esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Knowledge Graph Embeddings  
A knowledge graph is a structured representation of knowledge that captures general knowledge relationships between entities in a domain, often represented as nodes and edges in a graph. Examples of large-scale knowledge graphs are Wikidata [PERSON] et al. (2017), YAGO Li et al. (2018), Freebase [PERSON] et al. (2017), and DBpedia [PERSON] et al. (2017). Knowledge graph embeddings refer to techniques that transform entities and relationships in a knowledge graph into numerical representations in a feature space that can capture semantic relationships and patterns within the graph. When it comes to modeling methods, a variety of approaches are available. One traditional mathematical method involves representing the relational space using a term-term matrix, positive pointwise mutual information, and singular value decomposition, as demonstrated in ConceptNet [PERSON] et al. (2017). Another approach is based on relation paths, representing relations linearly as translations, for instance, as exemplified by TransE Tian et al. (2018). Alternatively, graph neural networks can be employed, utilizing graph convolutional layers or similar architectures to aggregate information from neighboring entities, as seen in models like GraphSAGE [PERSON] et al. (2017) and R-GCN [PERSON] et al. (2017). Random walk-based methods utilize graph traversals to generate sequences of entities, which are used to train the embeddings, as seen in Node2 Vec [PERSON] et al. (2017), capturing both local and global structural information. Attention-based mechanisms, such as KGAT (Knowledge Graph Attention Network) [PERSON] et al. (2017), have also been applied. Additionally, enriching knowledge graphs with descriptive external information is another strategy, which involves incorporating supplementary details like entity type [PERSON] et al. (2017) or descriptive information of entities [PERSON] et al. (2017); [PERSON] et al. (2017); [PERSON] et al. (2017). Knowledge graph embeddings are usually derived from diverse sources, including crowd-sourced data and hybrid methods.
# A Proposal for a New Python Library Implementing Stepwise Procedure
## 2 Background
### Diagnosis of Heteroskedasticity  
In addition to the assumptions and statistical tests presented above, each random error term's probability distribution is represented by \(u_{i}\) in Equations (2) and (3) must present the same variance; that is, they must be homoscedastic [6].  
As [PERSON] and [PERSON] [32] explain, heteroskedasticity can indicate a correlation between the terms of the error and the explanatory variables, causing problems with the hypothesis tests of the t statistics [41; 42]. However, it does not necessarily affect the consistency of the parameter estimates, and it can be addressed by robust standard errors, such as Huber-White, in order to provide valid inference by adjusting the standard errors of the estimated coefficients, ensuring that statistical tests remain reliable even in the presence of heteroscedasticity.  
For the diagnosis of heteroscedasticity, the Breusch-Pagan/Cook-Weisberg test stands out, based on the Lagrange multiplier, with \(H0\) corresponding to the variance of the constant error terms (homoskedasticity) [6]; hypothesis \(H1\) corresponds to the variance of error terms non-constantly, indicating that the terms \(u_{i}\) are a function of one or more explanatory variables (heteroskedastic errors) [32]. [PERSON] and [PERSON] [6] indicate this test when the assumption of normality of the residues is verified.  
It is necessary to obtain each standardized residual using Equation (6) to apply the Breusch-Pagan test:  
\[u_{BP,i}=\frac{u_{i}^{2}}{\sum_{n=1}^{n}u_{i}^{2}/n} \tag{6}\]  
where \(u_{i}\) represents the residual vector, and \(n\) is the sample size. Next, the regression model, Equation (7), through which the sum of squares of the regression is calculated, divided by two, obtaining the \(\chi^{2}_{BP}\) statistic [6].  
\[u_{BP,i}=\alpha+\beta\hat{Y}_{i}+\varepsilon_{i} \tag{7}\]  
\(\hat{Y}_{i}\) represents the dependent variable's predicted value vector, and \(\varepsilon_{i}\) corresponds to the regression's error term.  
The Breusch-Pagan test presents \(H0\) as the calculated statistic \(\chi^{2}_{BP}\) with a chi-square distribution with \(1\) degree of freedom for a given significance level. In practice, if the error terms are homoskedastics, the squared residues do not increase or decrease with the increase of \(\hat{Y}_{i}\)[6].
## 1 Introduction
NTPs in atmospheric pressure have been used for agricultural applications such as decontamination or inactivation of microorganisms in seeds, postharvest products, the plant tissue culture process, pork cuts and chicken meat, and the decontamination of pesticide residues [17; 18; 19; 20; 21]. The antimicrobial activity of NTP is associated with the generation of high concentrations of ROS during the discharge process; these ROS consist of superoxide (O\({}_{2}\)'), hydroxyl (OH) and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) which are toxic to microbial cells and cause oxidation of their macromolecules, thereby resulting in cell death [22]. In this study, we applied and compared three non-thermal plasmas, GA, TC and DBD, for the inactivation of _C. lactucae-sativa_ in laboratory conditions and observed the resulting fungal pathogenicity after plasma treatments.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis
### Application of Proposed Methodology with Other Satellite Data  
In order to test the adaptability and the effectiveness as in [56], we applied the proposed framework on the WHU-Hi-HongHu dataset [57; 58; 59]. This data consists of a complex agricultural area with a variety of crops. The data were acquired in Honghu City, China. The number of pixels is 940 \(\times\) 475, with 270 bands acquired from 400 to 1000 nm wavelength. The data were acquired using an unmanned aerial vehicle (UAV)-borne hyperspectral system with high spatial resolution of 0.043 m. Out of 22 class regions, 15 classes were found with class numbers 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20. Table 8 shows class-wise accuracies after the application of the proposed framework on the WHU-Hi-Hong Hu dataset. We used all the parameters similar to the Indian Pines and Salinas datasets. It can be concluded that the proposed framework was successful in classifying, with an average accuracy of 98.56% for 15 crop classes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Partition** & \multicolumn{5}{c}{**Ranking**} & \\ \hline
**Coarser** & \multirow{2}{*}{
\begin{tabular}{c} **Parition in to** \\ **3 Regions** \\ \end{tabular} } & **Proposed Partition Based on** & **Entropy** & **NDVI** & **NDWI** & **OA** & **AA** \\
**Partition in to** & **VIS, NIR, and SWIR Regions** & **Based** & **Based** & **Based** & **Based** & & \\  & & **Ranking** & **Ranking** & **Ranking** & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation experiments of proposed network.
## 3 Results and Discussion
### Temperature Influences on Aerobic Metabolisms of PAOs in Conjunction with Cu(II) Presence
#### 3.3.2 Aerobic PHA Degradation  
Figure 3c shows the relations between the Cu(II) concentration and aerobic PHA degradation rate at different temperatures. Clearly, the aerobic PHA degradation rate reduced with an increasing Cu(II) concentration. The effect of temperature on the extent of metabolism inhibition in the presence of Cu(II) was also substantial. The order of inhibition extent was 10 \({}^{\circ}\)C > 20 \({}^{\circ}\)C > 30 \({}^{\circ}\)C, as shown in Figure 3d. PAOs completely stopped degrading PHAs at 20 \({}^{\circ}\)C when 0.5 mgL\({}^{-1}\) of Cu(II) was present, whereas PHA degradation stopped at 2.0 mgL\({}^{-1}\) at 30 \({}^{\circ}\)C, revealing PAOs had a higher tolerance of Cu(II) toxicity in a high temperature environment than in a low temperature environment. It is interesting that the PAOs synthesized PHA instead of consuming it in a 10 \({}^{\circ}\)C environment, regardless of Cu(II) levels, indicating a low temperature environment was unfavorable to the aerobic metabolism of PHA degradation.
## 3 Implementation Method
### Cross-Correlation Analysis
#### 3.1.2 Denoise Principle of CCA  
The correlation between FIV--especially in the vertical direction--and FIS was excellent [23; 24]. Both were poorly correlated with random noise signals. The CCA between  
Figure 2: Workflow of the proposed method.  
the FIV and FIS could attenuate random noise and highlight target signals. The denoising principle of CCA is as follows:  
\[x(t) =f_{v}(t)+n(t) \tag{4}\] \[y(t) =f_{n}(t)+v(t) \tag{5}\]  
where \(x(t)\) is the measured vibration signals; \(f_{v}(t)\) is the FIV components with periodic noise; \(n(t)\) is the random noise; \(y(t)\) is the measured sound pressure signals; \(f_{n}(t)\) is the FIS components with periodic noise; \(v(t)\) is the random noise; \(t\) is the signal recording time.  
Considering that both \(v(t)\) and \(n(t)\) were not correlated to \(f_{n}(t)\) or \(f_{v}(t)\) and \(v(t)\) was not correlated to \(n(t)\) (which was consistent with most of the actual measurements), the CCF of \(x(t)\) and \(y(t)\) can be defined as:  
\[\begin{array}{ll}R_{xy}(\tau)&=E\big{\{}y(t)x(t-\tau)\big{\}}\\ &=E\big{[}\big{[}f_{n}(t)+v(t)\big{]}[f_{v}(t-\tau)+n(t-\tau)]\big{\}}\\ &=R_{f_{n}f_{v}}(\tau)+R_{f_{n}n}(\tau)+R_{vf_{v}}(\tau)+R_{vn}(\tau)\end{array} \tag{6}\]  
where \(R_{f_{n}f_{v}}(\tau)\) is the CCF of FIV and FIS components with periodic noise; \(R_{f_{n}n}(\tau)\) is the CCF of the FIS components with periodic noise \(f_{n}(t)\) and random noise \(n(t)\); \(R_{vf_{v}}(\tau)\) is the CCF of the random noise \(v(t)\) and the FIV components with periodic noise \(f_{v}(t)\); \(R_{vn}(\tau)\) is the CCF of the random noise \(v(t)\) and \(n(t)\).  
Because of the independence and non-correlation between FIV signals, FIS signals, and random noise, the last three terms on the right of Equation (6) should be zero, that is:  
\[R_{f_{n}n}(\tau)=R_{vf_{v}}(\tau)=R_{vn}(\tau)=0 \tag{7}\]  
Thus, the CCF of \(x(t)\) and \(y(t)\) can be simplified as:  
\[R_{xy}(\tau)=R_{f_{n}f_{v}}(\tau) \tag{8}\]  
Therefore, the CCF of measured vibration and sound pressure signals were obtained, the random noise was denoised, and the target signals were highlighted.
## 3 Experimental Section
### Instrumentation Settings  
HPLC-DAD analyses were carried out on an Agilent 1100 Series LC system (Agilent Technologies, Santa Clara, CA, USA), constituted by the following modules: vacuum degasser (G1322A), quaternary pump (G1311A), autosampler (G1313A), thermostatted column compartment (G1316A) and the diode array detector (G1315B). The data acquisition and instrumental control were performed by the software LC3D ChemStation (version Rev.A.10.02 [1757], Agilent Technologies). Analyses were performed on a Tracer excel 120 ODS-A column, 150 mm \(\times\) 4.0 mm, 5 \(\upmu\)m particle size (Teknokroma, Barcelona, Spain). The mobile phase consists on a mixture of MeOH (solvent A) and water with the following gradient: 0-4 min: 60% A, 4-10 min: 60%-90% A and 10-20 min: 90% A, using a flow of0.5 mL/min. The injection volume was 20 \(\upmu\)L with a draw speed of 200 \(\upmu\)L \(\text{min}^{-1}\). The detector was set at 226 nm. For identification purposes, standard addition was used by spiking the samples with pure standards, as well as by comparing the retention parameters and peak purity with the UV/vis spectral reference data. For recovery calculations, peak areas obtained from each assay were compared with the peak areas of standard controls used for spiking. For quantification purposes on real matrices, calibration plots using the standard addition methodology was also performed.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 5 Fabrication Technologies of Carbon Electrode-Based Micro-Supercapacitors
### Deposition Techniques
#### 5.1.3 Electrolytic Deposition  
Electolytic deposition uses redox reactions, in a solution containing precursors, to induce growth of the electrode material under an electric field. The concentration of the electrodeposition solution and the deposition time needs to be tightly controlled in order to avoid the growth of active materials in a lateral direction, which could lead to an electrical short circuit in micro-supercapacitor arrays in series [50].  
Porous nano-flake-like \(\alpha\)-Co(OH)\({}_{2}\) was deposited onto graphene nanosheets by electrodeposition and then functionalized and employed as electrode in hybrid micro-super capacitors [148]. A two-step electrochemical method was developed to deposit reduced graphene oxide and MnO\({}_{2}\) onto Ni fibres, which acted as electrodes in flexible fibre-shaped micro-supercapacitors with PVA-LiCl gel solid electrolyte [149]. Synthesis of Ni(OH)\({}_{2}\) thin film electrodes involved in situ doping, with carbon nanotubes/reduced graphene oxide by cathodic electrodeposition on titanium mesh, in order to improve the electrochemical performance, as an effect of the doping induced porous structure and conductive network of the thin film [79].
# A Denoising Method for Multi-Noise on Steel Surface Detection
## 5 Comparison of the Proposed Denoising Method with Other Denoising Methods  
The Pytorch framework was chosen for the experiments, the system environment was Windows 11, and the GPU acceleration was CUDA 11.5.1, CUDNN8.3_1_CUDA11.5. The Intel(r) Core (TM) i7-10750H CPU @ 2.60 GHz was used, and the graphics card used was an NVIDIA GeForce RTX 2060 6G for computing. The program script compilation environment was PyCharm, and the program language used was Python. In order to ensure that the model can effectively reduce noise for different types of noise and scenes, this paper selects the surface defect dataset constructed by Prof. [PERSON]'s team at the School of Mechanical Engineering and Automation, Northeastern University, which was introduced in the first part of this paper. On the other hand, the collected images of steel surfaces under various conditions, including different time periods, lighting conditions, shooting angles, etc., totaled 7200 images in six different categories. The extended dataset were divided into training and test sets in the ratio of 7:3. That is, 840 samples for each class of defective samples were used for training, another 360 samples were used to validate the model's classification performance metrics. The coverage of the training dataset was high enough to cover possible noise sources and variations to ensure the breadth of the training dataset.  
\begin{table}
\begin{tabular}{c c c} \hline
**Types of Defects** & **Number of Original Images** & **After Image Amplification** \\ \hline crazing & 300 & 1200 \\ inclusion & 300 & 1200 \\ patches & 300 & 1200 \\ pitted surface & 300 & 1200 \\ rolled-in scale & 300 & 1200 \\ scratches & 300 & 1200 \\ \hline \end{tabular}
\end{table}
Table 1: Number of defective samples per category after data amplification.
# Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data +
## 4 Results
The NTU RGB + D dataset is one of the most extensive skeleton-based action recognition datasets, consisting of 60 different actions performed by 40 subjects in front of an RGB + D camera, with data captured from multiple angles. The extended NTU RGB + D 120 dataset includes 120 action classes, covering a more comprehensive range of activities and more diverse scenarios. The results demonstrate that cutout augmentation provides a noticeable improvement in several cases, particularly in the XView setting. For instance, in NTU RGB + D, the accuracy for ST-GCN with cutout increased from 94.89% to 95.48%, while ST-GCN++ with cutout showed a slight improvement from 94.93% to 94.98%. Similarly, for the NTU RGB + D 120 dataset, cutout improved the performance of ST-GCN from 83.05% to 83.25% in the XSubject setting, and the accuracy of ST-GCN++ in the XView setting increased from 88.64% to 88.68%. These results suggest that the cutout augmentation technique helps the models generalize better, particularly in scenarios involving different views or subjects, by forcing the model to focus on incomplete data and improving its robustness. Overall, the results clearly indicate that both mixup and cutout augmentations, whether used alone or in combination with clean data, enhance the accuracy of models for HAR tasks. Both mixup applied to sensor-based data and cutout to skeleton-based data contribute to improving model robustness. The combination of augmented and clean data often yields the best performance, suggesting that integrating these augmentation techniques can effectively address data scarcity and variability issues, leading to more robust and generalizable models across different HAR modalities.
## 4 Multi-Hop Data Forwarding Strategy Based on Annulus-Sectors
### Data Forwarding Strategy with the Help of the Candidate Cluster Heads
That is to say, the following formula should be met:  
\[{r_{k-1}}^{2}/{r_{k}}^{2}\geq 2 \tag{27}\]  
According to Equations (17) and (27), we have:  
\[\frac{\left(\left(\frac{4k-4}{2k-1}\right)N_{k}-1\right)e_{r}+\left(\frac{4k- 4}{2k-1}\right)N_{k}e_{t}}{(N_{k}-1)e_{r}+N_{k}e_{t}}\geq 2 \tag{28}\]  
When \(k>4\), it can be approximately regarded that the constraint about in Equation (28) could be met. Therefore, under the condition that the density of nodes in the network is low, the number of annuli should be more than 4.
# Performance Analysis of the Korean Positioning System Using Observation Simulation
## 3 Simulations
### Precise Orbit Simulation
Therefore, it is possible to predict the performance of KPS POD by orbit simulations. Figure 4 shows the averaged orbit RMS errors in the along-track, cross-track, and radial directions for 24 GPS satellites. These errors were compared with the true orbits that we simulated. The orbit accuracy of the GPS satellites is less than 5 cm in all directions. The GPS satellites exhibit higher orbit accuracy in all directions than the KPS satellites. In general, while the GEO and IGSO satellites have limited coverage, MEO satellites like GPS provide global coverage. This indicates that GPS has much better observation geometry compared to KPS. [PERSON] et al. (2019) suggested that the quality of GEO orbits can be improved by LEO satellites. However, KPS does not consider LEOs. \begin{table}
\begin{tabular}{c c} \hline
**Item** & **Models/Methods** \\ \hline
**Force model** & \\ Earth gravity & EIGEN6C (\(12\times 12\)) ([PERSON] et al., 2019) \\ N-body & JPL DE405 ([PERSON] et al., 2019) \\ Relativity & IERS conventions 2010 ([PERSON] et al., 2010) \\ Solar radiation pressure & ECOM model ([PERSON] et al., 2010) \\ Atmospheric drag & Not considered \\ Earth orientation & IERS C04 ([PERSON] et al., 2010) \\ Attitude model & Orbit normal ([PERSON] et al., 2010) for KPS GEO, yaw steering and orbit normal for KPS \\
**Observation model** & \\ Observations & Undiferenced ionospheric free linear combination \\ Signal & L1/L2 \\ Elevation cutoff & \(7^{\circ}\) \\ Weight for observations & Elevation (\(E\))-dependent angle stochastic model (\(\sigma^{2}=1\) for \(E>30^{\circ}\), \\  & otherwise \(\sigma^{2}=1/2\sin(E)\)) \\ Sampling rate & 30 s \\ Arc length & 1 day \\ Reference frame & ITRF2014 \\ Satellite PCO/PCV & Igs14.atx \\ Receiver PCO/PCV & Igs14.atx \\ Phase wind-up & [PERSON] et al. (2019) \\ Solid earth tide, ocean tide, pole tide & IERS conventions 2010 ([PERSON] et al., 2010) \\ Ionospheric delay & Eliminated \\ Tropospheric delay & Saastamoinen with GMF ([PERSON] et al., 2019), estimated zenith wet delay \\ Satellite clock error & Estimated as white noise \\ Receiver clock error & Estimated as white noise \\ Ambiguity & Fixed solutions \\ \hline \end{tabular}
\end{table} 
Table 2: Force and observation models applied for the KPS/GPS POD simulation. As shown in Figure 4, the averaged RMS value of the along-track component is larger than that of the others, whereas the radial component of GPS has slightly better accuracy than the along-track and cross-track components. IGS has provided orbit products for GPS satellites [24]. The IGS final products have a nominal orbit accuracy of about 2.5 cm [40], which are available at the website ([[http://www.igs.org/products](http://www.igs.org/products)]([http://www.igs.org/products](http://www.igs.org/products))). Our results derived from simulations are similar to those provided by IGS.
# EEMIP: Energy-Efficient Communication Using Timing Channels and Prioritization in ZigBee
###### Abstract  
With the expansion of the Internet-of-Things, energy-efficient communication is becoming vital. The communication among energy-limited devices (e.g., powered by batteries or harvesting the energy from their environment) must be energy-efficient, prolonging their lifetime or increasing data throughput. This article aims at proposing energy-efficient periodic communication for devices over the ZigBee protocol and powered by a battery. We propose using timing channels for different data priorities, thus, more important data are sent more frequently. The priority is also considered in case of congested traffic, where a central device (coordinator) prioritizes more important communication. We have implemented a simulator, which serves for verification of the proposed solution, and conducted experiments comparing the proposed EEMIP method with the standard nonbeacon ZigBee communication. The experimental results show that the proposed method is more energy efficient.  
energy-constrained device; energy efficiency; Internet-of-Things; low-power communication; wireless sensor network; ZigBee Article
# A Watermarking Method for 3D Printing Based on Menger Curvature and K-Mean Clustering
## 3 The Proposed Algorithm
### Watermark Embedding
When \(\omega_{g}=1,K_{mean}^{m_{g}}\) will become greater than \(\Delta_{mg}\) if it is less than \(\Delta_{mg}\). After embedding the watermark bit \(\omega_{g}\) into the mean Menger curvature of the group \(m_{g}\), we compute the change rate \(\alpha_{g}\) between the watermarked mean Menger curvature \(K_{mean}^{m_{g}*}\) and the reference value \(\Delta_{mg}\) as shown in Equation (8):  
\[\alpha_{g}=\frac{K_{mean}^{m_{g}*}}{\Delta_{mg}} \tag{8}\]  
\[v_{ij}^{\prime}=\alpha_{g}\times v_{ij}+(\alpha_{g}-1)\times v_{ij}\ \forall\ j\in[1,3] \tag{9}\]  
The change rate \(\alpha_{g}\) is used to generate the watermarked 3D triangle mesh \(\mathbf{M}^{w}\) in the watermarked 3D triangle mesh generation process according to the watermarked mean Menger curvature \(K_{mean}^{m_{g}*}\) of each group \(m_{g}\). To generate the watermarked 3D triangle mesh, in each group \(m_{g}\) we select a facet that has the Menger curvature value closest to the watermarked mean Menger curvature \(K_{mean}^{m_{g}*}\) and change the selected facet according to the change rate \(\alpha_{g}\). Assume that, in the group \(m_{g}\), we found that the facet \(f_{i}\), has the Menger curvature value closest to the watermarked mean Menger curvature \(K_{mean}^{m_{g}*}\), so the facet \(f_{i}\) will be transformed into the facet \(f_{i}^{\prime}\) in the watermarked 3D triangle mesh \(\mathbf{M}^{w}\) as shown in Equation (9). Therein, \(v_{ij}^{\prime}|j\in[1,3]\) are three vertices of the facet \(f_{i}^{\prime}\).