esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 A Case Study
### Calculating the RIF for Each Subsystem

Table 1: The “best-to-others” and “worst-to-others” evaluation matrices. S: structural complexity; T: technical maturity; M: manufacturing stability; A: assembly stability; E: environmental conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Linguistic Variable** & **Fuzzy Number** & **No.** & **Linguistic Variable** & **Fuzzy Number** \\ \hline
1 & Extremely low (N) & (0, 0, 0.2) & 2 & Very low (VL) & (0, 0.2, 0.4) \\
3 & Low (L) & (0.2, 0.4, 0.6) & 4 & Moderate (M) & (0.4, 0.6, 0.8) \\
5 & High (H) & (0.6, 0.8, 1.0) & 6 & Very high (VH) & (0.8, 1.0, 1.0) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The correspondence of linguistic terms and linguistic values. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Subsystem**} & \multicolumn{2}{c}{**Similar Product**} & \multicolumn{2}{c}{**New Product**} \\ \cline{3-5}  & & **Shape Parameter** & **Scale Parameter** & **RIF** & **Scale Parameter** \\ \hline
1 & Driving & 1.830 & 25,831.0 & 0.490 & 50,669.4 \\
2 & Transmission & 0.9549 & 17,219.1 & 0.525 & 36,232.5 \\
3 & Lock \& Loosen & 0.8882 & 18,470.6 & 0.578 & 43,786.3 \\
4 & Signal & 0.740 & 1969.2 & 0.917 & 23,697.39 \\
5 & Sealing & 1.020 & 9479.5 & 0.739 & 36,325.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The reliability model of each subsystem before and after the upgrade. RIF: reliability improvement factor.
## 6 Halophile PGPB for Sustainable Agriculture
### Bioremediation  
The concept of using PGPB to facilitate bioremediation of contaminated soil has only recently emerged. For example, [PERSON] et al. [155] proposed that two petroleum hydrocarbon-degrading _Rhodococcus_ spp., with plant growth promoting traits, are likely to be good candidates for the phytoremediation of petroleum hydrocarbon contaminated sites. In recent years, the ecological functions of halophyte symbiotic microorganisms, including the growth promotion of host and crops, enhanced salt tolerance, and joint phytoremediation of contaminated soil, have been clarified by increasing studies [156; 157]. As bioremediation agents, _Streptomyces_ (in particular halophiles), participate in alleviating the toxic effect of xenobiotics on plants, using the strategies previously mentioned. _Streptomyces_ are widely used in the remediation of soils contaminated with different pollutants. Moreover, they are studied as PGP in salt-affected soils, where they provide plants with salt resistance to face this major problem for agriculture [158; 159].  
Table 1 illustrates various roles of PGPB in enhancing yield and growth of different plants/crops under salt stress.
# An FPGA-Oriented Baseband Modulator Architecture for 4G/5G Communication Scenarios
## 4 Results and Discussion
An ITU-R report about technical performance specifications for 5G radio interfaces recommends control plane latencies below 10 ms [43]. Therefore, the latencies observed in our architecture are \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(f_{clk}\)} & \multicolumn{3}{c}{**Mode 1**} & \multicolumn{3}{c}{**Mode 2**} \\ \cline{2-7}  & **OFDM** & **FBMC** & **UFMC** & **OFDM** & **FBMC** & **UFMC** \\ \hline
100 MHz & 113 & 148 & 180 & 123 & 161 & 233 \\
66.7 MHz & 74 & 84 & 119 & 78 & 79 & 155 \\
33.3 MHz & 34 & 25 & 60 & 33 & 28 & 77 \\
16.7 MHz & 14 & 8 & 30 & 10 & 10 & 39 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dynamic power consumption estimates for the six implemented baseband modulator cores (in mW). Device: xc7z020; analysis tool: Vivado 2015.2; post-implementation power analysis with a high confidence level; node activity derived from post-implementation simulation. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RP1** & **RP2** & **RP3** \\ \hline DPR latency & 400 μs & 677 μs & 767 μs \\ Partial bitstream size & 309 kB & 526 kB & 596 kB \\ \hline \hline \end{tabular}
\end{table} 
Table 7: DPR latency (worst-case scenarios). acceptable. Nonetheless, in critical, high-priority scenarios, our architecture allows a make-before-break approach to eliminate DPR latency: use a spare RP to load the new transmission mode before the old one is terminated. This is illustrated in Figure 8: OFDM Mode 1 communication carried out in RP1 has to be adapted to OFDM Mode 2; the idle RP2 is reconfigured to OFDM Mode 2 before communication using RP1 terminates. Then, the communication handover from RP1 to RP2 is not affected by the DPR latency.
## 5 Discussion
### Implications for Public Health and Cancer Prevention  
The findings highlight the critical role of advanced forecasting models in improving cancer prevention and resource allocation strategies. Rising incidence rates in middle-aged and older adults necessitate robust screening programs and preventive measures tailored to these demographics. Similarly, region-specific public health initiatives that address unique risk factors are essential to mitigating the cancer burden. By leveraging accurate forecasting models, policymakers and healthcare professionals can develop more effective intervention strategies, optimize resource allocation, and ultimately improve cancer outcomes across diverse populations.  
Our study provides a foundation for further integrating AI-driven forecasting models in epidemiological research. Future research could explore ensemble methods, combining traditional epidemiological models (e.g., APC) with deep learning architectures to refine cancer incidence predictions further. Additionally, incorporating real-time clinical and genetic data could enhance prediction accuracy and improve personalized cancer risk assessments.
# Asphalt Pavement Transverse Cracking Detection Based on Vehicle Dynamic Response
## 4 Optimized Short-Time Fourier Transform (STFT) Algorithm
### Parameter Optimization Results  
The optimized STFT parameters for transverse cracking damage detection includes three key parameters: using a Hanning window type, with a window length of 32 and overlap equal to 16.
## 3 Results and Discussion  
### Moisture and ash Contents of the Flour-Bran Blends  
The moisture and ash contents of the flour-bran blends are shown in Table 2. Before blending, the moisture content of bran was 14.8%, which was significantly higher than that of flour alone (12.1%). In general, bran is a by-product generated from the wheat milling process, and flour is mainly produced from the endosperm, resulting in a relatively lower moisture content than bran [19]. Although the flour and bran samples used in this study were obtained from different wheat cultivars, they were milled under similar conditions using the same mill. Thus, the trends in moisture content for flour and bran were similar. The moisture content of the flour-bran blends was 12.9-12.9%, demonstrating a proportional increase as the blending ratio of wheat bran increased because of the high moisture content of bran. The moisture contents of flour and bran are positively correlated with the amount of water added during the tempering step during wheat milling [20].  
The ash content of bran was 3.75%, which was approximately eight times higher than that of flour alone (0.48%). [PERSON] et al. [6] reported that the ash content of imported wheat bran is 5-7%. The \"Ariheuk\" bran used in this study showed a somewhat lower value than the results from different wheat varieties and milling methods. In general, minerals are located in the bran, which is the husk of wheat. Thus, the ash content of bran was significantly higher than that of flour. In this study, as the blending ratio of bran increased, the ash content increased proportionally to 0.48-1.46%. [PERSON] [21] reported that the quality of bread is affected by protein, ash, and pentosan among flour components. The higher the ash content and the lower the protein content, the lower the bread volume, which negatively affects the bread color. Therefore, it can be predicted that making bread with flour-bran blends with an increasing blending ratio of bran significantly affects bread volume and color.
## 6 Results and Discussion
### Overall Performance  
To evaluate the overall performance of our model, we compared our model with the state-of-the-art knowledge tracing models, such as DKT, DKT+, DKVMN, SAKT, AKT, and CL4 KT, on all four of the datasets mentioned above. As shown in Table 2, we compared our model with all the baseline methods under RMSE and AUC. Figures 4 and 5 visualize the RMSE and AUC values, respectively, with bar plots. They show that our model outperformed the other models in both metrics, especially Assist17. In the Assist17 dataset, the method with the best performance apart from ours is the CL4 KT method. The RMSE and AUC values of our method are 37.36% and 85.75%, respectively, while the CL4 KT method has RMSE and AUC values of 42.27% and 78.06%. Compared with the CL4 KT method, our method improved the RMSE and AUC values by 4.9% and 7.7%, respectively. Although Assist17 has fewer learners and questions compared with Assist09, the average number of interactions per learner is significantly higher in Assist17 than in Assist09. Our approach achieved a more significant performance on Assist17, which demonstrates that our model excels at learning patterns from long-term sequence data and that it can also adapt to short-term sequence data. Additionally, the proposed model shows only marginal improvements on Algebra05 and Bridge06 datasets, primarily due to the intricate nature of the latent knowledge structure within the datasets. Both datasets entail a significantly higher number of questions, posing a significant challenge
## 2 Materials and Methods
### Preliminary Analysis and Optimisation of the Extraction Method
#### 2.1.2 Satellite Data  
For the purpose of the study, we selected the Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery to retrieve the vegetation index from which to extract SOS, POS and EOS, corresponding to what was assessed for GPP data. Despite its lower spatial resolution compared to other more recent satellites (e.g., Sentinel-2), MODIS provides a long-time series of data allowing the analysis of phenological trends in European grasslands over the last decades.  
The vegetation index used to reproduce the growing season of grasslands was the kernel NDVI (kNDVI), a specific vegetation index used to reconstruct GPP patterns in different environments, including grasslands [47]. The kNDVI is calculated as follows:  
\[\text{kNDVI}=\frac{1-k(n,r)}{1+k(n,r)} \tag{1}\]  
where \(n\) and \(r\) refer to the reflectance in the near-infrared (NIR) and red bands (RED), respectively, BAND 2 and BAND 1 in MODIS at 250 m spatial resolution and 8 days of temporal resolution (product: MODIS/006/MOD09Q1), while the kernel function \(k\) measures the similarity between these two bands. We used the RBF kernel proposed by the same authors [47]:  
\[k(a,b)=exp\ \frac{\left(-(a-b)^{2}\right)}{((2\sigma)^{2})} \tag{2}\]  
where the \(\sigma\) parameter controls the notion of the distance between the NIR and RED bands, elaborated as the mean distance between these two bands:  
\[\sigma=0.5(n+r) \tag{3}\]  
For each site, kNDVI values were then processed from the MODIS RED and NIR bands according to the previous formulas, using the specific coordinates of the grassland sites with eddy covariance stations.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 5 Hardware Impairments
### Carrier Frequency Offset (CFO)
To improve the performance of the precoder proposed in [58] in terms of MUI reduction, the authors in [60] have proposed a precoder which produces signal with constant envelop where the signal envelope can be variable in a pre-defined period. The MUI reduction is better but the complexity and PAPR is larger than [58]. The precoders in [58; 60] use sequential gradient descent algorithm to find the precoder coefficients, which has high complexity. To solve this issue, in [61] the authors proposed another technique which uses an approximate message passing algorithm. Results show that the PAPR of this technique is lower than [60] and higher than [58]. In [62], the authors have used the degrees of freedom of the system to reduce the PAPR. In this technique, the precoding, modulation and reducing the PAPR operations are done jointly. In summary, the techniques introduced in [58; 59; 60; 61; 62] are complex, since the precoder coefficients are dependent to the signal and, therefore, for every block of data the optimization problem for obtaining the precoder coefficients has to be solved. A simple technique for reducing the PAPR was proposed in [63] using a clipping operation. Since the clipping is a nonlinear operation, it causes distortion. Therefore, to overcome this problem, the BS antennas are divided into two groups. The first group is used for transmitting the clipped signal and the second group is used to send correction signals to compensate the distortion caused by clipping.
## 3 Methodology
### Encoder
#### 3.1.2 Dual-Branch Feature Extraction Module
Transformer Feature Extraction Branch: In this article, we employed the ViT [22] encoder as the feature extraction component of the Transformer, comprising 6 ViT layers, excluding classification heads and the [CLS] token. The input image \(F_{x}\) is consistent with the input to the CNN branch, representing the feature map output of the filtering module with a width of \(W\), a height of \(H\), and a channel count of \(C\). The input image \(F_{x}\) is divided into \(N\) patches, denoted as \(x_{p}\), where \(N=WH/wh\), with \(w\) and \(h\) representing the length and width of each patch. In this paper, \(w=8\), \(h=4\). Subsequently, each patch is fed into the embedding layer, producing \(N\) tokens. Using the \"Add\" operation, we incorporate position encoding into each token. Finally, all tokens are input into Transformer encoding blocks, each encoding block consists of a multi-head self-attention (MSA) mechanism, layer normalization (\(LN\)), multi-layer perceptron (MLP), and residual connections. We represent the feature extraction process of the Transformer branch through Equation (3). \[ \begin{split} z_{0}&=\left[x_{p}^{1}E;x_{p}^{2}E; \cdots;x_{p}^{N}E\right]+E_{pos},\\ z_{l}^{\prime}&=\text{MSA}(LN(z_{l-1}))+z_{l-1}, \quad l=1,\cdots,L\\ z_{l}&=\text{MLP}(LN(z_{l}^{\prime}))+z_{l}^{\prime },\qquad l=1,\cdots,L\\ F_{t}&=z_{L}\end{split}  \tag{3}\]  
where \(E\in R^{(wh\cdot C)\times D}\) represents the linear projection matrix, and \(E_{pos}\in R^{(N+1)\times D}\) corresponds to positional encoding. The variable \(z_{0}\) denotes the input to Transformer encoding blocks, where \(L\) represents the number of encoder blocks set to \(L=6\) in this article. Additionally, \(F_{t}\) represents the output of the final encoding module in Transformer.
# Rotation Accuracy Analysis of Aerostatic Spindle Considering Shaft's Roundness and Cylindricity
## 1 Introduction
Based on the stochastic method and the Ng-Pan turbulent model, [PERSON] et al. [14] calculated the turbulent lubrication performance of a journal bearing with an isotropic rough surface. [PERSON] et al. [15] proposed an approach with both stochastic and contact characteristics to evaluate the effects of surface roughness for a slider bearing operating under partial or boundary lubrication, and found that the influence of roughness parameters on friction and load capacity increased rapidly upon the application of asperities contact. [PERSON] et al. [16] proposed a stochastic analysis of hydrodynamic journal bearings including the effect of surface roughness and development of the efficient radial basis function based uncertainty quantification algorithm. By using [PERSON]'s technique to solve the Reynolds equation, [PERSON] [17] indicated that the performance of a journal bearing system is significantly deteriorated due to the geometric imperfections. In the above references, the waviness and roughness in the shaft shape errors are mostly studied. However, the roundness and cylindricity are the largest errors among the shaft shape errors, usually between 1 and 4 micrometers. These errors have the most obvious impact on the aerostatic spindle's rotation accuracy. Although [PERSON] et al. [7] studied the radial error motion of the bearing in a quasi-static state considering roundness errors of the shaft, the rotation accuracy of the aerostatic spindle will change with rotation speed, which is not considered in the present study. Therefore, it is necessary to study the dynamic rotation accuracy of the aerostatic spindle under the effects of roundness and cylindricity errors. Therefore, a dynamic mathematical model considering roundness, cylindricity errors and spindle speed is established by using the finite difference method based on the Reynolds equation in this paper. The roundness and cylindricity errors of the shaft are measured by [PERSON] 585 LT cylindricity meter. The measured data are resampled and filtered and then brought into the model for calculation. Through the comparison of the simulation and the experiment, it is proved that the shaft roundness and cylindricity errors will produce an unbalanced gas film force on the shaft and affect the spindle's rotation accuracy. Based on the measurement data of roundness error, the evaluation index of the dispersion coefficient is proposed. The research shows that this discrete coefficient is a more effective index to predict the spindle's rotation accuracy compared with the roundness and cylindricity.
## 2 NEAMTWS System Architecture  
The original operational concept for the NEAMTWS architecture was to establish, where possible, national tsunami warning centers in each country responsible for issuing warnings to the relevant authorities in the member states. In addition, some of the centers were envisaged to act as regional tsunami watch centers (RTWC) with responsibility for providing tsunami alerts in particular regions. Following further consideration of the developments made by other ICGs and recommendations by the IOC Working Group on Tsunamis and Other Hazards Related to Sea Level Warning and Mitigation Systems (TOWS-WG), encouraging standardization where appropriate, this concept evolved to include tsunami watch providers (TWP) and tsunami watch recipients (TWR). The concept of \"watch providers\" and \"watch recipients\" was later revised as \"service providers\" and \"service recipients\", respectively, by the IOC Working Group on Tsunamis and Other Hazards Related to Sea Level Warning and Mitigation Systems (TOWS) in 2014.  
Tsunami Service Providers (TSP) are those NTWCs willing and able to provide tsunami alert information to other member states and their respective institutions at designated tsunami forecast points (TFP). Service recipients are Tsunami Warning Focal Points choosing to receive such information through subscription. A key aspect of this approach is that tsunami service providers do not have pre-determined geographical areas of responsibility. Each TSP is allowed to decide on its own service area as a function of its monitoring and analysis capabilities. NEAMTWS member states have the freedom to decide from which candidate tsunami service provider(s) they would like to receive tsunami messages, meaning that a Member State will be able to receive tsunami messages from more than one provider. As of submission of this manuscript, the list of subscribers to KOERI-TSP includes CDH (Cyprus), NIOF (Egypt), CENALT (France), BSH (Germany), DWD (Germany), NOA (Greece), PMO (Israel), INGV (Italy), NCGR (Lebanon), IPMA (Portugal), NIEP (Romania), TYPHOON (Russian Federation), DGPCE (Spain), IGN (Spain), CCS (United Kingdom), ERCC (EU) and the IOC Secretariat.  
In order to be recognized as part of NEAMTWS, tsunami service providers must meet a number of requirements and be approved by the ICG of NEAMTWS. Until these conditions are met, they will be referred to as 'candidate' tsunami service providers. The relevant accreditation procedure was approved at the 9 th session of ICG/NEAMTWS in 2012 in Southampton, UK. CENALT (France), INGV (Italy), KOERI (Turkey) and NOA (Greece) were accredited by ICG/NEAMTWS in 2016, followed by the accreditation of IPMA in 2019.
# Environmentally Friendly Anticorrosive Polymeric Coatings
## 2 Anticorrosive Coating Market and Its Challenges
Therefore, the industry and academic entities have focused their effort in the development of inorganic coatings  
Figure 2: Metric tons of corrosion protection materials consumed by region in 2018 and expected growth in 2023.  
based on sol-gel technologies and hybrid inorganic-organic pre-treatments such as nano ceramic coatings using titanium [25], zirconium [26], silicate, rare earth metals [27], and molybdates [28] compounds as greener alternatives. These inorganic coatings are generally prepared in highly diluted solutions, which means employing large volumes of water. Hence, the resulting residual water requires to be post-treated before discharge to comply with emission limit values for metals and other pollutants set by the Environmental Quality Standards. These legal limits are established to minimize the environmental impact and especially to protect aquatic life [29]. However, the anticorrosive coating market is dominated by organic coating which is generally divided in segments based on the type of resin or binder: epoxy, polyurethane, acrylic, alkyld, or polyester. Most of the organic coatings are applied in liquid form and therefore the organic solvents are one of the main components of the coating formulation. The European community recently started looking into controlling or limiting the emission of volatile organic compounds (VOC), leveraging the need to develop new VOC compliant products, with low amount of organic solvent [30]. To solve this challenge, several potential solutions were identified, including high-solid coating [31], waterborne [32], and ultra-violet (UV) light-cured coatings [33]. The replacement of hazardous materials, such as isocyanate to produce polyurethane is still an important challenge for the coating sector due to its consequence on human health. In fact, the main effects of hazardous exposures to isocyanate are occupational asthma and other lung problems, as well as irritation of the eyes, nose, throat, and skin [34]. The implementation of REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals) regulation, the stringent regulations imposed by government and the rise in consumer awareness for green and more sustainable solutions drive the anticorrosive coating market to environmentally friendly polymeric coatings. Furthermore, the development of bio-based raw materials for coatings such as resin [35], solvent [36], and additives [37] is becoming the focus of attention for substituting fossil ones. However, the availability of raw materials and the cost of environmentally friendly coating are still two major constrains for the growth of this market.