esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Graph Neural Networks
### Temporal Graph Networks  
To review more theoretical information about what is presented in this section, see [25]. A static graph \(G\) is defined as the tuple \((V,E,V,\mathcal{E})\), where \(V\) is the set of vertices, \(E\subseteq V\times V\) the set of edges; notice that directed graphs are being considered. Each vertex \(v\in V\) has  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(\mathbb{R}^{d_{v}}\) & The set and dimension of vector features \\ \(\mathbb{R}^{d_{v}}\) & The set and dimension of edge features \\ \(V_{T}\) & The set of temporal vertices defined by a tuple \\ \(\mathcal{E}_{T}(n)\) & The temporal neighborhoods of vertex n \\ \(V_{j}\) & The set of vertices associated to a snapshot \\ \(E_{j}\) & The set of edges associated to a snapshot \\ \(\epsilon_{V}^{\pm}\) & Vertex insertion/deletion event \\ \(\frac{\epsilon}{E}\) & Edge insertion/deletion event \\ \(h_{R}(t)\) & Embedding of vertex \(v\) at time \(t\) and iteration \(k\) \\ \(\mathcal{M}_{v}(t)\) & Message aggregation of vertex \(v\) at time \(t\) \\ \(s_{v}(t^{+})\) & Updated state of vertex \(v\) due to events at time \(t\) \\ \(z_{v}(t)\) & The embedding of vertex \(v\) after a process of an GNN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Math definitions used in this work.  
\(x_{v}\in\mathcal{V}:\mathcal{V}\in\mathbb{R}^{d_{\mathcal{V}}}\) features and each edge \((u,v)\in E\) has \(e_{uv}\in\mathcal{E}:\mathcal{E}\in\mathbb{R}^{d_{E}}\) features. In addition, \(\mathcal{N}(u):=\{v\in V|(u,v)\in E\}\) is the set of neighborhoods of \(u\) in \(G\).  
Temporal Graphs: A temporal graph \(G_{\mathcal{T}}\) is defined as the tuple \((V,E,V_{\mathcal{T}},E_{\mathcal{T}})\), where \(V\) and \(E\) are the set of vertices and edges, respectively, that may appear at any time. Some temporal vertices and edges have time-dependent features such that \(V_{\mathcal{T}}:=\{(v,x_{v},t)\mid v\in V,x_{v}\in\mathcal{V},t\in\mathcal{T}\}\), \(E_{\mathcal{T}}:=\{(e,x_{e},t)\mid e\in E,x_{e}\in\mathcal{E},t\in\mathcal{T}\}\), where \(\mathcal{T}=\{t_{1},t_{2},...,t_{f}\}\) is a set of timestamps. The temporal neighborhood of \(u\) is given by  
\[\mathcal{N}_{\mathcal{T}}(u):=\{v\in V\mid\exists(e,x_{e},t)\in E_{\mathcal{ T}},e=(u,v)\}. \tag{1}\]  
If the timestamps on a temporal graph (TG) follow a fixed time step, the TG becomes a Discrete-Time Temporal Graph.  
Discrete-Time Temporal Graph (DTTG): Let \(\Delta\tau>0\) be a fixed time-step and let \(t_{1}<t_{2}<...<t_{n}\) be timestamps with \(t_{k+1}=t_{k}+\Delta\tau\). A DTTG \(G_{DT}\) is a TG where for each \((v,x_{v},t)\in V_{\mathcal{T}}\) or \((e,x_{e},t)\in E_{\mathcal{T}}\), the timestamps are taken from the set of fixed timestamps.
## 3 Results and Discussion
### Selection of AOA Estimation Method  
[PERSON] et al. [24] reported that methods of AOA determination have different sensitivity to the degree of roasting of coffee beans. The spectrophotometric analysis based on 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging was the most sensitive assay. However, the DPPH assay has a mixed mechanism that combines electron transfer (ET) and hydrogen atom transfer (HAT) reactions [53]. This means that the antioxidative (ET) and antiradical (HAT) properties of the analyzed sample cannot be differentiated, which challenges the interpretation of the obtained results. In the present work, the analysis of the AOA of extracts of untreated coffee beans was performed through two methods based on the electron transfer mechanism [18; 37] in order to select the most sensitive. The AOA evaluation results of untreated coffee beans by spectrophotometric and potentiometric methods are presented in Figure 2a,b, respectively.  
As can be seen from Figure 2, the AOA of coffee extracts declined: green coffee presented the highest AOA, followed by medium roast coffee and dark roast coffee, which is consistent with the literature data obtained through the methods based on electron transfer [24, 26]. The correlation between the spectrophotometric and potentiometric data was r = 0.94, \(p\) < 0.001. In assessing the AOA differences between medium roast and dark roast coffee extracts, both methods demonstrated similar sensitivity (\(p\) < 0.001), whereas, in green coffee and medium roast coffee extracts, the potentiometric method showed more significant differences (\(p\) < 0.001) than spectrophotometry (\(p\) < 0.01). In addition, the FCR, used in the spectrophotometric analysis, is a strong oxidizer. It was reported that the FCR is capable of oxidizing compounds other than phenolic antioxidants (ascorbic and uric acids), as well as organic compounds that are not antioxidants (carbohydrates, nucleotides, aromatic amines, etc.) [18, 38, 39]. Potassium ferricanide, used in potentiometry, is a milder oxidizer. Interference studies showed that K\({}_{3}\)[Fe(CN)\({}_{6}\)] does not oxidize carbohydrates [54]. By considering all these facts, potentiometry was used to evaluate the AOA of extracts of treated coffee beans (Section 3.3).
## 3 Proposed Procedure to Develop Predictive Models in Healthcare
### Stage 6: Interpretation of Results and Model Selection  
Once a statistically acceptable predictive model has been created, it can be deployed to be used in production, since it can provide predictions with good accuracy to support decision-making (Figure 4, stage 6). However, for the exploitation of the model, it is necessary first to evaluate the degree of correlation between the results obtained and the previously defined clinical target (in our case study, the prediction of cervical pain). Besides, some models (e.g., the tree-based classifiers) could be explored to analyze which particular features are the most decisive factors in the classification model. If the evaluation of the correlation does not yield satisfactory results--that is, if the consonance of the results with the target is not achieved or the model does not have enough predictive power according to the minimum goals established initially--the previous stages must be repeated with the objective of improving and optimizing the process according to the target initially set, leading to an iterative process (see Figure 4) [20; 77]. There can be several causes of low correlation or poor predictive power:  
* The target is difficult to achieve or too complex;
* Patients are inadequately characterized;
* The sample is insufficient;
* The data include variables that are not necessary (e.g., irrelevant variables, confounding factors, or redundant variables) or do not include those that are;
* Problems exist in the predictive model generation stage regarding the selected algorithm [78], overfitting, or over-parameterization. For effective deployment, not only the accuracy of the models but also the resources must be considered. For example, the scalability requirements are key when you have high volumes of data to avoid very long training times, lack of memory, etc. This is important in terms of productivity, portability, cost reduction, the minimization of staff involvement, etc. In cases where large volumes of data have been collected for many years (cancer studies, studies of discharge from the hospital and sick leaves, etc. ), scalability acquires a transcendental role [30]. **Application to Our Case Study**  
Based on the results collected in Table 4, a quite good performance of all the algorithms can be observed for most metrics. Considering the large number of variables available initially, the rigor with which all the data were obtained by the multidisciplinary team, and the adequate selection of variables carried out in previous phases (choosing the objective variables and with a greater predictive power), a suitable prediction performance was initially expected. Considering the minimal target values of the statistical measures established in Stage 1 \"Clinical target definition\", several algorithms fulfil the requirements in terms of the target metrics:  
* Accuracy (>85%): logistic regression, SVM, random forest, MLP neural network, and GBA. * Precision (>85%): logistic regression, SVM, decision tree random forest, MLP neural network, and GBA.
## 2 Research Significance  
A review of the literature showed that considerable research work has been carried out to evaluate the influence of SP and NS separately on the mechanical and durability performance of concrete. It showed that the contents of SP and NS should be limited and using high-contest of these materials leads to reducing the mechanical properties of concrete [12; 13; 14; 15]. On the other hand, previous investigations demonstrated that using different types of fibres plays a positive role on the mechanical behaviour of concrete especially in terms of compressive, tensile and flexural strength [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. [PERSON] et al. [33] evaluated the effect of PPF and SP on the static and dynamic characteristics of HSC. They found that adding SF resulted in brittle failure in the indirect tensile strength test, while the simultaneous incorporation of SF and PPF led to ductile failure. Besides, the resistance was substantially enhanced with increasing PPF and SF content due to the bridging role of the fibres and their higher tensile strength in comparison with the tensile strength of the concrete matrix. [PERSON] et al. [34] investigated the mechanical properties of PPF-reinforced concrete with rubber aggregates. They showed that adding PPF led to reducing the dilapidation rate of the dynamic bending stiffness. [PERSON] [35] used SF and PPF to improve the durability performance of concrete under natural weathering. The specimens were exposed to natural conditions for 360 days. Additionally, the compressive strength, water absorption and porosity specimens containing various PPF content were studied. It was found that PPF significantly improved the durability and mechanical characteristics of the specimens. Therefore, finding a way to increase the contents of SP and NS in order to improve the mechanical and durability performance of concrete by combining these materials was the main aim of this study. Moreover, PPF and SF were used to prevent the reduction effect of using a high content of SP and NS on the properties of concrete.
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 2 Error Correction Scheme (ECS)
### Modulation  
Another approach used in the literature other than pure error correction technique is adapting the modulation order according to channel condition. In this approach, the order of modulation is altered according to the quality of a communication link. When the quality of a communication channel is better i.e., better SNR, use higher order modulation, whereas the modulation order is decreased when the quality of communication link is poor to achieve the same BER [29]. Modulation scheme involves the PHY layer of the OSI model.  
In the literature, we found that there are several types of ECSs that have been proposed to enhance communication reliability, delay constrained, and data rates in WBAN. In February 2012, IEEE 802.15.6 [30] is published as a standard for WBANs where ECS for WBAN is proposed.
## 1 Introduction
### Motivations  
The problem of coverage optimization in WSNs poses several challenges, primarily due to the limited energy resources of sensor nodes. Achieving high coverage while considering energy consumption, network lifetime, and deployment costs requires a delicate balance. Despite notable progress, the problem is still very challenging, and there is still considerable scope for future advancements in this work. Particularly, the binary ant colony algorithm (BACA) has not been extensively researched in the context of coverage. To the authors' knowledge, no existing work has explored the combination of the BACA with an optimization algorithm such as hill climbing or simulated annealing to enhance the solution of coverage.
## 4 Results
### Segmentation Accuracy  
Figure 4 compares the accuracy of the segmentation (\(Q_{seg}\), \(S_{r}\), \(E_{s}\)) of the proposed method with that of four other methods. The HSV and EDA produce a relatively low \(Q_{seg}\) with the highest standard deviation (SD). Furthermore, DeepLabv3+ and RF are second and third, with an average \(Q_{seg}\) of 0.65 and 0.82 and a much lower SD. Of all these methods, the improved RF method produces the highest mean value of \(Q_{seg}\) and has the lowest SD. It also produces the highest \(S_{r}\) and the lowest SD. For the \(E_{s}\) index, HSV produces the most misclassified pixels, and the improved RF method produces the fewest misclassified pixels.  
Figure 3: Estimate of the maize leaf coverage generated by different methods. Panels (**a**–**e**) show leaf-coverage images selected at random from the dataset.  
To test the efficacy of the augmentation, the image dataset without the augmented images was used independently.  
From the results given in Tables 4 and 5, we conclude that two classifier-based segmentation methods (DeepLabv3+ and the improved RF) produce the highest mIOU scores (0.7984, 0.8237 and 0.7916, 0.8055, respectively) which indicates that these two methods perform well on alternative datasets. These two methods also produce the smallest change in the mIOU score for the two training sets (0.9% and 2.3%), which means that they maintain good segmentation even when the quantity of data changes. The mIOU score in Table 5 is better than that in Table 4 because the augmentation of the original dataset improves the final accuracy.  
Figure 4: Comparison of the mean accuracy rate (\(Q_{seg}\), \(S_{r}\), and \(E_{s}\)). Comparison of the segmentation quality for the hue-saturation-value (HSV), edge-detection-based algorithm (EDA), random forest (RF), DeepLabv3+, and the proposed method (improved RF). The color bars indicate the mean value of the indices, and the black lines represent their standard deviations.  
The introduced techniques were also compared with the three well-known color index methods, the excess green index (ExG), the excess green minus ExGR and the color index of vegetation extraction (CIVE) [21]. We used ExG to provide a clear contrast between kernels and background: ExG = 2 \(\times\) Green \(-\) Red \(-\) Blue. It used an automatic thresholding method that enabled background and foreground segmentation based on the bimodal distribution of the pixel. ExGR combined ExG and the excess red index to improve the performance of ExG: ExGR = ExG \(-\) (1.4 \(\times\) R \(-\) G). CIVE evaluated color features by providing a greater emphasis on the green area: CIVE = 0.441R \(-\) 0.811G + 0.385B + 18.78745.  
We concluded from the results in Table 6 that relative to the other three common methods mentioned in this paper, the proposed method could obtain a greater mean value of these four indicators. It produced the highest quality of segmentation indices among the other three algorithms and had the lowest misclassification rate.
## 3 Problem Formulation
### FlexE Architecture  
The service data flow of traditional Ethernet technology based on IEEE 802.3 is first encapsulated into MAC frames at the MAC layer and connected to the PHY layer through the RS Layer (Reconciliation Sublayer, physical coordination sublayer), and the PCS (Physical Coding Sublayer) in the PHY layer completes the 64B/66B encoding work; the PMA (Physical Medium Attachment) sub-layer completes the serial-to-parallel conversion, clock synthesis, and recovery work; and finally, the PMD (Physical Medium Dependent, physical media related) sub-layer completes the interface work for various actual physical media.  
As depicted in Figure 1, the FlexE architecture consists of the FlexE Client, FlexE Shim, and FlexE Group. Firstly, the FlexE Client represents various standard service interfaces, it mainly represents the data flow of MAC carried in the FlexE network. The main function is to convert the data stream into 66 bit code blocks after 64B/66B encoding and transmit it to the FlexE Shim layer. Secondly, the FlexE Shim is to implement the core function of FlexE technology between the MAC and the PHY defined via IEEE 802.3, which completes the mapping from the Client to the Group based on the calendar mechanism. At last, the FlexE Group is essentially a collection of multi-layered PHY, which carries the 66-bit data code blocks mapped via FlexE Shim from the FlexE Client layer and also supports by carrying multiple FlexE Instances.
## 4 Discussion  
### Uncertainty Analysis  
According to the validation result, the correlation coefficient between the estimated NPP and the observed NPP from FLUXNET2015 and ChinaFLUX was 0.64. FLUXNET provides the monthly observed data, but grasslands in arid regions tend to have low NPP by month, which resulted in NPP values generally in the low value region. In the meantime, the number of FLUXNET stations is limited. Moreover, it was reported that the spatial scale of remote sensing images has an impact on the estimation of GPP/NPP [84]. Limited to the spatial resolution, the NPP for a pixel represents an area of 250 \(\times\) 250 m\({}^{2}\), which impacts the accuracy verification result. The published estimation results for grassland NPP in Inner Mongolia were used to verify the estimated NPP in this work (Table 7). The estimation results obtained in this study are close to those of the previous studies, that is, the results reported in this paper are reliable and could be utilized for future research.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Research Case**} & **Average Yearly NPP** & **Growth Rate** & \multirow{2}{*}{**Time Range**} & **NPP Model or** \\  & **(g C/m\({}^{2}\))** & **(g C/(m\({}^{2}\).yr))** & & **Data Sources** \\ \hline [PERSON] et al. [85] & Around 250 & – & 2002 & CASA \\ [PERSON] et al. [86] & 281.30 & 0.33 & 2001–2010 & CASA \\ [PERSON] et al. [10] & 271.10 & 4.36 & 2001–2015 & CASA \\ [PERSON] et al. [37] & Ranged from 81.21 to & 4.53 & 2000–2014 & MODIS \\  & 365.53 & 4.53 & 2000–2014 & MOD17A3 \\ This study & 278.63 & 2.43 & 2000–2020 & CASA \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with other NPP estimation results.  
For the uncertainties, the maximum light use efficiency \(LUE_{max}\) in this work refers to the work of [PERSON] et al. (2017). In the original CASA model, the maximum light use efficiency \(LUE_{max}\) was assigned a fixed value, 0.389 gC\(\cdot\)M\(]^{-1}\)([PERSON] et al., 2017). Since the value of \(LUE_{max}\) has a great influence on the estimated NPP, its value is controversial ([PERSON] et al., 2017; [PERSON] et al., 2017). Some research tried to modify the value of \(LUE_{max}\) based on field sampling data ([PERSON] et al., 2017; [PERSON] et al., 2017). In future research, \(LUE_{max}\) should be modified using more flux measurement sites.
# Musical Mix Clarity Prediction Using Decomposition and Perceptual Masking Thresholds
## 6 Conclusions  
A new perceptually motivated model for the prediction of mix clarity was proposed, based on the masking relationship between residual, transient and steady-state components of a musical signal. The model consists of a median filter-based separation system, which feeds the MPEG Psychoacoustic Model II used to calculate signal-to-mask ratios of the component parts which were then compared. Both layer 2 and layer 3 implementations of the MPEG Psychoacoustic Model II were tested along with a modified version of the layer 3 implementation, forming L2 PM, L3 PM, and modified L3 PM variants, respectively.  
Each variation was evaluated through both [PERSON] and [PERSON]'s rank correlation to subjective scores gathered in a controlled listening test. Their response to an independent dataset of stimuli degraded though the addition of pink noise, reverberation, and clipping was also evaluated. The L2 PM clarity model showed the strongest correlation to the subjective scores, followed by the modified L3 PM, with the L3 PM clarity model showing the weakest relationship. Although the L3 PM is most efficient in coding applications, the stronger correlation achieved by the modified L3 PM clarity model showed that the linearly grouped scale-factor bands of the L2 PM were advantageous to performance in this case. All variations of the model responded similarly to the degradation introduced in the independent dataset; the L3 PM clarity model's response was less extreme to that of the modified L3 PM and L2 PM clarity models, whose responses were very similar. Addition of pink noise and reverberation caused an increase in masking score, reflecting a decrease in clarity. Clipping caused a somewhat more complex response, where stimuli which were noise-like and received high masking scores at their reference level gained higher masking scores when clipped. Stimuli that had a low level of residual energy and a low masking score at their reference level received even lower masking scores when clipped.  
Further work is ongoing to validate the proposed model's performance against a larger subjective data set.  
Conceptualisation, A.P. and S.F.; methodology, A.P. and S.F.; software, A.P.; validation, A.P.; formal analysis, A.P.; investigation, A.P.; resources, A.P.; data curation, A.P.; writing--original draft preparation, A.P.; writing--review and editing, S.F.; visualisation, A.P.; supervision, S.F.; project administration, S.F.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.  
The article processing charge (APC) was funded by The University of Huddersfield.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors would like to thank participants of the subjective listening test. Moreover, the authors would like to thank the reviewers, whose insightful comments contributed to the quality of the work presented.  
The authors declare no conflict of interest.
## 4 Results
### Data of Bands with a Spatial Resolution of 20 m
#### 4.2.2 Regression Models  
Following, we analyze for the clay soil the regression models that allow estimating soil moisture based on remote sensing. In this case, no regression model for other types of soil is found. Simple and multiple regression models are calculated. For simple regression  
Figure 5: Multiple Regression Models for clay soil for bands with 10 m of resolution.  
Figure 6: Summary of multivariate analyses. (**a**) all soil types, (**b**) sandy soil, and (**c**) clay soil. Significance levels (Sig): \(\texttt{{}^{***}}\)\(p\)\(<\) 0.001; \(\texttt{{}^{**}}\)\(p\)\(<\) 0.010; \(\texttt{{}^{*}}\)\(p\)\(<\) 0.05.  
models, models based on band 4 and band 12 are displayed in Figure 7a,b, respectively. As in previous sections, in Figure 7, we can identify the regression model, the prediction and confidence intervals, correlation coefficient, and R\({}^{2}\). Both bands, band 4 and band 12, are combined for a multiple regression model; see Figure 8. In Figure 8, we can see the equation of correlation models, R\({}^{2}\), and predicted vs. observed results.