esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The main contributions of this paper are listed as follows:  
* We achieve comprehensive optimization of scientific data services by combining compression and indexing, achieving loosely coupled error-controlled lossy compression. With data precision guaranteed (PSNR of at least 39 dB), the compression ratio reached 68, which is twice that of SZ. * We design a multi-level indexing strategy based on a tree structure, which can segment and organize spatiotemporal data at different levels, achieving effective error control. By utilizing the flexibility of quadtrees, we can more effectively manage complex interactions between entities at multiple scales, thereby improving query efficiency. * We achieve comprehensive optimization for scientific data services by combining compression and indexing. This integration optimizes data storage and retrieval processes, providing more efficient and accurate support for data services. The rest of this paper is organized as follows: In Section 2, we mainly provide a review of related works. In Section 3, our lossy compression technique based on error control and our indexing structure, along with their corresponding algorithms, are described. Section 4 shows experimental results, and the performance of our compression and indexing methods is evaluated. Our final conclusions are given in Section 5.
## 2 Results
### Trait-Specific Genotype-by-Year Interaction (GxY)  
Multi-environment trial (MET) linear mixed models were fitted using a one-step approach, combining data from 2021 and 2022. The effect of cultivation year on phenotypic responses was evaluated using Wald test statistics. The extent of genotype-by-year interaction (GxY) was assessed using type-B additive genetic correlations.  
The year of cultivation had a highly significant effect on all traits, with \(p\)-values \(\leq 1.426\times 10^{-10}\), except for C18:2, which had a higher \(p\)-value of 0.014 but was still statistically significant.  
The extent of GxY was trait-specific (Table 3). Overall, oil and fatty acid content (C18:0, C18:1, C18:2) were quite stable across the years, with \(\rho_{\text{type-B}}>0.73\). Tannins were also rather stable, with \(\rho_{\text{type-B}}\) ranging from 0.55 to 0.81; quercetin showed the highest stability (\(\rho_{\text{type-B}}>0.82\)) among phenolic acids and flavonoids. Contrarily, protein, caffeic and p-coumaric acid, and myricetin were more affected by GxY, as indicated by \(\rho_{\text{type-B}}<0.5\).
## 4 Conclusions  
In this study, SAEOs were investigated via E-Nose, GC-MS, and GC-IMS combined with PCA and LDA, and the differences in the SAEO components were also determined. Generally, combining E-nose with PCA could effectively distinguish SAEOs extracted by ESE and CSD, while combining E-nose with LDA could accurately distinguish SAEOs extracted by HD and SE. The results of GC-IMS and GC-MS indicated that the SAEOs extracted using different methods could be effectively distinguished by combining non-targeting feature markers with PCA analysis. Moreover, GC-MS and GC-IMS have satisfactory discrimination, whereas E-nose and GC-IMS have the advantages of easy operation, faster analysis speed, and low cost. Thus, it can be concluded that E-nose and GC-IMS are more suitable for the discrimination of SAEOs, whereas GC-MS is more suitable for the qualitative and quantitative analysis of essential oils. In this regard, the results could provide theoretical guiding significance for further studies, which may include exploring additional extraction methods, conducting quantitative analyses of key compounds, or investigating the biological activities of the SAEOs extracted by different methods.  
Conceptualization, methodology, resources, data curation, writing--review and editing, and funding acquisition, X.C.; methodology, software, validation, investigation, data curation, and writing--original draft preparation, X.H.; resource and writing--review and editing, T.Z.; project administration and writing--review and editing, K.W.; software and writing--review and editing, X.D.; visualization and supervision, H.Y.; supervision and project administration, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Forestry Scientific and Technology Innovative Project of Guangdong Province (2020 KJCX010), the Science & Technology Planning Project of Guangzhou City (202103000078, 202206010181), the Guangdong Provincial Key Laboratory of Plant Resources Bioefinery (2021 GDKLPRB01), the Science and technology plan project of Guangdong Province (2020705101471437), and the Science & Technology Planning Project of Guangdong Province (19 ZX0364).  
This study does not involve any human or animal testing.  
Not applicable.  
Data Availability Statement:Data is contained within the article.  
The authors declare no conflict of interest.  
Figure 8: The PCA analysis load diagram of GC\(-\)MS of the SAEOs extracted by the four different methods.
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### Data Augmentation Methods
#### 2.3.4 Online Augmentation  
Online data augmentation involves applying random transformations to spectrogram features after generating them from the training data. SpecAugment [25] applied to speech recognition is a technique that masks spectrogram features to achieve data augmentation effects. It is not a simple preprocessing of the raw audio but rather a feature-based (in the visual domain) method aimed at enriching the time-frequency characteristics of audio. Randomly masking certain regions helps prevent model overfitting and enhances the model's generalization ability. Frequency masking works by randomly selecting\(f\) frequency bins to be masked from the interval \([0,F]\), where \(F\) is a known parameter. Then, it selects the random frequency bin \(f_{0}\) from \([0,v-f]\) along the vertical axis, where \(v\) represents the number of Mel frequency channels. Finally, all pixel values in the frequency bin range \([f_{0},\ f+f_{0}]\), are replaced with a constant mask value. Similarly, time masking uses the mask to the \(t\) consecutive time step \([t_{0},\ t+t_{0}]\), and \(t\in[0,T]\), \(T\) is the time mask parameter, \(t_{0}\in[0,\ \tau-t]\), where \(\tau\) represents the number of time bins.  
This paper focused on sound event recognition, which, compared to speech recognition, often involves shorter durations and wider frequency coverage for anomalous sound events. Therefore, we combined this method with the original audio processing techniques of Time Stretching, Pitch Shifting, and Background Noise, proposing a combination data augmentation method that enhances the diversity of anomalous sound events in small datasets. This method is straightforward, computationally efficient, and does not require additional data; it can be directly applied to audio. The MFCC of these various data augmentation methods is illustrated in Figure 4.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 2 Rubber Toughening
### Research Progress in Rubber Toughtening Methods
#### 2.3.1 Liquid Rubber Toughening Method
Additionally, the fracture toughness increased from 0.91 MPa\(\cdot\)m\({}^{1/2}\) to 1.49 MPa\(\cdot\)m\({}^{1/2}\), representing a 1.5-fold enhancement. [PERSON] et al. [9] investigated the impact of adjusting the acrylonitrile content in amine-terminated butadiene nitrile rubber (ATBN) on the phase separation of ATBN rubber-toughened epoxy resin networks. The study found that the particle size of the rubber is highly dependent on the acrylonitrile content in ATBN, which significantly affects the microstructure of the ATBN-toughened epoxy resin network. The phase separation scale is a crucial factor influencing network performance. Networks with larger phase separation scales exhibit longer elongation at break, while networks with smaller phase separation scales are more effective in improving impact strength. The addition of rubber to epoxy resins for toughening can also lead to a reduction in mechanical properties such as tensile strength and flexural strength. To mitigate the impact on mechanical performance, nanoparticles can be introduced into the rubber/epoxy (EP) system to enhance its properties. [PERSON] et al. [41] incorporated carboxyl-terminated butadiene-acrylonitrile (CTBN) into epoxy resin to improve fracture toughness, and then introduced graphene nanoplatelets (GnP) with two different lateral sizes (nominal diameters <1 \(\upmu\)m (GnP-C750) and 5 \(\upmu\)m (GnP-5)) into the CTBN/epoxy system to prepare multiphase composites. Although the tensile strength and flexural strength of the CTBN/EP system are significantly lower compared to pure EP, the mechanical strength of the resin system improves with the addition of 1 wt% and 3 wt% GnP-C750. The inclusion of CTBN markedly enhances the fracture toughness of the epoxy resin, increasing it by approximately 70.7% compared to pure EP. When subjected to damage, cracks in the GnP/CTBN/EP composite system not only interact with CTBN but are also impeded by GnP, altering the crack path and thereby further enhancing the toughness of the epoxy resin composites.
# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 5 Results
### Categorization  
Table 6 contains the summarized data collected from the volunteers' medical history and questionnaire answers. PT refers to Parkinsonian tremor patients, ET to essential tremor and NT means person with no diagnosed tremor. The status \(ON\) refers to a person who is taking medication and has even taken medication within the last 12 h. Instead, in the \(OFF\) mode, the person has not taken any medicine for a 12 h period, but is under pharmaceutical supervision. Moreover, 33% of PT subjects were at the early stage of the disease.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Category** & **PT** & **ET** & **NT** \\ \hline Gender (M/F) & 7/5 & 3/0 & 19/9 \\ Age (range) & 47–89 & 50–69 & 14–76 \\ Years since tremor appearance (range) & 1–18 & 5–42 & - \\ Early stage (yes/no) & 0/3 & 4/8 & - \\ Medication (Yes(ON/OFF)/No) & 10(7/3)/2 & 1(0/1)/2 & - \\ Tremor frequency in Hz (range) & 0–8.7 & 0–10 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Volunteer categorization groups.  
Figure 4: Indicative accelerometer measurements and respective frequency spectrum and power spectral densities, as acquired from the index measurement point and the extended arm pose \(l_{2}\) of a (**a**) Parkinson’s-diagnosed patient, (**b**) ET-diagnosed patient and (**c**) volunteer with no visible tremor evidence.
## 4 Results and Discussion  
The experimental results and comparative analysis of the proposed system are described in this section. The implementation and simulation of the indoor MIMO-VLC system using precoding is carried out in MATLAB R2018a with 4 GB RAM and an i3 processor. The main objective of the indoor MIMO-VLC communication using BBD precoding is to mitigate the interferences in the data transmission. The specifications used for the LED transmitter and PD receiver are specified in Table 2. In this proposed system, the LEDs are initialized with the power of 10 mW. Moreover, the semi angle of transmitter and FOV of PD receiver are fixed as \(15^{\circ}\). Here, the \(4\times 4\) indoor MIMO-VLC model is considered to analyze the communication performances.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Description** \\ \hline \(M_{t}\) & Number of transmitting LEDs \\ \(M_{r}\) & Number of receiving LEDs \\ \(y\) & Input signal vector \\ \(z\) & Received signal vector \\ \(\mathbf{DC}\) & DC bias matrix \\ \(m\) & Noise vector \\ \(\mathbf{H}\) & Square matrix \\ \(h_{jk_{a}}\) & Desired channel paths \\ \(h_{jk_{i}}\) & Interference channel paths \\ \(\gamma\) & Responsivity \\ \(p\) & Transmitting power of LED \\ \(m_{j}\) & Receiver noise \\ \(\sigma_{hh_{j}}^{2}\) & Thermal noise \\ \(\sigma_{sh_{j}}^{2}\) & Shot noise \\ \(q\) & Electronic charge \\ \(B\) & Bandwidth \\ \(l_{b}\) & Background current \\ \(l_{2}\) & bandwidth factor of noise \\ \(k\) & [PERSON]’s constant \\ \(\Gamma\) & Absolute temperature \\ \(G\) & Gain of open-loop voltage \\ \(A_{j}\) & Area of PD \\ \(\eta\) & PD’s fixed capacitance \\ \(\Gamma\) & Channel noise factor of field effect transistor \\ \(g\) & Transconductance of the FET \\ \(I_{3}\) & Weighting function \\ \(SINR_{j}\) & Signal to interference plus noise ratio (SINR) in jth PD \\ \(d_{jk}\) & Distance among the transmitting LED and PD receiver \\ \(\varphi_{jk}\) & Emergence angle \\ \(\phi_{jk}\) & Incidence angle \\ \(\phi_{c}\) & PD’s field of view \\ \(t_{s}\)(\(\phi_{jk}\)) & Optical filter’s gain \\ \(g_{a}\)(\(\phi_{jk}\)) & Optical concentrator’s gain \\ \(r_{0}\)(\(\varphi_{jk}\)) & Transmitting LED’s Lambertian radiant intensity \\ \(\varphi_{1/2}\) & Transmitter semiangle at half power \\ \(\mathbf{H}_{bbd}^{j}\) & SVD of BBD’s Square matrix \\ \(\mathbf{U}_{bbd}^{(j)}\) & Left singular vector matrix \\ \(\mathbf{v}_{bbd}^{(j)}\) & Right singular vector matrix \\ \(O\) & Zero matrix \\ \(B_{bbd}\) & Precoding matrix \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the indoor MIMO-VLC system.
## 2 Background
### Support Vector Regression  
SVR was first introduced by [PERSON] [25] as a method for estimating regressions and approximating functions as a generalization of their previous work support vector machines [26]. Support vector regression operates by finding the optimal hyperplane that best fits the training data while maximizing the margin between data points. The key components of SVR include kernel functions to map input data into high-dimensional feature spaces; epsilon (\(\epsilon\)) and regularization (\(C\)) parameters as hyperparameters to control the model's behavior with epsilon as the margin width within which data points are considered correctly predicted, while \(C\) balances the trade-off between achieving a small margin and minimizing training error; and support vectors as points lying exactly on the margin or within it, which heavily influence the construction of the regression model.  
This modeling technique offers several advantages that make it a valuable tool in data modeling. It performs effectively in high-dimensional spaces, is memory-efficient, and demonstrates robustness against overfitting. Additionally, SVR excels at modeling complex, non-linear relationships. However, it also has certain limitations, including computational complexity when dealing with large datasets as its computational cost scales between \(\mathcal{O}(n_{features}\times n_{samples}^{2})\) and \(\mathcal{O}(n_{features}\times n_{samples}^{3})\)[27], sensitivity to hyperparameter tuning, and challenges in handling noisy data. Despite these drawbacks, it has been successfully applied in various fields such as financial forecasting, environmental modeling and bioinformatics [28]. As a powerful regression technique, SVR remains relevant even in the deep learning era, providing a mathematically sound approach for capturing intricate patterns within data.
## 3 Results and Discussion
### Human Factor  
#### 3.2.1 Definition  
The \"human factor\" has been studied in many research fields, resulting in variable definitions, as discussed in Section 1.1. Herein, we focus on \"human factors\" in maritime safety and its definition. Table 2 shows the relevant definitions. HSE's definition refers to environmental, organizational, and job factors, as well as human characteristics. [PERSON] and IEA's definitions, given from the perspective of scientific discipline, are relatively general and macroscopic. The IMO's definition emphasizes human actions or omissions (intentional or unintentional) that have caused certain maritime casualties or safety incidents. The commonality between the four definitions is difficult to extract, implying that there is no consensus on the definition of human factor from an academic perspective.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Ref.** & **Definitions** \\ \hline IMO Resolutions A.884(21) [33] & Human factors which contribute to marine casualties and incidents may be broadly defined as the acts or omissions, intentional or otherwise, which adversely affect the proper functioning of a particular system, or the successful performance of a particular task. \\ HSE [34] & Human factors refer to environmental, organizational and job factors, system design, task attributes and human characteristics that influence behavior and affect health and safety. \\ [PERSON] [35] & Human factor is the discipline that seeks to understand the contribution of the human operator within the system and to influence design and operation accordingly. \\  & Human factor is the scientific discipline concerned with the understanding of interactions among humans and other elements of a system, and the profession that applies theory, principles, data and methods to design in order to optimize human well-being and overall system performance. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definitions of human factors.  
Figure 3: The “human element” and its components.
# Efficient On-Off Keying Underwater Acoustic Communication for Seafloor Observation Networks
## 4 Undersea Application and Experimental Results  
In this section, based on the SON application scenario, the seafloor communication experiment is described. As an important preliminary work, the placement of the communication nodes near the seafloor is analyzed to ensure good communication quality. Then, the feasibility and performance of the proposed scheme are verified by sea trial data.
## 2 Materials and Methods
### Evaluation Metrics and Model Interpretability  
To comprehensively evaluate model performance, both quantitative and visual interpretability analyses were conducted. Classification performance was assessed using standard metrics, including precision, recall, and F1-score for each growth stage class. These metrics were computed using the classification report and confusion matrix functions from the Scikit-learn library, based on the predictions made on the test set. The results were summarized in tabular form and supplemented with confusion matrices to analyze class-wise prediction reliability and error patterns.  
In addition to numerical evaluation, Grad-CAM (Gradient-weighted Class Activation Mapping) was employed to interpret the spatial focus of each model during classification. Activation maps were extracted using the final block of layer 3 in the ResNet50 backbone, which captures mid-level feature representations. Grad-CAM visualizations were generated for representative samples from class 0 (Week1) across four evaluation scenarios: Model 1, Model 2, Model 1-CDE, and Model 2-CDE. These visualizations enabled the identification of key morphological features that the models relied on and provided insight into model robustness and generalization across domains.
## 2 Results and Discussion
### Concentration of Individual Aromas in the Different Families of Volatile Compounds
#### 2.3.1 Acids  
The acids identified in the wines were hexanoic, octanoic, and 9-decenoic acids. While these compounds are not primarily associated with wine quality, their presence plays an important role in the complexity of the aroma [32]. Many of them are thought to be responsible for \"green\" aromas in grape juice, although they may have less impact in wines [33]. Hexanoic acid is related with cheesy, rancid, and fatty aromas [34]; octanoic acid is related with sweat and cheese aromas [35], and 9-decenoic acid with fatty aromas [34].  
The concentrations of all these acids were similar in all the wines analyzed for 2018 (Table 3), ranging from 3 to 7 mg L\({}^{-1}\), Verdejo wine having the lowest values. By contrast, the concentrations of fatty acids were higher in all the 2019 wines (Table 4), especially as regard hexanoic acid (up to 18 mg L\({}^{-1}\)), while 9-decenoic acid was not identified in Verdejo or MV11 wines during this campaign. These results agree with those of [PERSON] et al. [36], who found that the wines made from Albillo and Muscat varieties had higher fatty acid concentrations when less ripe grapes were used than the wines made with very mature grapes. Therefore, one of the factors influencing the content of acid compounds in a given wine could be the stage of ripening of the grapes.  
It has also been suggested that low impact odorants may act to change the perception of other odorants in a mixture, and, as they may interact synergistically or antagonistically, they may significantly influence perception without being recognized for their OAV (Odour Activity Value) [37]. In addition, the combination of two or more odorants can affect the final aroma, making it more or less intense, and so provide nuances different from those induced by individual odorants [38]. In summary, this kind of aroma was not detected in the sensory tasting because they would have been overwhelmed by other, more intense aromas [36].
## 2 Study Area and Data
### Data  
In this study, major weather factors, geographical data considering accessibility, fuel data, and time-related data related to the seasonal pattern of forest fires were used as input data to develop forest fire risk models. The input variables are summarized in Table 1.
## 3 Results
### TCs Impacts: Emily, Stan, Wilma, and Dean
#### 3.2.4 Coastal Chl-\(a\) Concentrations
Such upwelling in areas with average Chl-_a_ values before the storm showed an increase in the total area depending on TC strength, track, and landing site: Stan (24,816 km\({}^{2}\)) \(>\) Dean (17,520 km\({}^{2}\)) \(>\) Emily (7968 km\({}^{2}\)) \(>\) Wilma (7792 km\({}^{2}\)). Overall, most of the Chl-_a_ increase was observed near the shoreline, along northern Quintana Roo, Yucatan, and near the Terminos Lagoon (Figure 10B). The passage of the Stan and Dean landings illustrate the degree of influence of surface water discharge on Chl-_a_ values along the YP western coastline (Figures 10 and 11). For example, the Champion River discharge into the GOM on 11 October after [PERSON]'s landing (Figure 8C) suggests a direct relationship between river discharge (up to 320 m\({}^{3}\)/s) and Chl-_a_ up to 16 mg/m\({}^{3}\). These conditions contrast with typical values (0.5-1 mg/m\({}^{3}\); Figure 10A) found in oligotrophic waters and generally observed under normal (pre-storm) conditions [87; 88; 89]. In contrast, within one week after [PERSON]'s passage across Campeche, river discharge was low (35-37 m\({}^{3}\)/s) with little change following landfall  
Figure 11: Chl-_a_ (mg/m\({}^{3}\)) average estimated along the marine profile (T1; water depth, 25 m) surrounding the coast of western Campeche and northern Yucatan and Quintana Roo (see Figure 10). Averages are from seven-day periods before (blue) and after (red) landfall; the dashed line indicates the location of site \(M\) in Figure 10A. (Figure 8B). Under this river stage, Chl-_a_ remained low along the coastline between the Champion River and Terminos Lagoon (Figure 10A), where maximum Chl-_a_ ranged from 1-3 mg/m\({}^{3}\).