esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Temporal Variability
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Calciano** & **Calciano** & **Potenza** & **Potenza** & **[PERSON] 2** & **[PERSON]. [PERSON] 2** \\ \hline
**Year of Sampling** & **2018** & **2019** & **2018** & **2019** & **2018** & **2019** \\ \hline
**Compound** & **r.t. [min]** & & & & **Area (\%)** & & \\ \hline \(\alpha\)-Pinene & 7.83 & 3.02 & 10.63 & 6.43 & 7.87 & 0.46 & 0.24 \\ \(\beta\)-Pinene & 8.74 & & 0.42 & 0.35 & 0.39 & & \\ Sulatcone & 8.91 & & & 0.19 & & & \\ \(\beta\)-Mycrene & 9.03 & & 0.65 & 0.33 & & 0.37 & 0.29 \\ \(\beta\)-Phellandrene & 9.65 & & & 0.06 & & & \\ 2-Ethyl-1-hexanol & 9.83 & & & 0.18 & & & \\ D-limonene & 9.84 & 0.10 & 1.31 & 0.95 & 0.11 & 1.65 & 1.00 \\ Eucalypt & 9.88 & & 0.35 & 0.35 & & & \\ \(\alpha\)-Terpinolene & 11.05 & & & 32.45 & & & \\ trans-Sabinene idrate & 11.13 & & & 1.46 & & & \\ 6-Methyl-2- & & 11.19 & & & 0.36 & & \\ pyridincarboxaldehyde & & & 1.03 & & & \\ Undecane & 11.3 & & & 1.03 & & & \\ Linalool & 11.43 & & & & 0.24 & & \\ Nonanal & 11.45 & & & 1.35 & & & \\ Rose oxide & 11.64 & & & 0.59 & & & \\ 2-Phenylethanol & 11.67 & & & 0.42 & & & \\ p-Menth-8-en-1-ol & 12.26 & & & 1.66 & & 0.83 & \\ Citronellal & 12.29 & & 1,12 & 0.4 & & 0.42 & 1.50 \\ \(\alpha\)-Terpineol & 13.17 & & 0.45 & 0.17 & & & 0.78 \\ 1-Butoxy-2-ethylhexane & 13.23 & & & 0.18 & & & \\ Verbenone & 13.45 & & 4.72 & 55.18 & & 31.48 & 0.81 \\ Decanal & 13.72 & & & & & 2.12 & \\ Carvone & 14.07 & & & & & 0.42 \\ Citronellol & 14.14 & & & & 1.53 & 1.34 & 42.54 \\ _cis_-Octahydro-3a- methyl-2H-iden-2-one Methyl (S)-citronellellate & 14.24 & & 1.80 & & & & \\ D-carvone & 14.49 & & & 7.92 & & & \\ Citronellyl formate & 14.52 & 0.33 & & & & & \\ (_E_)-cinnamaldehyde & 14.59 & & 8.25 & & & & \\ Methyl citronellate & 15.03 & 0.30 & & & & & \\ Tridecane & 15.12 & & & 1.55 & & & \\ Citronellyl acid & 15.56 & & & 0.72 & & & \\ Cinnamyl alcohol & 15.80 & & & 0.15 & & & \\
4-Carene & 15.84 & & 1.53 & & & & \\ (E,Z)-2,6-Dimethyl-2,6-octadiene & 15.87 & & & & & & \\
2,6-dimethyl-2,6-octadiene & 16.72 & 0.42 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Inter-annual variations in VOCs emitted by three individuals of _Barlia robertiana_ sampled in two consecutive years.
## 1 Introduction
The other advantage of ISL is that the accuracy of the BDS-3 satellite orbit can be improved by using both ISL and L-band measurements. [PERSON] et al. used 16 global distributed stations and 6 China regional stations to verify the improvement of orbit determination accuracy when adding ISL measurement and finally achieved a great improvement of 42% and 83%, respectively [15]. [PERSON] et al. explored that with the help of ISL, the 3D root mean square (RMS) of eight satellites orbit position computed using 10 regional stations was decreased from 1 to 0.5 m [16]. In general, orbit accuracy at the few decimeters level could be achieved by the different studies. Nevertheless, there are still many aspects of BDS-3 ISL that have not been studied. For example, there are two ISL observation models applied for joint POD using ISL and ground-tracking data. One is to treat a pair of raw one-way ISL pseudoranges as two independent observables [17], and the other is to derive the ISL clock-free observables from a dual one-way pseudoranges [2; 11; 15; 18]. These derived clock-free observables contain only geometric information, so the observation model excludes the satellite clock offset parameters. In the case of joint POD, it is unreasonable to use only the clock-free observables. Because the satellite clock offsets and the orbit in the radial component are highly correlated in the L-band POD results [19], the addition of ISL data can reduce the orbit impact on the estimation of satellites clock offsets and adjust the clock parameters. Besides, as more and more receivers are upgraded, more than 100 ground tracking stations have access to receive the signal broadcast by BDS-3 satellites. Considering the addition of a large amount of ISL data, there will be a lot of L-band redundant data for joint orbit determination, which cannot contribute to the accuracy, but reduce the calculation efficiency. On the other hand, since BDS-3 is a hybrid constellation, the distribution of stations required for the POD of satellites operating in different orbital planes is not the same. The addition of ISL measurements can optimize the geometry of the observation model. Therefore, further research is needed to explore whether the introduction of ISL to joint POD has requirements on the distribution of stations. This article continues to analyze the issue of ISL's contribution to the joint POD. An improved joint POD strategy using ground-tracking and ISL observations of the BDS-3 satellites is applied. This strategy makes full use of both clock-free and geometry-free ISL observables derived from the raw dual one-way ISL observations, which has not been used by previous studies. The satellite orbit parameters, as well as satellite clock offsets estimated at the same time, are evaluated and compared. Through this improved strategy, the ISL's contribution to joint POD in the case of the selection of stations, including the distribution of stations and the number of stations, is analyzed separately for three types of BDS-3 satellites.
# An Optimized Genetic Algorithm-Based Wavelet Image Fusion Technique for PCB Detection
## 5 Conclusions  
In this study, we proposed an advanced image fusion framework that integrates a two-dimensional wavelet transform with an enhanced genetic algorithm, employing a comprehensive evaluation scheme based on RSD, PSNR, SF, and image clarity. The designed model preprocesses the input images through normalization and dimensional alignment before decomposing them via wavelet transform. By incorporating an improved genetic algorithm, the method efficiently identifies the optimal fusion weight combination. Experimental results across multiple test sets demonstrated that, although the RSD values remained near unity, which indicates consistent image stability, the enhancements in PSNR, SF, and image clarity were substantial. Specifically, the enhanced method yielded significantly higher PSNR values in several tests, reflecting superior noise reduction and detail preservation. Compared with traditional pixel averaging and PCNN-based fusion methods, the proposed approach consistently outperformed them in maintaining image sharpness and structural details.  
Figure 7: A PCB error testing. (**a**) Visual; (**b**) Infrared; (**c**) Fused; (**d**) Detected.  
Furthermore, the applicability of the proposed fusion technique was verified in a practical scenario involving PCB fault detection. By integrating both infrared and visible light imaging in a custom-designed prototype, the system successfully captured, processed, and fused multimodal images to highlight temperature anomalies accurately. This capability facilitates the early detection of potential component failures and underscores the practical significance of the fusion method in real-world applications.  
In summary, the proposed image fusion framework demonstrates a well-balanced performance by effectively enhancing image quality while preserving critical details. Its robust performance across diverse metrics and successful application in PCB fault detection highlights its potential for broader use in various imaging domains. Future work may focus on further optimizing the algorithm, extending its adaptability to different imaging conditions, and exploring additional real-world applications to fully harness its capabilities.  
Conceptualization, T.Z., Q.Y. and T.G.; methodology, T.Z. and S.L.; software, T.Z. and S.L.; validation, T.Z. and Z.F.; formal analysis, T.Z. and Q.Y.; writing--original draft preparation, T.Z. and S.L.; writing--review and editing, Q.Y., T.G. and Z.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shanghai Polytechnic University OF FUNDER grant number C80 JX244001.  
Not applicable.  
The data are included in this article. For detailed information, please contact the corresponding author.  
The authors declare no conflicts of interest.
## 3 Data and Methodology
### Methodology
#### 3.2.3 Hurst Index  
Hurst index can not only be used to describe the sustainability of the NPP in Northeast China but also predict the future development trend from time series. Therefore, this study attempts to explore the future ecological environment changes of forests in Northeast China by using Hurst index to provide reference for the sustainable management of forests in the future. Hurst index (H), which is based on the re-scale range (R/S) analysis, is an effective method to describe quantitatively the long-term dependence of time series information [56; 57]. The following sequences are all established at any moment \(\left\{\text{i}=1,2,\dots,\text{ n}\right\}\) after given time series variable of NPP. For any positive integer m, define the above time series. Here this study mainly predicts the future changes based on the NPP from 2001 to 2019, so n is 19.  
Difference sequence:  
\[\Delta\text{NPP}_{\text{i}}=\text{ NPP}_{\text{i}}-\text{NPP}_{\text{i}-1}. \tag{9}\]  
Mean sequence:  
\[\overline{\Delta\text{NPP}(\text{m})}=\frac{1}{\text{m}}\sum_{i=1}^{\text{m}} \Delta\text{NPP}_{\text{i}}\text{ \ m }=1,2\,\dots\text{ n} \tag{10}\]  
Cumulative dispersion:  
\[\text{X}(\text{t})=\sum_{i=1}^{\text{m}}\left(\Delta\text{NPP}_{\text{i}}- \overline{\Delta\text{NPP}(\text{m})}\right)\text{ \ }1\leq\text{ t }\leq\text{ m} \tag{11}\]  
Range:  
\[\text{R}(\text{m})=\text{X}(\text{t})_{\text{max}}-\text{X}(\text{t})_{\text{ min}}\text{m}
# C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
## 6 Conclusions and Future Work  
In this research, we introduced a unique strategy for source code search and recommendation based on the CodeT5 and Bi-LSTM models. Existing code search tools frequently struggle to comprehend the semantics and purpose underlying code snippets, and they may fail to capture the sequential patterns inherent in code. We developed the \"CodeT5 and Bi-LSTM\" (C2B) hybrid model, which combines the advantages of CodeT5's domain-specific pretraining with Bi-LSTM's contextual knowledge, to overcome these difficulties. In order to perform better on tasks involving code, our goal was to improve code representation, code interpretation and creation, and the capturing of sequential dependencies. Our C2B hybrid model's empirical evaluation on the large CodeSearchNet dataset produced encouraging findings. Our solution performed better than earlier approaches that relied on keyword- or syntactic-based strategies for retrieving pertinent code snippets. The C2B hybrid model significantly improved the code search and recommendation tasks by better understanding the semantics of code and capturing sequential patterns.  
Several possible paths for future study might be investigated to further improve the suggested C2B hybrid model for source code search and recommendation. To begin, fine-tuning and refining the C2B model's efficiency may result in increased code comprehension and generating capabilities. Furthermore, improving the C2B paradigm to include multilanguage code search would boost its practical value for developers working on a variety of projects involving different programming languages. The code search tool's usability would be improved by including user interaction and feedback, which would also enable customized code suggestions. It would be beneficial to test the C2B model on actual projects and include developers in user studies to learn more about how successful and practical it is.  
The model might perform better in real-world circumstances if code variability and resilience in retrieval results for code snippets with variations were addressed. In order to give developers a more effective and convenient tool for successfully reusing code and enhancing software development workflows, the C2B hybrid model may be improved and enhanced by following the above suggestions.  
Conceptualization, N.B.; Methodology, N.B.; Software, N.B.; Resources, F.A.; Data curation, A.M.; Writing--review & editing, A.M.; Supervision, T.R.; Project administration, T.R. and A.A.K.; Funding acquisition, F.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Dataset supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 3 Data Collection II: Stationary Target Reflection
### Signal Model at the Receiver  
Since the maximum bandwidth generated by our operating parameters \(B(N-1)=20\,\mathrm{kHz}\cdot 2=40\,\mathrm{kHz}\ll f_{0}\), we can simplify Equation (7) using the monochromatic assumption. Furthermore, we assume that the target radar cross section (RCS) is relatively constant during each sampling period, i.e., \(\sigma_{n}(t)\approx 1\). Under these assumptions, the received baseband signal model for a target at \((R,\theta)=(R_{t},\theta_{t})\) is  
\[\begin{split} F_{R,bb}(t;R_{t},\theta_{t})=\\ e^{2\pi\
u_{0}t}\cdot\bigg{[}\sum_{n=0}^{N-1}\,e^{2\pi n\Delta ft }\cdot e^{-2\pi f_{0}(R_{n}+R_{r})/c)}\bigg{]},\end{split} \tag{8}\]  
where \(R_{r}\) is the common distance from the target to the receive probe.
###### Abstract  
In this study, a novel 2-vinylpyridine functionalized magnetic nanoparticle (Mag-PVP) was successfully prepared. The prepared Mag-PVP was characterized by transmission electronic microscopy (TEM), Fourier transform infrared spectrophotometry (FT-IR), vibrating sample magnetometry (VSM) and thermogravimetric analysis (TGA), and was used for the adsorption of bisphenol A (BPA) from aqueous solutions. Mag-PVP, which is composed of Fe\({}_{3}\)O\({}_{4}\) nanoparticles and poly divinylbenzene-2-vinylpyridine (with a thickness of 10 nm), exhibited magnetic properties (\(M_{\mathrm{s}}\) = 44.6 emu/g) and thermal stability. The maximum adsorption capacity (\(Q_{m}\)) of Mag-PVP for BPA obtained from the Langmuir isotherm was 115.87 mg/g at 20 \({}^{\circ}\)C, which was more than that of Fe\({}_{3}\)O\({}_{4}\) nanospheres. In the presence of NaCl, the improved adsorption capacity of Mag-PVP was probably attributed to the screening effect of Mag-PVP surface charge and salting-out effect. In the presence of CaCl\({}_{2}\) and humic acid (HA), the adsorption capacity of BPA decreased due to competitive adsorption. The adsorption of BPA by Mag-PVP increased slightly with the increase in pH from 3.0 to 5.0 and obtained the largest adsorption amount at pH 5.0, which was probably attributed to hydrogen bonding interactions. Moreover, in actual water, Mag-PVP still showed excellent adsorption performance in removing BPA. The high adsorption capacity and excellent reusability performance in this work indicated that Mag-PVP was an effective adsorbent for removing BPA from aqueous solutions.  
2-vinylpyridine; magnetic nanoparticles; bisphenol A; adsorption; reusability 2018 10 1136 10.3390/polym101136
# A Hyper-Attentive Multimodal Transformer for Real-Time and Robust Facial Expression Recognition
## 7 Discussion
The triple attention block, comprising spatial, channel, and cross-patch mechanisms, significantly enhances both discriminative capacity and interpretability. Visualizations of attention maps reveal that the model consistently attends to biologically and psychologically salient facial regions, validating its human-aligned reasoning. Moreover, the cross-patch attention enables the model to capture long-range facial interactions--such as co-activation of the brow and mouth in expressions of fear--that would otherwise remain fragmented in traditional CNNs or narrow receptive field architectures. From a computational perspective, FERONet maintains a competitive efficiency-to-accuracy ratio. Despite the architectural complexity of transformers, the hierarchical token merging mechanism reduces computational overhead substantially without sacrificing expressive power. Inference latency remains consistently under 16 ms per sample across datasets, which supports deployment in real-time applications such as emotion-aware virtual agents, driver fatigue detection systems, and clinical affect analysis tools. Nevertheless, several limitations and future opportunities should be acknowledged. First, the current model assumes access to optical flow and depth inputs during training, which may not always be available in low-resource environments. While the model remains functional in RGB-only settings, performance declines marginally in highly dynamic or cluttered scenes. Second, FERONet has been evaluated primarily on categorical emotion labels. Extending the model to continuous affective spaces or compound expression recognition could further broaden its applicability. Finally, although attention maps offer some level of interpretability, integrating post hoc explanation techniques or embedding facial action unit (AU) priors could offer even deeper alignment with psychological models of emotion. While FERONet is evaluated on single-subject datasets, it can be extended to multi-person scenarios by integrating a face detection module to localize and extract each face region before emotion inference. Each face can then be processed independently through FERONet, allowing simultaneous recognition of diverse emotional states in a group setting. This adaptation will be considered in future research and deployment settings. FERONet presents a well-balanced, scalable, and context-aware FER architecture that not only advances the state of the art in terms of recognition accuracy but also aligns with practical requirements of interpretability, robustness, and efficiency. Its modular design opens the path for seamless integration with future modalities and tasks, serving as a solid foundation for next-generation affective computing systems.
## 3 Data Sources and Preprocessing
### GlobeLand30 Data  
Data from the American land resources satellite (Landsat) Thematic Mapper (TM5), an Enhanced Thematic Mapper Plus (ETM+) multi spectral image, China's Environmental Disaster Monitoring and Forecasting Small Satellite Constellation (HJ-1A/B) and other 30 m multispectral images are applied in the GlobeLand30 data. These data are also subsequently improved via the use of other reference data to support processes such as sample selection and auxiliary classification. The integration of pixel- and object-based methods with knowledge (POK) is used to control quality, which combines pixel level and object-oriented classification [38]. The data processing is made in accordance with the order of water, wetland, ice and snow to reduce the synonyms spectrum phenomenon, _i.e._, the same object has different spectra. China donated this global 30 meters surface coverage dataset to the United Nations on 23 September 2014. This product is based on the World Geodic System (WGS) 84 coordinate system and the Universal Transverse Mercator (UTM) projection, and comprises ten land cover types including \"water bodies\", \"wetland\", \"artificial surfaces\", \"tundra\", \"permanent snow and ice\", \"grassland\", \"barren land\", \"cultivated land\", \"shrubland\" and \"forest\" (Table 3). Furthermore, the overall accuracy (_OA_) of GlobeLand30-2010 data is 83.50% and the Kappa coefficient (_K_) is 0.78. Some research on the analysis and application of the GlobeLand30 data has been made and their results have shown good performance [39,40]. In the present study, the GlobeLand30 product for the 2010 reference year is selected, with the time period ranging from 1 January 2010 to 31 December 2010, and map numbers N50_25 and N50_30.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
###### Abstract  
Forest fires are a significant threat to the ecological system's stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today's world, research on flying ad hoc networks (FANETs) is a thriving field and can be used successfully. This paper describes a unique clustering approach that identifies the presence of a fire zone in a forest and transfers all sensed data to a base station as soon as feasible via wireless communication. The fire department takes the required steps to prevent the spread of the fire. It is proposed in this study that an efficient clustering approach be used to deal with routing and energy challenges to extend the lifetime of an unmanned aerial vehicle (UAV) in case of forest fires. Due to the restricted energy and high mobility, this directly impacts the flying duration and routing of FANET nodes. As a result, it is vital to enhance the lifetime of wireless sensor networks (WSNs) to maintain high system availability. Our proposed algorithm EE-SS regulates the energy usage of nodes while taking into account the features of a disaster region and other factors. For firefighting, sensor nodes are placed throughout the forest zone to collect essential data points for identifying forest fires and dividing them into distinct clusters. All of the sensor nodes in the cluster communicate their packets to the base station continually through the cluster head. When FANET nodes communicate with one another, their transmission range is constantly adjusted to meet their operating requirements. This paper examines the existing clustering techniques for forest fire detection approaches restricted to wireless sensor networks and their limitations. Our newly designed algorithm chooses the most optimum cluster heads (CHs) based on their fitness, reducing the routing overhead and increasing the system's efficiency. Our proposed method results from simulations are compared with the existing approaches such as LEACH, LEACH-C, PSO-HAS, and SEED. The evaluation is carried out concerning overall energy usage, residual energy, the count of live nodes, the network lifetime, and the time it takes to build a cluster compared to other approaches. As a result, our proposed EE-SS algorithm outperforms all the considered state-of-art algorithms.  
clustering; energy efficiency; WSN; FANETs; LEACH; IoT 1