esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Research on vehicle trajectory prediction based on road monitoring video data often utilizes a global map as an input, disregarding the fact that drivers rely on the road structures observable from their own positions for path planning. This oversight reduces the accuracy of prediction. To address this, we propose the CVAE-VGAE model, a novel trajectory prediction approach. Initially, our method transforms global perspective map data into vehicle-centric map data, representing it through a graph structure. Subsequently, Variational Graph Auto-Encoders (VGAEs), an unsupervised learning framework tailored for graph-structured data, are employed to extract road environment features specific to each vehicle's location from the map data. Finally, a prediction network based on the Conditional Variational Autoencoder (CVAE) structure is designed, which first predicts the driving endpoint and then fits the complete future trajectory. The proposed CVAE-VGAE model integrates a self-attention mechanism into its encoding and decoding modules to infer endpoint intent and incorporate road environment features for precise trajectory prediction. Through a series of ablation experiments, we demonstrate the efficacy of our method in enhancing vehicle trajectory prediction metrics. Furthermore, we compare our model with traditional and frontier approaches, highlighting significant improvements in prediction accuracy.  
conditional variational autoencoder; variational graph auto-encoders; trajectory prediction Article
###### Abstract  
Facial semantic segmentation, as a critical technology in high-level visual understanding, plays an important role in applications such as facial editing, augmented reality, and identity recognition. However, due to the complexity of facial structures, ambiguous boundaries, and inconsistent scales of facial components, traditional methods still suffer from significant limitations in detail preservation and contextual modeling. To address these challenges, this paper proposes a facial parsing network based on the Deeplabv3+ framework, named FP-Deeplab, which aims to improve segmentation performance and generalization capability through structurally enhanced modules. Specifically, two key modules are designed: (1) the Context-Channel Refine Feature Enhancement (CCR-FE) module, which integrates multi-scale contextual strip convolutions and Cross-Axis Attention and introduces a channel attention mechanism to strengthen the modeling of long-range spatial dependencies and enhances the perception and representation of boundary regions; (2) the Self-Modulation Attention Feature Integration with Regularization (SimFA) module, which combines local detail modeling and a parameter-free channel attention modulation mechanism to achieve fine-grained reconstruction and enhancement of semantic features, effectively mitigating boundary blur and information loss during the upsampling stage. The experimental results on two public facial segmentation datasets, CelebAMask-HQ and HELEN, demonstrate that FP-Deeplab improves the baseline model by 3.8% in Mean IoU and 2.3% in the overall F1-score on the HELEN dataset, and it achieves a Mean F1-score of 84.8% on the CelebAMask-HQ dataset. Furthermore, the proposed method shows superior accuracy and robustness in multiple key component categories, especially in long-tailed regions, validating its effectiveness.  
face parsing; fine-grained segmentation; boundary refinement; facial image analysis; deep learning +
Footnote †: journal: Applied sciences  
Article
# Modeling of Vessel Traffic Flow for Waterway Design-Port of Swinoujscie Case Study
## 3 Results
### Regression Analysis of Traffic Flow Parameters
#### 3.3.4 Model Including Distance to Isobaths D\({}_{10m}\)  
The parameters of the traffic flow changed with the distance from the hazard. In the studied area, the correlation was defined for the distance to 10 m isobaths on the starboard side \(D_{10m}\). As the distance to the defined isobaths increased, the spatial parameters of the traffic flow increased, as shown in Figure (a)a,b. This behaviour is understandable. The more complex (the narrower) the area is for navigation, the more accurate the steering of the vessel.
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 3 Green Computing for WBANs
### ELR-W: Operational Steps  
The proposed protocol has three phases; initial phase, next-hop selection, and forwarding phase. The flow chart of ELR-W is shown in Figure 3.  
Figure 3: Flow chart of ELR-W protocol.
# Communication Protocol Design for IoT-Enabled Energy Management in a Smart Microgrid
## 4 Error Probability Analysis
### \(k\) Odd, \(\ell\) Even
If two or four events take place at the same time, the error will be automatically corrected for the BPSK signals. At a high SNR, the probability of having one of the four events is significantly larger than the probability of having three of the four events at the same time. As a result, the chances of error for the \((\frac{|\ell-k|+1}{2})\)th HEMS's estimation of the \(k\)th HEMS's message is expressed using (14). Similarly, the chances of error at the \((\frac{|\ell-k|+1}{2})\)th HEMS about the message from the \(\ell\)th HEMS can be represented using (14), with the term \(\frac{|\ell-k|+1}{2}+1\) replaced by \(\frac{|\ell-k|+1}{2}\), and vice versa. Moreover, the terms \(k\) and \(\ell-1\) are replaced by \(k+1\) and \(\ell\), respectively. After the \((\frac{|\ell-k|+1}{2}+3)\)th time slot, the \((\frac{|\ell-k|+1}{2}+1)\)th and the \((\frac{|\ell-k|+1}{2})\)th HEMSs forward the signals from the \(k\)th and the \(\ell\)th HEMS to the nearby HEMSs until the \(k\)th and the \(\ell\)th HEMS receives the signal from each other. Then, using Lemma 3 and 4, the error performance at the \(k\)th HEMS for the erroneous detection of the \(\ell\)th HEMS's signal is given by the following:  
\[\Pr_{k,\ell,\text{even}} \approx\sum_{a=1}^{\frac{|\ell-k|+1}{2}+1-k}\Pr_{k+a-1,k+a}\prod _{b=1}^{\frac{|\ell-k|+1}{2}+1-k,b\
eq a}(1-\Pr_{k+b-1,k+b})(1-\Pr_{\frac{| \ell-k|+1}{2}+1,k})\] \[+\prod_{a=1}^{\frac{|\ell-k|+1}{2}+1-k}(1-\Pr_{k+a-1,k+a})\Pr_{ \frac{|\ell-k|+1}{2}+1,k} \tag{15}\]  
Similarly, the error performance at the \(\ell\)th HEMS when detecting the \(k\)th HEMS's messages can be written using (15), with \(k\) replaced by \(\ell\) and vice versa. Now, if there are \(K\) number of HEMSs in total, then, there will be \(K+1\) cases where an odd-indexed HEMS will decode the signal of an even-indexed HEMS and \(K-1\) cases where an even-indexed HEMS will decode the signal of an odd-indexed HEMS when \(K\) is odd. On the other hand, if \(K\) is even, in \(K-2\) cases, an odd-indexed HEMS will decode the signal of an even-indexed HEMS, and in \(K\) cases, an even-indexed HEMS will decode the signal of an odd-indexed HEMS. As a result, the average error probability for decoding a HEMS's signal in the microgrid can be represented as follows:  
\[\Pr_{avg}= \begin{array}{ll}\frac{1}{4K}(2(K+1)\Pr_{k,\ell,\text{odd}}+2(K- 1)\Pr_{k,\ell,\text{even}})&K\text{=odd}\\ \frac{1}{4K-4}(2(K-2)\Pr_{k,\ell,\text{odd}}+2\Pr_{k,\ell,\text{even}})&K \text{=even}\end{array} \]  
Note that the error performance at the HEMSs for the scenarios when \(k\) is even and \(\ell\) is odd can be written in a similar manner as in (14). On the other hand, the error performance at the \(k\)th and \(\ell\)th HEMSs for the scenarios when both \(k\) and \(\ell\) are even will be the same as that for the scenarios when both \(k\) and \(\ell\) are odd.
# Quantitative Evaluation Method for the Severity of Surface Fuzz Defects in Carbon Fiber Prepreg
## 2 Materials and Methods
### Quantitative Evaluation Score  
To comprehensively evaluate the overall severity of fuzz defects, a composite scoring metric S is introduced, which integrates both the severity per unit area l and the effective area S of the detected defect. This scoring mechanism is formulated as follows:  
\[\text{S}=\sqrt[]{\text{S}}*\log_{\Theta}\text{A}_{\text{eff}} \tag{31}\]  
In Equation (31), l represents the normalized grayscale deviation-based severity index defined by (30), while S is the effective area defined in 2.4. The parameter \(\theta\) is introduced as an adjustable weighting factor that governs the relative contribution of the unit severity and the spatial extent of the defect in the final assessment. The value of \(\theta\) should be empirically determined according to the specific application requirements and the desired emphasis on either the intensity of the defect or its physical size.  
The use of a square root function on the product of \(\theta\) and s ensures a balanced contribution between the two components, while the logarithmic term with base \(\theta\) on the effective area \(\text{A}_{\text{eff}}\) mitigates the overemphasis of large-area defects and enhances the sensitivity to moderate or small defects with high severity. This formulation allows for a more nuanced and application-adaptive quantification of fuzz defect severity, improving the discriminability and interpretability of the final evaluation results.
# Shared eHMI: Bridging Human-Machine Understanding in Autonomous Wheelchair Navigation
## 4 Results and Discussion
### Questionnaires for Pedestrians  
The pedestrian acceptance questionnaire results shown in Figure 9 indicate most participants were positive towards our wheelchair. Over 85% believed the projection interaction can help pedestrians avoid risks and react quickly, and most found it easy to interact with the wheelchair. Notably, only about 70% were willing to cross the road in front of the autonomous wheelchair, as evidenced by nearly 30% finding it difficult to cross the road in question 4 of Figure 9.  
Figure 8: Response to the questionnaire about users’ attitude towards our wheelchair. The bar heights indicate the number of answers per category. Higher scores indicate greater acceptance of the wheelchair, with the exception of Q4. Detailed questions of the questionnaire are shown in Table A1.  
Figure 9: Response to the questionnaire about pedestrian acceptance. The bar heights indicate the number of answers per category. Detailed questions of the questionnaire are shown in Table A2.  
The UEQ-S questionnaire results for pedestrian reveal that the pragmatic quality is 1.37, the hedonic quality is 1.17, and the overall score is 1.26. The higher score in pragmatism compared to hedonism reflects pedestrians' recognition of the shared eHMI's practicality, especially in enhancing safety.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 3 Sliding-Mode Controller Design
### Synthesis of the Control Law  
The first step for the synthesis of the control law is the calculation of the switching function, which has two main terms: \(K_{\wp}\cdot(v_{dc}-v_{r})\) and \(K_{i}\cdot i_{m}-i_{dc}\). The first term is simple to calculate since \(K_{\wp}\) is the constant given in (36), \(v_{dc}\) is measured using a voltage sensor, and \(v_{r}\) is defined as an input of the SMC. The calculation of this first term is illustrated in Figure 3 by using a subtractor and a gain for \(K_{\wp}\).  
Figure 3: Proposed sliding-mode controller for the battery charger/discharger.  
The second term, instead, requires the dynamic calculation of \(K_{i}\), and \(i_{m}\) is not physically measured in the circuit. The calculation of \(K_{i}\), reported in (33), requires calculating the duty cycle as given in (16), which is done by measuring both \(v_{dc}\) and \(v_{b}\) to perform the required mathematical operation with the constants \(n\) and \(L_{q}/L_{m}\). Then, \(1-d\) is calculated, which is divided by \(n\). The dynamic calculation of \(K_{i}\) is defined in (39) and illustrated in Figure 3 (green block) using gains, an adder, and a divider.  
\[K_{i}=\frac{v_{b}}{v_{dc}\cdot Lm/L_{q}+v_{b}\cdot n} \tag{39}\]  
The second term requires the value of \(i_{m}\), which is not measured directly. However, from the switched expressions for \(i_{p}\) and \(i_{s}\), given in (8) and (9), it is possible to reconstruct \(i_{m}\) from the measurement of \(i_{p}\) and \(i_{s}\) as follows:  
\[i_{m}=\left\{\begin{array}{ll}i_{p}&\text{if }u=1\\ \\ i_{s}\cdot n&\text{if }u=0\end{array}\right. \tag{40}\]  
The previous \(i_{m}\) reconstruction enables the calculation of the switching function (19) from \(i_{p}\) and \(i_{s}\), which is used to modify the control law given in (38) as follows:  
\[u=\left\{\begin{array}{ll}1&\text{if }\ \ \ \Psi_{s}<-\delta\mathbf{\Psi} \ \ \ \text{where}\ \ \ \Psi_{s}=K_{v}\cdot(v_{dc}-v_{r})+K_{i}\cdot n\cdot i_{s}-i_{dc}\\ \\ 0&\text{if }\ \ \Psi_{p}>+\delta\mathbf{\Psi}\ \ \ \text{where}\ \ \ \ \Psi_{p}=K_{v}\cdot(v_{dc}-v_{r})+K_{i}\cdot i_{p}-i_{dc}\end{array}\right. \tag{41}\]  
Finally, such a practical control law is implemented using multipliers, adders, sub-tractors, comparators, and an S-R Flip-Flop. The Flip-Flop is used to keep the value of \(u\) inside the hysteresis band, where \(u=1\) is imposed by activating the set (S) signal, while \(u=0\) is imposed by activating the reset (R) signal. Moreover, the Flip-Flop also produces the complementary control signal \(\bar{u}\) without the need for additional hardware. Finally, the complete synthesis of the practical control law is illustrated in Figure 3 (red block), which corresponds to an adaptive sliding-mode controller due to the dynamic calculation of the parameter \(K_{i}\). In such a figure, the blue signals correspond to physical measurements, while the red signals are the outputs of the SMC.
# A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
## 4 Discussion  
In this section, we discuss the results of our proposed method and the main reasons for these results. To the best of our knowledge, most of the existing methods use at least two vanishing points to estimate the orientation and position of the camera. In the case of two vanishing points, other constraints, e.g., two sets of parallel lines that are perpendicular or a 2D-3D point correspondence, are needed. In addition, when only the constraint of the vanishing points is given, the existing methods cannot estimate the orientation or position independently, but do it simultaneously. Moreover, if some prior knowledge is used, e.g., angles given by IMUs or positions given by the total station or RTK, the problem of estimation can be simplified with 3D control points or feature points, not the vanishing points for most of the existing methods. Based on an analysis of the drawbacks, we proposed a fast and simple method for absolute orientation estimation using only one vanishing point in this paper. Fewer vanishing points needed means fewer constraints are needed, and this can extend the range of applications for our proposed method. Estimating the orientation independently can reduce prior knowledge and improve the computational speed. The difference and advantage of our proposed method, as well as future work, are discussed as follows.
## 2 Background
### Learning from Imbalanced Data  
The well-known problem of imbalanced data, or more generally Sample Selection Bias (SSB), refers to cases where either or both of the training and testing datasets for some phenomenon of interest have a different distribution than the actual ground truth. This undermines machine learning algorithms, because changing the distribution of examples also changes their influence on how the current representation of a problem (weights in a DNN, support vectors in SVM, etc.) is updated in the learning algorithm (see [13; 14] for reviews). Note, however, that [15] argues that SSB's impact on a classifier varies with the dataset, as well as its inductive bias.  
In general, it is desirable to mitigate SSB in order for a learning algorithm to faithfully model the phenomenon under study. One approach focuses on building new classification algorithms that incorporate SSB mitigation directly, e.g., [16; 17]. However, these approaches require the creation of a bespoke learning algorithm. Attempts to mitigate SSB more generally in machine learning can broadly be divided into stratification and cost-sensitive classification approaches.  
Stratification approaches usually undersample a majority class, oversample a minority class, or both. Uniform random selection is one method for undersampling, but numerous others exist [18]. Oversampling approaches include the simple replication of individual minority-class examples or copying the minority class to form ensembles [19]. The well known SMOTE algorithm [20] creates synthetic examples randomly placed on the line connecting a pair of minority-class nearest neighbors. There are several extensions of the basic SMOTE algorithm. The SMOTE + Tomek and SMOTE + ENN algorithms were proposed in [21]. The authors in [22] created a new variant of SMOTE called Borderline-SMOTE, in which only the minority examples near the class boundary are over-sampled. Ref. [23] proposes combining random undersampling and SMOTE. Ref. [24] uses a different method to generate synthetic examples.  
The other general mitigation for SSB, Cost-Sensitive Classification (CSC), imposes differential costs on errors in the majority and minority classes and revises the learning algorithm to minimize the total error cost. This can again be performed by creating bespoke algorithms, but the more common approach follows the MetaCost technique [25] of creating a meta-classifier that provides cost-sensitivity to arbitrary learning algorithms. Hybrids of stratifications and cost-sensitive learning are presented in [26].
## 3 Numerical Simulation Analysis
### Sound Scattering Field of Elastic Structure in a Typical Deep-Sea Channel  
The submarine model that is selected in this section is the internationally published standard submarine model, i.e., the benchmark submarine target model [22]. The length of the submarine is 61.5 m, the diameter of the hull is 7.5 m, the height of the podium is 3.5 m, and the thickness of the hull is 0.03 m.  
A 5000-m deep ideal waveguide is established when considering the target scattering sound field of the submarine in the deep-sea waveguide. The upper boundary of the waveguide is an absolute soft boundary, and the lower boundary is an elastic boundary. The longitudinal wave velocity is 3800 m/s, the transverse wave velocity is 1800 m/s, the density is 1800 kg/m\({}^{3}\), and the attenuation coefficient 0.5. Figure 7a shows the sound velocity profile of the deep-sea waveguide, which is from South China Sea; the density of water is \(\rho\) = 1000 kg/m\({}^{3}\) and the submarine dives at a depth of 150 m. The sound field is incident from the bow direction, and the side scattered sound field is drawn. Figure 7b shows the calculation result.  
Figure 7b is the sound scattering field of the submarine model in the deep-sea waveguide. The sound field propagation trajectory is obvious, and the time that is required for the directional point source equivalent method to calculate the complex model is 1399 s. By contrast, the sound field of the deep-sea complex model cannot be calculated while using the FEM because the number of grids required is extremely large, which highlights the high efficiency of the directional point source equivalent method.
## 2 System Model and Problem Formulation
### Threat Model  
While many vehicles behave honestly in the IoV ecosystem, others may be compromised by malicious entities aiming to disrupt the FL process. Adversarial threats in this context can take several forms [37; 38; 39]:  
* Model poisoning and misrepresentation: Malicious participants may send tampered model updates or corrupted training data, degrading the accuracy and reliability of the global FL model.
* Backdoor or Trojan attacks: Hidden triggers embedded into the model can cause specific malicious behaviours when activated, compromising safety and exposing the system to significant risks.
* Training process disruption: Adversaries may send outdated or incomplete updates, exploit connectivity issues, or deliberately induce delays, undermining model convergence and system performance.
* Exploitation of in-vehicle vulnerabilities: Attackers can target internal protocols (e.g., CAN Bus) to inject malicious commands, jeopardising vehicle operations and data integrity.  
To mitigate these risks, we assume the presence of an IDS in each vehicle, capable of detecting many common attacks and anomalies [40; 41]. Although IDS systems are not infallible, they play a pivotal role in trust evaluation. Furthermore, communications between vehicles and the central server are encrypted (e.g., using AES [42]) to ensure confidentiality and integrity. However, tampering at the source remains a potential vulnerability, underscoring the importance of our trust-based mechanism as a critical first line of defence. This mechanism evaluates and selects participants based on trustworthiness, safeguarding the FL process from adversarial interference.
# Refractive Indices of Ge and Si at Temperatures between 4-296 K in the 4-8 THz Region
## 2 Materials and Methods
The results of the fringe method as applied to Ge are presented in Figure 1b, where the entire extracted spectrum is shown for different temperatures. The main difference between the two algorithms is that the \"phase\" method is more robust and yields a smoother, more densely sampled spectrum [14] so only these results are presented here. The lack of refractive index dispersion in this region signals low absorption and the absence of resonances, either carrier or phonon. The calculated refractive index \(n(f)\) was averaged over the data at each temperature. The uncertainty was taken to be the range of the data at each temperature. The main remaining source of uncertainty in the analysis is the systematic error due to the uncertainty in the thickness of the samples. Samples were mounted in a fixed holder aligned normal to the transmitted beam, ensuring normal incidence in all measurements. Thickness variation due to linear thermal expansion was accounted for by using literature values for the thermal expansion coefficient of each material as a function of temperature [17,18], and these corrections to the thickness are of the order of 0.25% for Si and 0.5% for Ge.