esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Feature Extraction  
Feature extraction means finding the most important and relevant features from the ECG signal to determine the existence of OSA or not. Feature extraction methods have  
Figure 3: Filtered ECG signalbased on IIR notch filter.  
Figure 2: Proposed method.  
been widely used in ML applications [33]. In the present work, nine general features have been extracted from ECG signals, as shown in Table 3. The ECG feature extraction code for all these nine features available on [[https://www.mathworks.com/matlabcentral/fileexchange/63645-ecg-feature-extractor](https://www.mathworks.com/matlabcentral/fileexchange/63645-ecg-feature-extractor)]([https://www.mathworks.com/matlabcentral/fileexchange/63645-ecg-feature-extractor](https://www.mathworks.com/matlabcentral/fileexchange/63645-ecg-feature-extractor)), accessed on 8 July 2021. The window size used in this work is one minute. From the experiment, we found that these extracted features are the most valuable ones that can reduce the data's high dimension and improve the ML classifiers' overall performance.  
\[AvgHR=\frac{\sum_{w=1}^{N}n_{r_{w}}\times 60\ seconds}{t_{w}} \tag{1}\]  
\[meanRR=\frac{\sum_{r=1}^{n_{r}}d_{r+1}-d_{r}}{n_{r}} \tag{2}\]  
\[RMSSD=\sqrt{\frac{(d_{rr})^{2}}{n_{r-1}}} \tag{3}\]  
\[NN50=\forall(n_{r})(NN50++)\leftarrow\sum_{r=1}^{n_{r}}d_{r+1}-d_{r}>50\ millisecond \tag{4}\]  
\[pNN50=(\forall(n_{r})(NN50++)\leftarrow\sum_{r=1}^{n_{r}}d_{r+1}-d_{r}>50\ millisecond)\times 100 \tag{5}\]  
\[SD\_RR=\sqrt{\sum_{r-1}^{n_{r}-1}(d_{rr}-meanRR)^{2}} \tag{6}\]  
\[SD\_HR=\sqrt{\sum_{rr-1}^{n_{r}-1}(\alpha_{rr}-avgHR)^{2}} \tag{7}\]  
\[PSE=-\sum_{f=-\frac{f_{k}}{2}}^{+\frac{f_{k}}{2}}PSD_{n}(f)\times log_{2}[PSD_{n}(f)] \tag{8}\]  
\[average\_hrv=average(AvgHR) \tag{9}\]  
where:  
* \(N\) = Number of windows.
* \(t_{w}\) = Sampled time for each window.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Feature** & **Description** \\ \hline \(f_{1}\) & Average Heart Rate (AvgHR)- Equation (1) \\ \hline \(f_{2}\) & mean R-R interval distance (meanRR)- Equation (2) \\ \hline \(f_{3}\) & Root Mean Square Distance of Successive R-R interval (RMSSD)- Equation (3) \\ \hline \(f_{4}\) & Number of R peaks in ECG that differ more than 50 millisecond (NN50)- Equation (4) \\ \hline \(f_{5}\) & percentage NN50 (pNN50)- Equation (5) \\ \hline \(f_{6}\) & Standard Deviation of R-R series (SD\_RR)- Equation (6) \\ \hline \(f_{7}\) & Standard Deviation of Heart Rate (SD\_HR)- Equation (7) \\ \hline \(f_{8}\) & Power Spectral Entropy (PSE)- Equation (8) \\ \hline \(f_{9}\) & Average Heart Rate Variability (average\_hrv)- Equation (9) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of features extracted from each signal.  
* \(n_{rw}=\) Number of R peaks in each window.
* \(\alpha_{rr}=\) the heart rate at R-R peak location.
* \(d_{rr}=\sum_{r=1}^{n_{tr}-1}(d_{r+1}-d_{r})\)
* PSD = Power Spectral Density
* PSD_n(f) = \(\frac{PSD(f)}{\Sigma_{f--\frac{f_{R}}{2}}^{+\frac{f_{S}}{2}} PSD(f)}\)
## 4 Challenges
### Economic Aspects  
The precondition of the successful commercialization of UWCGES depends on economics. Many theoretical studies support the potential acceptable economy of UWCGES technology [51; 103; 104; 105; 106]. At the significant stage of the energy transition, the utilization of marine renewable energy is accelerating. By integrating UWCGES technology, the contradiction between the rapid growth of marine renewable energy power generation and the slow speed of power grid construction can be effectively solved, which is conducive to solving the consumption of marine renewable energy and improving the utilization rate of marine renewable energy. Marine renewable energy power generation is an integrated system integrating UWCGES technology, which helps to accelerate the reduction in the price of marine renewable energy power. Furthermore, it can also help the development of marine renewable energy and UWCGES technology to achieve sustainable development in the economy, society, people, and nature. However, when commercial companies disclose the economy of their UWCGES technology, they lack transparency in specific details. It could be inferred the technical and economic indicators disclosed by commercial companies are the results obtained under relatively ideal conditions. For example, the deployment site is always ideally deep-water or ultra-deep-water, and the overall efficiency of the system is always very high. In fact, like the onshore systems, the technical economy of UWCGES mainly depends on the specific application mode, technical scheme, energy storage scale, gas storage depth, and distance from the application site. In particular, the UWCGES is generally limited by the local water depth conditions. Specific and detailed technical and economic analysis is needed for specific applications.
###### Abstract  
Addressing the limitations of restricted coding capacity and material dependency in acoustic identity tags for autonomous underwater vehicles (AUVs), this study introduces a novel passive acoustic identification tag (AID) design based on multilayered elastic cylindrical shells. By developing a Normal Mode Series (NMS) analytical model and validating it through finite element method (FEM) simulations, the work elucidates how material layering strategies regulate far-field target strength (TS) and establishes a time-domain multi-peak echo-based encoding framework. Results demonstrate that optimizing material impedance contrasts achieves 99% detection success at a 3 dB signal-to-noise ratio. Jaccard similarity analysis of 3570 material combinations reveals a system-wide average recognition error rate of 0.41%, confirming robust encoding reliability. The solution enables the combinatorial expansion of coding capacity with structural layers, yielding 210, 840, and 2520 unique codes for three-, four-, and five-layer configurations, respectively. These findings validate a scalable, hull-integrated acoustic identification system that overcomes material constraints while providing high-capacity encoding for compact AUVs, significantly advancing underwater acoustic tagging technologies through physics-driven design and systematic performance validation.  
Autonomous Underwater Vehicles (AUVs); passive acoustic identification tags; multilayered elastic cylindrical shells; Normal Mode Series (NMS); Finite Element Method (FEM); Target Strength (TS); jaccard similarity analysis; encoding capacity scaling +
Footnote †: journal: Applied Sciences
## 4 The Reconfiguration-Dependency Non-Consistent Algorithm Based on Simulated Annealing (RDNC-SA)
### Scheduling Length Calculation  
As mentioned, SL is a important parameter when measuring the feasible solution quality. The smaller SL means the better solution; otherwise, the solution has worse quality.  
When the obtained solution is a feasible solution, the scheduling length needs to be calculated to evaluate the solution. We present pseudo code to calculate the length of the scheduling, as shown in Algorithm 1.  
**O** and **PR** have decided the entire solution, we need to calculate the scheduling length according to the reconfiguration order and the mapping relationship between tasks and regions. For each reconfiguration node, it is necessary to calculate \(Rs\) and \(Es\), and then calculate \(Re\) and \(Ee\) according to expression (2) and (3). When calculating the execution start time, if the parent task of a task of the current reconfiguration node has not yet started or has not been completed, this task needs to be pushed into the waiting queue **Q**. In the next step, the algorithm is going to continue to calculate the start time of the other nodes. When the parent tasks of the waiting task are all executed, they can start to execute the waiting task. The reconfiguration start time of the first reconfiguration node is set to 0, then formula (9) is simplified to \(SL=max(Ee_{i})\), that is, the scheduling length is the execution end time of the last reconfiguration node.  
```
1:Reset each task \(Rs\),\(Re\),\(Es\),\(Ee\).
2:for each \(RN_{i}\in\textbf{O}\)do
3:for each \(n_{j}\in RN_{i}\)do
4: compute \(Rs\) and \(Re\) of \(n_{j}\).
5:if all \(pn\) of \(n_{j}\) has accomplished execution phase do
6: compute \(Es\) and \(Ee\) of \(n_{j}\).
7:else
8: push \(n_{j}\) into the waiting queue **Q**.
9:endif
10:endfor
11:for each \(n_{j}\in\textbf{Q}\)do
12: Repeat the steps in lines 5 and 6
13:endif
14:endfor
15:endfor
16: Assign the maximum execution end time \(Ee_{j}\) to SL.
17:return SL
```  
**Algorithm 1** Calculate scheduling length  
Figure 6: Infeasible solution.
## 4 Simulation and Analysis
### Analysis of Computational Complexity  
As mentioned above, the calculation of the LPSA algorithm is mainly determined by the number of orthogonal sampling points and the sparsity of the input signal, assuming that the input signal has a bandwidth of \(B\) and a sparsity of \(\eta_{s}\). Consider a two-stage frequency resolution (\(\Delta f_{1}\sim\Delta f_{2}\)) in the LPSA algorithm. For the first stage, the channel number under the frequency resolution of \(\Delta f_{1}\) is \(N_{1}=\frac{B}{\Delta f_{1}}\), and the number of the orthogonal sampling points \(n_{1}\) can also be obtained from the derived relationship between the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Classical Time-Domain Pulse Compression** & **Linear Phase Sampling and Accumulation** \\ \hline sampling rate & \textgreater{}2 \(\text{GHz}\) & 8 \(\text{GHz}\) \\ amplitude accuracy & \textgreater{}99\% & \textgreater{}98\% \\ frequency resolution & 101.6 \(\text{kHz}\) & 99.8 \(\text{kHz}\) \\ multiplication & \(8\times 10^{8}\) & 633 \\ addition & \(8\times 10^{8}\) & \(5.606\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classical time-domain pulse compression method versus the LPSA method.  
Figure 7: The measured amplitude accuracy with different number of orthogonal sampling points at different sampling rates.  
compression time and frequency resolution in Section 3. So, the computational complexity of additions and multiplications in this stage is \(o(2n_{1}N_{1})\) and \(o(N_{1})\), individually. After the process of the first stage, the effective channel number was \(\eta_{s}N_{1}\). For the second stage, there were m (\(m=\frac{\Delta f_{1}}{\Delta\eta_{2}}\)) subdividable channels. Similarly, the number of the orthogonal sampling points \(n_{2}\) under the second-stage frequency resolution could also be obtained. Thus, the computational complexity of the addition and multiplication were \(o(2 mn_{2}\eta_{s}N_{1})\) and \(o(m\eta_{s}N_{1})\). So, the total computational complexity for the LPSA algorithm were \(o(2n_{1}N_{1})+o(2 mn_{2}\eta_{s}N_{1})\) and \(o(N_{1})+o(m\eta_{s}N_{1})\). It can be seen that the main calculation was focused on addition. The number of orthogonal sampling points and the sparsity of the input signal were the main factors influencing the calculation.  
Usually, in the time-domain pulse compression method, the sampling rate is larger than double the band of the input signal. This indicates that the sampling number used in the time-domain pulse compression method is much larger than the orthogonal sampling points used in the LPSA algorithm. The computational complexity in the time-domain pulse compression method is \(o\left(n^{2}\right)\). So, compared to the classical time-domain pulse compression method, the proposed LPSA method can save a lot of computation, especially for the measurement of sparse signals with low dynamic range.
## 2 Data Analysis and Problem Statement
### Problem Statement  
Typically, the EOL model of the Li-ion battery is closely related to that of a battery capacity [26]; the specific Li-ion battery remaining capacity model can be acquired in the literature [27]. This model gives all parameters (except some constant coefficients) by various experimental curve fittings. Despite knowledge of the complicated Li-ion mechanisms, the state of health (SOH) can be defined as:  
\[SOH(\mathbf{\tau})=\frac{C(\mathbf{\tau})}{C(0)} \tag{1}\]  
where \(C(0)\) is the capacity value at the initial stage of Li-ion battery, and \(C(\mathbf{\tau})\) is the capacity value at time \(\mathbf{\tau}\) (it is usually the index of cycle number).  
Figure 2 gives the battery capacity changing curve according to the measured capacity of the discharge process. The curve clearly presents the degradation trend of a Li-ion battery. In the literature [28], Li-ion is regarded as being at EOL when its capacity drops to 70% (1.38 Ah) of its initial value. For satellite Li-ion batteries, it is inconvenient or even impossible to measure battery capacity values during in-orbit service. The main reason is that battery capacity measurements require the performance of a full charge and discharge cycle [29]. Hence, the problem is: how can we use basic measurements to construct a novel HI which can replace the capacity, and realize a dynamic RUL prognosis of Li-ion batteries used in satellites.  
Figure 1: Charge and discharge process of Li-ion battery in one cycle [11].  
Figure 2: Capacity changing curve during the whole Li-ion battery life.
## 3 Result and Discussion
### Frost Heave, Thawing Settlement, and Change of Moisture Content in Model Test  
During the test, the surface displacement is monitored, and the frost heave and thaw settlement at different distances from the upstream box wall are shown in Figure 9.  
The colored solid lines in the figure above show the surface displacement recorded by dial indicators at different distances from the upstream box wall. The dotted line on the left is the time when the pump is turned off, and the dotted line on the right is the time when the soil in the box is completely melted (the temperature of any part of the model inward is >0 \({}^{\circ}\)C).  
Frost heave develops with the beginning of freezing, but after freezing stopped, the area of the frozen area did not decrease, and water in the unfrozen area still migrated to the frozen area and changed into ice. After the pump is shut down for about 15 h, the surface heave reaches max, at 8 mm. Then the melting settlement increased to 40 mm,  
Figure 8: Temperature distribution nephogram during the melting stage (**x** stand for the horizontal position and **y** stand for the distance from the freezing center).  
which was much larger than the frost heave. Combined with the cloud diagram analysis, it can be concluded that when there is an area with temperature >0 \({}^{\circ}\)C in the frozen clay, the settlement begins to develop rapidly. When the soil in the box is completely melted (the temperature at each point in the box is >0 \({}^{\circ}\)C), the settlement tends to be stable in a short time.  
The thaw settlement develops first at 11 cm and the settlement reaches stability first, indicating that the area near the upstream melts first and completes the consolidation first under the action of seepage. That also confirms the temperature development trend in the temperature cloud map, the upstream melts first and gradually develops to the downstream area.  
Before and after the test, some soil samples were extracted to determine the moisture content. The test results are shown in Figure 10.  
From the water content distribution before and after freezing and thawing, the water content of each soil layer during filling is near the set value of 48%, mainly distributed in the range of 45-50%, which proves that the soil quality of remolded soil is relatively uniform. After the freeze-thaw cycle, the moisture content of the soil layer far from the  
Figure 10: Change of water content.  
Figure 9: Frost heaving and thawing settlement.  
freezing pipe changes little compared with that of the filling soil. The water content in the frozen area is low, and the lowest water content is only 30%, indicating that a large amount of liquid water in the frozen area is discharged in the melting stage, which is also the cause of melting settlement. Compared with other areas within the freezing range, the water content in the freezing pipe is higher, at 39%. The same trend was also observed in the dry density distribution shown in Figure 11. The dry density increases in the frozen area but decreases in the freezing pipe area.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 10 Numerical Simulations and Results
### Comparison of Algorithms with Low Measurement Accuracy and Low Data Rate  
In previous cases, realistic measurement SDs and measurement time intervals were used. As a result, the state estimation accuracies of the various IMM filters are nearly the same. In order to evaluate the performance of the IMM filters with high SDs and measurement time intervals, the accuracy of state estimation among the three IMM filters is quite similar. In order to evaluate the performance of the various IMM filters with high SDs and measurement time intervals, we used \(\sigma_{r}=50\) m, \(\sigma_{\beta}=5^{\circ}\), \(\sigma_{\text{by}}=5\) m/s, and \(T=2.0\) s. Table 9 presents the measurement time indices of various dynamic models. The RTAMS position and velocity errors for the NCT- and last NCV segment are presented in Table 10. The results in Table 10 show that the IMM-CKF and IMM-EKF have the best and the worst performances, respectively, and the performance of the IMM-UKF is close to that of the IMM-CKF. The RMS position and velocity errors are presented in Figure 15. Figure 16a,b show the ANEES and \(\xi_{k}=\text{Trace(Cov)/Trace(MSEM)}\). The above findings indicate that the IMM-UKF and IMM-CKF have better covariance consistency than the IMM-EKF.  
We kept all the parameters the same and used \(\sigma_{\beta}=1^{\circ}\) to examine the RTAMS errors for both position and velocity in the IMM filters, thereby highlighting their performance variations. Table 11 presents the resulting RTAMS position and velocity errors. The results in Tables 10 and 11 show that as the measurement accuracy increases, the performance of the IMM-EKF approaches those of the IMM-UKF or IMM-CKF.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Segment** & **RTAMS Error** & **IMM-EKF** & **IMM-UKF** & **IMM-CKF** & **PCRLB** \\ \hline \multirow{3}{*}{NCT-} & Position (m) & 146.341 & 127.031 & 126.935 & 47.459 \\ \cline{2-6}  & Velocity (m/s) & 6.376 & 3.572 & 3.568 & 1.105 \\ \hline \multirow{3}{*}{Last NCV} & Position (m) & 170.592 & 122.847 & 122.193 & 54.900 \\ \cline{2-6}  & Velocity (m/s) & 2.761 & 2.328 & 2.327 & 2.012 \\ \hline \hline \end{tabular}
\end{table}
Table 10: RTAMS errors with \(\sigma_{r}=50\) m, \(\sigma_{\beta}=5^{\circ}\), \(\sigma_{v_{r}}=5\) m/s, and \(T=2.0\) s.
## 5 Practical Applications in Cannabis Cultivation
### Integration with Other Elicitors or Stressors  
The idea of combining MeJA with other plant elicitors or mild environmental stresses represents a promising frontier in cannabis cultivation. Research has demonstrated that chitosan, for instance, can enhance specific defense pathways in a manner complementary to MeJA, thereby potentially amplifying secondary metabolite production [51]. Such combinations are also used in other medicinal plants, where pairing distinct elicitors leads to synergistic gains in valuable phytochemicals. In the case of cannabis, growers may experiment with combining moderate MeJA treatments alongside moderate water-deficit stress or low-intensity UV-B exposure to encourage increased trichome development without excessively harming yield [63]. This approach does, however, require precise monitoring, since even mild water-deficit stress or excessive UV could stress cannabis to the point of diminishing returns, even considering that cannabis presents evident plasticity traits. As a result, small-scale pilot studies are recommended to fine-tune methods before applying them across larger commercial plots.  
Despite the potential benefits of multifactorial elicitation, growers should remain cautious about unintended interactions. For example, SA may antagonize certain JA- or MeJA-driven pathways. Simultaneously elevating SA and JA responses can disrupt metabolic allocation, reducing efficiency for the desired type of defense or secondary metabolite accumulation. While future research may clarify how best to stagger SA and MeJA treatments, blindly co-applying multiple elicitors can inadvertently undermine gains in cannabinoid yield or cause physiological imbalances. Another concern is cost-effectiveness; if products like chitosan or certain micronutrient formulations are expensive, measuring their impact on the final cannabinoid output is crucial for assessing a favorable cost-benefit ratio. Ultimately, combining MeJA with other elicitors or mild environmental stressors exemplifies the growing complexity of cannabis in agronomy/horticulture systems, underscoring the need for integrative studies that draw on genetics, multi-omics analyses, and field-scale trials.  
By employing MeJA strategically--whether in tightly controlled indoor settings or under field conditions--cannabis cultivators can target improved trichome formation, elevated cannabinoid profiles, and potentially stronger defenses against pests and diseases. As research expands, standardized protocols for optimal doses, application timings, and compatible elicitors will likely emerge. This knowledge will enable the cannabis industry to refine cultivation methods that boost product quality while maintaining desirable growth characteristics.
# Wind Pressure Coefficient on a Multi-Storey Building with External Shading Louvers
## 3 CFD Simulation
### Reference Case
#### 3.1.2 Turbulence Model  
To investigate the airflow fields around buildings through CFD technology, the two-equation Reynolds-averaged Navier-Stokes models (RANS) were frequently applied as these models have a good balance between accuracy and computational consumption [46; 47]. The most widely validated RANS model in urban environment is \(k\)-\(\varepsilon\) turbulence model [48]. For \(k\)-\(\varepsilon\) models, it is not necessary to investigate the turbulent fluctuations thoroughly when simulation is performed for wind characteristics [17; 44]. The Reynolds-averaged and time-averaged properties of airflow have sufficient accuracy to perform the simulation around shaded buildings. One of the most commonly used RANS models for urban climates is the standard \(k\)-\(\varepsilon\) model [49]. In addition, some modified \(k\)-\(\varepsilon\) models, like RNG \(k\)-\(\varepsilon\) model and realizable \(k\)-\(\varepsilon\) model, can provide more accurate data for some situations because of their better reproduction of swirling flows [44]. The realizable \(k\)-\(\varepsilon\) model was used in the reference case according to the simulation performed by [PERSON] et al. [44]. Some other CFD studies of wind pressure coefficient on building surfaces show that \(k\)-\(\varepsilon\) models, especially realizable \(k\)-\(\varepsilon\) model, have good performances at various angles of attack on buildings. For example, [PERSON] and [PERSON] investigated the wind pressure coefficient on a building with balconies by using five turbulence models [17]. The results show that the wind pressure coefficient, obtained from the realizable \(k\)-\(\varepsilon\) model, has good agreement with experimental data. [PERSON] et al. [50] investigated wind pressure coefficient on a high-rise building at different angles of attack and by using five turbulence models. The results show that realizable \(k\)-\(\varepsilon\) model has good performances at angle of attack from \(0^{\circ}\) to \(90^{\circ}\).  
Figure 5: Flowchart for investigating airflow fields of the shaded building.