esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Computational Methods
### CALPHAD Method  
The CALPHAD method was employed in this study to extract the reliable experimental thermodynamic properties for comparison with our computed results, which validated our theoretical approach. In general, the major function of the CALPHAD approach is to model the Gibbs energy and establish a phase diagram [45; 46; 47]. CALPHAD is an iterative method that adjusts the parameters that describes the Gibbs energy functions of various phases in a system in order to construct a phase diagram that best reproduces the available experimental thermodynamical and phase-diagram data. Once such functions have been assessed, they are compiled in a database that can be used for computational thermodynamical predictions of multi-component systems.  
In addition to obvious comparison between the existing CALPHAD data and the theoretical calculations, we emphasize that the coupling of CALPHAD and ab initio calculations enhances the thermodynamic modeling capability when material systems with many thermodynamic unknowns are considered. First of all, ab initio calculations (e.g., the heats of formation and transformation) can directly provide essential inputs to the CALPHAD modeling framework when experimental data are sparse or missing. Second, it is worth noting that the process of parameter optimization and error minimization involved in the CALPHAD method is an inverse problem with infinite degrees of freedom [64]. Thus, many possible combinations of values of user-defined parameters could produce phase boundaries and diagrams that perfectly coincide. Therefore, the use of ab initio energetics during the CALPHAD assessment constrains the optimization process and validates the resulting thermodynamical database, not only from a diagrammatic basis, but also from a reliable energetics basis. Examples of ab-initio-informed CALPHAD assessments for actinide systems can be found in [65; 66]. Following the present work, ab initio predictions of specific heat and free energy versus temperature for actinide systems is extremely useful to the CALPHAD community.  
The CALPHAD thermodynamical modeling of the U-N binary system was initially performed by [PERSON] et al. [31] from a critical assessment of most of the available experimental information on this system. The agreement between experimental and calculated phase diagram or thermodynamical properties was quite satisfactory. However, [PERSON] et al. [67] mentioned that the calculated UN decomposition nitrogen pressure versus temperature was significantly lower than almost all reported measurements. Thus, [PERSON] [67] adjusted the UN Gibbs free energy by +12 kJ/mol from that of [PERSON] et al. [31] to bring the calculated pressures into relative agreement with reported measurements. This last CALPHAD assessment of the UN Gibbs free energy is considered in the present study.
## 2 Basic Theory
### Proposed Method  
Aiming at the limitations of traditional methods for fault diagnosis of rotor system with sliding bearing, a stepwise intelligent diagnosis method based on statistical filter and SAE is proposed for sliding bearing fault diagnosis, which adequately take advantages of the statistical filter to remove the affective of noise and apply SAE to perform stepwise intelligent diagnosis. Moreover, the SAE training process is divided into two phases: unsupervised feature learning and supervised fault identification. The detailed information of the proposed method is described as follows:  
Figure 5: Brief architecture of SAE network.  
Figure 6: Brief sketch of SAE.  
1. Raw signals of sliding bearing in normal state and 4 kinds of abnormal states using professional experimental platform with sensors;
2. Filter noise with the statistical filter between normal state and each abnormal state;
3. Decompose five kinds of bearing states into four groups according to Figure 4 and perform the stepwise bearing fault diagnosis with SAE to distinguish two states at each time;
4. Establish the deep hierarchical structure with the rule of greedy training, where the auto-encoders are utilized to obtain characteristics of the training sets; output the diagnosis results and stop diagnosis until distinguishing the static unbalance and dynamic unbalance.
5. Apply the testing sets to confirm the accuracy of the fault diagnosis with the trained SAE.
## 4 Experimental Results and Discussion
### Energy Consumption  
The energy consumption of the introduced algorithm is shown in Figure 5, along with two conventional algorithms: a cross-layer-based adaptive threshold technique and a cross-layer fuzzy logic approach.  
From the experimental results, the proposed CERP consumed less energy (1.97 mJ), whereas the existing cross-layer-based adaptive threshold technique consumed 7.75 mJ  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Approaches**} & \multicolumn{8}{c}{**Average Energy Consumed (J) vs. Cluster Size**} \\ \cline{2-10}  & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline K-means & 0.024 & 0.029 & 0.0343 & 0.038 & 0.041 & 0.045 & 0.049 & 0.059 & 0.0638 \\ \hline Fuzzy C-means & 0.029 & 0.039 & 0.041 & 0.046 & 0.052 & 0.056 & 0.061 & 0.069 & 0.074 \\ \hline Proposed & 0.019 & 0.023 & 0.028 & 0.029 & 0.033 & 0.035 & 0.037 & 0.039 & 0.0423 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the average energy consumption of the proposed ASFO algorithm.  
Figure 4: Comparison of CH selection by the proposed method with existing approaches.  
and the cross-layer fuzzy logic approach consumed 8.43 mJ. For 200 nodes, the E-CERP approach consumed 1.10 mJ of energy, and the existing techniques, cross-layer-based adaptive threshold and cross-layer fuzzy logic, consumed 5.51 mJ and 10.36 mJ of energy, respectively. For 300 nodes, the proposed technique consumed 5.13 mJ of energy, the cross-layer-based adaptive threshold technique consumed 7.91 mJ of energy, and the cross-layer fuzzy logic approach consumed 9.53 mJ. For 400 nodes, the introduced E-CERP approach consumed 4.8 mJ, whereas the cross-layer-based adaptive threshold technique and cross-layer fuzzy logic approach consumed 7.45 mJ and 11.23 mJ, respectively. For 500 nodes, the introduced approach consumed 4.19 mJ of energy, whereas the existing cross-layer-based adaptive threshold technique and the cross-layer fuzzy logic approach consumed 7.55 mJ and 10.37 mJ of energy, respectively. So, the proposed method is shown to be very effective in data transmission.
## 8 Challenges and Future Directions
### Future Directions
#### 8.2.4 Personalised HAR  
The development of personalised HAR will account for individual user preferences and profiles. It will improve the accuracy of the HAR system by considering individual bio-mechanics, activity preferences or environmental factors. Personalised HAR systems can adapt to changes in user behaviour or environmental conditions. LLMs can be used in this instance to adapt existing models to the patterns of individual users or develop adaptive HAR models. Future research can be conducted on personalised and adaptive HAR systems using LLMs.
## 8 Fixed and Adaptive Wave Domain Equalization  
The equalization approaches reviewed so far considered the reproduced sound field at one or more points in space. These points should ideally coincide with a potential listener position or restricted listening area. A broader view of equalization can be gained by taking the entire reproduced sound field within the desired--potentially large--listening area into account. This can be achieved by taking the spatio-temporal character of the sound field instead of the sound pressure at a limited number of points into consideration. In order to lay the grounds, the background of equalization following such a field-centered view is reviewed in the next subsection. This is followed by a review of representative approaches in the subsequent subsections.
# Transcriptional Analysis of Masson Pine
## 3 Results
### Gene Expression and KEGG Enrichment Analysis under CO\({}_{2}\) Stress  
A total of 7088 genes were differentially expressed between the samples from the three CO\({}_{2}\) stress treatments and the control samples. Of these, 4988, 5225 and 4167 genes were differentially expressed between 6 h and CK, 12 h and CK, and 24 h and CK treatments, respectively (Figure 1A). Among the differentially expressed genes (DEGs), 3080/1908, 3110/2115 and 2684/1483 genes were up-/down-regulated at 6 h, 12 h, and 24 h, respectively, compared with CK (Figure 1B). Gene enrichment analysis of the DEGs based on KEGG analysis revealed that these genes were mainly involved in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Transcript** & **Unigenes** \\ \hline \(<\)500 bp & 61,696 & 48,592 \\
501–1000 bp & 37,586 & 22,267 \\
1001–2000 bp & 25,994 & 12,887 \\ \(>\)2000 bp & 15,587 & 8,678 \\ Total & 140,863 & 92,424 \\ Min length (bp) & 201 & 201 \\ Max length (bp) & 15,491 & 15,491 \\ Mean length (bp) & 891 & 935 \\ N50 (bp) & 1463 & 1550 \\ TransRate score & 0.20045 & 0.30498 \\ BUSCO score & 77.7\% & 74.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Length distribution and software evaluation of unigenes and transcripts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CK** & **6 h** & **12 h** & **24 h** \\ \hline Raw reads & 49,314,299 & 47,459,322 & 45,980,036 & 60,876,932 \\ Raw bases & 7,446,459,199 & 7,166,357,723 & 6,942,985,436 & 9,192,416,732 \\ Clean reads & 48,795,571 & 46,976,134 & 45,496,760 & 60,205,674 \\ Clean bases & 7,292,383,178 & 7,017,302,463 & 6,802,876,221 & 9,000,454,730 \\ Error rate (\%) & 0.02 & 0.02 & 0.03 & 0.03 \\ Mapped reads & 16,879,027 & 16,459,249 & 15,859,340 & 21,108,414 \\ Mapped ratio (\%) & 0.69 & 0.70 & 0.70 & 0.70 \\ GC content (\%) & 47.47 & 46.74 & 46.24 & 46.32 \\ Q20 (\%) & 98.12 & 98.20 & 98.06 & 97.86 \\ Q30 (\%) & 94.04 & 94.22 & 93.86 & 93.40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of sequencing data quality control.  
several pathways at different time points, including photosynthesis, carbon fixation (the Calvin cycle), glycolysis, the tricarboxylic acid (TCA) cycle, starch and sucrose metabolism, fructose and mannose metabolism, galactose metabolism and plant hormone signal transduction, etc. (Figure 1C,D,E). In the three KEGG enrichment Figures that compare the different treatment time points with CK, all of the above pathways ranked within the top 20 pathways (except the \"TCA cycle\" in 6 h/CK, Figure 1C), indicating that energy metabolism, carbohydrate synthesis, cell wall synthesis and hormone regulation may be the main metabolic activities in Masson pine under high CO2 stress.
### Fatty Acid Profile
The closest values were found for parboiled milled rice, with an SFA content of 55.5% [116]. These results highlight the strong influence of the rice variety and the technological process on the fatty acid profile of rice flour [116]. The main fatty acids in the flours analyzed were palmitic acid, followed by oleic, linoleic and stearic acids, in contrast to the findings of previous studies, which reported that the main fatty acids were oleic, palmitic and linoleic acid for white rice flours and oleic, linoleic and palmitic acids for brown rice flours [118; 119]. Fatty acids of specific chain length and saturation are required by humans for their structural and metabolic needs. Linoleic acid is responsible for the production of prostaglandlandins, which prevent the concentration of heavy cholesterol on the blood vessel walls [120], but is also correlated with an improved blood lipid profile, lower incidence of type 2 diabetes, improved insulin sensitivity and anti-arrhythmic effects. Oleic acid is known to have beneficial effects on autoimmune and inflammatory diseases, as well as cancer and the healing of wounds [121]. \(\alpha\)-Linolenic acid is an essential fatty acid correlated to inflammatory and immunosuppressive activities by means of the formation of several intermediates such as leukotrienes, thromboxane and prostaglandlandins [122]. However, unsaturated fatty acids are susceptible to lipid oxidation, which results in undesirable changes in organoleptic properties owing to the presence of off flavors [123]. In this sense, oleic and linoleic acids show less propensity towards oxidation compared to linolenic acid during processing [124]. Pulses and hemp flours were characterized by high levels of all these fatty acids, which are correlated with the above-mentioned health benefits, with pea and hemp flours showing the highest levels of linolenic acid. However, not only PUFA levels but also the n-6/n-3 PUFA ratio should be taken into account. According to the World Health Organization, this ratio should be below 4:1, as is shown by both brands of both pea and hemp flours. In this context, if any flour with a high n6/n3 ratio is to be partially replaced, it would be desirable to use hemp flour, owing to its low n6/n3 ratio or pea flour, as this contains only a small amount of fat with a large amount of oleic acid and an adequate n6/n3 ratio.
# Research on Lifespan Prediction Methods Using Ultrasonic Microimaging for Electronic Packaging
###### Abstract  
Addressing the issue of lifespan prediction for electronic packages under thermal loading, this paper proposes a method for predicting the lifespan of electronic packages based on ultrasonic microimaging. Firstly, experimental samples equipped with flip-chip packages were designed and fabricated and subjected to aging through thermal cycle acceleration tests. Ultrasonic microscopy was utilized to periodically acquire ultrasonic image data for monitoring solder joint degradation. Secondly, the internal ultrasonic wave propagation mechanism within electronic packages was investigated, establishing a qualitative relationship between the intensity in the central region of the solder joint's ultrasonic image and internal defects within the joint. Image processing techniques were applied to enhance the quality of the solder joint images, and the mean intensity in the central region of the solder joint image was extracted as a failure feature. Finally, based on the extracted failure feature, a data-driven failure model for solder joints was developed, which predicts the lifespan of the solder joints based on cumulative failure probability. The research results indicate that the proposed model accurately describes the failure process of solder joints and effectively differentiates the lifespan variations among solder joints at different locations on the chip. This provides theoretical support for the reliability assessment of electronic package solder joints and holds practical value for enhancing the overall reliability of electronic packaging components.  
electronic packaging; lifespan prediction; ultrasonic microimaging; failure modeling 2025  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], M. Research on  
Microimaging for Electronic Packaging. _Appl. Sci._**2025**, 15,3246. [[https://doi.org/10.3390/app15063246](https://doi.org/10.3390/app15063246)]([https://doi.org/10.3390/app15063246](https://doi.org/10.3390/app15063246))
## 4 Statistical Multi-Node MIMO Switching in Mobile WMSNs
### Motivation  
Previous works showed that there are many benefits to switching between multiplexing and diversity modes of operation based on the current channel state [30,35,36]. When fast switching is not available, it is reasonable to switch based on the statistics, such as the transmit correlation matrices [38]. In MN MIMO, there are four MIMO possibilities: SN SM, MN SM, SN OSTBC and MN OSTBC. Excluding MN OSTBC, as described in the previous section, SN SM, MN SM and SN OSTBC can be selected in MN MIMO mode switching according to channel environment. In MN MIMO, a capacity crossing can exist between SN SM and MN SM, as well as between SM and OSTBC. To justify this, consider the examples in Figures 2, 3 and 4, which show the link level capacity of \(2\times 2\) SN SM and MN SM when there is no spatial correlation at the receive antennas (\(\rho=0\)). In Figure 2, we assume that the spatial correlation of transmission antennas, \(\rho\), is 0.8 and the path gain ratio, \(\mu\), is 0.9. In this case, MN SM has significantly better performance than SN SM. In Figure 3, we plot the capacity for low spatial correlation (\(\eta=0.2\)) and low path gain ratio (\(\mu=0.4\)), where SN SM shows higher capacity than MN SM. As shown in Figure 4, when \(\eta\) is \(1/\sqrt{2}\) and \(\mu\) is 0.125, the upper bound crossing and the exact analysis crossing between SN and MN SM occur at SNR = 3 dB (\(\gamma_{MN-SN}=2\)) and around 2 dB, respectively. These results show that MN SM can provide higher capacity than SN SM based on the spatial correlation and path gain ratio. Therefore, adaptive switching between SN and MN MIMO schemes can yield significant capacity gains over SN MIMO switching schemes.  
Figure 2: Spatial multiplexing (SM) \(2\times 2\) MIMO capacity (\(\eta=0.8\) and \(\mu=0.9\)).  
Simple switching between SN and MN modes of operation can be improved further using the concept of antenna subset selection [39]. For example, let us consider the \(4\times 4\) MIMO case, where more than two correlated transmit antennas may be chosen from one or both sensor nodes. In this case, creating a diagonal transmit correlation matrix is no longer possible, even in MN MIMO schemes, because at least two or more antennas should be chosen from the same SEN. As expected from Equation (11), the lower the correlation among the selected antennas, the higher the capacity of MN SM is. Therefore, adaptive antenna selection according to channel conditions is also required to maximize the MN MIMO capacity.
## 2 Battery Modeling
### State-Space Model
#### 2.3.2 State Space Model for Battery Parameter Estimation  
Because the battery model parameters vary with changes in the batteries' SOC, degree of aging, and environmental temperature, online recursive battery parameter estimation is needed. So here we establish the state-space equations of the battery parameters as:  
\[\left\{\begin{array}{ll}\theta_{k+1}=\theta_{k}+r_{k}\\ d_{k}&=h(X_{k},I_{L,k},\theta_{k})+e_{k}\end{array}\right. \tag{11}\]  
where \(r_{k}\sim\mathcal{N}(0,Q_{k}^{\rm g})\) is a small white noise with covariance \(Q_{k}^{\rm g}\) that reflects the time-varying parts of the parameters, \(d_{k}\) is a measurement function of \(\theta_{k}\), and \(e_{k}\sim\mathcal{N}(0,\Sigma_{k}^{\rm g})\) is the measurement noise to account for the sensor noise and modeling uncertainties. \(\Sigma_{k}^{\rm g}\) is assumed unknown here.  
The Jacobian measurement matrix of \(\theta_{k}\) is calculated as follows [20]:  
\[H_{\theta,k}\!=\!\frac{dh(\cdot)}{d\theta_{k}}\bigg{|}_{\theta_{k}=\hat{\theta }_{k}^{-}}=\frac{\partial h(\cdot)}{\partial\hat{\theta}_{k}^{-}}+\frac{ \partial h(\cdot)}{\partial\hat{X}_{k}^{-}}\cdot\frac{d\hat{X}_{k}^{-}}{d \theta_{k}^{-}} \tag{12}\]  
\[\frac{\partial h(\cdot)}{\partial\hat{\theta}_{k}^{-}}\!=[\begin{array}{cc}-I _{L,k}&0&0\end{array}] \tag{13}\]  
\[\frac{\partial h(\cdot)}{\partial\hat{X}_{k}^{-}}=H_{x,k} \tag{14}\]\[\frac{d\hat{X}_{k}^{-}}{d\hat{\theta}_{k}^{-}} = \left[\begin{array}{ccc}0&0&0\\ 0&a_{22}&a_{23}\end{array}\right] \tag{15}\]  
where  
\[a_{22} = -I_{L,k}\cdot(\exp(\Delta t/\tau_{1}^{2})-1)\] \[a_{23} = (\Delta t/\tau_{1}^{2})\cdot(\hat{\tau}_{2,k}^{-}-R_{1}I_{L,k}) \cdot\exp(\Delta t/\tau_{1}) \tag{16}\]
## 3 Experimental Results
### Thrust Tests  
The main objective of the thrust test consists of checking the dynamometer value and power consumption with different throttle demands that will be used in a typical flight mission, while the ALO is installed in the thrust test bench.  
When available power, P\({}_{\text{A}}\), and power required, P\({}_{\text{R\,criusser}}\), are equal to each altitude, h, the maximum and minimum speeds of the system were obtained under these conditions. Figure 22 (from Annex 3) shows available power and required power for each flight altitude.  
Figure 21: Battery pack test. Individual cells voltage behavior under flight load profile.  
Figure 20: Battery pack test. Battery pack behavior under flight load profile.  
Experimental results of the electric ALO thrust test are presented in Figure 23. Thrust and power consumption increase with motor input. The launching could be performed at 60% of engine input, with a power consumption of around 4.5 KW and a thrust of 12 kg. In order to maintain cruise speed when the RPA reaches its objective altitude, a very low thrust is needed and less than 35% of motor input is enough.  
Figure 24 shows a thrust comparison between the Electric ALO and ICE configuration. Thrust results confirm that both configurations reach around 12 kg thrust at 60% motor input, which would be enough for the launching needs.  
Figure 23: Electric ALO thrust test. Electric power vs. thrust.  
Figure 22: Available power and required power for each flight altitude. Where P\({}_{\text{A}}\) is power available (W) and h is altitude (m).  
Since the modifications performed in the original ALO do not affect the weights nor the surfaces, the flight envelope for both configurations are the same. With this static thrust test, it can be assumed that the performance of the modified ALO will be the same when in flight.
## 3 Results and Discussion  
### Results of Emulsion Stability  
The percentage of separation in the emulsions produced with different ratios of wall materials is shown in Table 2. The emulsion stability is related to the physical stability of emulsion systems, which allows the emulsion to remain homogeneous over long periods of time. A high percentage of separation of emulsions indicates that the system is unstable, while a low percentage of separation indicates high stability.  
\begin{table}
\begin{tabular}{c c} \hline
**Emulsion Formulations** & **Separation, \% 1 ** \\ \hline NY (1:1) & 11.55 \(\pm\) 0.06 2 \\ NY (1:4) & 11.44 \(\pm\) 0.03 2 \\ NY (1:10) & 10.98 \(\pm\) 0.04 2 \\ KY (1:1) & 11.44 \(\pm\) 0.03 2 \\ KY (1:4) & 11.09 \(\pm\) 0.04 3 \\ KY (1:10) & 10.45 \(\pm\) 0.06 4 \\ \hline \end{tabular}
\end{table}
Table 2: Separation values of emulsions after 24 h (%).  
In emulsion formulations with both oil types, it was observed that the percentage of separation decreased as the MD ratio increased (from 1:1 to 1:10). This indicates that the stability of the emulsion increases with the addition of more MD. Similarly, [PERSON] et al. (2018) used coating agents such as maltodextrin and whey protein isolate in the microencapsulation of olive oil and investigated the effects of different ratios on the emulsion stability. In the study, it was stated that the stability of emulsions varied depending on the proportions of the ingredients in the formulation.
## 2 Methodology
### A Combination of Fuzzy AHP and Fuzzy TOPSIS
#### 2.2.3 Data Collection  
The hybrid approach using Fuzzy AHP-TOPSIS provides a quantitative logical and systematic framework to identify critical issues, attach relative priorities to those issues, choose best compromise alternatives, and facilitate communication towards general acceptance [29; 44]. This paper considered 8 criteria, representing the main economic sectors of Ky Khang, and 5 different alternative sites, representing conflict hotspots. Fuzzy AHP is combined with Fuzzy TOPSIS to estimate weights of the criteria and to prioritize alternative sites according to the intensity of environmental conflicts. Data on weighting sector criteria and prioritizing alternatives sites in the Ky Khang commune are collected using a questionnaire with 15 locals, 8 communal authorities, 2 district authorities, and 12 scientific experts on titan mining, environmental geology, and sustainability. Data were collected during a field trip in March 2018. All respondents inhabit in the study area (locals and authorities) or are knowledgeable about the study area and about scientific problems related to titan mining. The questionnaire allows inventory the opinion of the respondents on the pair wise comparison matrix by using Likert 5 scale, indicating five pre-coded responses with the neutral point (point 3) being neither agree nor disagree. Respondents took about 30 min to complete the questionnaire.