esa-sceva/satcom-chunk-collection huggingface dataset:
# An Oligoimide Particle as a Pickering Emulsion Stabilizer
## 3 Results and Discussion
### Microencapsulation  
Microencapsulation of Pickering emulsion droplets was performed as a preliminary evaluation of the applicability of the Pickering emulsion stabilized by oligoimide particles. An oil phase containing _n_-hexadecane, MMA, DVB, and AIBN was added to the PMDA-ODA particle aqueous dispersion (0.5 wt %). A stable oil-in-water Pickering emulsion was obtained by homogenizing the sample at 20,000 rpm for 5 min. The emulsion was heated to induce radical crosslinking polymerization of MMA and DVB inside the droplets [51]. As shown in Figure 9, spherical microcapsules were obtained. By pressing the microcapsules between two slide glasses, the core material flowed out from the capsules. An FT-IR spectrum of the core was in accord with that of _n_-hexadecane (data not shown), indicating that _n_-hexadecane was successfully microencapsulated via Pickering emulsions. FT-IR spectroscopy was conducted to confirm the chemical composition of the shell wall (Figure 10). PMDA-ODA showed absorption bands of the imide ring at 1778, 1728, and 1378 cm\({}^{-1}\), as described above. Poly(MMA-_co_-DVB) has a CH stretching vibration band at 2840-3140 cm\({}^{-1}\) and an ester carbonyl stretching band at 1730 cm\({}^{-1}\). The above absorption bands were all observed in the spectrum of the shell wall, indicating that the shell wall is composed of PMDA-ODA and poly(MMA-_co_-DVB).  
TGA curves of PMDA-ODA, the shell wall, poly(MMA-_co_-DVB), and microcapsules are shown in Figure 11. Poly(MMA-_co_-DVB) and PMDA-ODA start to degrade around 270 and 350 \({}^{\circ}\)C, respectively. T\({}_{10}\) values for poly(MMA-_co_-DVB) and PMDA-ODA were 343 and 513 \({}^{\circ}\)C, respectively. The shell wall showed a T\({}_{10}\) of 354 \({}^{\circ}\)C, which is higher than that of poly(MMA-_co_-DVB). This is attributable to the oligomide particles contained in the shell wall. The curve for microcapsules is composed of two weight loss steps: the weight loss at 80-180 \({}^{\circ}\)C is induced by volatilizatio  
Figure 10: FT-IR spectra of PMDA-ODA, poly(MMA-_co_-DVB), and the shell wall. The shell wall sample was obtained by pressing the microcapsules between two slide glasses and drying.  
Figure 9: FE-SEM images of (**a**) _n_-hexadecane-loaded microcapsules prepared by oil-in-water Pickering emulsion templating and (**b**) the microcapsule shell wall.  
and the decomposition of shell wall occurs around 370 \({}^{\circ}\)C. From the weight loss of \(n\)-hexadecane, the encapsulation ratio of \(n\)-hexadecane is calculated to be about 86%.
## 3 Results and Discussion
### Comparison between Different Numerical Inversion Routines
Considering the same geometry as for the previous section, we computed the pipeline current \(i_{\text{pipe}}(t)\) when phase 1 of the transmission line was subject to a unity impulse voltage. The employed settings were the same as employed for the previously discussed case featuring the response to a current impulse on the same conductor, the only difference being \(\sigma_{\text{phase 1}}\
eq 0\) for the present simulation. We selected three time instants for the comparison, corresponding to an early stage of the response (\(\tau_{1}=2.63665\times 10^{-9}\) s), when the pipeline current was close to its maximum absolute value (\(\tau_{2}=4.54878\times 10^{-5}\) s) and at a latter stage of the response (\(\tau_{3}=0.194748\) s). The results of the comparison are reported in Table 2. We took the results yielded by the algorithm proposed by [PERSON] et al., and verified in the former sections, as the reference for this comparison. The pipeline current values computed with the above-described implementations of the Talbot and Euler techniques were collected for several different values of the parameter \(M\). Instead of directly reporting the computed currents, we directly computed the percentage difference with respect to the reference current values, meaning that, e.g., using the Talbot algorithm with \(M=4\) we had \(\bigg{|}\frac{i_{\text{DYAmore}}-i_{\text{Talbot}}}{i_{\text{DYAmore}}}\bigg{|} \times 100=1.60\times 10^{-2}\). For both algorithms the obtained accuracy was low or very low for values of \(M\) close to the upper or lower investigated limit. For \(M=4\), the round-off error was negligible, but from (27) only \(\approx\)2 digits of accuracy could be expected theoretically. The result was thus dominated by the truncation error. The large errors for values of \(M\) larger than the machine precision (\(\approx\)16) were instead due to round-off errors for the reasons discussed above and--in greater detail--in [53]. Not surprisingly, the most accurate results were obtained for both algorithms for \(M=8\) and \(M=16\). We note, however, that the Talbot algorithm seemed to consistently perform better than the Euler algorithm over the observed results. This was also true for the number of required function evaluations \(n\), where the Talbot strategy required \(n-1\) less evaluations for the given value of \(M\). Overall, for \(M=8\) and \(M=16\) the two techniques yield results in close or very close agreement with the reference values, for a considerably lower number of function evaluations compared to the technique by [PERSON] et al.
###### Abstract
(2020) analyzed the influence of different factors on the stability of a horizontal wellbore by establishing a multi-physics coupling model between a drilling wellbore and marine natural gas hydrate reservoir. [PERSON] et al. (2020) derived analytical solutions of temperature, seepage, and mechanical parameters during hydrate reservoir drilling by coupling multiple fields. Most existent studies took vertical wells as research objects, but few studies covered the stability of horizontal wells during drilling, failing to reveal the strong coupling effect between the wellbore and hydrate reservoir in depth. In summary, current research on the stability of hydrate wellbores is mostly focused on vertical wells, while there is relatively little research on the stability of wellbores during the drilling process of hydrate horizontal wells. Moreover, research on hydrate reservoir drilling still regards the wellbore and reservoir as two relatively independent physical models, and has not deeply revealed the strong coupling characteristics between the wellbore and hydrate reservoir. Therefore, it is necessary to establish a model coupling heat, fluid, solid, and chemical fields of the near-well zone, which takes the impact of drilling fluid loss on the reservoir into consideration in light of the drilling conditions, and to realize the analysis of wellbore stability in the drilling process by numerical simulation software-COMSOL. In this study, a model of horizontal well in the hydrate reservoir at sea during open-circuit drilling was established, and, on this basis, a comprehensive model for stability analysis was constructed, both with errors of less than 10%. This study can provide theoretical references for stability analysis of a horizontal wellbore and control of safety pressure in the wellbore during drilling at sea areas.
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 4 Experiments and Discussion  
### Experiment Setup  
To validate the effectiveness and efficiency of HMVFS, we conducted comparison experiments using the mentioned field data previously. Three strategies for utilizing multiview data with feature selection were considered, namely single-view learning, feature concatenation after selection and feature selection after concatenation. The last two are the simplest early fusion methods. Single-view learning is represented via best single view (BSV) method, through which the most informative view achieves the best performance among views. As for the dataset in this paper, contextual features are more representative than hand-crafted waveform features. Feature concatenation after selection (FSFC) employs a feature selection technique separately and concatenates features selected from different views. Feature selection after concatenation (FCFS) concatenates original feature sets of two views and then performs feature selection. Adaptable feature selection methods listed in the next subsection are applied to select discriminative features.  
The fault dataset was split into training data and testing data in a stratified fashion according to the ratio of 3:1. All samples were normalized by standard deviation after zero-mean standardization. Then, feature selection methods were used to seek the optimal feature combination using training sets and transform all samples for fault-cause classification. ML classifiers were utilized to finish the classification. In the presence of imbalanced data, criteria such as G-mean and accuracy were used to quantitatively assess classification performance. Since G-mean is a metric within biclass concepts, its microaverage was computed and adopted. The final results of each metric were calculated as the average of the 5 trials.
## 3 Results and Discussion
### Boundary and Initial Conditions  
It is assumed in the model that the concentration of the soluble polymers at the outer surfaces is always zero; indeed, they are debound (hydrated) at the beginning of the process [28]. When considering the diffusion along a cylindrical component, the equation can be solved by applying the following initial boundary conditions, i.e., a \(2L\) slab length (\(-L\) to \(L\)), with one end and its surfaces sealed (support):  
\[C=C_{o},\text{ for }0<x<L\text{ at }t=0 \tag{3}\]  
\[C=C_{l}=0,\text{ for }x=L\text{ at }t>0 \tag{4}\]  
\[\frac{\partial C}{\partial x}=0,\text{ for }x=0\text{ at }t\geq 0. \tag{5}\]  
Figure 3: Removed weight fraction of polyethylene glycol (PEG) versus the temperature in cylindrical components after debinding by supercritical carbon dioxide (CO\({}_{2}\)) under 40 MPa for four hours.
## 4 Numerical Simulation Results and Validation
### The Influence of Different Factors on TVMS and LTE  
#### 4.2.1 The Influence of Load on TVMS and LTE  
For this set of gear parameters, a working load range of 600 N\(\cdot\)m to 850 N\(\cdot\)m was selected in this paper to study the effect of load on the TVMS and LTE. The influence of load  
Figure 11: Comparison of results.  
Figure 10: Finite element model.  
on the TVMS and LTE of the modified internal helical gear is shown in Figures 12 and 13. As the load gradually increased from 600 N\(\cdot\)m to 850 N\(\cdot\)m, both the TVMS and LTE continued to rise. At the same time, it was evident that the two-tooth mesh area decreased and the three-tooth mesh area increased. The increase in TVMS can enhance the ability of gears to resist deformation. However, the increase in transmission error may result in a more serious mesh impact. In order to determine the appropriate load, a load range can be determined based on the fluctuation degree of the transmission error, thereby minimizing the impact on the gear mesh.  
In order to clarify the influence of load on the amplitude fluctuation of LTE, the load was increased from 600 N\(\cdot\)m, with an interval of 10 N\(\cdot\)m, to 850 N\(\cdot\)m. The fluctuation amplitude curve is shown in Figure 14. The LTE fluctuation value reached its minimum value when the load was 750 N\(\cdot\)m, after which it began to increase gradually. It can be  
Figure 12: Time-varying mesh stiffness of different loads.  
Figure 13: Load transmission error of different loads.  
concluded that although the TVMS increases with the increase in load, the stability of the system can be improved. However, the fluctuation of LTE is not conducive to reducing vibration noise. Therefore, in engineering practice, the appropriate working load range needs to be determined on the premise that the LTE fluctuation is small.
## 3 New Techniques Used in Detection of Forest Fire in China  
### Estimation of Sub-Pixel Fire Burned Areas and Temperature  
Many algorithms developed for detecting forest fire use a mid-infrared band (3.5-4.0 \(\upmu\)m) and a thermal band (10.5-12.5 \(\upmu\)m) [19-24]. The burning objects are separated from the background by  
Figure 5: Forest fire hot spot detection using EOS/MODIS in Northern China.  
various image processing algorithms. However, the spatial resolution of the infrared channels in the majority of the meteorological and environmental satellites is no finer than 1 km; it limits the ability to accurately quantify spatial extent of the fires, because the actual size of many burned areas can be less than 1 km\({}^{2}\), especially in the beginning of the fire occurrence. [PERSON] _et al._ developed two kind of methods for estimating sub-pixel forest fire burned areas and temperature using the multiple infrared channels from NOAA/AVHRR data [59]. One method uses both MIR band (Ch. 3, 3.7 \(\upmu\)m) and TIR band (Ch. 4, 11 \(\upmu\)m). A set of non-linear equations were established for the mixed pixels using different infrared bands, and a [PERSON]'s iteration method was used to estimate forest fire burned areas and associated temperature at sub-pixel scale. The other method uses two TIR bands (Ch. 4 and Ch. 5, 11 and 12 \(\upmu\)m), and an equation of BT increment in Ch. 4, and another equation of BT increment differences in Ch. 4 and Ch. 5 of all mixed pixels were established. Finally, by using a Look-Up Table (LUT), the fire burned areas and associated temperature can be retrieved at sub-pixel scale [59].
# WiGR: A Practical Wi-Fi-Based Gesture Recognition System with a Lightweight Few-Shot Network
## 4 Results
### The Influence of The Number of Antennas  
Since only some high-end mobile devices are tailored for multiple input, multiple output (MIMO) communication with several antennas, it is necessary to study the influence of the number of antennas on the recognition performance of the WiGR model.  
With different numbers of receiving antennas, we conducted cross-domain recognition evaluation and single-domain recognition evaluation. Specifically, the CSI data collected in Room 2 are selected as the test data in cross-environment evaluation, the CSI data performed by User 5 are selected as test data in cross-user evaluation, and the CSI data collected in Location 3 are selected as test data in cross-location evaluation. In single-domain recognition evaluation, we selected some CSI data of six gestures performed by User 1 at Location 1 of Room 1 as training data, and the remaining CSI data of each gesture as testing data. Similarly, there are three support samples provided for each gesture. From Table 8, we can see that the larger the number of receiving antennas, the better the recognition performance. This is because multiple receiving antennas can transmit richer CSI data, which helps the WiGR model recognize gestures more accurately. In addition, when only one transmitting antenna and one receiving antenna are used, the cross-domain recognition accuracy of the WiGR model can only reach 70.2-73.2%, and the single-domain recognition accuracy of the WiGR model can reach 91.3%. To a certain extent, it can still show a cross-domain recognition ability and a good single-domain recognition ability, although the effect is not as good as using MIMO.
# Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints
## 3 PL-MMSE Kalman Filter with Linear State Constraints  
Assume that the motion state is bounded by linear constraints  
\[Gx_{k}=g \tag{24}\]  
\begin{table}
\begin{tabular}{l} \hline \hline \multicolumn{2}{l}{1. Initialization} \\ \(\hat{x}_{0}=\hat{x}_{0}\) \\ \(P_{0}=E\big{[}(x_{0}-\hat{x}_{0})(x_{0}-\hat{x}_{0})^{T}\big{]}\) \\ \hline \multicolumn{2}{l}{2. State Prediction} \\ \(\hat{x}_{k|k-1}=F_{k-1}\hat{x}_{k-1}\) \\ \hline \multicolumn{2}{l}{3. Covariance Prediction} \\ \(P_{k|k-1}=F_{k-1}P_{k-1|k-1}F_{k-1}^{T}+DQ_{k}D^{T}\) \\ \hline \multicolumn{2}{l}{4. Filter Gain} \\ \(K_{1}=\sum_{j=1}^{n}\lambda_{j}e^{\frac{-j^{2}}{2}}P_{k|k-1}\left(H_{k}^{1} \right)^{T}\) \\ \(K_{2}=H_{k}P_{k|k-1}H_{k}^{T}+\sum_{i=1}^{n}\rho_{i}e^{\frac{-2\pi^{2}_{i-1}} {2}}[P_{k|k-1}(1,1)+P_{k|k-1}(2,2)]+R_{k}\) \\ \(K_{k}=K_{1}K_{2}^{-1}\) \\ \hline \multicolumn{2}{l}{5. State Update} \\ \(\hat{x}_{k}=\hat{x}_{k|k-1}+K_{k}(z_{k}-H_{k}\hat{x}_{k|k-1})\) \\ \hline \multicolumn{2}{l}{6. Covariance Update} \\ \(P_{k|k}=P_{k|k-1}-K_{k}K_{1}^{T}\) \\ \hline \multicolumn{2}{l}{7. \(k=k+1\), go to 2.} \\ \end{tabular}
\end{table}
Table 1: PL-MMSE under Gaussian mixture noise.  
where \(G\) is a known \(R^{d\times n}\) matrix while \(g\) is a known \(R^{d\times 1}\) vector with \(d<n\). It is also assumed that \(G\) has full rank. Next, we introduce two methods to encapsulate the PL-MMSE with linear state constraints.
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 7 Applications and Contributions  
### Integrated Wind Speed-Power Forecasting System (WSPFS)  
The unique aspect of our integrated WSF-WPF model lies in its holistic approach to wind energy forecasting, where the output of the wind speed forecasting model directly informs the wind power prediction model, as shown in Figure 21. This real-time data integration improves forecasting accuracy using real-time computation (RTC) to manage the wind station using informed decision-making effectively and feed smart and zero-energy cities with reliable wind electricity supply.  
WSPFS addresses power supply interruptions and associated revenue losses in manufacturing by enhancing the predictability and reliability of wind energy. The WSPFS enables more informed and precise management of wind stations. This real-time computational approach allows industrial microgrids to operate more effectively, supporting (near) zero-emission processes and combined heat and power (CHP) generation. The integration of reliable wind energy forecasts into microgrid systems ensures a stable and resilient energy supply, minimizing downtime and optimizing operational efficiency in industrial settings. On the other hand, WSPFS can significantly enhance remote off-grid and electricity microgrids by providing accurate, real-time wind energy forecasts. This capability allows for optimal utilization of wind resources, improving energy supply reliability and efficiency. By reducing dependency on external power sources and lowering operational costs, WSPFS supports the sustainability of these microgrids in isolated locations, making renewable energy a more feasible and stable option.  
Figure 21: An integrated real-time computing wind speed—power forecasting system (WSPFS) from 10 min to 36 h ahead.  
The integration of general weather forecasts into long-term wind speed and power predictions is valuable; however, the contribution of this paper lies in developing a multi-horizon forecasting framework that effectively captures both the dynamics of actual values and the intricate relationships between wind speed and power. By leveraging machine learning models, including tree-based and ensemble approaches, the system bridges the gap between short- and long-term forecasting through robust data-driven methodologies. The incorporation of over 200,000 meteorological data points, combined with the innovative use of wind speed forecasts as direct inputs for power predictions, enhances predictive accuracy across diverse time scales. While general weather forecasts offer broader insights, this study demonstrates how a data-centric approach, complemented by precise AI models and computational optimization, can yield high-resolution, location-specific predictions that address the unique challenges of wind energy systems. This dual focus ensures both practical relevance and scientific advancement.
# Sirtuin 1 and Hormonal Regulations in Aging
## 3 SIRT1, Hypothalamus, and Endocrine Regulations
### SIRT1 and the Hypothalamic-Pituitary-Adrenal Axis  
The hypothalamic-pituitary-adrenal (HPA) axis involves a complex signaling cascade that begins with the hypothalamic secretion of corticotropin-releasing hormone (CRH). CRH stimulates the pituitary gland to secrete adrenocorticropic hormone (ACTH), which in turn prompts the adrenal cortex to produce corticoids, primarily the glucocorticoid cortisol, that mediate the stress response [14; 15]. SIRT1 influences various components of the HPA axis, thereby modulating the body's response to stress and maintaining homeostasis (Figure 1B).  
SIRT1 is highly expressed in the paraventricular nucleus (PVN) of the hypothalamus, where it plays a crucial role in regulating CRH production. Studies have shown that SIRT1 enhances the expression of prohormone convertases 1 and 2 (PC1/2), which are essential for the conversion of the CRH precursor to its active form in 15 weeks old Sprague-Dawley male rats, thereby facilitating CRH secretion and maintaining the activity of the HPA axis [15]. Furthermore, SIRT1-activating molecules, such as resveratrol, increase the half-life and activity of the adrenal P450 enzyme and thus enhance cortisol synthesis  
Figure 1: SIRT1 regulations on the hypothalamus-pituitary-thyroid (**A**) [10; 11; 12; 13] and hypothalamus-pituitary–adrenal (**B**) [13; 14; 15] axes. Sirt1 with down arrow (liver) means decreased SIRT1 activity in the liver by T3 and thyroxine.  
and release in human H295R adrenocortical cells [16]. With aging, cortisol production increases, especially during morning peaks of secretion when the hypothalamus and pituitary gland become less sensitive to the negative feedback from increased plasma cortisol. For this reason, stress-induced cortisol secretion is higher in the elderly. All these factors contribute to the adrenocorticoid-dependent predisposition to hypertension, hyperglycemia, immunosuppression, and a correspondingly higher risk of malignant, cognitive, and other diseases [10].
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 2 Photoacoustic Signal Generation
### Breakdown and Bubble Collapse Shockwaves
The number of acoustic transients generated by the cavitation bubble is related to the total mechanical energy made available by the laser pulse energy. Here, three transients are generated by bubble collapse before the energy needed for additional transients is dissipated. The generated acoustic signal is also dependent on the focusing geometry. Because short and highly absorbing plasmas can be generated at large focusing angles, which allows a well-localized energy deposition at a low breakdown threshold. Large focusing angles are associated with a high conversion efficiency into mechanical energy and thus a large potential to induce higher acoustic pressure. However, the mechanical effects can be significantly reduced by decreasing the laser pulse duration [54]. In this section, we have summarized prior research work related to linear and nonlinear PA signal generation. In the next two sections, we describe some properties of linear and nonlinear PA.
## 2 Related Work of Compressed Data Gathering
It is easy to see that these sparse measurement-based CDG schemes would outperform prior dense schemes, because the formation of each measurement only involves several nodes, and the rest of the nodes can still stay in an idle/sleeping mode to reduce energy consumption.