esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Volatiles in Lapsang Souchong Black Tea of Different Grades  
A total of 73 volatile compounds were identified in the various grades of Lapsang Souchong black tea (Tables S1-S3, Figures S1-S3). These compounds encompassed alcohols (n. 7), arenes (n. 4), phenols (n. 13), aldehydes (n. 7), terpenes (n. 12), ketones (n. 5), esters (n. 3), and others (n. 22) (Figure 2a). In terms of the variety of aromatic components, the order was C \(>\) B \(>\) A (Figure 2b). PCA analysis indicated differences in the aroma components of the Lapsang Souchong black tea samples across grades (Figure 2c). Principal component 1 accounted for 92.82% of the variation, while principal component 2 explained 4.27%, with R = 0.9918 and \(p\) = 0.006, reaching significance. Among the volatile components detected, terpenoids had the highest total relative content, followed by phenols and aldehydes (Figure 2d). Heat map analysis (Figure 2e) revealed that terpenes, alcohols, and phenols were significantly enriched in super-grade tea (A). Terpenes typically contribute floral, fruity, and woody notes [18]. Key compounds like Geraniol and D-Limonene possess floral or floral-fruity aromas [18; 19]. Alcohols often present green, woody, and floral scents [20; 21], with compounds such as 2-Furammethanol providing strong woody-floral aromas [22].  
In contrast, first-grade tea (B) showed a notable presence of terpen such as \(\alpha\)-Terpineol and Citral, all associated with floral and fruity aromas. Second-grade tea (C) exhibited a significant quantity of phenolic compounds, including various phenols known for their antioxidant and anti-inflammatory properties, which can enhance the complexity of tea aromas [23]. These phenolic compounds are primarily generated during the oxidation of polyphenols during fermentation, contributing to the flavor and aroma profiles of black tea [24].  
Overall, the highest concentration of terpenoid compounds was found in Lapsang Souchong black tea, followed by phenols and aldehydes, which are generally linked to fermentation and floral characteristics. The analysis highlighted significant differences in the aroma components of Lapsang Souchong black tea across grades, particularly in the presence of floral and fruity terpenoid compounds. Further investigation is warranted to identify key differential volatile compounds in Lapsang Souchong black tea of varying grades.  
Figure 2: Analysis of volatile compounds and their classification in three grades of Lapsang Souchong black box: (**a**) Duoghnut chart illustrating the distribution of volatile compounds across all samples. (b) Venn diagram comparing the presence of compounds in grades A, B, and C. (c) Principal component analysis (PCA) showcasing the variance among the grades. (d) Relative abundance of various volatile compounds in grades A, B, and C. (e) Heatmap representation of the normalized relative content of each volatile compound, with rows corresponding to tea samples and columns to individual volatile components. Grades A, B, and C denote special grade, first grade, and second grade, respectively.
# Distributed Consensus for Global Matrix Formation in the Principal Component Pursuit Scenario
## 1 Introduction
In scenarios where the cost of communication between agents is prohibitively high--such as in privacy-sensitive environments, conflict zones, or geographically remote areas where energy resources are scarce--it becomes imperative to minimize inter-agent communication while still achieving an accurate consensus on matrices \(L\) and \(S\). This research introduces an innovative approach that significantly reduces the necessity for frequent  
Figure 1: Removing shadows, peculiarities, and saturation from face images [16]. Figure 2: Schematic of PCA filtering applied to corrupt flow-field data [14]. communication by optimizing the local computations at each agent. We propose a method where agents communicate only the essential information required to converge towards a unified decomposition of the global matrix \(M\). This methodology leverages the inherent efficiency of local optimization steps, thus enabling the recovery of \(L\) and \(S\) with minimal communication overheads. Our approach is particularly suited for a distributed principal component analysis (PCA) under the constraints of a high communication cost or stringent privacy requirements. We demonstrate through theoretical analysis and empirical validation that this strategy not only preserves the accuracy of matrix decomposition but also ensures a substantial reduction in communication costs. The feasibility and effectiveness of our method are supported by recent advancements in distributed optimization and consensus algorithms that emphasize communication efficiency [25; 26]. More specifically, we propose an algorithm to recover a low-rank and a sparse matrix in a static undirected graph where each agent measures some components of the global corrupted matrix. The convexification process to relax the PCA into a computationally efficient problem is employed along with the Alternating Direction Method of Multipliers (ADMM) [27]. An application of mapping and image recovery is shown, demonstrating exponentially fast convergence.
## 5 Assessment of Algorithms Against Prior Research Results
The results indicate that RDE outperforms MLD and DFE algorithms in terms of EVM, demonstrating an improvement of 5.71% over MLD and 5.25% over DFE for QPSK. LMS, CMA, and AMF proved ineffective for a 120 km SMF length for our proposed setup. In the case of 16-QAM, RDE demonstrates superior EVM performance compared to MLD, DFE, LMS, and AMF algorithms. RDE demonstrates an improvement in EVM of  
Figure 8: (**a**) Comparisons of EVM results for MLD, DFE, and RDE obtained before and after equalization vs. the fiber lengths for QPSK constellation against LMS, CMA, and AMF algorithms, and (**b**) comparisons of EVM results for MLD, DFE, and RDE obtained before and after equalization vs. the fiber lengths for 16-QAM constellation against LMS, CMA, and AMF algorithms. 2.99% compared to MLD, 2.91% compared to DFE, 14.34% compared to LMS, and 13.59% compared to the AMF algorithm, whereas CMA was ineffective for 16 QAM. These findings highlight the superior performance of MLD, DFE, and RDE algorithms not only in improving signal quality (EVM for QPSK and 16-QAM) but also in extending the transmission distance and demonstrating robustness and effectiveness in high-performance optical communication systems. Moreover, when higher-order modulation schemes, such as 64-QAM and 128-QAM, are used for equalization approaches, like LMS, CMA, RDE, and AMF, do not give meaningful results. At the same time, the proposed MLD and DFE algorithms demonstrate their effectiveness in managing the challenges posed by higher modulation formats and dispersion in optical fibers, preserving signal quality and meeting the 3 GPP EVM threshold limits. Table 6 provides a comprehensive summary of the various performance indicators associated with all equalization algorithms implemented in the simulation setup.
# State-of-Charge Prediction Model for Ni-Cd Batteries Considering Temperature and Noise
## 1 Introduction
Manual parameter adjustment proves challenging in attaining the optimal model parameters, hindering its adaptability to varying battery working conditions. Furthermore, the prediction outcomes are vulnerable to noise interference and ambient temperature fluctuations. To address these limitations, we propose an adaptive particle swarm optimization APSO-GRNN prediction model. The APSO-GRNN model employs the particle swarm algorithm to optimize the parameters of the GRNN model. It incorporates a scoring function to evaluate the prediction effectiveness of each neuron within the model. Additionally, it dynamically adjusts the weight of the particle swarm's velocity based on the prediction results. This adaptive adjustment allows the model to prioritize training data that exert a more significant influence on the current SOC prediction, thereby mitigating the impact of battery rebound voltage on the model's prediction outcomes under various working conditions [24]. Furthermore, it enhances the model's resilience to diverse ambient temperatures. To assess the robustness and anti-interference capability of the APSO-GRNN model, experiments are conducted considering two factors: noise and temperature. A comparative analysis is performed, where APSO-GRNN is compared with SVR, XGBoost, and GRNN to elucidate the advantages of APSO-GRNN over other prevalent regression algorithms. Ultimately, the efficacy of the APSO-GRNN model is confirmed based on the experimental findings.
## 2 Proposed Architecture
### Recovery Analysis
#### 2.2.1 SEU at Q  
When an SEU flips the state of node Q from '1' to '0', the transistors N2 and N6 are turned off, while P7 is activated. This scenario keeps the pull-up paths P2 and P4 for the nodes QB and S0 in the off state, resulting in both nodes entering a high impedance (high-Z) state while still maintaining their initial value of '0' [4]. For the S1 node, although P7 is activated, N1 remains off, allowing the S1 node to retain its original state. Consequently, the unaffected S1 node enables node Q to restore its initial state of Vdd through the action of transistor P3.
# Review of Recent Advances in QEPAS-Based Trace Gas Sensing
## 6 3D-Printed QEPAS Sensor  
To be useful in practical applications, a QEPAS sensor should be portable and small in terms of size, weight, and power consumption. Typically, a group of block-shaped lenses is used for laser beam propagating and transforming [77; 78]. As a consequence, it results in unstable configuration and large size and therefore limits the sensor's performance and real applications. The 3D printing technique does not need traditional machine tools and multi-machining process. It holds the merits of \"free manufacturing\", reduction in processing time and procedure, high integration and stability, and so on. A 3D-printed QEPAS acoustic detection module (ADM) was first demonstrated by [PERSON] [79; 80].  
Figure 11: Schematic of the FEW-QEPAS sensor.  
Figure 10: FEW power as a function of fiber diameter for three tapers (inset: optical field distribution for fiber taper).  
The designed 3D model of ADM and the 3D-printed product are displayed in Figure 12. A UV-curable resin was used as the processing material for the 3D printing. The 3D-printed ADM had dimensions of 29 mm in length, 15 mm in width, and 8 mm in thickness, and was sealed by a quartz glass window. A fiber-coupled grin lens with a diameter of 1.8 mm was adopted for laser focusing. The optical components of fiber and grin lens and the photoacoustic detection components of QTF and a pair of micro-resonators (mRs) were assembled together. The total weight of the 3D-printed ADM, including all the above components, was \(\sim\)5 g.  
A portable QEPAS sensor was further developed based on the above 3D-printed ADM. The QEPAS system contains two floors, in which Floor I is for the circuitry section, including a diode laser driver, a custom lock-in amplifier, and a transimpedance amplifier. In order to facilitate its usage outside, a rechargeable battery with running time of 8 h was used as the power supply. Floor II is for the optical section, in which a fiber beam splitter (FBS) with a splitting ratio of 1:9, an integrated ADM, and a reference cell were assembled. The portable QEPAS sensor had a dimension of 250 \(\times\) 250 \(\times\) 50 mm\({}^{3}\). The weight of it was \(\sim\)3.5 kg.
## 3 Modeling Approach
### Establishment of Spatial Vibration Analysis Model for Medium-Low-Speed Maglev Train  
#### 3.3.1 Basic Assumption  
The medium-low-speed maglev train system is structurally composed of the following two primary subsystems: the carbody and the levitation frame. A standard train mode shape adopts a three-unit configuration, with each unit equipped with five levitation frames. Interconnection between the carbody and levitation frame is achieved through air spring. Considering the system complexity and operational characteristics at medium-low speeds, establishing a fully detailed dynamic model faces both theoretical and computational difficulties, particularly for the practical engineering analysis of such systems. Thus, this study utilizes a simplified train model for dynamic behavior investigation. To maintain the engineering relevance and computational reliability of the simplified modeling approach, the following fundamental assumptions are applied:  
1. Both structural components (carbody and levitation frame) are considered as rigid bodies, with their elastic demode shapes disregarded.
2. Suspension elements are represented as linear springs, accompanied by equivalent viscous damping for energy dissipation modeling.
3. System dynamics are analyzed within the small vibration range.
4. Longitudinal vibrations along the guideway direction are deemed insignificant for medium-low-speed operations.
5. The dynamic interaction from guideway longitudinal vibrations is not included in the current analysis scope.
## 5 Discussion  
The AR results in this paper were compared with the results over grassland from the literature. In the present study, the average AR value at the A'rou grassland station was 0.0101 m, and the AR values ranged of 0.0028 to 0.0234 m during the growing period. [PERSON] (1999) provided a general reference AR value for prairie grassland of approximately 0.03 m ([PERSON] et al., 2002). [PERSON] et al. (2002) studied the AR over the grassy marshland surface of the Tibetan Plateau using in-situ turbulent data and obtained an AR value of 0.00466 m at Anduo for grass heights of 5 cm and a value of 0.0139 m at the HPAM for grass heights of 15 cm ([PERSON] et al., 2003). [PERSON] et al. (2003) determined the AR at Anduo PBL and Naqu, which are covered with sparse and short grasses, in the monsoon season based on wind and temperature profiles, and the order of magnitude of the AR was from 0.001 to 0.01 m ([PERSON] et al., 2003). Thus, the AR results in the present paper are comparable to previously published in-situ results.  
The relationship between the average wind speed at the height of 1 m and the in-situ AR on the eight-day scale (Figure 6) indicates that the average wind speed has a significant influence on the AR over grassland ([PERSON] et al., 2002; [PERSON] et al., 2003), with \(R^{2}\) values reaching 0.7411 in the form of an exponential power function, resulting from Equation (5). However, the wind speed over land surfaces is difficult to obtain from remote sensing; therefore, field meteorological observations are conducted at the single-site level and spatial interpolations are performed for the limited number of ground stations at the spatial-distribution level. Therefore, wind speed is likely a limiting factor for the use of remote sensing applications in large regions. The findings in the present study indicate that a reasonable combination of the MODIS NIR BRDF_R index and NDVI can obtain a good fit and present an \(R^{2}\) value of 0.721, which is close to the value obtained using wind speeds. The CI provides a significant new method of estimating temporal AR values over grassland via remote sensing. However, there is also some limitation for the CI, as it was developed under grassland during its growing season with NDVI greater than 0. As a result, it is probably not suitable for poorly vegetated areas. In the next step, we will attempt to develop another index to estimate AR for sparse or non-vegetated areas.
## 3 Study Methods and Evaluation Process
### Selection and Quantification of Indicators
#### 3.1.2 Evaluation Indicator System  
This paper uses the hierarchical analysis method to construct an index system for evaluating the suitability of urban underground space resources for development and use. To evaluate the suitability of urban underground space resource development using the AHP method, it is necessary to first hierarchize and classify the problem. The problem is decomposed into different constituent elements, and afterward, according to the association and affiliation between the elements, different levels of clustering are combined to construct a multi-level hierarchical model, where the factors of the upper level play a dominant role over the elements of the lower level by a certain criterion, and these levels can usually be divided into three categories:  
Target level (A): represents the objective of the assessment decision and usually has only one element.  
Guideline level (B): is the intermediate level involved in achieving the objective level and consists of 1 or more levels, also known as the intermediate or indicator level.  
The bottom layer (C): consists of several evaluation elements that affect the objectives, also known as the measure layer, program layer, etc., and is a key part of building an evaluation system.  
Figure 2: Overall technical route of urban underground space resource assessment.  
In this evaluation work, the evaluation of the suitability of underground space resources development in the evaluation area is taken as the target layer (A), and the hydrogenological conditions (B\({}_{1}\)), engineering geological conditions (B\({}_{2}\)), environmental geological conditions (B\({}_{3}\)), and human engineering activities (B\({}_{4}\)) are taken as the secondary indicator layers; the bottom layer selects the main factors affecting each condition of the secondary indicator layers.  
In this paper, 11 factors, including minimum depth of groundwater, groundwater richness, groundwater quality, topography and geomorphology, distance from fracture structure, lithological combination, avalanche, and slip flow, mining collapse, seawater intrusion, ground space feature type, and developed underground engineering, are selected as influencing factors. The index system for evaluating the suitability of underground space resource development in Weihai City is shown in Figure 3.
# How Resilient Are Kolmogorov-Arnold Networks in Classification Tasks? A Robustness Investigation
## 5 Results and Discussion
### KAN and MLP Models
Under the FGSM attack with \(\epsilon=0.01\), the KAN model achieved 27.97% accuracy on the GTSRB, while MLP reached 34.76%. As \(\epsilon\) increased to 1, MLP maintained a significant advantage with 1.14% accuracy, compared to the KAN's 0.25%. SAR values were lower in randomized smoothing, with MLP showing better resistance across most attacks. For the BIM and PGD attacks, MLP consistently outperformed the KAN, achieving 8.57% and 4.98% accuracy, respectively, while the KAN reached 5.61% and 2.70%. In CW attacks, MLP achieved 38.92% accuracy on GTSRB, surpassing the KAN's 29.54%. In comparing the three training methods, randomized smoothing consistently outperformed both standard and adversarial training, particularly for MLPs, which achieved higher accuracy and lower SAR values. The KAN method showed some resilience but was less robust than MLPs, especially against high-intensity attacks like BIM and PGD. While adversarial training offered moderate improvements, it was less effective than randomized smoothing against stronger adversarial attacks. Figure 7 illustrates the loss values during the training of the KAN and MLP models across the GTSRB, BTSRB, and CTSRD datasets using different training strategies: standard, FGSM, and randomized smoothing. The \(x\) axis represents the training epochs, while the \(y\) axis shows the corresponding loss values. In the standard training scenario, both models demonstrate a sharp decline in loss during the early epochs, with MLP converging more quickly than the KAN, particularly on the GTSRB and CTSRD datasets. For the BTSRB dataset, the KAN exhibited a more gradual decrease in loss but eventually reaches stability, maintaining a performance level similar to that of MLP after several epochs. When employing the FGSM as an attack strategy, both the KAN and MLP show a comparable trend in loss reduction; however, the overall loss remains higher than in the standard training scenario. The MLP model displayed slightly better stability in reducing loss across all datasets, though the KAN's performance on the BTSRB dataset indicates greater resilience, especially in the later epochs. Under randomized smoothing, both models achieved significant loss reduction. However, the KAN consistently maintained a lower loss than MLP, particularly on the CTSRD and BTSRB datasets. This highlights the KAN's superior ability to mitigate the impact of adversarial examples, especially when trained with smoothing techniques. These results underscore the KAN's advantage in robustness when utilizing randomized smoothing, which is a key finding in this study.
# Tomato Leaf Disease Classification by Combining EfficientNetv2 and a Swin Transformer
## 3 Experimental Results and Analysis
### Result Analysis  
To validate the effectiveness and advancement of the algorithm proposed in this paper, comparisons were made between the proposed algorithm and existing mainstream image classification methods on the tomato disease and pest dataset, as shown in Table 3. All models were trained 300 epochs to provide a fair comparison. In addition, for fair contrast, the batch size was kept the same between comparators. It is important to note that all algorithms were evaluated under the same dataset and experimental conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy/\%** \\ \hline CBAM & 98.03 \\ ECA & 98.21 \\ SE & 98.54 \\ CA & 99.70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the results of different attention mechanism methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Accuracy/\%** \\ \hline Improved EfficientNetV2 & 99.21 \\ Swin Transformer & 96.02 \\ Eff-Swin & 99.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiment of the Eff-Swin algorithm.  
From the comparative experimental results, it can be observed that the performance of the proposed dual-branch network model in tomato leaf disease and pest identification outperforms that of the classical convolutional neural networks ResNet18 and Efficient-Netv2. Compared to the lightweight convolutional neural network model MobileNet-V2, the classification accuracy of the proposed model is improved by 7.45%. Compared to Transformer-based models such as Vision Transformer and Swin Transformer, the proposed model achieves classification accuracy improvements of 4.51% and 0.58%, respectively. This indicates that the combined dual-stream network model performs better in tomato leaf disease and pest classification than single-stream network models.  
Moreover, compared to mainstream feature fusion networks, including Conformer [15] and CMT [16], which also integrate features extracted from CNN and Transformer branches, the experimental results show that the proposed model achieves classification accuracy improvements of 1.15% and 0.8%, respectively. This suggests that the CA attention mechanism enhances the generalization ability of the network model, and the combination of a CNN and a Swin Transformer achieves superior classification accuracy compared to current mainstream methods.  
Although the training computational time of Eff-Swin model is a little longer than some lightweight models, the accuracy is much higher than them such as MobileNet-V2 and VGG-19 and so on. We believe that the high performance of Eff-Swin model makes it worth training for longer than others to a certain extent. In addition, for the same type of binding model, with the concise CNN backbone we designed, our model not only trained faster than other CNN branches but also had higher accuracy.