esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling and Research Methods
### Robustness  
The concept of network robustness is measured by the capability of a given network to continue to be connected, despite the failure or attack of a vertex [30]. Two main factors contributing to this problem are network attack strategies and the measurement of robustness.
## 2 Materials and Methods
### Probability of Cell Death Due to EI  
The probability of cell death by EI during IRE was calculated using the Peleg-Fermi model, as follows [14]:  
\[S=\frac{1}{1+e^{\frac{E-E_{c}(n)}{A(n)}}} \tag{4}\]  
where \(S\) is the proportion of surviving cells after IRE and \(E\) is the electric field strength. \(E_{c}(n)\) and \(A(n)\) are two functions that depend on the number of pulses:  
\[E_{c}(n)=E_{0}\exp\left(-k_{1}n\right) \tag{5}\]  
\[A(n)=A_{0}\exp\left(-k_{2}n\right) \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Musculars** & **Submucosal** & **Mucosal** & **Electrode** \\  & **Propria** & **Layer** & **Layer** & **Electrode** \\ \hline Electrical conductivity (S/m) & 0.202 & 0.251 & 0.511 & \(4.5\times 10^{7}\) \\ \(A\) & 1.5 & 1.8 & 1.8 & - \\ \(E_{delta}\) (V/cm) & 500 & 600 & 600 & - \\ \(E_{range}\) (V/cm) & 300 & 150 & 150 & - \\ Density (kg/m\({}^{3}\)) & 1090 & 1027 & 1088 & 19,300 \\ Specific heat capacity (J/kg K) & 3421 & 2372 & 3690 & 129 \\ Thermal conductivity & 0.49 & 0.39 & 0.53 & 317 \\ References & [14; 31; 32; 33; 34] & [14; 33; 31; 34] & [14; 32; 33; 31; 33; 34] & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrical and thermal properties of the gastrointestinal tract and electrode.  
where \(E_{0}\), \(A_{0}\), \(k_{1}\), and \(k_{2}\) are the regression coefficients that are related to the pulse duration and cell type, having values of 3996 V/cm, 1441 V/cm, 0.03, and 0.06, respectively, as described in the literature [35]. The probability of cell death due to EI can be calculated as follows:  
\[Probability\ of\ EI\ (PEI)=1-S. \tag{7}\]  
Thus, a certain region where the PEI is greater than 0.90 is determined to be treated with IRE.
## 6 Calculation Method of Tunnel Face Support Pressure Based on Seepage Control
### Comparison Considering Seepage Control  
The impact of considering seepage control is demonstrated using a specific engineering project as an example, where calculations are compared based on whether seepage control is considered or not. This project traverses fine sand, medium sand, and coarse sand layers. For simplification, focus is placed on the cross-section passing through the coarse sand layer. The geological and construction parameters for this cross-section are \(c\) = 0 kPa, \(\varphi\) = 36\({}^{\circ}\), \(D\) = 6 m, \(C\)/\(D\) = 2.8, \(\gamma_{sat}\) = 20.4 kN/m\({}^{3}\), \(k\) = 80 m/d, and \(h_{w}\) = 11.3 m. Calculations are compared using methods where the tunnel face support pressure is controlled based on seepage and where the tunnel face water pressure is assumed to be zero. The seepage-controlled method employs a standard of 2.0 m\({}^{3}\)/h/m\({}^{2}\) for unit area seepage. Results are presented in Table 2 where the safety factor is defined as the ratio of the actual support pressure used to the calculated support pressure. It is observed that significant differences in calculated outcomes arise based on whether seepage control is considered or not. In this project, a support pressure of 209 kPa was utilized, ensuring smooth progress in excavation. Had seepage control been considered, the safety factor would have been 1.4, whereas assuming zero water pressure at the tunnel face would have necessitated a safety factor of 5.4. Considering typical safety factors employed in engineering design underscores the necessity of designing tunnel face support pressures based on seepage control.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Support Pressure/kPa** & **Safety Factor** \\ \hline The actual support pressure used & 209 & - \\ Considering seepage control & 149.8 (\(\eta\) = 0.96) & 1.4 \\ Not considering seepage control & 38.8 (\(\eta\) = 0) & 5.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of calculation results.
# Stochastic Predictive Energy Management of Multi-Microgrid Systems
## 4 Two-Stage Stochastic Model Predictive Control Formulation  
In this section, the operation management problem of IMGs is formulated in the framework of two-stage stochastic MPC with recourse. In MPC, based on a dynamical model of the system under consideration, an optimal control sequence is derived over the desired control horizon. Intrinsic robust characteristics of MPC due to the rolling horizon strategy as well as its capability to account for operational and time-coupling constraints, make it a consistent solution strategy for power system applications.  
Based on the two-stage stochastic approach that has been outlined in the previous section, the operation management problem of MGs can be represented as follows:\[\begin{split}\min_{\begin{subarray}{c}P_{b_{i}}(t_{r,m,l}+H_{u}-1),\\ \gamma_{ij}^{+}\gamma_{ij}^{-}\gamma_{ij}^{-}(t_{r,m,l}+H_{p}-1)\end{subarray}} \sum_{k=0}^{H_{u}-1}c_{i}P_{b,i}(t+k)+\\ \sum_{k=0}^{H_{p}-1}\sum_{j=1}^{S}\pi_{i,j}(q_{i}^{+}\gamma_{ij}^{+}(t+k)+q_ {i}^{-}\gamma_{i,j}^{-}(t+k)),\end{split} \tag{22}\]  
\[s.t.\quad SOC_{i}(t+k+1)=SOC_{i}(t+k)+\frac{P_{b,i}(t+k)}{E_{b,i}}\Delta, \tag{23}\]  
\[P_{b,i}^{min}\leq P_{b,i}(t+k)\leq P_{b,i}^{max}, \tag{24}\]  
\[SOC_{i}^{min}\leq SOC_{i}(t+k)\leq SOC_{i}^{max}, \tag{25}\]  
\[\delta_{mis,i,j}(t+k)\leq\gamma_{ij}^{+}(t+k), \tag{26}\]  
\[-\delta_{mis,i,j}(t+k)\leq\gamma_{ij}^{-}(t+k), \tag{27}\]  
\[\gamma_{ij}^{+}(t+k),\gamma_{ij}^{-}(t+k)\geq 0, \tag{28}\]  
\[k=0,...,H_{p},\ j=1,...,S.\]  
In (22)-(28), \(H_{P}\) and \(H_{u}\) refer to the prediction and control horizons, respectively. In addition, \(\pi_{i,j}\) represents the occurrence probability of the \(j\)th scenario in the \(i\)th MG and \(\gamma_{ij}^{-}(t+k)\) and \(\gamma_{ij}^{+}(t+k)\) are the recourse actions to take if the \(j\)th scenario occurs in the \(i\)th MG at time instant \(t+k\) while staying in \(t\). Furthermore, \(\delta_{mis,i,j}(t+k)\) is the possible scenario of the uncertain parameter \(\delta_{mis}\) at the time-step \(t+k\) in the \(j\)th scenario and the \(i\)th MG.  
At 60-min time intervals, the LEMS of each MG measures the level of stored energy in the battery and solves the optimization problem (22)-(28) to obtain the optimal control sequence \(P_{b,i}(t,...,t+H_{u}-1)\) and recourse variables \(\gamma_{ij}(t,...,t+H_{p}-1)\). The first sample of the optimal control sequence is applied to the local controllers and the control and prediction horizons are shifted. Consequently, at each time step there are \(H_{u}+2\times S\times H_{p}\) decision variables and \(2\times(H_{u}+(2S+1)H_{p})\) constraints for each MG.
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
## 5 Experimental Results
### Reference IMU  
To better appreciate achieved performance, a tactical-grade inertial sensor has been adopted as benchmark. In particular, a MEMS sensor was used, STIM300 by SensoNor(tm), which includes a high-performance triaxial gyroscope and triaxial accelerometer. According to what is described in the datasheet [37], the sensors assure bias instability and angular random walk equal, respectively to \(0.3\,^{\circ}/\mathrm{h}\) and \(0.15\,^{\circ}/\sqrt{(\mathrm{h})}\) for angular rate measurements; as regards accelerations, bias instability and speed random walk values are equal to \(0.5\) mm/s\({}^{2}\) and \(0.07\) m/s/\(\sqrt{(\mathrm{h})}\). It is worth noting that the inertial measures provided by the STIM300 are already compensated by an internal processing routine; nevertheless, the STIM300 output are processed by means of the integration algorithm based on Kalman filter.
## 2 Exosome Features
### Exosome Formation  
According to their size, exosomes can be classified as large (90-120 nm) and small (30-80 nm) exosomes [12]. Exosomes are produced by all eukaryotic cells, mainly through intracellular multivesicular bodies [13; 14], which subsequently become ILVs in which the exosomes are encapsulated and subsequently released. ESCRT and soluble NSF attachment protein receptor (SNARE) functions are crucial for the mechanism of exosome formation [15; 16]. ESCRT-0, I, II, and play a critical role in the invagination and scission of the membrane, facilitating cargo sorting and sequestration necessary for endosomal trafficking and the formation of exosomes. In contrast, SNARE proteins, such as T-SNAREs like syntax-4 and V-SNAREs like VAMP, facilitate the efficient release of exosomes into the extracellular space from multivesicular bodies [17]. These proteins mediate membrane fusion, ensuring that the vesicles are successfully transported and released from the endosomal compartment [7].  
The exosome membrane is composed of a lipid layer comprising multiple forms of lipids, mainly cholesterol, sphingomyelin, and ceramides. It also contains numerous proteins that contribute to their structure [18]. These include antigen-presenting molecules, glycoproteins, adhesion molecules, and signaling receptors, which are essential for cellular communication [19].
# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
## 5 Conclusions  
In this paper, a fault detection and isolation scheme based on SAE was developed and applied in an industrial multivariate monitoring data set. Robust Mahalanobis distance was applied to compose multiple variables into one system-wide health indicator. The two-dimensional contribution map was calculated to help isolate variables that are responsible for the fault. In summary, the contribution of this paper includes: (1) The application of a state-of-the-art unsupervised fault detection model, SAE, on industrial multivariate process data, with each step, data pre-processing, parameters setting, and model evaluation explained in detail. Our experience with processing the industrial data set can benefit relevant readers. (2) The proposed fault detection scheme based on SAE and Mahalanobis distance has been shown to be more effective in detecting faults than the traditional PCA method, with a lower false alarm rate, higher fault detection rate and higher AUC values, and therefore offers a good alternative solution for complicated industrial process systems that experience high false alarm rates. (3) The proposed fault detection and isolation scheme can detect faults at its incipient stage and have accurate performance in fault isolation, which can assist operators and data analysts to infer the fault cause, making it easier for maintenance planning. This scheme can also be applied in the fault detection and isolation of other process machines, such as compressors and turbines.  
Conceptualization, X.L. and F.D.; investigation and methodology, X.L. and F.D.; data analysis, X.L.; validation, F.D., and D.M.; writing--original draft preparation, X.L.; writing--review and editing, F.D., D.M. and I.B.; funding acquisition, D.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
###### Abstract  
System-of-systems (SoS) architecture is crucial in managing complex and interconnected systems. However, the description and modeling of SoS architecture pose significant challenges and require a structured and organized approach. In this study, a metamodel for SoS architecture that considers both structural and behavioral perspectives is defined. The metamodel is then mapped to ontologies that are enhanced with a flow-based extension to characterize architecture views. On this basis, an SoS capability ontology (SoSCO) and an SoS operational ontology (SoSO) are built with factors, relationships, and flows. A four-step architecture process for developing capabilities and a five-step architecture process for operational modeling are provided based on the ontologies. The proposed approach is applied in a search and rescue case study, demonstrating its ability to improve operability in the early design stage. The process is implemented using the Unified Architecture Framework (UAF) so that various stakeholders and engineers can better understand and develop an SoS.  
system of systems engineering; ontologies; model-based system engineering; architecture modeling; UAF +
Footnote †: journal: Applied Sciences
## 5 DRR Correction Based on a Regression Analysis  
At near distances around the listener's head, the DRRs tend to diverge between a real sound source and the proposed method. To address this, the proposed method applies DRR correction for near distances around the listener's head.  
First, the room impulse responses are measured at 0.1 m, 0.2 m, and 0.3 m as near distances around the listener's head. The real DRRs are then calculated using these room impulse responses as follows:  
\[\text{rDRR}_{r}=\frac{\sum_{n=0}^{T_{\text{D}}\text{F}_{s}-1}\delta_{T}^{2}(n )}{\sum_{n=T_{\text{D}}\text{F}_{s}}^{T_{\text{M}}\text{F}_{s}-1}\delta_{T}^{2 }(n)}, \tag{11}\]  
where \(r\) (0.1 m, 0.2 m, and 0.3 m) is the distance between the listener's head and the real sound source, \(g_{r}(n)\) is the room impulse response at distance \(r\), \(T_{\text{D}}\) (=7 ms) is the signal length of the direct wave, \(T_{\text{M}}\) is the signal length of the room impulse response, and \(F_{s}\) is the sampling frequency. In the direct wave extraction of the conventional method [29], \(T_{\text{D}}\) was set to approximately 4 ms. However, when the impulse response was actually measured and the direct wave was extracted, 4 ms was found to be too short for direct wave extraction. Therefore, \(T_{\text{D}}\) was adjusted to 7 ms, which was the duration until the direct waveform became sufficiently small.  
DRRs are calculated at near distances using the proposed method as follows:  
\[\mathrm{pDRR}_{r,\xi_{r}}=\frac{\sum_{n=0}^{T_{0}F_{s}-1}(x_{r}(n)+\xi_{r}\cdot y _{r}(n))^{2}}{\sum_{n=T_{0}F_{s}}^{T_{0}F_{s}-1}(x_{r}(n)+\xi_{r}\cdot y_{r}(n))^ {2}}, \tag{12}\]  
where \(\xi_{r}\) (initially \(\xi_{r}=1.0\)) is the weighting function for DRR correction, \(x_{r}(n)\) is the RIR for the loudspeaker's output at distance \(r\), and \(y_{r}(n)\) is the RIR for the PAL's output at distance \(r\).  
At certain distances, the weighting coefficients are calculated as follows:  
\[\hat{\xi}_{r}=\arg\min_{\xi_{r}}|\mathrm{rDRR}_{r}-\mathrm{pDRR}_{r,\xi_{r}}| \tag{13}\]  
The weighting function \(\xi_{r}\) for DRR correction is calculated using the regression line for \(\hat{\xi}\) as follows:  
\[\xi_{r} = \alpha r+\beta, \tag{14}\]  
where \(\alpha\) and \(\beta\) are calculated using \(\hat{\xi}_{r}\). For 0 m \(\leq r\leq 0.3\) m, \(\xi_{r}\) is calculated using Equation (14). On the other hand, for 0.3 m \(<r\), \(\hat{\xi}_{r}=1.0\).
## 4 Discussion
### Potential of Landsat-8 and Sentinel-2 Imagery to Identify Cropping Intensity  
The combination of Landsat-8 and Sentinel-2 has demonstrated significant potential for mapping cropping intensity. Globally, the median revisit interval of their combination is 2.9 days [18]. For Shaanxi Province, our results showed an even shorter interval of 2.4 days. After eliminating cloud-affected, low-quality data, the fusion still offers frequent observations at a 3.9-day interval. This short revisit cycle allows for the creation of dense time series, enabling the capture of seasonal variability and the assessment of cropping intensity. Additionally, the high spatial resolution enhances the ability to monitor fragmented cropland, which is common in Shaanxi and similar agricultural regions, as compared to previous studies [11; 12]. The long historical archive of Landsat-8 and Sentinel-2 also supports continuous temporal analysis, facilitating the identification of cropland abandonment. The combination delivers the data needed for high-accuracy and consistent cropping intensity mapping, ultimately supporting more effective cropland management strategies.
## 3 Experimental Results
### Battery Tests
#### 3.1.1 Battery Individual Cell Tests  
As per the battery product specification datasheet [76], standard charge and discharge procedure were carried out to the battery individual cells to check their condition one month after delivery and to confirm the capacity stated by the manufacturer.  
The battery individual cells were charged to 4.2 V with 8 A (C0.5), from constant current to constant voltage, Figures 15 and 16, interval 0 \(<\) time \(<\) 1700 s. When the SOC achieves 95%, the charge is stopped. Then they were discharged at 8 A (C0.5) until the voltage decreased up to 3.0 V. After 10 min rest, the individual cells were charged again following the standard charging procedure.  
Figure 15: Battery individual cell testing: Voltage and current vs. time.  
Flight Load Profile  
In order to ensure the behavior of the battery individual cells during the flight, they were tested under a typical flight load profile.  
This profile, as can be seen in the Figures 17 and 18, starts with 4 min demanding 4 KW, which corresponds to the launching needs and, after that, the power demand decreases to 1 KW, which is enough to ensure a stable cruise speed at 30 m/s. The experiment shown in the Figures 17 and 18 also includes a 10 min rest period after the discharge under the flight load profile and the subsequent recharging of the battery.  
These power values are referred to the complete battery pack, so, in order to test the battery pack, individual cells were proportionally scaled taking into account the battery pack setup (14S2P).  
The power needed in the original ALO, both during launching and cruise, is stablished regarding the total power of the motor (6.8 HP) and the percentage of throttle commanded by the pilot.  
During the launching, this percentage of throttle will vary with the wind conditions but it can be considered as standard in most of the duration of the launching. The percentage of throttle in the majority of the launchings is stablished at 70% [85; 86; 87]:  
\[6.8\;\mathrm{HP}=5070.76\;\mathrm{W}\]  
\[5070.76\;\mathrm{W}\times 70\%=3549.53\;\mathrm{W}\]  
The percentage of throttle needed during cruise in the original ALO will be affected by weather conditions and flight altitude, but it can be considered as standard in the major part of the typical flights. The value of throttle in the majority of cruise situations was established at 15%:  
\[6.8\;\mathrm{HP}=5070.76\;\mathrm{W}\]  
\[5070.76\;\mathrm{W}\times 15\%=\mathbf{760.61\;W}\]  
Thus, it can be confirmed that the values of 4 and 1 kW, which were used during testing a typical flight load profile, are more demanding than during a normal flight.
## 4 Integration with Communication Technologies
### Synergy Between RISs and Current, Future Wireless Communication Systems Including 6G
RISs can contribute to more secure communications by selectively enhancing the signal strength in the intended directions while mitigating eavesdropping risks. Moreover, the dynamic nature of RISs can provide an additional layer of security through variable and unpredictable propagation environments, complicating potential interception or jamming attempts [110; 112]. The synergy between RISs and current and future communication systems, including the journey towards 6G, is a testament to the ongoing evolution of wireless technology. The RIS is a pivotal technology that enhances signal propagation, system capacity, energy efficiency, and security [112]. It aligns with the ambitious goals of future communication systems, promising to address the burgeoning demand for high-quality, ubiquitous, and sustainable connectivity. As research and development continue to unfold the potentials of RISs, their integration with current and future communication systems is likely to become increasingly central, paving the way for an era of adaptive, controlled wireless communication environments.
## 2 Materials and Methods
### Numerical Analysis  
Figure 5 depicts the equivalent circuit of the UWB antenna along with the UWB antenna with a single and dual-notch band. The equivalent model of the UWB antenna was designed using a series-connected combination of RLC components, where each group of RLC components show the independent single resonance. The operational region of each resonance overlaps due to closely spaced thus results in a Wide Band Antenna  
Figure 4: Current distribution of proposed antenna at notch-band of: (**a**) 2.82 GHz (**b**) 3.64 GHz.  
Figure 3: S\({}_{11}\) for wideband antenna and wideband antenna with single and dual-notch bands.  
(WBA) [30; 31]. Similarly, the notch bands occurred due to the presence of V-shaped slots were also model using an RLC group, where the LC equivalent notch frequency \(f_{no}\)[32].  
\[f_{no}=\frac{1}{2\pi}\sqrt{\frac{1}{L_{eq}C_{eq}}} \tag{4}\]  
where \(C_{eq}\) is the total equivalent capacitance and can be obtained by summing up the capacitances generated by individual leg of the V-shaped slot. \(L_{eq}\) is the equivalent inductance generated by the etched slot and can be calculated by using the following relation:  
\[L_{eq}=0.0002\text{E}\bigg{(}2.303\log_{10}(\frac{4E}{D})-\theta\bigg{)}\mu H \tag{5}\]  
where \(E\) was assumed to be the finite length of a wire of rectangular cross-section equals to the notch area, \(D\) is the length approximately equals to the cross-section of the slot, and \(\theta\) is the angle between the legs of the V-shaped slot.  
As the current distribution is perturbed due to slots on the radiating patch, the path of the current is elongated. The length of the current path exhibits the series inductance, whereas the etched gaps of the slots accumulate the charge, hence determines series capacitance. Collectively, this behavior can be represented by the equivalent lumped element circuit with LC configuration. The equivalent lumped element parallel RLC circuit for the proposed antenna is illustrated in Figure 5. \(L_{S1}\), \(C_{Si}\), and \(R_{Si}\) represent the  
Figure 5: RLC equivalent model of wide band antenna with and without notch-bands.  
inductance, capacitance, and radiation resistance for \(i\)th radiating mode at each resonant frequency band, respectively. The lumped effect of the slots causing the resonant frequency is represented as a series combination of all lumped elements with an inductive patch antenna and ground plane as a capacitive load. Both inductive and capacitive effects as well as feeding effects are considered for higher-order modes generation and significantly contribute to the input impedance of the antenna.