve as biomarkers for various cognitive and physiological states. Specifically, the study examined conditions such as the following:  
* Insomnia: increased beta and gamma activity during wakefulness, indicating cortical hyperactivation and difficulties in relaxation regulation.
* Stress: higher beta and gamma activity, which is associated with increased cortical excitability.
* Fatigue: increased theta and reduced beta activity, indicating cognitive slowing.
* High cognitive workload: increased theta and beta activity in prefrontal regions, reflecting sustained cognitive effort.
* High-performance athletes: higher alpha activity, which is linked to attentional regulation and cognitive efficiency.
* Healthy individuals: balanced spectral distribution.  
Additionally, tests were conducted in relaxation and active states, where variations in alpha activity served as a reliable indicator. The helmet's stable and precise electrode placement allowed for more reliable recordings, enhancing its applicability in neurophysiological and clinical studies.  
Table 2 presents the different EEG frequency bands and their association with various states of consciousness and neurophysiological activity in the subject.
## 4 Enabling Multi-RAT Indoor Handover
### Determining mmWave LoS  
Here, we describe the method [47] of building the LoS map of the mmWave AP within the considered environment. The main purpose of this method is to construct a 3D model of a particular environment in order to predict the LoS state w.r.t. the mmWave AP at any specific locations. In particular, the necessary steps are given as follows.  
1. Building 3D model. The first step is to obtain a considered environment in the 3D modeling software that provides sufficient details to reflect all the objects that obstruct the LoS path between the objects of interest (e.g., the mmWave AP and the robot).
2. Identifying 2D plane. In the 3D model, we add a plane on the height corresponding to the device antenna. The surface of the plane in the 3D environment represents the set of all possible points where the device antenna can be situated. For further image processing simplification, the plane is uniformly colored, while the choice of its material results in the absence of shadows and reflections (illustrated in Figure 3a).
3. AP point of view (PoV) LoS map. A 2D render from the AP's point of view is used to produce a \"warped\" map (shown as Figure 3a). The viewing angles are set to the antenna parameters of the AP.
4. Affine transform. The final step is to perform a geometric transformation to the projected PoV map in order to eliminate the z-coordinate as well as restore the proportions such that the orthogonal coordinate system (top view) of the LoS map of the area is obtained. In particular, the transform is carried out by applying three anchor points, which have been marked with the red cross shown in Figure 3b. It is important to note that the red line shown therein is employed as the ground truth of the robot trajectory in the simulation campaign (see Section 5.1), which is different from the ground truth of the robot trajectory in the experiments that is not measured and therefore remains unknown.  
The red line plotted in Figure 3b is applied as the robot trajectory for simulation-based study on the RSSI, AoA and proposed positioning algorithm. The information provided by the LoS map (such as the LoS-NLoS border) is first integrated in the robot, and then utilized to decide whether the robot resides in the LoS region w.r.t. the mmWave AP for the experimental evaluation of the proposed handover scheme, which will be discussed in Section 4.3.
# Assessment of Tire Features for Modeling Vehicle Stability in Case of Vertical Road Excitation
## 2 Investigation the Tire-Road Interaction
### Investigation of Tread Deformation  
Tread deformations were investigated using several models: porous composite and rubber prism models. The research studies analyzing the pneumatic tires specify fairly wide limits of the modulus of elasticity of rubber \(E_{r}=2\dots\) 20 MPa. In the study [36], a simplified model of a passenger car tire was used, \(E_{r}=18\) MPa, in order to obtain adequate deformation characteristics of the whole tire. In order to specify the initial characteristics, an experiment was performed to determine the 313 mm wide section of the tread band of the truck tire corresponding to 12.00R20 tire treads. The height of the tread part with a pattern was \(\delta_{1a}=17.8\) mm, without pattern \(\delta_{1b}=3.9\) mm. At compression, the tread was under the action of the load corresponding to the nominal radial load \(R_{znom}\) of the 12.00R20 type tires in terms of the pressure. An experimental setup for monotonous tension compression tests consisted of a 50 kN testing machine Instron (Norwood, MA, USA) 8801 series and an electronic device that was designed to record the stress-strain curves. The mechanical characteristics were measured with an error not exceeding \(\pm 1\%\) of the deformation scale. The data collection was performed by using the Bluehill Universal Software. Compression tests were performed in accordance with ASTM D575-91: Standard Test Methods for Rubber Properties in Compression. The obtained load-deformation dependence (Figure 6) is very close to the linear (correlation coefficient \(r_{xy}=0.947\)), proving the applicability of the description of the linear elastic material to the study of tread deformations. The obtained modulus of tread elasticity  
Figure 5: Layered composite element: 1-the direction along the fiber; 2-the direction transverse to the fiber. determined on the basis of the experimental results was 7.84 MPa, taking into consideration the tread part with a pattern according to the filling factor, \(E_{r}=6.16\) MPa, which was much lower than in the study [36].