esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Experimental Settings
#### 4.1.2 Baselines  
FedDeep for multi-urban \(PM_{2.5}\) forecasting was contrasted with other state-of-the-art baselines, as follows:  
* HA [20]: The average historical \(PM_{2.5}\) sequence was adopted to forecast future multi-urban \(PM_{2.5}\) concentrations.
* SVR [21]: A support vector with RBF Kernel was designed for multi-step \(PM_{2.5}\) forecasting.
* LSTM [23]: This is a variant recurrent neural network (RNN) used to mine dynamic temporal dependencies in \(PM_{2.5}\) data and produce predictive sequences.
* CNN-LSTM [24]: This model integrates CNN and LSTM to capture the spatio-temporal correlations in \(PM_{2.5}\) data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Urban Region**} & **The Number** & **The Variables of** & \multirow{2}{*}{**The Variables of**} \\  & **of Sites** & & **Air Pollution** \\ \hline Nanjing & 13 & & \\ Suzhou & 24 & & \\ Wuxi & 16 & & \\ Changzhou & 13 & & \\ Zhenjiang & 8 & & \\ Nantong & 9 & & \\ Taizhou & 7 & \(PM_{2.5}\) & weather conditions, temperature, and wind speed \\ Yangzhou & 6 & & \\ Yancheng & 7 & & \\ Huaian & 6 & & \\ Suqian & 5 & & \\ Xuzhou & 10 & & \\ Lian yungang & 9 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed descriptions about the distribution of the air quality monitoring sites in 13 prefecture-level urban regions, Jiangsu province, and the types of air pollution and external factors.  
* GAT-LSTM [25]: This model mines spatial dependencies from a non-Euclidean spatial perspective and then applies LSTM to model temporal dependencies.
# Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications
## 4 Problem Formulation  
The main objective is to maximize the sum data rate of all users while achieving minimum device delay through selecting the assignment index \(b_{k,i}\)  at every time slot. Hence, the resource allocation problem is formulated with data rate maximization objective function as follows:  
\[\underset{\{b_{k,i}(t)\}}{\text{Max}}\sum_{k=1}^{K}\sum_{i=1}^{N}\text{R}_{k,i} (t) \tag{17}\]  
\[s.t.\ C1:\sum_{k=1}^{K}b_{k,i}(t)R_{k,i}(t)\ \geq\ R_{min,i} \tag{18}\]  
\[C2:\sum_{k=1}^{K}b_{k,i}(t)p_{k,i}(t)\ \leq\ P_{max} \tag{19}\]  
\[C3:\ b_{k,i}(t)\ \in\{0,1\} \tag{20}\]  
\[C4:\ \sum_{k=1}^{K}b_{k,i}(t)p_{k,i}(t)\ \geq\ 0 \tag{21}\]  
\[C5:\sum_{i=1}^{N}b_{k,i}(t)\ \leq\ L \tag{22}\]  
\[C6:\ d_{avg,i}<D_{i} \tag{23}\]  
\[C7:\ h_{min}<h_{UAV}<h_{max} \tag{24}\]  
where \(P_{max}\) is the maximum available transmission power of the IoT device. \(C1\) is the minimum required data rate for all users to ensure QoS. \(C2\) means that any IoT device transmit power cannot exceed \(P_{max}\). \(C3\) and \(C4\) are the constraints of the assignment matrix \(b_{k,i}\) and power \(p_{k,i}\). \(C5\) mentions that one subcarrier cannot be allocated to more than \(L\) users at the same time. \(C6\) is the delay constraint, states that the average delay of the device \(i\) (\(d_{avg,i}\)) should not exceed the delay limit requirement \(D_{i}\). \(C7\) is the UAV height constraint between the minimum and maximum allowable altitudes.  
The optimization problem is non-convex due to the coupled variables \(b_{k,i}\) and \(p_{k,i}\) in (20) and (21). Hence, the proposed algorithm uses a linear weighted utility function to overcome the complexity of the optimization problem (17).
## 3 Materials and Methods
### Description of Laboratory and Field Experimental Setup
The soil is classified as sandy clay loam, according to the United States Department of Agriculture classification (USDA) [39], with an average content of clay, silt and sand of 24%, 16% and 60%. The area is characterized by a typical Mediterranean climate with moderate precipitation during the fall and winter seasons, and a very high air temperature and the absence of or limited precipitation during summer. Hourly meteorological data (air temperature, air pressure, solar radiation, precipitation, relative humidity, soil heat fluxes and soil temperature) were acquired by an eddy covariance system, used to evaluate crop water requirements [40]. Sensor-based measurements of soil water content, as well as image acquisitions, were carried out over bare soil, between two rows of the olive orchard. The soil thermal properties assessed indoors were used. The dynamics of SWC in the topsoil was in particular investigated with two buried lysimeters (0.5 m long, 0.3 m wide and 0.3 m deep) opened only at the upper boundary and maintained under extremely different soil water contents (air-dried and fully wetted). Both lysimeters were filled carefully with the same soil extracted from the digs in which they were inserted.
# Improved Hybrid A* Algorithm Based on Lemming Optimization for Path Planning of Autonomous Vehicles
## 4 Hybrid Path Planning Algorithm
### Mechanism of Algorithm Fusion
#### 4.2.2 Penalty Function  
In the hybrid path algorithm system, the penalty function is mainly reflected in the fitness function through multi-objective weighting. It is the core constraint processing mechanism of the LOA and hybrid A* fusion, ensuring the feasibility and quality of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Function** \\ \hline Path length & Normalizes the path length, encouraging the generation of shorter paths \\ Curvature penalty & Penalizes sharp turns to ensure that the path satisfies the vehicle’s kinematic constraints (for example, min_r) \\ Distance penalty & Ensures that the path is free of obstacles to improve the safety \\ Collision penalty & If the path collides with an obstacle, the system returns directly to +\(\infty\) and infeasible solutions are eliminated \\ \hline \hline \end{tabular}
\end{table}
Table 5: Roles of objective function parameters.  
path through hard constraints (collision) and soft constraints (curvature, distance), as shown in the following Equation (26):  
\[F(\theta)=\omega_{1}\cdot\frac{L(\theta)}{L_{ref}}+\omega_{2}\cdot\max\lvert \kappa(\theta)\rvert+\omega_{3}\sum_{k=1}^{N}e^{-d_{k}/\sigma}+P_{hard} \tag{26}\]  
where \(\theta=[\theta_{1},\theta_{2},\theta_{3}]=[\min\_r,step,P_{\theta}]\) as the hybrid A* parameters to be optimized; \(L(\theta)\) is the path length generated by hybrid A*; \(L_{ref}\) is the standard parameter path length; \(x(\theta)\) is the curvature at each point along the path; \(d_{k}\) is the distance from the kth point on the path to the nearest obstacle; \(\sigma=0.5\) is the obstacle influence range parameter; \(P_{hard}\) is the hard penalty (returns the maximum value at the point of collision); \(\omega_{1}=0.6\) (path length weighting); \(\omega_{2}=0.3\)(curvature smoothing weight); \(\omega_{3}=0.1\) (obstacle distance weighting). The workflow of the penalty function in the LOA is shown in Figure 8.  
The penalty function achieves the collaborative optimization of global and local planning through a hierarchical penalty mechanism, where the penalty function of hybrid A* ensures the global feasibility of the path (such as obstacle avoidance constraints), while the LOA penalty function optimizes the local path quality (e.g., smoothness). By dynamically adjusting the adaptive penalty intensity of the safety distance, the multi-objective optimization problem is transformed into a mathematical expression of constraint conditions, thereby achieving a multi-dimensional balance of path safety, efficiency, and comfort at the algorithmic level.
## 3 Experimental Setup (Materials and Equipment)
### Characterization  
The morphology was examined by SEM. The optical microscopic graphs for the processing parameters at higher temperatures showed the degree of dispersion to be increased.  
Additionally, researchers investigated the wear characteristics of two high-velocity oxy-fuel (HVOF)-sprayed coatings through experimental and statistical analyses. Microstructural examinations of both coatings were performed using SEM and X-ray diffrac  
Figure 2: Plastic extrusion processing methods.  
tion (XRD). RSM was utilized to formulate a mathematical model and the findings indicate that both coatings exhibit similar responses in terms of individual RSM outputs [19].  
The main focus was to establish a quantitative methodology using a combination of particle size analysis (PSA), SEM, micro-computed tomography (MCT) scanning and direct observation method (DOM) observations to describe pigment dispersion in polycarbonate composites. This method enhances the evaluation of rheological properties and pigment dispersion, thereby reducing color differences during material processing and minimizing waste. For each processing parameter, three samples were produced at three levels and molded into color chips for the microscopy dispersion test. These color chips were then measured for their color output properties and particle size distributions.  
The characterization of the Algerian rock reservoir involved employing a range of techniques, including SEM, XRD, XRF and BET analysis. To determine the minimum adsorbed quantity, a full factorial design consisting of 8 experiments derived from 23 factors was employed. This design allowed for the examination of individual effects and interactions of operational parameters using methods such as variance analysis (ANOVA), the desirability method and response surface methodology [20].  
The scanning morphology was examined using a Joel scanning electron microscope at an accelerating voltage of 20 kV. Three parameters were varied individually from the center levels. In the SEM micrographs, relatively larger agglomerated pigments were observed at mid-level processing parameters compared to higher processing levels.
# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
## 3 The Adaptive Fusion of KCF and Kalman Filter
### Target Tracking Method Based on Kalman Filter
Considering that the direction of the vehicle does not change significantly in the two adjacent point cloud images, the target angular velocity is ignored and a uniform motion model is adopted to describe the vehicle motion between adjacent point cloud sequences. The state of the vehicle target to be tracked is shown in Equation (26):  
\[X=\left[x,y,z,\theta,v_{x},v_{y},v_{z}\right]^{\mathrm{T}} \tag{26}\]  
Figure 3: The Kalman filtering process. According to the uniform motion model, the state \(X_{t}\) at time \(t\) is found, as shown in Equation (27):  
\[X_{t}= \begin{bmatrix}1&0&0&0&\Delta t&0&0\\ 0&1&0&0&0&\Delta t&0\\ 0&0&1&0&0&0&\Delta t\\ 0&0&0&1&0&0&0\\ 0&0&0&0&1&0&0\\ 0&0&0&0&0&1&0\\ 0&0&0&0&0&0&1\end{bmatrix}\begin{bmatrix}x_{t-1}\\ y_{t-1}\\ z_{t-1}\\ \theta_{t-1}\\ \
u_{x_{t-1}}\\ y_{t_{t-1}}\\ \
u_{y_{t-1}}\\ \
u_{z_{t-1}}\end{bmatrix} + \begin{bmatrix}\omega_{x}&{}_{t-1}\\ \omega_{y}&{}_{t-1}\\ \omega_{z}&{}_{t-1}\\ \omega_{y}&{}_{t-1}\\ \omega_{z_{t-1}}\\ \omega_{z_{t-1}}\end{bmatrix} =A_{t}X_{t-1}+\omega_{t} \tag{27}\]  
where \(\Delta t\) is the time interval between two adjacent frames, \(A_{t}\) is the state transition matrix from \(X_{t-1}\) to \(X_{t}\), and \(\omega_{t}\) is the noise in the equation of motion and follows a zero-mean Gaussian distribution. Taking the output value of the model as the observed value of the state, the observation equation can be established, as shown in Equation (28):  
\[Z_{t}= \begin{bmatrix}x_{t}\\ y_{t}\\ z_{t}\\ \theta_{t}\\ v_{x_{t}}\\ y_{y_{t}}\\ \sigma_{z_{t}}\end{bmatrix} = \begin{bmatrix}1&0&0&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&1&0&0&0\\ 0&0&0&1&0&0\\ 0&0&0&0&1&0\\ 0&0&0&0&0&1\end{bmatrix}\begin{bmatrix}x_{t}\\ y_{t}\\ z_{t}\\ \theta_{t}\\ v_{x_{t}}\\ v_{y_{t}}\\ \
u_{z_{t}}\end{bmatrix} + \begin{bmatrix}v_{x_{t}}\\ v_{y_{t}}\\ v_{z_{t}}\\ v_{z_{t}}\\ \end{bmatrix}  \tag{28}\]  
where \(x_{t}\), \(y_{t}\), \(z_{t}\) are the 3D coordinates of the target centroid in the current frame, and \(\theta_{t}\) is the vehicle driving direction angle. \(v_{x_{t}}\), \(v_{y_{t}}\), \(v_{z_{t}}\) are the target velocity components in \(x\), \(y\), \(z\) directions, respectively. And the velocity in a specific direction is determined by dividing the displacement between two consecutive frames in that direction by the time interval of those two frames.
## 1 Introduction
In this study, the antenna dimensions were reduced by approximately 65.4% and produced as 34.6 mm \(\times\) 33 mm. In order to prevent antenna performance loss during the size reduction process, slots were dug into the antenna. A low-cost and compact UWB antenna operating in the 3.60-13 GHz range has been developed. The antenna design was carried out using the CST MWS simulation program. The Vivaldi antenna is designed with linear phase response and group delay. The designed Vivaldi antenna has been implemented in hardware. Many laboratory measurements were carried out using the hardware-generated Vivaldi antenna and Aritsu MS2028C VNA. A heterogeneous breast phantom with a tumor was designed using the CST MWS simulation program. Two heterogeneous phantoms with a radius of 50 mm were created from healthy and tumor tissues placed under a 2 mm thick skin layer for the breast model. A spherical tumor with a diameter of 0.9 mm was placed inside the heterogeneous phantom. Tests were performed on the heterogeneous breast phantom with the tumor designed in the Vivaldi antenna simulation environment with features such as real gain and directionality. A Gaussian signal was sent to the heterogeneous breast phantom with an antenna and the reflected signals were collected. Tumor responses were obtained by calibrating the unwanted parts of the collected signals. Tumor images were constructed as an energy map in the X-Y and X-Z planes using the DMAS beam forming algorithm [80]. Homogeneous and heterogeneous breast phantoms with tumors were produced in the laboratory environment using materials suitable for the real breast structure. These phantoms were automatically scanned in the measurement setup by designing a 3D device in the prone position. A 16 mm diameter tumor was detected and imaged in homogeneous and heterogeneous breast phantoms. It was shown that the measurements made with the proposed antenna in both simulation and experimental environments are suitable for the early-stage detection of breast cancer.
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 4 Problem Formulation and Performance Metrics
### BSM Transmission Interval  
According to the model in Section 3.1, the expected passing time of vehicles in each speed segment is calculated based on the average velocity of vehicles in that segment. Let \(t_{r}\) be the expectation of the passing time, which can be expressed as \(E(t_{r})=D/E\Big{(}v_{f}\Big{)}\). Then, the expected BSM transmission interval for each priority level, denoted as \(E(T_{BSM\;i})\), is given by the following equation:  
\[E(t_{ri})/E(T_{BSM\;i})\times K_{i}\times E(T_{i})\leq\ E(t_{ri}), \tag{38}\]  
where \(K_{i}\) represents the number of vehicles in priority level \(i\), and the minimum time interval for BSM transmission for vehicles in priority level \(i\) is denoted as:  
\[E(T_{BSM\;i})=K_{i}\times E(T_{i}) \tag{39}\]
# Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
## 4 Cases Studies
### Case 2: Vibratory Component Analysis in a Two Degrees of Freedom System
Figure 18: Amplitude analysis of vibratory components \(\widehat{x}_{11}\), \(\widehat{x}_{12}\), \(\widehat{x}_{21}\), and \(\widehat{x}_{22}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Reference** & **EMD** & **VMD** &
\begin{tabular}{c} **Hybrid** \\ **EMD–VMD** \\ \end{tabular} & **FFT** \\ \hline \multicolumn{5}{c}{**Position 1**} \\ \hline \hline \(\Omega_{11}\) **[rad/s]** & 50.00 & 50.14 & 44.17 & 49.95 & 50.20 \\ \(\Omega_{12}\) **[rad/s]** & 86.60 & 86.52 & 86.71 & 86.58 & 86.39 \\ \(X_{1}^{(1)}\) **[m]** & 0.05 & 0.051 & 0.036 & 0.05 & 0.043 \\ \(X_{1}^{(2)}\) **[m]** & 0.05 & 0.048 & 0.052 & 0.05 & 0.042 \\ \hline \multicolumn{5}{c}{**Position 2**} \\ \hline \(\Omega_{21}\) **[rad/s]** & 50.00 & 50.33 & 44.05 & 49.95 & 50.20 \\ \(\Omega_{22}\) **[rad/s]** & 86.60 & 86.83 & 86.71 & 86.58 & 86.39 \\ \(X_{2}^{(1)}\) **[m]** & 0.05 & 0.051 & 0.034 & 0.049 & 0.043 \\ \(X_{2}^{(2)}\) **[m]** & 0.05 & 0.048 & 0.035 & 0.049 & 0.042 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detailed comparative analysis of frequency and amplitude estimates derived from multiple signal decomposition methods.
# Characterization of the Intelligibility of
## 3 Results
### Characterization of VCV Intelligibility
Statistical analysis confirmed that the mean STOI values for English VCVs (Figure 2a) are significantly higher than for VCVs in French, German, and Portuguese in the range from \(-8\) to \(-2\) dB SNR (\(p\) < 0.05), whereas the observed differences with VCVs in Italian were not statistically significant. Regarding the \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**English**} & \multicolumn{2}{c}{**French**} & \multicolumn{2}{c}{**German**} & \multicolumn{2}{c}{**Italian**} & \multicolumn{2}{c}{**Portuguese**} \\ \hline  & **SRT** & **Slope** & **SRT** & **Slope** & **SRT** & **Slope** & **SRT** & **Slope** & **SRT** & **Slope** \\ \hline avg & \(-4.86\) & \(2.58\) & \(-1.83\) & \(3.05\) & \(-0.89\) & \(2.93\) & \(-2.82\) & \(3.06\) & \(-0.85\) & \(3.26\) \\ s.d. & \(1.74\) & \(0.47\) & \(2.18\) & \(0.47\) & \(3.10\) & \(0.56\) & \(2.42\) & \(0.71\) & \(3.31\) & \(0.63\) \\ range & \(-6.79\): \(-0.63\) & \(2.07\): \(3.52\) & \(-6.94\): \(2.34\) & \(2.05\): \(3.79\) & \(-6.96\): \(2.64\) & \(2.15\): \(3.82\) & \(-5.64\): \(3.10\) & \(2.19\): \(4.18\) & \(-4.36\): \(7.07\) & \(2.22\): \(4.30\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average, standard deviation, and range of the SRT (dB) and slope (%/dB SNR) of the psychometric functions. Figure 2: Analysis of STOI values (percent values) as a function of the SNR (dB) observed in the five sets of VCVs, averaged within each set: (**a**) mean values of STOI; (**b**) standard deviation of STOI. variable of STOI (Figure 2b), statistical analysis showed that the observed differences were not statistically significant (\(p>0.05\)). Statistical analysis of the distributions of SRTs shown in Table 4 revealed that the estimated SRTs were significantly lower with VCVs in English compared to those in French, German, and Portuguese (\(p<0.05\)), whereas the observed difference between the average SRTs for VCVs in English and Italian was not statistically significant. Regarding the distributions of slope in Table 4, the differences in average values were small, and the range of slope values was similar across languages. Statistical analysis showed that the observed differences are not statistically significant (\(p=0.098\)), although there is a trend for English to provide lower slope values compared to the other languages. In summary, Figure 2 and Table 4 shows that among the five languages here studied, the set of spoken VCVs in English fulfilled the three criteria for application in multilingual settings, i.e., (1) high mean STOI (Figure 2a), (2) low variance of STOI (Figure 2b), and (3) shallow slope and low SRT (Table 4). As such, these results suggested that the subset of VCV recordings in English may be the best candidate language among the five languages here considered for the implementation of speech-in-noise tests to be administered to listeners whose native language is not known in advance.
## 7 Soil-Pile Interaction Study and Frequency-Dependent Stiffness and Damping  
Another sample model has been used to investigate the effect of soil-pile-foundation interaction on the vibration performance of the equipment foundation. The material property of the foundation is assumed to be 27.6 MPa cylindrical concrete strength with 24.856 MPa Young's modulus. The unit weight of concrete is of typical 2403 kg/m\({}^{3}\).  
A total of 2706 joints, 70 links, and 1170 solid elements were used in the example model (Figure 8, 3D solid element model with link element). The 3-D solid elements are expected to predict displacement/velocity response with higher accuracy, and the behaviour of the pile foundation with higher efficiency. This model used only three translational degrees of freedom at each joint. A direct soil-pile interaction study has been conducted using the DYNA6 program. A total of 70 CIP (600 mm diameter, 18 m long Cast-In-Place with 34.5 MPa cylindrical concrete strength) piles are installed beneath the large compressor foundation and are modelled by the FE link elements. Two layers of soil media, with shear wave velocities equal to Vs = 300 m/s for the upper 3 m of soil and V\({}_{\text{b}}\) = 538 m/s below the soil, are expected to interact with the CIP piles, including a weak zone (Figure 9). The weak zone parameters are assumed to have a Poisson ratio of 0.3 and material damping of 5%. The compressor vendor has set the single machine speed at 66.5 Hz. Thus, all the foundational vibration responses up to 120% (80 Hz) of the operating speed were calculated and considered for resonance checking at any frequency. For a reference purpose, an eigenvalue analysis using frequency independent stiffness was performed. The fundamental modal period returned was 0.395 s (horizontal mode coupled with rocking) and  
The individual soil-pile interactions were numerically simulated in the DYNA6 platform [8]. The dynamic pile stiffness and damping along the frequency range have been generated and plotted in Figure 11. The stiffness and damping values were incorporated into the FE model (Figure 8, Right) via frequency-dependent link elements in SAP2000.  
Figure 11: Soil-pile stiffness and damping vs. frequency: (**a**) Horizontal stiffness vs. frequency, (**b**) Horizontal damping vs. frequency, (**c**) Vertical stiffness vs. frequency, (**d**) Vertical damping vs. frequency.  
Figure 9: Soil-pile section and weak zone (l: Pile length, r: Radius of the pile, l/r: Slenderness ratio of the pile, t: Thickness of the weak zone, vs.: Shear wave velocity of soil, V\({}_{\text{b}}\): Shear wave velocity below the pile tip).  
In general, the horizontal stiffness has little variation with the frequency range (less than 2%) while the vertical pile stiffness gradually increases up to 30%. Horizontal and vertical damping is drastically reduced as frequency increases. In particular, the frequency-dependent values are very different from frequency-independent values, as shown in Table 6. The frequency-dependent vertical stiffness is 10-13 times higher than the frequency-independent value. The frequency-dependent horizontal stiffness is 3.5 times higher.