esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Parameter Estimation for Frequency-Modulated (FM) Sound Waves
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Algorithm** & **Best** & **Mean** & **Median** & **Worst** & **Stdv** \\ \hline LO & **0.0115150 \(\times\) 10\({}^{-3}\)** & 0.0115150 \(\times\) 10\({}^{-3}\) & 0.0115150 \(\times\) 10\({}^{-3}\) & 0.0115150 \(\times\) 10\({}^{-3}\) & 4.53430 \(\times\) 10\({}^{-20}\) \\ HMA & **0.0115150 \(\times\) 10\({}^{-3}\)** & 0.0115150 \(\times\) 10\({}^{-3}\) & 0.0115150 \(\times\) 10\({}^{-3}\) & 0.0115150 \(\times\) 10\({}^{-3}\) & 9.97110 \(\times\) 10\({}^{-19}\) \\ DE & **0.0115150 \(\times\) 10\({}^{-3}\)** & 0.0115150 \(\times\) 10\({}^{-3}\) & 0.0115150 \(\times\) 10\({}^{-3}\) & 0.0115150 \(\times\) 10\({}^{-3}\) & 2.00390 \(\times\) 10\({}^{-19}\) \\ APS 9 & **0.0115150 \(\times\) 10\({}^{-3}\)** & 0.0115150 \(\times\) 10\({}^{-3}\) & 0.0115150 \(\times\) 10\({}^{-3}\) & 0.0115150 \(\times\) 10\({}^{-3}\) & 4.80700 \(\times\) 10\({}^{-19}\) \\ DOE-RHC & **0.0115150 \(\times\) 10\({}^{-3}\)** & 0.0115150 \(\times\) 10\({}^{-3}\) & N/A & 0.0115150 \(\times\) 10\({}^{-3}\) & 0.00000 \(\times\) 10\({}^{0}\) \\ CHIO & **0.0115150 \(\times\) 10\({}^{-3}\)** & 0.0115150 \(\times\) 10\({}^{-3}\) & 0.0115150 \(\times\) 10\({}^{-3}\) & 0.0115150 \(\times\) 10\({}^{-3}\) & 4.53430 \(\times\) 10\({}^{-10}\) \\ HDE & **0.0115150 \(\times\) 10\({}^{-3}\)** & 0.01151 All of the previous experiments and observations support the proposed algorithm's ability to solve complex problems with unknown search spaces. As a result, this efficient optimization algorithm is being provided to be utilized for optimization problems in various fields. It is worth mentioning that other well-defined optimization problems such as text documents clustering [83, 84], EEG signals denoising [85, 86, 87], feature selection [88, 89, 90, 91], and scheduling problems in smart home [92, 93, 94, 95] can be handled by the proposed algorithm.
# Edge and Cloud Computing in Smart Cities
## 5 Challenges, Open Issues, and Future Research Directions
### Data Caching and Content Distribution  
Data caching is essential for reducing redundant transmissions, minimizing latency, and improving system throughput in edge-cloud environments. However, efficient cache placement and eviction strategies remain a major challenge due to the dynamic nature of smart-city applications [197; 198].  
One key issue is predicting data access patterns. Traditional Least Recently Used (LRU) and Least Frequently Used (LFU) caching policies do not account for real-timevariations in data demand. Recent studies propose AI-driven predictive caching, where DL models forecast future requests based on historical data. However, challenges such as scalability, model retraining overhead, and adaptability to unseen traffic patterns remain unsolved [199; 200; 201].  
Another challenge is cooperative caching in multi-tier architectures. With frequent interactions between cloud and edge nodes, data redundancy across tiers must be minimized while ensuring timely content availability. Hierarchical cache synchronization mechanisms, where edge nodes dynamically adjust caching decisions based on cloud-side intelligence, have shown potential but require further optimization [202; 203].  
Additionally, cache consistency in highly dynamic environments poses a critical problem. When data are updated at the cloud or an edge server, outdated cached copies can cause stale content delivery. Existing solutions rely on synchronous updates, which introduce delays, or asynchronous replication, which risks inconsistencies. Hybrid cache coherence mechanisms, leveraging edge-consensus algorithms and real-time synchronization techniques, need to be explored to ensure accuracy without compromising efficiency [204; 205].  
Advancements in AI-driven cache management will improve content placement strategies, ensuring minimal redundancy in distributed storage. Future research should focus on collaborative caching schemes, where multiple edge nodes cooperatively manage storage to optimize retrieval speed and reduce bandwidth consumption. Hierarchical caching frameworks could further enhance data accessibility across multi-tier edge-cloud environments [206; 207; 208].
## 5 Conclusions  
In this paper, two systems, CHPVC and HPTVC, have been proposed for waste heat recovery and water vapor supply with temperatures above 150 \({}^{\circ}\)C. The thermal performances of these two systems have been investigated and compared based on the proposed thermodynamic model. A compressor model is developed to accurately calculate the isentropic efficiency of R134a and R245 fa compressors. The efficiency of the water vapor compressor has been calculated according to the pressure ratio, and the power consumption of the pump has also been considered. Four different parameters (inlet water temperature, R134a condensation temperature, suction temperature of twin-screw compressor, and required vapor temperature) have been used to analyze energy efficiency, and the optimal COPs at the nominal operation conditions have been calculated. According  
Figure 8: COP versus the R134a condensation temperature and the suction temperature of the vapor twin-screw compressor (**a**) CHPTV; (**b**) HPTVC.  
to the calculation results, the two systems show advantages at different working conditions, and the results of each analysis are given in detail below:  
1. While the mass flow rate of the inlet water is the same, CHPVC provides 16.8% more required water vapor than HPTVC at the nominal working conditions. Meanwhile, the mass flow rate of R134a in HPTVC is 8.21% more than that in CHPVC.
2. While the inlet water temperature is 45 \({}^{\circ}\)C and the required vapor temperature is 150 \({}^{\circ}\)C, the optimal COPs of CHPVC and HPTVC are 2.432 and 2.436 respectively.
3. CHPVC is more suitable for the required vapor with high temperatures. Moreover, the temperature lift provided by the cascade heat pump should not be too large, and the suction temperature of the vapor twin-screw compressor is generally around 90 \({}^{\circ}\)C.
4. HPTVC is more suitable for a relatively small temperature difference between the inlet water and the required vapor. In addition, the temperature rise provided by the R134a heat pump cycle should be relatively small, and a large temperature lift provided by the vapor centrifugal compressor is increasingly efficient as its pressure ratio allows.  
Investigation, C.Z.; Data curation, D.C.; Writing--original draft, X.P.; Writing--review & editing, J.C.; Supervision, J.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare
# Spinning of Endless Bioactive Silicate Glass Fibres for Fibre Reinforcement Applications
###### Abstract  
Bioactive glasses have been used for many years in the human body as bone substitute. Since bioactive glasses are not readily available in the form of endless thin fibres with diameters below 20 \(\upmu\)m, their use is limited to mainly non-load-bearing applications in the form of particles or granules. In this study, the spinnability of four bioactive silicate glasses was evaluated in terms of crystallisation behaviour, characteristic processing temperatures and viscosity determined by thermal analysis. The glass melts were drawn into fibres and their mechanical strength was measured by single fibre tensile tests before and after the surface treatment with different silanes. The degradation of the bioactive glasses was observed in simulated body fluid and pure water by recording the changes of the pH value and the ion concentration by inductively coupled plasma optical emission spectrometry; further, the glass degradation process was monitored by scanning electron microscopy. Additionally, first in vitro experiments using murine pre-osteoblast cell line MC3T3E1 were carried out in order to evaluate the interaction with the glass fibre surface. The results achieved in this work show up the potential of the manufacturing of endless bioactive glass fibres with appropriate mechanical strength to be applied as reinforcing fibres in new innovative medical implants.  
glass fibre spinning; bioactive glass fibres; surface modification; silanes +
Footnote †: journal: Applied Sci.
# Beyond Correlation: Mutual Information to Detect Damage in Nonlinear Systems
## 2 Damage Detection Using Transmissibility Coherence
\(TMAC\) is given by the equation  
\[TMAC(i,j)=\frac{\left|d_{ij}^{T}u_{ij}\right|^{2}}{\left(d_{ij}^{T}d_{ij} \right)\left(u_{ij}^{T}u_{ij}\right)}, \tag{5}\]  
where \(u_{ij}\) is the transmissibility coherence \(TC_{ij}\) at the reference (undamaged) state, and \(d_{ij}\) is that of the new, potentially damaged state. Statistically, \(TMAC(i,j)\) is the square of the Pearson's correlation coefficient between vectors \(u_{ij}\) and \(d_{ij}\) and as such is a number between 0 and 1, where 0 indicates that the two signals are uncorrelated, and 1 indicates that one of the signals can be written as a linear function of the other. Damage detection using transmissibility coherence and metrics derived from it is an active area of research. Various damage metrics comparing the baseline transmissibility to that of a potentially damaged state have been proposed so far. The majority of these formulations can be grouped by their method of comparison into difference-based (e.g., ([PERSON] et al., 2015; [PERSON] et al., 2016; [PERSON] et al., 2016)) and correlation/coherence-based approaches (e.g., ([PERSON] et al., 2015; [PERSON] et al., 2016)). [PERSON] and [PERSON] (2011) define a new indicator for damage detection and relative damage quantification by examining the correlation between pairs of operational deflected shapes at the same frequency. According to [PERSON] and [PERSON] [42], the appeal of the transmissibility concept, when properly defined, lies in its ability to describe the dynamics of a portion of a structure independent of the rest of the structure. This property, called sub-structural invariance, makes it possible not only to detect but also to locate damage in complex structures, provided that the transmissibility is defined between a set of internal and interface degrees of freedom. In practice, however, localization of damage is more challenging than detection. While the integral of the transmissibility contributions along the frequency axis--captured by the Accumulated Transmissibility Coherence (ATC) equation--can be used for damage detection, it is not necessarily suitable for locating damage. The difference between the transmissibility of baseline and damaged states tends to be large near modal frequencies, so any errors in the predicted damage locations accumulate and may mask the true location of damage for frequencies higher than the first modal frequency. To circumvent this problem, [PERSON] [43] proposed a normalization scheme based on counting the number of times the transmissibility difference is maximized at each potential damage location. A similar technique, which uses the transmissibility of several neighboring sensors, has been experimentally verified with data from static and fatigue tests of a full-scale wind-turbine blade [44].
# Metrics for Evaluating Synthetic Time-Series Data of Battery
## 1 Introduction
The DTW evaluation technique can evaluate data that repeat a specific cycle and are classified under the same label, and it is difficult to evaluate the non-clusterable data (i.e., battery) used in estimation and prediction algorithms. Generally, the use of qualitative techniques (which express results visually) reflects the subjective evaluation of the evaluator; however, their accuracy and reliability are poor. In quantitative techniques, learning loss is generally used as an evaluation criterion; however, an objective comparison is difficult because the loss varies based on the learning environment. Data augmentation addresses the insufficiency problem of battery data. To ensure the reliability of such augmented battery data, this study proposes three quantitative indicators (namely, the rate of change of linear correlation coefficients, the Dunn index, and silhouette coefficients) that directly evaluate the quality of synthetic battery data, specifically, its similarity to the original data. Similarity can be visualized through existing qualitative evaluation methods, such as t-SNE, but they are not suitable for evaluating the battery data of estimation algorithms. Moreover, visual evaluation is affected by the subjectivity of the evaluator. Therefore, to overcome this limitation, the quality of synthetic battery data is quantitatively evaluated using the adopted correlation coefficient and clustering technique (Dunn index [17] and silhouette coefficient [18]) using visual evaluation (t-SNE [19]). The contributions of this study are as follows:  
1. The proposed method can satisfy and compensate for the quality of insufficient datasets in deep learning-based battery estimation and fault diagnosis. 2. By evaluating the quality of the data, high-quality data can be obtained for battery estimation and fault diagnosis. 3. The proposed method can efficiently evaluate battery data both visually and quantitatively, regardless of the learning environment. 4. The proposed method can also evaluate the synthetic battery data generated using data generation techniques other than TimeGAN for similarity. The proposed method can be used for data other than battery data to evaluate the similarity of data.
## 2 Data and Method
### Sources of Uncertainty and Datasets
#### 2.2.1 Constellation Changes-Induced Uncertainties
Therefore, assuming a multi-platform PMW rain retrieval database, variability in the overall rain distributions and its performances was expected which could also be attributed to the  
Figure 1: The uncertainty distribution due to constellation changes (\(y\)-axis, in \(\%\)) as a function of the daily precipitation (\(x\)-axis, in mm day\({}^{-1}\)) for two constellation configurations, during June-July-August of 2014, across the \(55^{\circ}\)N/S global zones and over-land surfaces. Panels show density scatter-plots (colored scale), superimposed by the IQR distribution (black lines), of the relative error (**a,b**) vs. the daily accumulated precipitation (log-scale distributed). Outliers are represented by blue dots. retrieval algorithm [10; 32; 33; 34; 35]. For instance, according to [PERSON] et al. [21], the systematic uncertainties associated with PMW rain retrievals ranging between 2 and 10 mm h\({}^{-1}\) lead to an impact on the 1\({}^{\circ}\)/daily precipitation accumulations, resulting in non-Gaussian distributions. In this case, the constellation configuration-induced variability impacted the entire daily accumulated precipitation range. The impact is clearly noticeable through the modification of the RE distribution shape at each precipitation category, which can even be seen in the density spread or peak location.
## 2 Materials and Methods
### Test Design  
#### 2.4.1 Single-Factor Test  
The greater the output pressure of the gas storage tank, the greater the thrust of the cylinder and the greater the clamping force of the manipulator, which affects the clamping effect. The pulling angle has an important influence on the pulling force [22], which not only affects the pulling effect but also affects the power consumption of the whole machine. Slow removal speed affects both the efficiency and effectiveness of the operation. Therefore, by designing a single-factor test, we studied the influence of the output pressure on the clamping force of the manipulator and the influence of the output pressure (\(X_{1}\)), the pulling angle (\(X_{2}\)) and the pulling speed (\(X_{3}\)) on the rape uprooted removal rate (\(Y_{1}\)), the rape removal breakage rate (\(Y_{2}\)) and the pulling force (\(Y_{3}\)). The rape uprooted removal rate is the ratio of the number of rapes successfully removed by the clamping manipulator in a test group to the number of all rapes in a test group, which can be expressed as Formula (1). The rape removal breakage rate is the ratio of the number of rapes broken by the clamping manipulator in a test group to the number of all rapes in a test group, which can be expressed as Formula (2). The pulling force is the ratio of the total pull-out force of the rape successfully pulled out by the clamping manipulator in a test group to the number of rapes successfully pulled out in a test group, which can be expressed as Formula (3).  
\[Y_{1}=\frac{M_{1}}{M}\times 100\% \tag{1}\]  
where \(Y_{1}\) is the rape uprooted removal rate, %; \(M_{1}\) is the number of rapes successfully removed by a clamping manipulator in a test group; and \(M\) is the number of all rapes in a test group.  
\[Y_{2}=\frac{M_{2}}{M}\times 100\% \tag{2}\]  
where \(Y_{2}\) is the rape removal breakage rate, %; \(M_{2}\) is the number of rapes broken by a clamping manipulator in a test group; and \(M\) is the number of all rapes in a test group.  
\[Y_{3}=\frac{F_{1}+\cdots+F_{n}}{N} \tag{3}\]  
where \(Y_{3}\) is the average pulling force of rape in the experimental group, \(N\); \(F_{n}\) is the pulling force of the clamping manipulator to successfully pull out the nth rape plant; and \(N\) is the number of rapes successfully uprooted in a test group.  
The fixed parameters used in the single-factor test were output air pressure 0.5 MPa, pulling angle 75\({}^{\circ}\) and pulling speed 10 cm/s. The specific test factors and levels are shown in Table 2. Each group of experiments was carried out 20 times, and the rapes were selected with a diameter of about 14 mm. According to Formulas (1)-(3), the rape uprooted removal rate, the rape removal breakage rate and the pulling force were obtained, and the data were recorded and analyzed.
## 3 Description of Comprehensive Techno-Economic (CTE) Model
### Signal Measurement Results for the Three Independent Mobile Telecom Operators
\begin{table}
\begin{tabular}{c c} \hline \multicolumn{3}{c}{**Comparison of Mobile Telecom Operators—I**} & **Comparison of Mobile Telecom Operators—II** \\ \hline RefADD & 300 Mb/s & 134,22 Mb/s \\ \hline RefADU & 150 Mb/s & 42.41 Mb/s \\ \hline RefDEL & 10 ms & 40 ms \\ \hline \end{tabular}
\end{table} 
Table 5: Reference values for the MDMG item (Ref). The ground floor and four floors above ground are covered by the signal of all three operators. The FMO shows the best results, but it can be noticed that the signal samples of all three operators are quite unstable. It indicates the fact that the coverage is made through external base stations and that there are no micro and pico base stations inside the mall (or, alternatively, there is a negligible number). Finally, MDUA and MDMG values will be calculated for both approaches. The final values will be shown in Tables 7 and 8. The measured and obtained results clearly indicate the fact that none of the observed three mobile operators is ready to provide and support more demanding IoT services. Parallel signal measurement was performed to the server (for FMO) within the city (average delay is 18.28 ms) and the result of MDUA + MDMG increases to 0.069. The maximum amount of each item is 0.1 (the sum of these items is 0.2), and it is clear that the FMO has some potential to provide a certain level of IoT services. The recommendation for all three operators is that it is necessary to significantly improve the quality of the network in the city, especially since the observed city is located in Urban Zone 1. The measurement results show that the FMO has the best results. It should be noted that the FMO has a significant place for progress and that the coverage of both open and closed space needs to be improved through the addition of new locations in the city, but also through the determination of antenna redirection. TMO showed better results than SMO, but this difference is not significant. Both operators must greatly improve the quality of coverage in the outdoors, but especially indoors, because it is not acceptable that both operators do not have a signal in the underground area of the largest shopping center in the city. The analyses presented in this chapter show how the CTE model can be used modularly (by items and areas) and for the purposes of the comparison of mobile operators. This analysis confirms the robustness and modularity of the CTE model, as well as its effectiveness in order to obtain concrete proposals for the improvement of certain areas of operation of the operator.
## 3 Results and Discussion
### Response Profile of E-Nose for Fermented Specimens
#### 3.3.2 PCA of E-Nose Dataset of Fermented Specimens  
Figure 5 show that PC1, PC2, and PC3 of fermented specimens accounted for 56.5%, 80.9%, and 91.6% of the accumulative variability. The total contribution rate was \(>\)90%, indicating that the three PCs could explain all the properties of the volatile aroma of fermented specimens. From the perspective of PC2, the specimen CPBY-12 h was distributed in the positive portion of the \(y\)-axis. CP-12 h and CPY-12 h were located in the negative areas of the \(y\)-axis. Moreover, the specimens differed chiefly in PC1. CP-12 h and CPB-12 h were distributed in the positive areas of PC1. CPY-12 h and CPBY-12 h were located in the negative area of PC1. The data locations of the four specimens with different means of preparation were dispersed, implying that they possessed varied aromas, and PCA could completely distinguish the aroma substances of the four groups of specimens. Enzymatic hydrolysis and fermentation after adding yam had sufficient effects on the volatile flavor components of chickpea milk. This finding was consistent with that of a previous study, in which chickpea milk specimens that were prepared via dissimilar means displayed diverse spatial locations in PCA [2]. Hence, E-nose can be used to verify the flavor characteristics of chickpea milk before and after fermentation. Nevertheless, accurately identifying the substances in these specimens is difficult. Thus, GC-MS detection was conducted.
## Theory
### Thermal Energy Model for Tissue Heating  
The transfer of heat in the tissue under HIFU irradiation is modeled using the Pennes bioheat transfer equation [20]:  
\[\rho_{t}C_{t}\frac{\partial T}{\partial t}=k\
abla^{2}T-W_{b}C_{b}(T-T_{a})+Q _{ext} \tag{4}\]  
where \(C_{t}\) and \(\rho_{t}\) are the specific heat and density of tissue, respectively; \(C_{b}\), \(W_{b}\), and \(T_{a}\) are the specific heat, perfusion rate, and ambient temperature of blood, respectively. \(Q_{ext}\) is the ultrasound heat deposition term, which can be calculated by employing the time averaged over one acoustic period by numerical integration [14]:  
\[Q_{ext}=\frac{2\kappa}{\rho c\omega^{2}}\left\langle\left(\frac{\partial p}{ \partial t}\right)^{2}\right\rangle \tag{5}\]  
To evaluate the performance of the HIFU treatment, thermal dose is usually used to estimate the tissue damage. The thermal dose depends on the final time \(t_{f}\) and temperature level \(T\), which was developed by [PERSON] and [PERSON] [21]:  
\[t_{43}=\int_{0}^{t_{f}}R^{(T-43)}dt\approx\sum_{0}^{t_{f}}R^{(T-43)}\Delta t \tag{6}\]  
where \(t_{43}\) is the thermal dose equivalent time at 43 \({}^{\circ}\)C. \(R=2\) if \(T\geq 43\)\({}^{\circ}\)C, and \(R=4\) if \(37\)\({}^{\circ}\)C \(<T<43\)\({}^{\circ}\)C. The threshold value of an isothermal dose value of 240 min at 43 \({}^{\circ}\)C was usually selected to predict the size of the thermal lesion region.  
Another way to quantify the tissue thermal damage is to use the Arrhenius equation [22]:  
\[\Omega=\int\limits_{0}^{t_{f}}A\exp(\frac{-E_{a}}{R_{a}T})dt \tag{7}\]  
where \(A\), \(E_{a}\), and \(R_{a}\) are the frequency factor, activation energy, and universal gas constant, respectively. For liver thermal damage, \(A=9.4\times 10^{104}\) s\({}^{-1}\), \(E_{a}=6.68\times 10^{5}\) J mol\({}^{-1}\), and \(R_{a}=8.31\) J mol\({}^{-1}\) K\({}^{-1}\)[23]. The undamaged fraction of the tissue and the damaged fraction can be estimated by \(f_{u}=\exp(-\Omega)\) and \(f_{d}=1-f_{u}\), respectively [24].