esa-sceva/satcom-chunk-collection huggingface dataset:
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 3 LTE Schedulers
### Proportional Fair (PF) Scheduler  
The PF algorithm is considered to be one of the best effort scheduling algorithms where QoS requirements are not guaranteed. The available radio resources are assigned via PF algorithm to the users, taking into account the experienced channel quality as well as the last value pertaining to user throughput. It can also be regarded as a weighting factor pertaining to the assumed data rate. The PF algorithm is designed to increase the total bit rate, as well as ensure fairness is maintained for the flows [3; 22].  
\[M_{i,k}^{PF}=\frac{d_{i,k}(t)}{R_{i}(t)}, \tag{1}\]  
This metric also helps to determine the proportion that exists amongst the current accessible data rate \(d_{i,k}(t)\) as well as the middling past data rate \(R_{i}(t),\text{ with }i\) denoting the flow in \(k\) flow sub-channel.
## 4 Numerical Simulation of Roof Cutting Parameters
### Numerical Simulation of Cutting Angle  
4.1 Vertical Stress Distribution Law of Surrounding Rock of Roadway with Different Roof Cutting Angles  
According to scheme II, the roof cutting angle is numerically simulated, and the influence of different roof cutting angles on the vertical stress distribution law of roadway surrounding rock is analyzed, as shown in Figure 11. When the roof cutting angle is 10\({}^{\circ}\), the peak stress of the solid coal pillar side is 4.1 MPa, the peak abutment pressure of the working face is 3.2 MPa and the roadway is in a low-stress environment. When the roof cutting angle is 15\({}^{\circ}\), the peak lateral stress of the solid coal pillar is 3.3 MPa and the peak abutment pressure of the working face is 3.8 MPa. When the roof cutting angle is 20\({}^{\circ}\), the lateral cantilever length of the roof increases, and the overlying strata will have an impact on the roof of the roadway when the overlying strata rotate and sink. The peak stress of the solid coal pillar is 4.1 MPa and the peak value of the advanced abutment pressure of the working face increases to 3.7 MPa. The roof of the roadway in the advanced working face also has obvious stress concentration, resulting in the poorest pressure relief effect.  
Figure 10: Stress concentration characteristics of coal pillar under different roof cutting heights.
# Sources of Extraterrestrial Rare Earth Elements: To the Moon and Beyond
## 4 Lunar Differentiation
### Lunar Mineralogy  
Mineralogically, the Moon is very simple with only four major phases: olivine ((Mg,Fe)\({}_{2}\)SiO\({}_{4}\)); pyroxene ((Ca,Mg,Fe)\({}_{2}\)Si\({}_{2}\)O\({}_{6}\)); plagioclase (Ca\({}_{2}\)Al\({}_{2}\)Si\({}_{2}\)O\({}_{8}\)); and ilmenite (FeTiO\({}_{3}\)) [92; 93]. These phases dominate the Moon's major lithologies, the anorthositic lunar highlands, and the mare basalts (Figure 4). Figure 4a shows the lunar nearside with the location of the six Apollo landing sites also shown, note that Apollo 16 was the only mission which targeted the lunar highlands. The other missions targeted various regions of the lunar maria. Figure 4b shows a characteristic lunar highlands sample, 60025, dominated by plagioclase feldspar. Figure 4c shows a typical mare basalt, characterized by pyroxene, plagioclase, ilmenite, and olivine. This particular sample is highly vesiculated and has been determined to originate from depths >250 km in the lunar mantle [94].  
Minor phases also present throughout the lunar (Apollo, Luna, and meteorite) sample collection include apatite (Ca\({}_{5}\)(PO\({}_{4}\))(F,CII)); baddeleyite (ZrO\({}_{2}\)); chromite-ulv\(\ddot{\text{o}}\)spinel (FeCr\({}_{2}\)O\({}_{4}\)-Fe\({}_{2}\)TiO\({}_{4}\)); iron (Fe(Ni,Co)); k-feldspar ((K,Ba)AlSi\({}_{3}\)O\({}_{8}\)); merrillite ((Ca\({}_{3}\))(PO\({}_{4}\))\({}_{2}\)); pleonaste ((Fe,Mg)(Al,Cr)\({}_{2}\)O\({}_{4}\)); rutile (TiO\({}_{2}\)); silica (SiO\({}_{2}\)); ternary feldspar ((Ca,Na,K)AlSi\({}_{3}\)O\({}_{8}\)); troilite (FeS); zircon (ZrSiO\({}_{4}\)); and zirkelite-zirconolite ((Ca,Fe)(Zr,Y,Ti)\({}_{2}\)O\({}_{7}\)) [93]. Several other minerals enriched in refractory elements, are also found on the Moon (some uniquely), including: dysanalyte (Ca,Fe)(Ti,REE)O\({}_{3}\), thorite (ThSiO\({}_{4}\)); titanite (CaTiSiO\({}_{5}\)); tranquillityite (Fe\({}_{8}\)(Zr,Y)Ti\({}_{3}\)Si\({}_{3}\)O\({}_{24}\)); yittrobetafite (Ca,Y,U,Th,Pb,REE)\({}_{2}\)(Ti,Nb)\({}_{2}\)O\({}_{7}\)); zirconolite (CaZrTi\({}_{2}\)O\({}_{7}\)); and zirkelite ((Ca,Th,Ce)Zr(Ti,Nb)\({}_{2}\)O\({}_{7}\)) [95; 96].
# Delamination Localization in Multilayered CFRP Panel Based on Reconstruction of Guided Wave Modes
## 3 Results and Discussion
### Localization of Delamination Using an Analytical Model

Table 3: Delamination position estimation using analytical model. the transmitted A\({}_{0}\) mode continues to propagate within the delamination and is received as the second wave packet (\(t_{2}\)), as shown in Figure 13.  
Analyzing the guided wave (GW) signals becomes challenging due to the differing velocities of the converted S\({}_{0}\) modes combined with the transmitted A\({}_{0}\) mode or other modes. Estimation of delamination location in case of overlapped modes is complex. For this reason, an analytical model was developed to match the overlapped modes, and then the ToF of the converted S\({}_{0}\) mode (\(t_{2}\)) can be estimated. The delamination location (\(X_{d}\)) can be estimated by calculating the ToF between the converted S\({}_{0}\) mode and the transmitted A\({}_{0}\) mode. The estimated distance from the delamination to the receiver and the absolute error of the estimated delamination position are presented in Table 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Receiver**} & \multicolumn{2}{c}{**Distance from**} & \multirow{2}{*}{**Propagation**} & \multirow{2}{*}{**ToF (dt) (\(\mu\)s)**} & \multicolumn{2}{c}{**Estimated**} & \multirow{2}{*}{**Absolute Error**} \\  & & **Deceiver (mm)** & & & & & \\ \hline
2-1 & R1 & 10 & \(92.4-88.4\) & 4 & 6.4 & 3.6 \\
2-2 & R2 & 20 & \(99.5-89.4\) & \(10.1\) & 16.3 & 3.7 \\
2-3 & R3 & 30 & \(105.6-91.1\) & 14.5 & 23.5 & 6.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Localization of delamination when the delamination is between transmitter and receiver. Figure 13: Guided wave signal when the delamination is between excitation source and receiver.
## 3 Proposed Solution
### Feature Extraction and Selection
#### 3.3.3 TF-IDF and SVD  
The most traditional way to handle text data has four pathways: hashing of words, count vectorization, term frequency-inverse document frequency (TF-IDF), and singular-value decomposition (SVD).  
\[TF(t)=\frac{\textit{How many time a term }^{\prime}t^{\prime}\textit{ appears in a document}}{\textit{Number of total terms present into the document}} \tag{1}\]  
\[TF(IDF)=Log\bigg{(}\frac{\textit{Number of total documents}}{\textit{Document numbers having term }^{\prime}t^{\prime}\textit{ in it}}\bigg{)} \tag{2}\]  
\[TF-IDF(t)=TF(t)*IDF(t) \tag{3}\]  
Likewise, in SVD, one can follow further steps: latent semantic analysis and scikit learn version of SVD for n numbers of components. It has already been verified that by applying TF-IDF and SVD together, a model can improve the result of logistic regression and XGB.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 7 Results and Discussion
### Handover Performance
#### 7.2.4 Average Handover Latency  
Figure 7(a) shows the average latency of successful handovers for both algorithms. The results show the latency of the proposed algorithm is marginally lower than the 3 GPP scheme. Since a handover may be attempted multiple times due to retransmission of failed handover communications, we also analyzed the proportion of handovers that made a different number of attempts before they became successful.  
Figure 7: (a) Number of handover failures and (b) handover failures as a percentage of total handovers.  
Figure 8b shows the percentage of handover attempts after a set number of retransmissions (abbreviated as reTx in the figure). It shows that most of the successful handovers were attempted only once (0 retx) or twice (1 retx) for both algorithms. Despite having a slightly lower proportion of handover that becomes successful after the first attempt (0 retx) than the 3 GPP scheme, the proposed algorithm is still found to incur a lower average latency. This may be due to the simultaneous activation of admission control and UE handover procedure by the proposed algorithm as described in Section 5.2.2, which helps to reduce the impact of the handover retransmissions.
## 1 Introduction  
WPT systems present a complex characterization challenge due to their inherent nonlinear behavior [1, 2, 3, 4, 5]. Traditional analytical methods often struggle to accurately predict and optimize WPT system performance due to these nonlinearities [6, 7, 8, 9, 10, 11, 12]. Consequently, research efforts have increasingly focused on advanced computational techniques, including machine learning (ML) approaches, for WPT system modeling and optimization [13, 14, 15]. NNs have proven particularly effective in this domain [16]. Their capacity for high prediction accuracy, contingent on rigorous training and validation, is essential for capturing the intricate dynamics of WPT systems [17, 18, 19, 20, 21, 22, 23]. Furthermore, NNs offer considerable flexibility, adapting readily to diverse scenarios, such as variations in coil geometry or system configuration. Finally, NNs excel at modeling the complex nonlinearities inherent in WPT systems, often surpassing the capabilities of conventional analytical methods [24, 25, 26, 27, 28]. This study presents a compact NN architecture designed for efficient and practical implementation. The compact design facilitates potential deployment on microcontrollers, enabling real-timeoperation. This real-time capability is particularly advantageous for dynamically assessing inter-coil distance, implementing real-time impedance matching adjustments and real-time changes of frequency to increase the power factor [29, 30, 31, 32, 33, 34]. By optimizing these parameters dynamically, the proposed method enhances overall system performance, specifically energy transfer efficiency and adaptability [25, 35]. Furthermore, NNs can be employed to minimize the phase angle between voltage and current by adjusting the WPT system frequency [36, 37, 38]. Minimizing the phase means increasing the power factor, improving transfer efficiency. This application of NN tries to address some longstanding challenges associated with the characterization of WPT systems, trying to make a step forward in the development of more efficient and versatile wireless power solutions.
# Intrinsic and Extrinsic Factors Affecting the Color of Fresh Beef Meat--Comprehensive Review
## 3 Results and Discussion
### Intrinsic Factors
#### 3.1.4. Breed  
Commercial meat cattle breeds are divided into two big groups: Bos taurus, which are cattle of European ancestry, and Bos indicus, which are cattle typically designed for production under tropical weather conditions. Pure Bos taurus crosses (Hereford x Angus) exhibited higher carcass weight, improved fat deposition, and intramuscular marbling than Bos indicus x Bos taurus animals (Sahiwal or Brahman x Hereford or Angus) [54]. Due to hybrid vigor, beef producers also noted that crosses between the two cattle groups resulted in higher resistance and increased potential for muscle accumulation [55]. However, more aggressive temperament from pure Bos indicus (Brahman) or Bos indicus crosses resulted in darker muscle color and higher pH values in meat compared to Angus (Bos taurus) cattle, especially when animals were pasture-fed [56, 57]. Significantly lower L*, a*, and b* scores and higher pH values were also found in the meat from feedlot-finished Nellore x Aberdeen Angus crosses in comparison to meat from the pure Nellore breed [58]. It was described that although a more excitable temperament of Braford (Bos indicus x Bos taurus) crosses than pure Hereford (Bos taurus) animals, no effects in meat color were found [55]. A study using only pure Hereford (Bos taurus) steers showed that calmer animal temperament had no major influence on muscle glycogen concentration or meat color [59]. On the other hand, a study performed in three Bos indicus African races suggested that aggressive behavior seen in long-horn Red Bororo cattle was also correlated with higher injury frequency, especially during unloading operations [60]. Indeed, more bruises in Bos indicus x Bos taurus cattle were correlated to high pH values and DFD-related problems in meat [61, 62]. It was also found that Bos indicus carcass showed similar differences between the so-called color-stable and non-color-stable muscles as the Bos taurus counterparts [45]. In our view, pure Bos taurus breeds or crosses with a high percentage of Bos taurus blood offer the best temperament profiles for enhancing meat color. Also, due to higher fat deposition in the meat of Bos taurus animals, muscles had a brighter color compared with the meat of Bos indicus cattle [56]. Breed selection should also take into consideration individual nutritional requirements and management systems to obtain brighter beef muscles.
Gene Expression of Monoterpene Synthases Is Affected Rhythmically during the Day in _Lavandula angustifolia_ Flowers  
[PERSON]  
1 Department of Botany, School of Biology, Faculty of Science, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; [EMAIL_ADDRESS] (E.S.); [EMAIL_ADDRESS] (S.P)1 Department of Botany, School of Biology, Faculty of Science, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; [EMAIL_ADDRESS] (E.S.)1 Department of Botany, School of Biology, Faculty of Science, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; elefthe@bio.  
These terpen occur widely in plant species, performing essential ecological functions, including deterring herbivores, repelling insects and repressing the growth of competing plants [11, 12, 13].  
Monoterpenes are derived from geranyl diphosphate (GPP) by the activity of various terpene synthases [14]. Some monoterpenes, like camphor and linalool acetate, are further modified through acetylation, oxidation or reduction reactions. The product of linalool acetyltransferase enzymatic reaction is linalool acetate [15]. The monoterpene synthases initially form cationic intermediates, such as geranyl, linalyl diphosphate, linalyl cation and the \(\alpha\)-terpinyl cation (Figure 1). Then, several cyclizations are formed in the intermediate products until a stable component is produced. For example, \(\alpha\)-terpinyl cation forms all cyclic monoterpenes, such as limonene. Linalool, \(\beta\)-myrrene, geraniol and ocimene are derived from geranyl and linalyl cations [9, 14].  
Several genomic and transcriptomics resources for _L. angustifolia_ were developed to investigate the regulation of EO production in leaves and flowers [16, 17, 18, 19, 20]. Finally, the first chromosome-level genome assembly of _L. angustifolia_ was recently reported [21]. Environmental and developmental conditions affected Lavender oil production during the flowering period [22]. Linalool quantity, a primary compound of lavender oil, was influenced by temperature, flower development and linalool synthase gene expression [22]. As a result, the expression of _LaLINS_ correlated with linalool concentration during flowering development, suggesting that linalool production in lavender flowers is regulated at the transcriptional level. Linalool production is also affected by diurnal harvest time [23].  
In this study, we report that the expression of genes involved in lavender EO, including linalool and limonene synthases, is accompanied by oscillations, picking at mid-day and leading to linalool and linalool acetate accumulation in the afternoon.
## 3 Application
### RUE-PRI Relationships Across Scales  
We calculated the mean \(R^{2}\) between RUE and PRI at short daily and long seasonal scales from foliar to ecosystem levels (Figure 9). All correlations were strong across spatiotemporal scales, with the mean \(R^{2}\) ranging between 0.45 and 0.66. Mean \(R^{2}\) values at daily timescales were similar to those in Figure 10 in [PERSON] et al. [20] at foliar and canopy levels. The mean \(R^{2}\) values at seasonal scales, however, were 20% and 7% higher for foliar and canopy levels, respectively, than those in Figure 10 in [PERSON] et al. [20]. Also, the mean \(R^{2}\) was higher for seasonal than diurnal relationships at the canopy scale. Forty relationships linked changes in RUE with PRI at the ecosystem level, which was the second largest number of relationships, with a standard error of 0.04. Thirty-four of these relationships were reported after 2010. Daily PRI was thus better correlated with RUE at the foliar than the canopy level, whereas seasonal PRI tracked the changes in RUE better at the canopy than the foliar and ecosystem levels.  
Figure 8: Boxplots of the coefficients of determination of the relationships between PRI and (**a**) Radiation-use efficiency (RUE) and (**b**) Net CO\({}_{2}\) uptake at the ecosystem level and seasonal timescale. Central lines represent medians, boxes represent 50% of the data and whiskers represent minima and maxima. The numbers of correlations reported in the literature are shown in brackets.
## 6 Conclusions  
In this paper, we introduce MNH-FGR, an enhanced variant of the FzGR protocol, with the objective of achieving satisfactory Quality of Service (QoS) and Quality of Experience (QoE) for video transmission in VANET networks. Our approach incorporates a multi-hop strategy into the existing fuzzy system of FzGR, allowing for the dynamic utilization of the network topology. The link weights needed for packet scheduling represent the multi-constrained QoS metrics extracted by the fuzzy system. The MNH-FGR routing protocol has been successful in improving the QoS and QoE of video transmission compared with the GPSR-2P and GPSR-kP protocols that use the classic greedy mechanism, but also those with respect to fuzzy protocol FzGR. The proposed technique is consistent with the requirements of revocability, diversity, and security.  
While this proposed approach has shown effective results, there are limitations that require further research to address. One major limitation is that the effectiveness of the proposed method highly depends on the accuracy of link quality metrics, which can be difficult to achieve in some scenarios and may lead to suboptimal routing decisions. Addressing these limitations is one of the main perspectives of our research. We also presented the results of our DNN model, which achieved an accuracy of 87% in predicting the optimal routing protocol based on a set of features and input parameters. These results  
Figure 9: Impact of Features on Protocol Performance.  
show the potential of artificial intelligence in the analysis and optimization of routing protocols for wireless networks, providing a promising direction for future research in this area.  
However, there was a delay degradation observed for the MNH-FGR protocol. This degradation was due to the fact that the multi-next-hop variant took a little longer time to calculate the quality of the links in real time in order to select the most optimal ones. In perspective, the improvement of MNH-FGR should be continued in terms of delay, and this protocol should be applied for FANET networks (Flying Ad Hoc Networks).  
Conceptualization, I.Z.; Methodology, I.Z. and M.O.; Validation, A.B. and M.O.; Writing--original draft, I.Z.; Writing--review & editing, I.Z. and B.J.; Visualization, B.J., M.H.A. and A.E.M.; Supervision, M.O.; Project administration, I.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
All data has present in main text.  
The authors declare no conflict of interest.
# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
## 3 Methodology
### Identification of QRS Peaks  
Because it is possible that multiple candidate QRS peaks present within the same QRS complex and the candidate QRS peaks also include sharp P- and T-peaks, we propose the following decision rules to identify the most prominent QRS peak. The distance thresholds of 130 and 100 samples to define the following Cases 1, 2, and 3 are determined by pre-analyzing the relationships between the positions of all candidate QRS peaks and true QRS peaks annotated by the MIT-BIH arrhythmia database.  
Case 1: The distance between the current candidate QRS peak and the previous QRS peak is greater than 130 samples. In this case, it is considered that the current candidate QRS peak is far enough from the previous QRS peak and, hence, is identified as a new QRS peak. This identified QRS peak may also be replaced by the next candidate QRS peak, according to the following Cases, 2.3 and 3.  
Case 2: The distance between the current candidate QRS peak and the previous QRS peak is greater than 100 samples and less than 130 samples. In this case, it is considered that the current candidate QRS peak may be a new peak or a sharp T-peak.  
Case 2.1: The current candidate QRS peak and the previous QRS peak both have a candidate extremum pair in the higher frequency d2'. In this case, the current candidate QRS peak is identified as a new QRS peak.  
Case 2.2: The current candidate QRS peak does not have a candidate extremum pair in the higher frequency d2', but the previous QRS peak has one. In this case, the current candidate QRS peak is considered as a lower frequency T peak, not a QRS peak.  
Case 2.3: The current candidate QRS peak has a candidate extremum pair in the higher frequency d2', but the previous QRS peak does not have one. In this case, the current candidate QRS peak is identified as a new QRS peak and the previous QRS peak is identified as a lower frequency P peak, not a QRS peak.  
Case 3: The distance between the current candidate QRS peak and the previous QRS peak is less than 100 samples. In this case, it is considered that the two peaks are located in the same QRS wave and only a sharper peak remains. A sharpness parameter \(\alpha\) of a peak based on the corresponding extremum pair is defined as follows:  
\[\alpha=\frac{\left|y_{1}y_{2}\right|}{x_{2}-x_{1}} \tag{9}\]  
The larger the extreme values, or the shorter the distance between the first and second extremum points, the larger the sharpness parameter \(\alpha\). If the sharpness of the current candidate QRS peak is larger than that of the previous QRS peak, we remove the previous QRS peak and identify the current candidate QRS peak as a new QRS peak. Otherwise, we remove the current candidate QRS peak and reserve the previous QRS peak.
## 2 Machine Learning Applications in Big Data
### Telecommunications  
The telecommunications industry has embraced ML to enhance network performance, improve customer experience, and optimize operations. One of the primary applications is in network optimization, where ML models analyze data from network sensors, user devices, and traffic patterns to optimize network performance, reduce latency, and improve service quality. These models can predict network congestion, optimize routing, and allocate resources dynamically based on real-time data [102; 103; 104; 105].  
Alongside network optimization, ML is used in CRM to analyze customer data, predict churn, and personalize marketing campaigns. By analyzing customer behavior, usage patterns, and feedback, ML models can identify customers at risk of leaving and develop targeted retention strategies. This leads to improved customer satisfaction and loyalty [106; 107; 108; 109].  
ML is also applied in fraud detection, where models analyze call data records, transaction data, and usage patterns to detect fraudulent activities such as subscriber identity module (SIM) card cloning, account takeovers, and identity theft. These models continuously learn from new data, improving their ability to detect and prevent fraud in real time [110; 111; 112; 113].