esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Approach for the Classification of Refrigerant Gases
## 6 Discussion  
In this study, we demonstrated the effective application of machine learning techniques, particularly the Random Forest classifier, for the classification of refrigerant gases using IoT-enabled sensor arrays. The results showed high accuracy, precision, recall, and F1 scores, particularly when compared to traditional statistical methods which often struggle with real-time data and complex gas mixtures. Our approach uniquely combines real-time IoT data acquisition with advanced machine learning, significantly outperforming older methods that rely on singular, less dynamic datasets.  
Our analysis, presented in detailed tables and visualizations, clearly highlights the superior performance of the Random Forest algorithm. For instance, the confusion matrices in Table 3 illustrate the classifier's ability to distinguish between gases R32 and R134a with minimal misclassification, a significant improvement over traditional spectroscopy methods that often require lengthy calibration and are prone to interference from similar gas types. The precision and robustness demonstrated here suggest a viable path forward for the deployment of these systems in critical areas such as industrial safety and environmental monitoring.  
Furthermore, the use of t-SNE visualizations provided a clear depiction of the clustering of data points by gas type and dilution levels, as shown in our figures. These visualizations not only confirmed the effectiveness of our feature extraction techniques but also offered intuitive insights into the data structure that are not readily apparent from numerical data alone. This graphical representation helps in understanding complex patterns and interactions within the data, facilitating better decision-making and model improvements.  
Despite these promising results, our study acknowledges certain limitations. The specificity of the sensors and potential interference from other gases require further exploration to ensure broader application. Future research will aim to expand the dataset to include more types of gases and test the models under a variety of environmental conditions to assess their robustness and reliability.
# An Enhanced Contrastive Ensemble Learning Method for Anomaly Sound Detection
## 3 Results
### Experiment Results  
The AUC test results for the model are presented in Table 3 below. Furthermore, to fully demonstrate the performance of the proposed model in this paper, we also calculated the partial-AUC (pAUC) value and recall score within the MIMII dataset and plotted the FAR-FRR curve. In this paper, the pAUC is calculated as the AUC over a low false-positive rate \(p\), as this score reflects the reliability of the model. In this paper, the \(p\) is set to 0.1. The pAUC score and recall scores are shown in Table 4, and the FAR-FRR curve is shown in Figure 7. FAR represents False Acceptance Rate and FRR represents False Rejection Rate, while FRR is the likelihood that a legitimate user will be rejected by the system, and FAR is the likelihood that an impostor will be accepted by the system. In the evaluation of any classification model, the FAR-FRR curve can represent the model performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Algorithm** & **Fan** & **Pump** & **Slider** & **Valve** & **Toycar** & **Toyconveygor** & **Gearbox** \\ \hline \multirow{8}{*}{MIMIMII} & AE [27] & 63.24\% & 61.92\% & 66.74\% & 53.41\% & \multirow{8}{*}{59.37\%} & \multirow{8}{*}{61.72\%} \\  & IDNN [28] & 64.64\% & 61.48\% & 69.8\% & 59.37\% & & \\  & MobilenetV2 [27] & 80.61\% & 83.23\% & 96.26\% & 91.26\% & & \\  & AADCL [16] & 80.11\% & 70.12\% & 77.43\% & 84.17\% & & \\  & SLFE-AE [29] & 80.0\% & 88.5\% & 95.4\% & 93.1\% & & \\  & Complex-network [14] & 89.55\% & **96.4\%** & 98.75\% & 96.87\% & & \\  & This research & **95.56\%** & 93.56\% & **98.86\%** & **98.75\%** & & \\ \hline \multirow{8}{*}{DCASE2020} & Self-encoder [30] & 69.92\% & 59.57\% & 95.79\% & 94.31\% & 92.74\% & 80.85\% \\  & Reference [31] & 59.64\% & 60.32\% & 76.29\% & 85.42\% & 79.30\% & 61.72\% \\  & Glow-aff [15] & 75.90\% & 83.40\% & 94.60\% & 91.40\% & 92.20\% & 71.50\% \\  & STGram-MFN [32] & 80.14\% & 80.4\% & **97.09\%** & 82.5\% & 91.80\% & 70.90\% \\  & Reference [33] & 90.07\% & 86.44\% & 91.84 \% & **97.67\%** & 89.84\% & 67.64\% \\  & LMTnet [34] & 88.81\% & 87.83\% & 94.27\% & 91.04\% & 90.05\% & 62.45\% \\  & This research & **94.47\%** & **91.93\%** & 95.79\% & 95.31\% & **92.74\%** & **79.85\%** \\ \hline \multirow{8}{*}{MIMIII DUE} & AE [35] & 64.36\% & 63.66\% & 58.09\% & 52.7\% & & 66.7\% \\  & MobilenetV2 [35] & 63.65\% & 64.12\% & 64.62\% & 54.01\% & & 68.4\% \\ \cline{1-1}  & UADA [36] & 65.31\% & 62.32\% & 59.19\% & **69.05\%** & & **71.05\%** \\ \cline{1-1}  & This research & **68.72\%** & **64.62\%** & **72.72\%** & 65.98\% & & 70.07\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: AUC scores in three datasets: Since most open-source papers and code evaluation metrics only provide the AUC value, the AUC is primarily used as the main indicator for performance comparison. Meanwhile, to ensure fairness, all models only used the development dataset to train.
## 5 Lattice Structures in Medical Devices and Applications
### Reticulated Cellular Structure  
Orthopedic implants often incorporate reticulated structures due to their versatile and adaptable isotropic properties, which are achieved through the repetition of unit cell geometry across the entire scaffold [118; 119; 121]. These structures are characterized by their regular and predictable architecture, which repeats a consistent pattern along multiple axes. This uniformity provides a reliable mechanical performance and makes reticulated structures ideal for applications, where consistent load distribution and mechanical strength are critical [118].  
Reticulated topologies can be designed with various geometric shapes, ranging from simple forms like cubic, triangular, and honeycomb patterns to more complex shapes, such as dodecahedral and diamond configurations [118; 121]. Each geometric shape influences the scaffold's overall compressive strength and mechanical properties. For example, honeycomb and triangular designs are known for their high stiffness and strength, making them suitable for load-bearing applications, while more intricate shapes like dodecahedral and diamond structures may offer enhanced flexibility and better adaptability to different mechanical demands [122; 123].  
The ability to tailor mechanical properties by adjusting the porosity and orientation of the unit cells is one of the key advantages of reticulated designs. Increasing the porosity of the scaffold can reduce its stiffness, making it more flexible and better aligned with the mechanical behavior of natural bone [124; 125]. This reduction in stiffness helps alleviate stress shielding. However, increasing porosity or altering the orientation of unit cells to lower stiffness may also lead to a decrease in the scaffold's ability to withstand compressive forces [124]. This trade-off between flexibility and compressive strength is a critical consideration in the design of reticulated structures for orthopedic implants, particularly in weight-bearing applications like spinal fusion or joint replacement.  
The compressive strength of a reticulated scaffold is highly dependent on the geometry of the unit cells and how they are oriented relative to the applied load. For instance, cubic and honeycomb structures may provide higher strength in specific directions, while more complex shapes like dodecahedral structures might offer a more even distribution of forces [118; 126]. This flexibility in design allows for engineers to customize the mechanical properties of the implant to meet the specific demands of different anatomical locations [124; 118].
# Epoxy/Glass Fiber Nanostructured p- and n-Type Thermoelectric Enabled Model Composite Interphases
## 4 Discussion
### Raman Analysis of the TE-Enabled GF Tows  
The p-type and the n-type SWCNTs coatings onto the GFs possess all the expected vibrational modes commonly encountered in graphitic materials. More specifically, the most prominent band of the spectrum of SWCNTs was the graphitic or G band with E\({}_{26}\) symmetry located at about 1590 cm\({}^{-1}\). The radial breathing mode, which is a unique phonon mode of SWCNTs was also observed at the 120 cm\({}^{-1}\) to 140 cm\({}^{-1}\) region. Another disorder-induced band appearing in the first order spectrum of the SWCNTs is the D band, at about 1340 cm\({}^{-1}\). The D band is caused by either amorphous carbon or sp\({}^{3}\) carbon atoms at defect sites on the CNT sidewalls [44]. Furthermore, the 2D peak dominated the second order spectrum of the SWCNTs coatings. This band was located at about 2685 cm\({}^{-1}\). This overtone results from a double resonance process and is a symmetry allowed band which requires two phonons to be completed [45; 46].  
As can be seen in Figure 6b,c, no changes were observed in the Raman spectra of both the p-type and the n-type SWCNTs coatings onto the GFs when they were subjected to temperature gradients of 50 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, apart from minor positive frequency shifts of all vibrational modes which were attributed to the increased temperature. This result indicates that the hierarchical GFs which are coated with the organic SWCNTs inks of both p-type and n-type TE behavior are chemically and structurally stable.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 2 CO2 Observation, Datasets, Global Related Research  
Earth observation for atmospheric GHG, including ground-based observation networks and GHG satellites, are summarized. Compared to ground-based observations, satellite remote sensing has been providing more and more accurate and higher-resolution global GHG detection. This paper outlines the chronological development over time of various countries.
# Model Tests of Soil Reinforcement Inside the Bucket Foundation with Vacuum Electroosmosis Method
###### Abstract  
Offshore wind turbine foundations are commonly subjected to large horizontal, vertical, and bending moment loads. Marine soils have high moisture content, high compressibility, high sensitivity, and low strength, resulting in insufficient foundation bearing capacity. In order to improve the bearing capacity of wind turbine foundations and reduce foundation settlement, an internal vacuum preloading method combined with electroosmosis reinforcement is used to reinforce the soil within bucket foundations. The pore water pressure, vertical settlement, pumping quality of the soil during the reinforcement process, soil moisture content before and after the reinforcement, and undrained shear strength were analyzed. Horizontal and vertical bearing capacity model tests were carried out on the reinforced and nonreinforced soil inside the bucket foundation. Results show that vacuum preloading combined with electroosmosis reinforcement reduces soil moisture content inside the bucket foundation by approximately 20%, and the undrained shear strength of the internal soil increases by approximately 20 times. Soil reinforcement has high spatial uniformity. Results of the bucket foundation bearing capacity model show that when the soil inside the bucket foundation is strengthened, horizontal bearing capacity increased by 2.9 times and vertical bearing capacity increased by 2.1 times. Vacuum preloading combined with electroosmosis reinforcement can effectively improve the shear strength of soft soil and enhance the bearing capacity and stability of bucket foundations.  
marie soft soil; electroosmosis reinforcement; bucket foundation; offshore wind; bearing capacity +
Footnote †: journal: Applied Sciences
## 4 Rare Earth Flotation from Secondary Resources
### Froth Flotation  
As described earlier, froth flotation (Figure 1) is a classical method to separate solid particles based on their different surface wettability. To date, the only solid waste amenable to flotation is phosphor, which comes from waste fluorescent lamps. Phosphors in fluorescent lamps can be classified into three groups: (i) red phosphors ((\(\mathrm{Y}_{0.95}\mathrm{Eu}_{0.05}\mathrm{z}_{0}\mathrm{y}_{0}\mathrm{z}_{0}\))\(\mathrm{z}_{0}\)), (ii) green phosphors ((\(\mathrm{Ce}_{0.67}\mathrm{Tb}_{0.33}\mathrm{MgAl}_{11}\mathrm{O}_{19}\)), and (iii) blue phosphors ((\(\mathrm{Ba}_{0.9}\mathrm{Eu}_{0.1}\mathrm{Mg}_{2}\mathrm{Al}_{16}\mathrm{O}_{27}\)) [130]).  
[PERSON] et al. [130] carried out the successful separation of phosphors and glass from waste fluorescence lamps using reverse flotation. In contrast to conventional direct flotation, reverse flotation removes impurities in the froth product while target REEs-bearing materials are recovered in the tailings. [PERSON] et al. [130] used dodecyl ammonium acetate (DDA) as a collector--usually called \"surfactant\" in resources recycling--to physically adsorb on glass particles while alkaline starch was utilized as a depressant for waste phosphors. The results showed that glass particles had high floatability and were recovered in the froth products while phosphors were mostly retained in the pulp. Among the three phosphors, red phosphors had the lowest floatability, followed by blue phosphors and green phosphors. The highest purity of TREO was obtained at pH 9, and the purity and recovery of model samples were 27.0% and 71.4%, respectively, while, in real wastes, the purity was slightly lower at 26.0% but with a slightly higher recovery of 73.3%.  
Figure 3: Schematic illustrations of advanced flotation techniques used in REEs recycling: (**a**) ion flotation, (**b**) solvent sublation, (**c**) electroflotation, and (**d**) absorbing colloid flotation.
## 2 Materials  
The material selected for this study belongs to the Krion(r) family of advanced solid surfaces, developed by Systempool, a division of the Porcelanosa Group (Vila-real, Castellon, Spain). Krion products are known for their unique composition of approximately 67% natural minerals, mainly alumina trihydrate (ATH), and 33% high-strength resins. This combination provides a wide range of properties that are intrinsic to the Krion brand, making it a leading choice for applications requiring performance, hygiene, and durability [12]:  
* **Thermoformability**: The material can be heated and molded into intricate shapes without compromising its mechanical properties. Optimal forming temperaturesrange between 130 and 160 \({}^{\circ}\)C, depending on the complexity of the shape and the desired radius.
* **Non-porosity and hygiene**: It is non-porous and has proven antibacterial properties, making it suitable for environments where hygiene is paramount, such as children's play areas.
* **Mechanical strength**: Exhibiting superior mechanical performance, it achieves flexural strength values between 60 and 78 MPa and tensile strength ranging from 40 to 60 MPa, ensuring robust resistance under mechanical loads.
* **Environmental resistance**: Engineered to withstand UV radiation (\(\Delta E<1.5\) after 10 years), thermal fluctuations, and exposure to external agents, this material is well adapted for prolonged outdoor use.
* **Sustainability**: Krion is 100% recyclable and features Eco-Active Solid Technology, contributing to environmental sustainability by purifying air and easing maintenance through self-cleaning properties.  
Among the Krion product range, Krion K LIFE 1100 was selected for its enhanced functionality, which surpasses not only alternative solid surfaces such as Corian but also other Krion models. This choice was driven by its exceptional performance in hygiene, sustainability, and environmental resilience.  
* **Eco-Active Solid Technology**: This unique feature enables air purification and contributes to maintaining cleaner and healthier environments, making it particularly well suited for children's spaces.
* **GREENGUARD gold certification**: Achieving this stringent certification demonstrates compliance with the highest standards for low chemical emissions, ensuring improved indoor air quality, particularly in sensitive environments such as nurseries and healthcare facilities.
* **Antiviral properties**: Studies conducted by Virology Research Services Ltd. (Sittingbourne, UK), using a modified ISO 21702 protocol [15], demonstrated the superior antiviral performance of the selected material, achieving an 82.22% reduction in infectivity of human coronavirus NL63 within 24 h (compared to 55.28% on plastic) and an 88.75% reduction in infectivity of the Influenza A virus within 2 h (compared to 18.44% on plastic). These properties make it an ideal choice for environments with high sanitary demands, supporting the creation of ergonomic and hygienic double-curved surfaces for children's play areas.
# Analyzing Markov Boundary Discovery Algorithms in Ideal Conditions Using the d-Separation Criterion
## 3 Bayesian Networks and Markov Boundaries
The Markov boundary is also unique for a specific variable \(X_{i}\)[2]. Due to this property, knowing the variables in the Markov boundary of \(X_{i}\) renders \(X_{i}\) itself obsolete, because the behavior of \(X_{i}\) can be predicted entirely by its Markov boundary. A Markov boundary is illustrated in the same Figure 1.  
It is highly desirable to know the Markov boundary of any individual variable of interest in a dataset because it holds such predictive power for the variable. However, knowing enough boundaries allows for the construction of the complete Bayesian network, which is a much more useful source of information. As a corollary, knowing the entire Bayesian network that underlies a dataset will trivially reveal all the Markov boundaries of the variables within it [2]: the boundary of a target node consists, by definition, of its parents, children and spouses (the nodes that share children with the target node). However, as stated above, the Bayesian network is usually unknown and the sole source of information is the dataset itself. The Markov boundary and the Markov blanket are concepts originating from Markov networks, which are similar encodings to Bayesian networks, but are undirected graphs, which limits their expressive power [2]. For this reason, Bayesian networks are preferred when encoding causal information. However, there are relationships that Bayesian networks cannot represent but Markov networks can [2].
## 4 Conclusions  
Shaded leaves, as well as sunlit leaves, contribute quite differently to reflectance and GPP at canopy level. Therefore, separating the canopy into sunlit and shaded parts for estimation of GPP using remote sensing technology is very necessary. Penumbra effect increases the difficulty for quantifying the fractions of sunlit and shaded leaves. In this paper, we investigated three methods with different hypotheses on treatments of the penumbra region for estimating the fraction of sunlit  
Figure 8: Relationships of PRI\({}_{b}\) and PRI\({}_{t}\) with APAR and LUE at daily temporal scales averaged from half-hourly data from 10:00 to 15:30 throughout the study period. The two \(R^{2}\) represent the coefficients of determination of linear (red) and exponential (green) regressions, respectively.  
leaves. Inversely retrieved PRI (PRI\({}_{\text{inv}}\)) from estimated \(P_{T}\) were compared to multi-angle observed PRI (PRI\({}_{\text{obs}}\)) to evaluate the accuracy of the three methods. The high accuracy of model I demonstrated that the penumbra region should be mostly treated as sunlit leaves, of which the fraction could be simply estimated using the ratio of canopy reflectance to leaf reflectance.  
The simple two-leaf approach presented by [PERSON] et al. (2017) was used to process the remote sensing and flux data, aiming to evaluate the ability of using PRI as a proxy for LUE or APAR. Generally, PRI was able to capture half-hourly and daily changes in LUE as well as APAR. At either half-hourly or daily time steps, the two-leaf approach significantly enhanced the correlations between PRI and LUE and APAR. The correlation between two-leaf PRI and APAR was very significant under all weather conditions, especially at half-hourly temporal scale. These results suggested that two-leaf PRI was probably a better proxy for APAR than LUE. However, in order to make PRI effective for understanding plant photosynthetic status under different weather conditions, a lot of works need to be done before a general method for reducing the effects of non-physiological factors on PRI can be wildly used.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Datasets
#### 4.1.2 NWPU VHR-10 Dataset  
The NWPU VHR-10 dataset, curated by Northwestern Polytechnical University, is an openly accessible benchmark for remote sensing detection tasks. Comprising 800 high-resolution images sourced from Google Earth and the Vaihingen database, this collection includes 650 object-bearing scenes and 150 background-only references. It contains 3755 annotated instances spanning ten categories: vehicle (VE), bridge (BR), harbor (HA), ground track field (GTF), basketball court (BC), tennis court (TC), baseball diamond (BD), storage tank (ST), ship (SH), and airplane (AP). All objects are manually labeled with horizontal bounding boxes. We adopt a 75-25% split for training and testing phases to ensure experimental reproducibility.
# A Survey of Vehicle to Everything (V2X) Testing
## 5 V2X Testing Methods
### Accelerated Testing  
To solve the slow testing process of a vehicle, the accelerated test method can effectively reduce the cost and time spent on the vehicle reliability verification process [95]. Accelerated testing requires a significant amount of real-world data, and critical scenarios are used to analyze potential vehicle problems [96]. The analysis results will be applied to additional scenarios and the above process will be repeated. Some technologies such as importance sampling are used to accelerate the testing process [97]. Therefore, accelerated testing can greatly speed up a vehicle's test process. Accelerated testing can be used in lab simulations, human-in-the-loop tests with driving simulators, hardware-in-the-loop tests, or field testing [98]. Accelerated testing can also be used to test the reliability of the V2X in working conditions such as heavy rain, fog, and other extreme weather, and the network communications performance during traffic jams.  
Some challenges should be considered before taking accelerated testing. One is that it performs badly in the filed because it's hard to build the real-world environment. The other is how to build the model with the real-world data and how to ensure that iterative data is valid. From the perspective of communication, accelerated testing can be used in function and performance testing in the lab. For example, testers build the application scenarios according to the real world and generate new scenarios with the accelerated method.