esa-sceva/satcom-chunk-collection huggingface dataset:
# The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Standley Model
## 2 Fuzzy-Based Stanley Model  
In the path tracking of agricultural machinery, the bicycle model is often used for kinematic analysis, where it is assumed that the agricultural machinery is running on a smooth road, and only longitudinal pressure is generated between the tires and the ground.  
As shown in Figure 1, \(\delta\) is the front wheel rotation angle, \(\theta\) is the heading angle of the vehicle and \(L\) is the distance between the front and rear axles of the vehicle. Kinematic analysis of this model can be summarized as  
\[x^{\prime}(t)=v\sin\theta\] \[y^{\prime}(t)=v\cos\theta\] \[\theta^{\prime}(t)=\frac{v\tan\delta}{L} \tag{1}\]  
where \(v\) is the vehicle longitudinal speed; \(x^{\prime}(t)\) is the vehicle speed in the \(x\)-axis direction; \(y^{\prime}(t)\) is the vehicle speed in the \(y\)-axis direction; and \(\theta^{\prime}(t)\) is the vehicle angular velocity. The simplified bicycle kinematic model will be employed in our simulation.  
SM needs parameter adaptation similar to other geometric-based path tracking. Typical parameter self-adaptation methods include the intelligent search algorithm [19], neural networks algorithm [20] and fuzzy algorithm [21]. Intelligent search algorithms mainly include the genetic algorithm [22], PSO [23] and differential evolution algorithm [24]. The neural networks algorithm can be classified into BP [25], RBF [26], PINN [27] and GRNN [28] according to their excitation functions. Compared with other methods, parameter self-adaptation based on fuzzy algorithm has the advantages of low computational load and strong robustness, which is suitable for online implementation in an embedded controller [29]. In this section, the fuzzy algorithm is used to adjust the output gain of  
Figure 1: Vehicle kinematics model.  
SM, which provides an algorithm basis for global intelligent optimization under varying environmental disturbances.
## 5 Experimental Results
### Comparison with Tesseract for Key Character Recognition  
As we know, Tesseract OCR (Optical Character Recognition) software provided by Google is well-known for character recognition. To compute the AP rate, the threshold of IoU is determined as 0.5. Here, there are 112 ROIs extracted from the testing set for performance evaluation. Figure 15 shows the AP rates of the proposed scheme and Tesseract OCR software. As we can see in Figure 15, the proposed scheme can detect these key characters from noisy document images and is compared with Tesseract OCR software. Furthermore, the AP rates of the Tesseract OCR tool are at most 36.2%, shown in Figure 15. This means that the Tesseract OCR tool cannot deal with noisy document images well.  
The AP rates of the proposed scheme are at least 56% for key characters in noisy document images. Therefore, the experimental results demonstrate that the proposed scheme is superior to Tesseract OCR software for detecting key characters in noisy document images.  
Figure 14: The AP results of the proposed keyword-detection algorithm.
# The Reproduction and Evaluation of Star Fields with the Milky Way in a Planetarium
## 2 Experiment
### Experimental Stimulus  
We selected stars around Scorpius (constituting 1/32 of the night sky) as experimental stimuli because the area around Scorpius has more stars and thus, presents a brighter segment of the Milky Way than other areas. Therefore, the area around Scorpius is a suitable star field to assess the reproduction of the Milky Way, as it ensures that observers can detect a difference when the reproduction pattern changes. For the faithful reproduction of a starry sky, including the Milky Way, we considered the astrophysical factors in the real environment, such as the brightness of the surrounding environment, effect of light pollution, and gradation by zenith angle. The twinkling of stars is caused by atmospheric extinction [11]. However, we excluded external factors such as atmospheric extinction in this experiment after determining that it was better to display a temporally stable star image. Therefore, the twinkling of stars was not included to prevent observers from paying attention to the twinkling star, instead of the Milky Way. In addition, it was necessary to eliminate the influence of sunlight reflected within the solar system (zodiacal light) on the perception of stars and that of air and air lights from the Earth's atmosphere (light pollution), to avoid distracting the observers. The experiments were conducted in complete darkness to reproduce an environment of scotopic vision, which is typical of a general planetarium environment.  
We prepared 12 types of experimental patterns by changing the transmittance, representation size and star density of the star image. Table 1 lists the projection patterns used as experimental stimuli. As explained previously, we assumed that the transmittance, representation size and star density in the Milky Way influenced the evaluation of the star image reproduced in the planetarium. Therefore, we first determined a standard pattern (Std), following the procedure described in Section 2.2.1. This pattern ensures perceptual faithfulness to the real starry sky. Second, we prepared additional patterns in which an individual parameter or a combination of parameters had been changed. We provide a detailed explanation of each pattern in the following subsections.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Pattern**} & \multicolumn{2}{c}{**Changed Parameters, with**} & \multirow{2}{*}{**Remarks**} \\  & **Std as the Benchmark** & \\ \hline Std & N/A & \\ Std-L1 & Transmittance & Pattern Std but half transmittance \\ Std-L2 & Transmittance & Pattern Std but double transmittance \\ S1 & Size & Pattern Std but half diameter \\ S1-L1 & Size and Transmittance & Pattern S but half transmittance \\ S1-L2 & Size and Transmittance & Pattern S but double transmittance \\ D1 & Density & Pattern Std but 2/3 times density \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of experimental projection patterns.  
Figure 1: Mechanism of optical projection.
## 2 Electrodes and Electrolytes for Lithium-Based Batteries
### Solid Electrolytes
There are glass, glass-ceramic and crystalline sulfide electrolytes (thio-LISICON and argyrodites). Many remarkable ionic conductivities at room temperature were reported with sulfide electrolytes including Li\({}_{2}\)S-P\({}_{2}\)S\({}_{5}\) glass-ceramic (\(1.7\times 10^{-2}\) S/cm) [52], Li\({}_{10}\)GeP\({}_{2}\)S\({}_{12}\) (\(1.2\times 10^{-2}\) S/cm) [53] and Li\({}_{9.54}\)Si\({}_{1.74}\)P\({}_{1.44}\)S\({}_{11.7}\)Cl\({}_{0.3}\) (\(2.5\times 10^{-2}\) S/cm) [27]. Argyrodites, named for the mineral Ag\({}_{8}\)GeS\({}_{6}\), are the class of solids that have F-43 m or P\({}_{2}\)13 structure. Deiseroth et al. [225] reported lithium argyrodites (Li\({}_{6}\)PS\({}_{5}\)X, X = Cl, Br, I) that have conductivities ranging 10\({}^{-2}\)-10\({}^{-3}\) S/cm at room temperature. Crystalline Li\({}_{6}\)PS\({}_{5}\)Br samples prepared by mechanical milling followed by annealing showed the conductivity up to \(6.8\times 10^{-3}\) S/cm at ambient temperature [195]. However, some concerns were raised with halogen atoms because they can potentially cause the corrosion of current collectors, in particular, Al. Schneider et al. [196] replaced the halogen atom with Si to form lithium argyrodite, Li\({}_{22}\)SiP\({}_{2}\)S\({}_{18}\), that presented a conductivity of \(3\times 10^{-3}\) S/cm. Recently, based on the fact that the ionic conductivity of argyrodite largely depends on the S\({}^{2-}\)/X\({}^{-}\) mixing at 4d sites, Wang et al. synthesized Br rich argyrodite to enhance S\({}^{2-}\)/Br\({}^{-}\) mixing and achieved a conductivity of \(1.1\times 10^{-2}\) S/cm with Li\({}_{5.3}\)PS\({}_{4.3}\)Br\({}_{1.7}\) at room temperature [197]. In this work, the S\({}^{2-}\)/Br\({}^{-}\) mixing at 4d sites was confirmed with \({}^{31}\)P NMR. Another type of halogen-rich lithium argyrodite, Li\({}_{5.5}\)PS\({}_{4.5}\)Cl\({}_{1.5}\), made by a rapid thermal annealing process also presented ionic conductivity of 10\({}^{-2}\) S/cm at 25 \({}^{\circ}\)C [198]. With such a high ionic conductivity, argyrodite-type electrolytes (Li\({}_{6}\)PS\({}_{5}\)Cl) were used as a solid electrolyte in a prototype pouch cell [226] of all solid-state lithium metal batteries--presenting higher than 900 Wh/L of energy density and long cycle life (1000 cycles). Despite their high ionic conductivity comparable to a liquid electrolyte, sulfide electrolytes showed severe performance degradation when they are exposed to air, requiring an inert environment for their handling and cell assembly.
## 3 Methods  
Since paired haze-free datasets are difficult to obtain in many scenarios, the extensive use of synthetic datasets may lead to overfitting issues in deep learning models, resulting in insignificant dehazing effects in real-world images. Using the CycleGAN unpaired image-to-image translation framework, the image dehazing problem is treated as an image transformation between two distinct style domains. Hazy images are considered the source domain, while haze-free images are regarded as the target domain. The core of the CycleGAN-based image dehazing method lies in mapping the source domain images to the target domain, transforming haze images into haze-free images. However, the physical properties of hazy environments in the real world are often ignored in end-to-end dehazing methods, resulting in generated haze that usually lacks realism and diversity, further impacting the learning of the subsequent dehazing network. To address the abovementioned issues, we propose the FA-CycleGAN model. Using the CycleGAN model can effectively solve the problem of difficult pairwise image acquisition. In the training process, convolu  
Figure 1: The light reflected from the surface of the target object is absorbed and scattered by the suspended particles in the atmospheric medium during propagation, resulting in light attenuation. Ambient light scatters in the atmospheric medium, which affects the brightness distribution of the imaging. Through the superposition effect of these two physical processes, the final imaging exhibits problems such as contrast drop and color distortion.  
tional operations are used to extract the parameters of the atmospheric scattering model and reconstruct the clear image. At the same time, a deep learning model is used to refine the process to make up for the parameter estimation error of the atmospheric scattering model. A feature fusion attention module is introduced into the generator structure for multi-scale feature fusion and different weight assignments, which can retain more feature information and focus on different regions' features to varying degrees. While ensuring the authenticity of dehazed images, more image details are retained, and the dehazing performance of the model is improved.
# Optimal Sizing of Battery Energy Storage System for Implicit Flexibility in Multi-Energy Microgrids
## 2 Methodology
### Loads  
In our case study, we only consider non-dispatchable electric, thermal, and cooling loads. From a modeling perspective, these loads are represented by the parameters \(P_{u,t}^{EL_{total}}\), \(P_{u,t}^{TH_{load}}\) and \(P_{u,t}^{COOL_{load}}\), respectively, which are indexed by both unit \(u\) and time \(t\). These parameters are treated as fixed inputs to the problem since their daily profiles are predefined and not subject to optimization. Consequently, to enable the use of HOMES for day-ahead scheduling, a dedicated forecasting model for energy loads or appropriate assumptions, must be adopted.
# Feature Transformation-Based Few-Shot Class-Incremental Learning
## 4 Simulation Experiments
### Baseline Experiments
On the CIFAR100 dataset, while our method ranks second in per-session accuracy--slightly lower than Cosine--it still achieves the lowest performance degradation, at 24.45 percentage points. Our approach can be viewed as an enhancement to the Cosine method by introducing a feature transformation layer. This layer maps both the support and query samples of few-shot tasks into a new space, refining the estimation of class prototypes and thus improving overall performance. Although our method performs slightly worse than Cosine on CIFAR100, it outperforms all baselines on the other two datasets. Upon further analysis, we attribute this observation to differences in the dataset characteristics and distribution. While our method may have some limitations in terms of robustness, it achieves the best overall performance across all three benchmarks. In addition, several other observations can be drawn from the figures and tables:  
Compared to traditional class-incremental learning methods directly applied to few-shot settings, methods specifically designed for few-shot class-incremental learning achieve significantly better performance. For example, TOPIC, DeepEMD, FeTrIL, PL-FSCIL, and Cosine consistently outperform iCaRL, EEIL, and NCM in terms of both per-session accuracy and overall performance degradation. This highlights a fundamental shift in the nature of the problem under the few-shot constraint: the limited number of samples for new classes increases the risk of overfitting during the incremental process, which in turn degrades model performance. Moreover, methods that keep the feature extractor fixed clearly outperform those that fine-tune the feature extractor during incremental learning. For instance, on the miniImageNet dataset, methods with a fixed feature extractor exhibit an average performance degradation of 25.44 percentage points, while those that fine-tune show a significantly higher average degradation of 42.51 percentage points. Our analysis suggests that this is due to the insufficient number of new class samples, which makes fine-tuning the feature extractor less effective. Whether to fix or update the feature extractor is a classic trade-off in incremental learning. Fixing the feature extractor helps preserve knowledge of previously learned classes but reduces adaptability to new classes. In contrast, fine-tuning may improve performance on new classes but carries a higher risk of catastrophic forgetting. Under the few-shot setting, fixing the feature extractor appears to be a better choice: while it may lead to suboptimal performance on new classes, it helps maintain classification accuracy on base classes. This observation will be further supported by the ablation studies presented later.
## 3 Results  
For the cloud-free case, the characteristic curve with high 10-min availability at lower measurement heights and the subsequent continuous drop up to the maximum measurement height can be seen. This is due to a higher probability of the CNR value not reaching the threshold to be accepted as valid at higher measurement distances. For the analyzed data set, the average 10-min availability under cloud-free conditions is 97.7% at the lowest measurement height (bin center 47.5 m), it slightly increases to a maximum value of 98.4% at height bin 62.5 m, and then drops to 81.7% at height bin 197.5 m (blue dots in Figure 4). In contrast to the CBA \(\leq 100\) m situation (red dots in Figure 4), with rising measurement height a sharper drop in 10-min availability, down to 52.8% at height bin 197.5 m, can be observed.  
Figure 4: Mean 10-min availability on lidar measurement height above ground for cloud-free cases and situations with cloud base altitudes \(\leq 100\) m. The red arrow marks the measurement height at which 10-min availability is below the 80% threshold in the cloud base altitudes (CBA) \(\leq 100\) m case.
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 4 Conclusions  
The advent of AM has expanded the horizons of cellular structure design. In this paper, a new cellular structures classification paradigm has been developed. The design methods for cellular structures have been classified by identifying all the geometric degrees of freedom that are involved in the construction of cellular structures. The classification was brought forth by reviewing the numerous case studies, with representative works highlighted. Moreover, cellular structure generation and CAD manipulation techniques have also been reviewed. The review explored the different design constraints imposed by AM processes on the quality of cellular structures. Finally, manufacturability issues that occur when printing CSs are also highlighted. In the future, more work is needed to develop new simulation methods and design tools for CSs, to also analyze the printing parameters that have an effect on the dimensional accuracy and the properties (mechanical, thermal, etc.) of CSs, to then establish CS design rules that should be implemented at the design stage.  
**Author Contributions:** Cellular structure design classification, overview of representative volume elements, RVE variation methods, RVE variation factors, K.M.N.; CS generation, K.M.N., M.W.; highlights of manufacturability challenges of cellular structures, M.W.; conceptualization, supervision and correction, M.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors appreciate the funding support from the Federal Economic Development Agency for Southern Ontario (FedDev Ontario) grant number 814654.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Typical RVE Design Constraints** & **LPBF** & **SLS** & **SLA** & **FDM** & **BJ** & **EBM** & **MJ** \\ \hline Need for dimensional fidelity & x & x & x & x & x & x & x \\ Need for material removal & x & x & - & - & x & x & - \\ Need for support overhang structures & x & - & - & x & - & - & x \\ Need for avoidance of pore defects & x & x & - & x & x & x & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: The common process design constraints for manufacturability of CS.
## 2 Materials and Methods
### Incubation Study  
Three sets of treatments were performed during the gob spoil soil incubation study. For the first set of treatments, gob spoil soil was thoroughly mixed with compost at three rates (5%, 7.5%, and 10%, \(w/w\)). For the second set of treatments, gob spoil soil was thoroughly mixed with biochar at three rates (5%, 7.5%, and 10%, \(w/w\)). For the third set of treatments, compost and biochar were first mixed at a 1:1 ratio (mixed amendment), and then gob spoil soil samples were amended with the mix at 5%, 7.5%, and 10% application rates (\(w/w\)). For all treatments, 150 g of gob spoil soil with amendments was prepared and 50 g was sampled on day 0. The other 100 g gob spoil soil was incubated in sealed polythene bags for 90 days. As a control, one batch of gob spoil soil was incubated without any amendment. All gob spoil soils were maintained at 70% of water-holding capacity at room temperature [23]. Periodic samplings (-15 g each) were carried out on days 7, 30, 60, and 90. All experiments were performed in triplicate. The incubation length of 90 days was determined based on the characterization results of the samples. Once the samples equilibrated, and insignificant changes were identified, the incubation study was stopped.
# Application of Label Correlation in Multi-Label Classification: A Survey
## 4 Applications of Label Correlation in MLC Methods
### Global-Local MLC Methods Considering Only Positive Label Correlation  
Previous research has primarily focused on utilizing global or local label correlations separately, while current studies on MLC aim to integrate both global and local correlations. To establish a unified learning framework that simultaneously addresses global and local label correlations, researchers have proposed various strategies and methods to achieve this goal.  
[PERSON] et al. [66] proposed the GLOCAL method to investigate label correlations. This method is based on the assumption that \"the stronger the positive correlation between labels, the closer the corresponding classifier outputs will be, and vice versa\". It employs manifold regularization by introducing global and local manifold regularizers to leverage both global and local label correlations in addressing both complete and missing labels. [PERSON] et al. [35] introduced the MLFS-GLOCAL method, which selects discriminative features among multiple labels by utilizing both global and local label correlations. The LCFM multi-label classification method proposed by [PERSON] et al. [71] accurately extracts label correlations by learning global and local label-specific features, effectively tackling the issue of missing labels. [PERSON] et al. [67] proposed the GLkEL method, which employs a higher-order label correlation assessment strategy based on approximate joint mutual information, selecting the most relevant k-label sets from the label space to evaluate global correlations. Additionally, this method clusters the training data into different groups, assesses local label correlations within each group, and linearly combines global and local correlations.  
[PERSON] et al. [69] calculated pairwise label similarities across the entire label space to create a global label correlation matrix. They then assigned a local label correlation matrix to each instance subset based on the cosine similarities of label vectors within clusters. Finally, they integrated global and local label correlation regularization terms into the objective function. [PERSON] et al. [70] introduced the LFCLC method, which determines global label correlations from label co-occurrence frequencies between pairs and learns local label correlations from the neighborhoods of individual instances.  
Overall, considering both global and local label correlations has demonstrated significant practical utility and advantages in real-world applications. However, this approach inevitably increases the complexity of the methods, requiring a balance between implementation efficiency and performance.