esa-sceva/satcom-chunk-collection huggingface dataset:
### Related Works  
#### 1.2.1 Merging Decision-Making Methods  
Merging decision-making methods can be categorized into five main types: model-based, learning-based, game-based, global optimization-based, and rule-based approaches. Model-based methods conceptualize the merging challenge as an insertion process, necessitating the creation of models for selecting optimal merging timing, gap, point, and trajectory planning [2; 3; 4; 5]. However, due to the highly non-linear nature of traffic systems, constructing these models is exceedingly complex.  
Learning-based methods offer an alternative perspective, harnessing historical trajectory data to learn from human merging behaviors and providing a more adaptive approach to the merging problem [6; 7; 8].  
Game theory is extensively applied to address conflicts between two vehicles. Game-based methods analyze the interplay of behavioral decisions between oncoming and merging vehicles, factoring in safety, comfort, and efficiency [9; 10; 11; 12; 13]. They operate on the rational premise that each vehicle aims to maximize its own benefit, using equilibrium concepts from game theory for right-of-way allocation, such as pure-strategy Nash equilibrium and mixed-strategy Nash equilibrium.  
Global optimization-based methods focus on objective optimization, either maximizing a single goal or balancing multiple indicators. These centralized approaches coordinate the movements of all CAVs within a controlled area to achieve optimal merging outcomes [14; 15; 16; 17; 18].  
Lastly, rule-based methods are distinguished by their low computational cost, stability, high efficiency, and strong interpretability [19; 20; 21; 22; 23; 24]. Examples include the \"First In, First Out (FIFO)\" rule and similar straightforward protocols that facilitate clear and consistent decision-making processes.
# Robust Control with Uncertain Disturbances for Vehicle Drift Motions
## 2 Vehicle Drift Motion Mechanism Analysis
### Vehicle Dynamics Model  
The mechanism analysis and the controller design are based on the 4-DOF nonlinear vehicle dynamics model depicted in Figure 1. The model is established with the nonlinear tire characteristics and the normal load transfer from front to rear wheels arising from the longitudinal acceleration of the vehicle to describe the motion in drift conditions.
# Electroencephalogram Emotion Recognition via AUC Maximization
## 4 AUC Maximization Algorithms  
The AUC metric evaluates classification model performance by measuring the area under the receiver operating characteristic (ROC) curve, reflecting the model's ability to differentiate between classes at various thresholds. Unlike traditional accuracy, which measures correct predictions irrespective of class distribution, AUC provides a comprehensive assessment across all thresholds, making it especially useful in imbalanced class scenarios. Most algorithms do not directly optimize AUC due to its non-decomposable and threshold-independent nature, using it instead as a post-training performance metric. However, AUC maximization can be approached through a strongly convex-strongly concave minimax optimization [57; 58], enabling direct optimization and offering a robust alternative to conventional loss functions focused on point estimates. In order to train a linear classifier, \(\mathbf{w}\), the objective function can be written as  
\[\min_{\mathbf{x}\in\mathbb{R}^{n}}\max_{y\in\mathbb{R}}f(\mathbf{x},y):=\frac {1}{N}\sum_{i=1}^{N}f_{i}(\mathbf{x},y)+\frac{\lambda}{2}\|\mathbf{x}\|^{2}-p(1 -p)y^{2}, \tag{6}\]  
where \(\mathbf{x}=[\mathbf{w};u;v]^{\top}\in\mathbb{R}^{n_{\mathbf{x}}},y\in\mathbb{R}\), \(\lambda\) is the regularization parameter and \(p\) denotes the proportion of positive instances in the dataset. Specifically, \(\mathbf{w}\in\mathbb{R}^{d}\) is the weight vector of the model, and \(u,v\in\mathbb{R}\) are auxiliary variables introduced to facilitate the reformulation of the AUC maximization problem into a convex-concave saddle point problem, and the variable \(y\) acts as a dual variable in this saddle point formulation. The objective function \(f_{i}(\mathbf{x},y)\) for each sample is defined as  
\[f_{i}(\mathbf{x},y)= (1-p)\big{(}(\mathbf{w}^{\top}\mathbf{a}_{j}-u)^{2}-2(1+y)\mathbf{ w}^{\top}\mathbf{a}_{j}\big{)}\mathbf{I}_{b_{j}=1}\] \[+p\big{(}(\mathbf{w}^{\top}\mathbf{a}_{j}-v)^{2}+2(1+y)\mathbf{ w}^{\top}\mathbf{a}_{j}\big{)}\mathbf{I}_{b_{j}=-1}, \tag{7}\]  
where \(\mathbf{a}_{i}\in\mathbb{R}^{n_{\mathbf{x}}-2}\) are features and \(b_{i}\in\{+1,-1\}\) is the label. Several numerical algorithms have been proposed to solve strongly convex-strongly concave minimax optimization problems. In Section 4.1, we introduce first-order algorithms, and in Section 4.2 we discuss a second-order framework for addressing such problems. In this paper, we only consider a linear classifier. However, this approach can be extensively applied to nonlinear models to achieve a better performance such as nonlinear models [27; 32; 59] by replacing \(\mathbf{w}\) with \(h(\mathbf{w};\mathbf{a}_{j})\).  
Figure 3: An illustration of cross-channel features, showing the PLV and correlation between segments across various channels in both synchronous and asynchronous fashions.
# Assessment of _Bacillus pumilus_ Isolated from Fresh Water Milieu for Bioflocculant Production
## 3 Results and Discussion
### Optimization of Culture Conditions for Biofoculant Production  
#### 3.2.1 Effect of Carbon Source on Biofloculant Production  
The choice of carbon source has a great influence on the production of biofloculants. The results of the effect of various carbon sources on biofoculant production by the bacterial strain are shown in Figure 1.  
Among the various sources of carbon tested, maltose was the preferred carbon source for the production of the biofoculant by the test bacteria with flocculating activity of 71.7%, followed by sucrose with a flocculating activity of 69.8%. Glucose, lactose and starch supported biofoculant production with flocculating activities of 67.8%, 61.3% and 55.3%, respectively. Fructose showed the least flocculating activity at 53.1%. Maltose was then used as a carbon source of choice for all subsequent experiments.  
Similarly, [PERSON] et al. (2013) reported that maltose was the preferred carbon source for biofoculant production by _Solibacillus silvestris_ exhibiting a flocculating activity of 88.7% [12]. [PERSON] et al. [41] reported that glucose, sucrose and fructose were preferred carbon sources for REA-11 production, a biofoculant produced by _Corynebacterium glutamicum_. It is well documented in the literature that many biofoculant-producing microorganisms prefer organic carbon sources for optimum biofoculant production [42]. However, the utilization of inorganic carbon sources for biofoculant production is still scant in literature.
## 2 SLAM Based on Multi-Sensor Fusion
### Map Construction and Loop Closure Detection  
Using the fused sensor data, the algorithm based on graph optimization is used to construct the map after the sensor data processing is completed. The SLAM algorithm framework based on graph optimization is shown in Figure 3.  
Loopback detection. Use the ICP algorithm to match the current laser point cloud information with the sub-map, obtain the pose transformation relationship, and form a closed-loop constraint inserted into the back-end optimization. The closed-loop detection process is to judge the similarity between the relative position of the mobile robot in the laser point cloud and the relative position of the carrier in the sub-map through the detection mechanism. The optimal candidate pose is calculated by optimization means, and the paranoia is eliminated to complete the loop closure detection.  
Assume that the LiDAR feature point of the current frame is \(P_{i}\). The carrier position coordinate point obtained by EKF fusion is the center of the circle, and \(R\) is the radius for pose distance detection. The detected point cloud of the target frame whose distance from  
Figure 3: SLAM algorithm framework based on graph optimization.  
the current pose is less than \(R\) is taken as the point cloud \(P_{j}\) to be detected. The detected point cloud of the target frame whose distance from the current pose is less than \(R\) is taken as the point cloud \(P_{j}\) to be detected. Then the ICP algorithm is run on \(P_{i}\) and \(P_{j}\) to output pose vector \(f\), and the loopback is considered to be detected. After obtaining \(f\), the position \(k_{t}\) of the detected pose in the historical pose can be calculated. Finally, optimize and correct the vehicle pose between the \(k_{t\_1}\) and \(k_{t}\) frames, and output the corrected pose trajectory and map.
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 6 The Impact of Processing on Vitamin D Levels in Foods and Mitigation Strategies
### Impact of Processing on Vitamin D Levels
#### 6.1.3 Light Exposure  
Vitamin D is sensitive to UV and visible light, leading to degradation through photo-oxidation. Foods exposed to light during processing or storage, especially those in transparent packaging, are at risk of losing their Vitamin D content. [PERSON] et al. have demonstrated the effects of package light transmittance on the vitamin content of milk, showing that transparent packaging led to more pronounced losses in Vitamin D3 compared to pigmented or less transparent packaging. This aligns with the general observation that light exposure can degrade sensitive vitamins like Vitamin D3 [109]. Furthermore, Vitamin D3-enriched bread exposed to light for 2 h showed a reduction of 15-20% in Vitamin D content, underscoring the sensitivity of both forms of Vitamin D to light during post-baking storage [110]. In detail, D2 and D3 vitamins exhibit different susceptibilities to oxidative degradation, with Vitamin D3 generally considered more stable, due to its structure, under similar conditions [110]. The impact of processing on Vitamin D underscores the importance of considering strategies to mitigate nutrient losses in processed foods.
## 1 Introduction
The simultaneous application of both spectroscopic methods enables direct comparison of the time-dependent concentration courses of the analytes. Moreover, the functionalities of Raman versus MIR-ATR spectroscopy have been discussed for bioprocess monitoring. The analytes monitored in this work were glucose, ethanol, and biomass. Since the fermentation media's composition is complex, multivariate methods for the evaluation of the Raman and MIR spectra are often required [15]. In this study, PLS regression models [14; 25; 26] and MLR models [27; 28] were applied to link the measured light intensities (Raman) or absorbances (MIR) to the analyte concentrations. As a reference method for the determination of glucose and ethanol, high-performance liquid chromatography (HPLC) was used. The biomass was determined gravimetrically. The simple data evaluation by MLR models can be used to simulate the analytical performance of sensor setups with two or more measurement channels [10] and to check if miniaturized sensors can replace spectrometer setups without loss of accuracy. To see whether this reduced spectral information yield similar good results as the PLS evaluations (with and without data pre-processing) of spectra, two different MLR models have been developed: a 4D MLR model based on four spectral ranges and a 2D MLR model that uses only an analyte-specific measurement channel and a spectrally-adjacent reference channel. For Raman and MIR spectroscopy in combination with the different multivariate calibration models (PLS, 4D MLR, 2D MLR), the achieved performance parameters root-mean-squared error of calibration (RMSEC) and root-mean-squared error of prediction (RMSEP) were compared. Therefore, the aims of this contribution are:  
1. to investigate if MLR models can offer as good performance parameters as the sophisticated PLS evaluation of spectra. This approach enables estimating if spectrometer setups can be replaced by miniaturized and low-priced multi-channel Raman or MIR-ATR sensors without loss of accuracy. the simultaneous application of Raman and MIR spectroscopy for real-time monitoring of yeast fermentations, allowing a direct comparison of both methods with regard to process implementation.
## 5 Parameter Analysis
### Surface Surcharge Size \(L\times B\)  
Based on the model parameters of new and existing tunnels, only the tunnel-load spacing \(d\) is changed in the following analysis. Figure 16 shows the relationship between the maximum longitudinal displacement of the tunnel and the length \(L\) of the surcharge. As shown in the figure, for a surcharge with a fixed width and the same surcharge and tunnel spacing, the maximum longitudinal displacement of the tunnel increases gradually as the length of the surcharge increases, but the growth rate gradually decreases. As the surcharge length increases from 8 m to 72 m, the maximum longitudinal displacement of the tunnel increases significantly. When the surcharge length increases from 72 m to 96 m, the increase in the maximum displacement is only 1%. For the same tunnel, when the length of the surcharge is greater than 72 m, the maximum displacement of the tunnel tends to be stable for different surcharge spacing \(d\). As the tunnel-load spacing \(d\) increases, the maximum longitudinal displacement of the tunnel decreases considerably. The analysis shows that the maximum displacement of the existing tunnel is affected by the space between the overload and tunnel.  
Figure 15: Influence of tunnel–surcharge angle on the maximum longitudinal displacement of the existing tunnels.  
Similarly, Figure 17 shows the relationship between the maximum displacement of the tunnel and the width of the load for different tunnel-load spacing and a constant load length. As the surface surcharge width increases from 8 m to 72 m, the maximum displacement of the tunnel gradually increases, and the change rate gradually decreases. As the load width increases to 96 m, the maximum displacement of the tunnel gradually decreases. The increase in maximum displacement tends to be stable when the load width is greater than 72 m. The values of maximum longitudinal displacement of the tunnel for different spacing \(d\) converge toward the same value, and the rate of change tends towards 0 as the width \(B\) increases. Therefore, the maximum longitudinal displacement of the tunnel is no longer affected by the tunnel-load spacing as the width of the load increases.  
According to the above analysis, the maximum longitudinal displacement of the tunnel is related to the buried depth of the tunnel \(z_{0}\), the tunnel-s  
Figure 16: Influence of surface surcharge length L on the maximum displacement of the existing tunnel.  
Figure 17: Influence of surface surcharge width B on the maximum displacement of the existing tunnel.  
surcharge size \(L\times B\), and the tunnel-surcharge angle \(\alpha\). Next, previously published cases of the response of an existing tunnel to the overload are analyzed to verify the reliability and practicability of the prediction formula for the maximum longitudinal displacement of the tunnel.
# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 3 Methodology
### Semantic Segmentation Module
#### 3.4.2 Refine Segmentation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Main Steps** & **Operation Content** \\ \hline \multirow{3}{*}{Step 1} & Select a semantic mask from the semantic segmentation results of the image by the BiSeNet V2 network. After the initial seed point \((u_{0},v_{0})\) is determined by Equation (2), the corresponding 3D coordinates \((x_{0},y_{0},z_{0})\) of the seed point are determined according to the camera internal reference. Add the three-dimensional coordinates \((x_{0},y_{0},z_{0})\) to the semantic point cloud set \(P\). \\ \hline \multirow{3}{*}{Step 2} & Taking the seed point \((u_{0},v_{0})\) as the center. In the range of semantic mask, search the spatial coordinates \((x_{i},y_{i},z_{i})\) that correspond to the eight-pixel points \((u_{i},v_{i})\) around the seed point. Calculate the Euclidean distance between \((x_{i},y_{i},z_{i})\) and the spatial coordinates of the seed point \((x_{0},y_{0},z_{0})\). If the distance is less than the threshold \(T\), \((u_{i},v_{i})\) is pressed into the stack and \((x_{i},y_{i},z_{i})\) is added to \(P\). \\ \hline \multirow{2}{*}{Step 3} & Take a pixel from the stack as a new seed point \((u_{0},v_{0})\), and return to step 2. When the stack is empty, the regional growth of the current semantic mask ends, and the point cloud set \(P\) is returned as the optimized result. \\ \hline \multirow{2}{*}{Step 4} & Return to Step 1, optimize the next semantic mask, and end the algorithm after all masks are completed. \\ \hline \end{tabular}
\end{table} 
Table 1: Region Growth Algorithm with Depth Information. Figure 7: Schematic diagram of 3D semantic annotation.
# Covert Channel Based on Quasi-Orthogonal Coding
## 4 Simulation Tests  
A preliminary evaluation of the feasibility of using the system in Figure 2 to receive a covert signal in the presence of a channel estimation error resulting in a non-perfect SIC was carried out in the MATLAB environment. The following graphs are shown in the figures to better illustrate the phenomena taking place:  
1. [label=()]
2. Averaged FFT spectrum of cover and covert signal (before and after sorting)
3. Averaged value and variance of cross-correlation of signals
4. Probability distribution of cross-correlation of signals estimated using histograms  
In order to provide an understanding of the phenomena occurring during the sorting process, the signals were assumed to be unnoisy and of equal power to produce charts containing spectra and cross-correlations (Figure 5a-f). This corresponds to the situation when the \(\hat{x}\) signal in Figure 2 consists of a cover and covert signal of equal strengths. This is to show how distinguishable the signals are in the time and frequency domain.  
Detection capabilities Figure 5f were investigated in accordance with the diagram in Figure 1 (system without sorting) and Figure 2 (system with sorting), assuming that the signal-to-noise ratio (SNR) of the channel (calculated for the aggregate signal) is 45 dB, and the estimation error of channel \(\varepsilon\) has a variance \(\sigma_{\varepsilon}^{2}\). It was assumed that the covert symbol is transmitted with \(l\) IQ samples that are submultiples of the number of cover symbols  
Figure 3: 64 QAM signal. (a) Random and sorted (\(x_{1\_sort,l}\)) 64 QAM signal-real part of the signal, (b) Random and sorted 64 QAM signal-imaginary part of the signal (c) Sorted 64 QAM signal with frequencies of \(f_{0}\) and \(16\cdot f_{0}\) (d) Instantaneous FFT spectrum for 64 samples of random and sorted signals.  
for which the sorting operation is performed. The simulation was performed for 10,000 (for each value of \(\sigma_{2}^{2}\)) executions of a random sequence of cover and covert data. It was assumed that the value \(\sqrt{P_{1}}=1\), \(\sqrt{P_{2}}=0.005\).  
In the case of frequency modulation, it was assumed that the M-FSK covert signal [25; 26; 27; 28; 29] in the baseband is defined as:  
\[g(t)=\exp(i\cdot\pi\cdot k\cdot\Delta f\cdot t+\theta) \tag{12}\]  
\(k=\pm 1,\ \pm 3,\ \ldots\pm M/2\), and \(\Delta f\) is a frequency deviation, \(\theta\) random initial phase (random for each symbol).
## 2 Overview of IoV
### Architectural Framework for IoV  
Different layered architectures for IoV frameworks have been proposed in the literature [27; 45; 51; 55], which differ in their design and framework for optimum functionality. Achieving an optimized number of layers and enhancing the differentiability among layers are the keys towards efficient IoV framework [27]. Among the vital considerations for an IoV framework are a strong integration with the internet, a service-based focus, and a plug-and-play based interface [27; 45; 51; 55]. Some of the major components in an IoV architectural framework include the perception layer, the coordination layer, the artificial intelligence (AI) layer, the application layer, and business, security, processing, service, and physical layers [27; 51]. [PERSON] et al. [27] reports that the six-layer architecture is the most common design for IoV implementation. The components considered in the framework include the physical, communication, processing, services, business, and security layers. Figure 3 vividly depicts the six-layer architectural design of the IoV system.
## 3 Prediction and Future of EVs in the UK
### Data Preparation  
The following datasets have been provided for this research (Tables 1 and 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**Target Share (\%)**} \\ \cline{2-5}  & \multicolumn{1}{c}{} & NA \\ \hline
2024 & 22\% & \\ \hline
2025 & 28\% & \\ \hline
2026 & 33\% & \\ \hline
2027 & 38\% & \\ \hline
2028 & 52\% & \\ \hline
2029 & 66\% & \\ \hline
2030–2035 & 80\% + 4\% (per year) & \\ \hline
2035 & 100\% & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Zero-emission vehicle (ZEV) mandate policy of the UK.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**(A)**} \\ \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Total EV**} & \multicolumn{1}{c}{**Battery Price**} & \multicolumn{1}{c}{**GDP Growth**} & \multicolumn{1}{c}{**Charger**} \\  & \multicolumn{1}{c}{**Market (Unit)**} & \multicolumn{1}{c}{**(USD/kWh)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**Density Index**} \\ \hline
2015 & 26,000 & 230 & 2.2 & 1 \\ \hline
2016 & 35,500 & 180 & 1.9 & 1.51 \\ \hline
2017 & 46,300 & 140 & 2.7 & 2.07 \\ \hline
2018 & 57,000 &  
* **Historical sales (2015-2024):** Society of Motor Manufactuerers & Traders (SMMT) registration data [18; 19].
* **Exogenous regressors:** Real lithium-ion battery pack price index [20]; UK GDP growth (ONS) [21]; public charge-point density [22]; UK zero-emission vehicle (ZEV) Policy [23].
* **Seasonality:** Monthly seasonality captured via Fourier terms; UK public-holiday effects enabled in Prophet. All series are converted to monthly frequency and log-transformed where appropriate to stabilize variance.  
It is worth mentioning that a potential limitation of this study is the relatively short historical window--only ten annual observations from 2015 to 2024--used for model training. While this raises legitimate concerns about statistical power, the decision to focus on this period is deliberate and well-justified. Prior to 2015, EV adoption in the UK was minimal, with sales volumes too low to yield meaningful trend signals or model training value. The period from 2015 onward captures the inflection point and sustained growth phase of the EV market, supported by policy interventions, infrastructure expansion, and industry momentum. Including earlier data would introduce disproportionate sparsity and could distort model calibration. Thus, the 10-year window reflects the most informative and relevant segment of the EV transition, even if it imposes some constraints on sample size.