esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparison with Previous Approaches
### Huang and Pan Example 2  
This PN example (see Figure 7) is taken from [22]. Based on Table 6, four MTSI conditions and 27 markings are necessary to be processed if _HP_ is used. On the other side, our control policy also needs four MTSI conditions but does not generate any markings. It is obvious that three PN monitors are calculated to control this example with the _HP_ approach against three, four and three control places for \(R_{1}\), \(R_{2}\), and \(R_{3}\), respectively. The controlled system net was obtained with the four methods and is shown in Table 7. The main advantage of the \(R_{3}\) algorithm is the gain of the computation time, which was decreased from 15,500 ms to 6700 ms. Indeed, without RG generation, one can design the same number of PN controllers using the theory of regions. Another important factor for the comparison of the four methods is not listed in Table 6; the _HP_ approach does not consider uncontrollable transitions in the used PN models, which is a weakness in this work. As a result, one can conclude that our new control policy is the most efficient among _HP_ method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Control** & **No. of PN** & **No. of** & **Maxlings in** & **Maxlally** & **No. of** & **Computation** & **Time (ms)** & **Approach** \\
**Criteria** & **Controllers** & **the RG** & **Permissive** & **MTSI** & **Time (ms)** & **Approach** \\ \hline \(R_{1}\) & 2 & 13 & Yes & 7 & 7190 & Minimal Cuts \\ \hline \(R_{2}\) & 2 & 13 & Yes & 7 & 7213 & Canonic markings \\ \hline \(R_{3}\) & 2 & 0 & Yes & 7 & 3560 & Without RG \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results in example 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Additional Control Places** & **Initial Marking \(M_{0}\)(\(P_{ci}\))** & **Incidence Vector \(C\)(\(P_{ci}\), )** \\ \hline \(R_{1}\) & \(P_{c1}\) & 1 & \((-1,\,-2,\,0,\,1,\,2)\) \\ \(P_{c2}\) & 1 & \((-2,\,0,\,0,\,0,\,0)\) \\ \hline \(R_{2}\) & \(P_{c1}\) & 1 & \((0,\,-2,\,0,\,0,\,0)\) \\ \(P_{c2}\) & 1 & \((-2,\,-2,\,0,\,0,\,1)\) \\ \hline \(R_{3}\) & \(P_{c1}\) & 3 & \((-1,\,-2,\,0,\,1,\,2)\) \\ \(P_{c2}\) & 1 & \((0,\,-1,\,0,\,0,\,1)\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Petri net controllers of example 1.
## 2 Materials and Methods
### Flow Cytometry Genotyping CMSCs  
Flow cytometry (FACS) was used to evaluate the expression of cell markers after the first passages. For the purpose of evaluation of surface marker expression, cell suspensions CMSCs of \(5.0\times 10^{5}\) cell/mL, volume of 100 \(\upmu\)L PBS were incubated for 30 min with fluorochrome, phycoerythrin (PE) with conjugated antibodies CD105 PE clone 43A3 (BioLegend, San Diego, CA, USA), CD44 PE (Miltenyi Biotec, Bergisch Gladbach, Germany) clone G44-26 a CD90 PE clone 5E10 (BD Biosciences, Franklin Lakes, NJ, USA), CD73 PE clone AD2 (Miltenyi Biotec), CD29 PE (Miltenyi Biotec) a fluorescein isothiocyanate (FITC)--conjugated antibodies against human antigens CD146 FITC (Miltenyi Biotec), CD45 FITC clone HI30 (Pharmingen, USA), CD34 FITC clone 581 (BD Biosciences), Toll-like receptor (TLR)4 FITC clone MTS510 (Invitrogen, Thermo Fisher Scientific), CD54 PE intercellular adhesion molecule (ICAM-1) clone REA266 (Miltenyi Biotec), and HLA DR FITC clone AC122 (Miltenyi Biotec). Intracellular markers were identified in suspension with a concentration of \(5.0\times 10^{5}\) cells/mL. The cells were fixed in suspension and then permeabilized with 150 \(\upmu\)L BD Cytoxix/Cytoperm (BD Biosciences). The cells suspension was incubated for 20 min at 4 \({}^{\circ}\)C. Afterwards, the sample was washed by BD Perm/Wash buffer (BD Biosciences). Single-cell suspensions were stained with fluorochrome labeled antibodies IL-10 PE Clone JES3-19F1 (BD Biosciences) and TLR3 PE clone TLR3.7 (Thermo Fisher Scientific). Cells samples were analyzed with a fluorescent activated cell sorter (FACS) Calibur (BD Biosciences). Collected data were analyzed and evaluated using BD CellQuest software (BD Biosciences) and FCS Express 5 (De Novo Software, Glendale, CA, USA).
# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
## 4 Experimental Results and Analysis
### Performance Comparison
#### 4.2.2 Quantitative Evaluation  
Our model is compared with 17 state-of-the-art RGB-D SOD methods. For both NJU2K and NLPR datasets, the four evaluation metrics achieve the best results, as shown in Table 1. Higher values of \(S_{\lambda}\), \(F_{\beta}\), and \(E_{\gamma}\) indicate better performance, while lower \(MAE\) values indicate better performance. Notably, our model achieves superior results compared to most RGB-D saliency detection models on the SIP and STERE datasets. For the SIP dataset, the results are similar to those of the RD3D model, and for the STERE dataset, the results are similar to those of the CMINet model. Overall, our CMANet model demonstrates excellent performance in RGB-D saliency object detection tasks, particularly in various indoor and outdoor scenes. Furthermore, we compared the performance of the model using different backbone networks. As shown in Table 2, when using Res2 Net-101 as the backbone network, the performance remains outstanding. But, in general, the model has a better detection effect when using Res2 Net-50 as the backbone network.
## 2 Mathematical Model
### Definite Problem of the \(TGF\_N\_V\)  
Let \(\psi(x,y,t)\) be the disturbed velocity potential of water due to the roll motion of a floating body. Let \(\Omega\), \(S_{\mathrm{F}}\), \(S_{\mathrm{B}}(t)\), \(S_{\infty}\) be the water domain, undisturbed water surface, instantaneous wetted surface of the floating body, and flow boundary at infinity, respectively. The definite conditions for the disturbed velocity potential \(\psi(x,y,t)\) can be formulated as  
\[\left\{\begin{array}{ll}\frac{\partial^{2}\psi}{\partial x^{2}}+\frac{ \partial^{2}\psi}{\partial y^{2}}=0,&\text{in }\Omega\\ \frac{\partial^{2}\psi}{\partial t^{2}}+g\frac{\partial\psi}{\partial y}+2 \epsilon\omega_{0}\frac{\partial\psi}{\partial t}=0,&\text{on }S_{\mathrm{F}}\\ \frac{\partial\psi}{\partial t}=V_{n}(t),&\text{on }S_{\mathrm{B}}(t)\\ \psi=0,\ \
abla\psi=0,&\text{on }S_{\infty}\\ \psi=0,\ \frac{\partial\psi}{\partial t}=0,&\text{when }t=0\end{array}\right. \tag{2}\]  
where \(\epsilon\) is the non-dimensional viscosity coefficient, \(\omega_{0}\) is the reference frequency, and \(V_{n}(t)\) is the normal velocity of the instantaneous wetted surface.  
From Equation (2), one can observe that in the definite conditions of the TGF_N_V, the normal velocity \(V_{n}(t)\) is satisfied on the instantaneous wetted surface of the floating body, while the normal velocity in TGF_V [21] is given on the mean wetted surface \(S_{\mathrm{B}}(t)\) of the floating body. This difference makes the definite problem of the TGF_N_V to be body-nonlinear.
## 3 Illustration of the Methodology
### Obtaining the Integrated Interruption Cost Model
For example, the southwest corner of the test system in Figure 7 lies in the high wind zone, and an option to reinforce the tower structures in the region is considered to enhance the resiliency against extreme winds. A resilience study can be conducted to estimate the duration and magnitude of load curtailment at each node and used in (16) to calculate the ECOST of the system. This analysis is conducted for two cases: one with tower reinforcement and the other without reinforcement. The reduction in total outage cost due to tower hardening is compared with the reinforcement cost to make an investment decision. Another example is a planning decision to twin a transmission line between Buses 15 and 24 to enhance grid reliability. A composite system reliability assessment of the test system can be carried out with and without considering line twinning, and the resulting duration and MW are curtailed at each load point and then used in (16) to assess the outage costs for the two cases. The decision to invest in a new transmission line should then be justified by the reduction in ECOST due to the investment. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Sector** & **1 min** & **20 min** & **1 h** & **4 h** & **8 h** & **24 h** \\ \hline Residential & 0.00 & 0.27 & 1.96 & 22.09 & 45.03 & 170.90 \\ \hline Farm & 0.65 & 2.89 & 5.02 & 8.72 & 9.63 & 18.79 \\ \hline Commercial & 3.05 & 9.02 & 24.36 & 122.75 & 197.25 & 237.57 \\ \hline Industrial & 2.53 & 4.19 & 8.25 & 27.25 & 52.51 & 154.65 \\ \hline \end{tabular}
\end{table} 
Table 12: Sector customer damage function (SCDF), CAD/KW. Figure 7: IEEE-RTS 24-bus system.
# Biomass-Based Chemical Looping Gasification: Overview and Recent Developments
## 3 Looping Materials in Chemical Looping Gasification of Biomass
### Performance of Looping Materials  
Oxygen carrier is a key factor in chemical looping processes. One of the main challenges of these processes is to stabilize their performance over prolonged redox cycling. However, many reasons may reduce the performance of oxygen carriers during Biomass chemical looping gasification. As discussed above, the agglomeration of oxygen carriers is one of the most serious challenges in chemical looping processes which can lead to bed defluidization and deactivation of oxygen carriers. The Cu metal has a low melting point which can cause agglomeration at high temperatures, and the redox rate of CuO would readily decrease after few reduction-oxidation cycles [98], while several agglomerations of Fe-based oxygen carrier has been reported when the phase change to Fe\({}_{3}\)O\({}_{4}\) during oxidized in air. Therefore, different support materials have been used to mitigate this problem. Another concern is the reduction of the mechanical strength of oxygen carriers during chemical looping processes. This problem can lead to attrition and decrease their lifetime. Along with physical attrition, chemical stress during redox cycles is also a reason for the reduction of mechanical strength [9]. In solid-gas reactions, carbon deposition on the solid oxygen carriers is a serious problem, because it can reduce their reactivity and shorten their lifetime. Carbon deposition is a complex phenomenon that is affected by several factors (pressure, temperature, availability of oxygen in the oxygen carrier, contents of water vapor, and CO\({}_{2}\) in the fuel gas) [99]. For Ni- or Cu-based oxygen carriers, carbon deposition was reported when the oxygen
# Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System
## 4 Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System (DAGAN)  
Industrial control systems (ICS) have some individual characteristics, which bring the following challenges to anomaly detection. (1) Most of the data collected in ICS are normal samples, and it is difficult to obtain abnormal samples. For wider applicability, an anomaly detection model needs to be able to perform accurate detection without relying on any anomalous samples. (2) The operation logic of ICS is relatively fixed, resulting in an obvious \"long-tail\" distribution of operating data, with a lot of data in dense areas and little data in marginal areas. This further leads to unseen marginal data being misidentified as outliers. (3) Affected by the complex working environment, the operating data in ICS have high noise. If anomaly detection is performed directly from the data space, the detection accuracy will inevitably decrease. Therefore, it is very necessary to perform anomaly detection from the feature space to improve the accuracy.  
To solve the above challenges, inspired by GANomaly and FenceGAN models, a dual auto-encoder GAN-based anomaly detection model (DAGAN) is proposed in this paper for ICS. This model tries to use the reconstruction ability of auto-encoder and the feature learning ability of GAN to design an accurate and efficient deep model for anomaly detection of ICS that does not rely on any abnormal samples and can accurately identify real marginal samples.
# Multidisciplinary Design Optimization of Cooling Turbine Blade: An Integrated Approach with R/ICSM
## 3 Multidisciplinary Design and Optimization of Turbine Stage Blade
### Subject Analysis of Turbine Stage Blade
#### 3.2.2 Strength Analysis of Rotor Blade Considering Internal Cooling Structure  
The cooling turbine blade in service should withstand significant mechanical strain caused by centrifugal loads and coupled with complex loads such as geometric stress concentration, airflow pressure, and temperature load. The stress state is extremely complex, so it is necessary to analyze the stress distribution of the turbine blade and evaluate the strength through the finite element method, providing a basis for the MDO of the blade.  
This section analyzes the stress on the first-stage rotor blade with linear elastic finite element analysis by ANSYS. The unit type is 10 nodes tetrahedral unit SOLID187. The temperature field distribution of the cooling turbine blade affects both thermal stress and material mechanical properties, thereby affecting blade strength. The use of a three-dimensional high-fidelity numerical simulation for the flow heat coupling calculation of the blade needs a long cycle, resulting in low efficiency in multidisciplinary design optimization iterations. Therefore, in the optimization analysis of this study, a simplified temperature field was adopted, which is to calculate the average temperature of the blade along the radial section and obtain the distribution of blade temperature along the radial direction. Figure 10 shows the grid and temperature field used in the optimized design.  
Based on a simplified temperature field and applying loads such as rotational speed, the equivalent stress cloud map of the blade is analyzed, as shown in Figure 11.  
Figure 10: The grid, boundary conditions, and temperature field used in the optimization design.  
Figure 9: The mesh scheme used in the optimization design (2.4 million).
# The Reverse Lunge: A Descriptive Electromyographic Study
## 4 Discussion
### Limitations  
During the completion of this study, several limitations were identified. First, the sample size is small and only includes healthy 22-25-year-olds, which limits the generalizability of the observed results to larger populations including different age groups. Second, typical of surface EMG, artifact and impedance may have negatively affected the ability to collect quality EMG data of key musculature intermittently, especially the GMax. It was essential to limit this as much as possible. This was conducted through precise EMG electrode placement via SENIAM protocol and real-time quality assurance of the EMG data during collection by a member of the research team. However, even with controls, there was potential signal interference with the EMG electrodes during data collection including crosstalk between other adjacent muscles, electrical wire disturbances, and EMG electrical wire port damage. We believe these factors primarily impacted on the validity of the data collected regarding the GMax. The raw data of the GMax indicated more muscle activity than expected with excessive EMG noise at baseline. Despite this, we believe the EMG data displayed a reasonably accurate representation of muscle activity in the lead and stationary limb. Another limitation was that we did not measure skin impedance and its relation in dampening the EMG signal. Utilization of the heel switch introduced potential human error. However, using a single researcher to manually hit the switch at key moments in the reverse lunge allowed for a systematic approach for sequencing the reverse lunge phases and adding context to the EMG data. Lastly, this study only analyzed the left limb acting as the lead limb despite also collecting data for the right limb acting as the lead limb.  
Since this study is a preliminary descriptive study, further research is needed for exploration of the reverse lunge. This study worked with primarily healthy, physically active individuals. Future research would be required to examine the differences in muscle activation across other populations, including those who are deconditioned, injured, or otherwise impaired. With this data, future research can analyze the differences between lunge types, i.e., forward, side, and reverse within participants. Since the current study only analyzed the left limb as the lead limb, future studies could analyze both limbs as lead limbs to decipher interlimb differences. Finally, this study only assessed the bodyweight lunge, and future research could examine other variables, including the effects of changes in arm position, trunk position, and addition of weight or resistance.
## 1 Introduction
### Steady-State Response Analysis
\begin{array}{l}SNR=\frac{P_{s}}{K-1}\sum\limits_{k=0}^{K-1}X_{N}(w_ {k})\\ P_{s}=\lim\limits_{\Delta w\rightarrow\infty}\sum\limits_{k=0}^{K-1}\int_{w_{ k}-\Delta w}^{w_{k}+\Delta w}X_{S}(w)dw\end{array} \right. \tag{53}\]  
The steady-state output SNR expression of the SUUBSR system is derived from Equation (53):  
\[SNR=\sum\limits_{k=0}^{K-1}\frac{R_{1,2}\pi{A_{k}}^{2}r^{2}\ln\left(\frac{2v}{ ar^{2}}\right)}{2}\left[1-\frac{4R_{1,2}^{2}r^{2}{A_{k}}^{2}\ln\left(\frac{2v}{ ar^{2}}\right)}{2D^{2}\left(4R_{1,2}^{2}+{w_{k}}^{2}\right)}\right]^{-1} \tag{54}\]  
Based on Equation (54), a relationship curve between the steady-state output SNR and the damping coefficient under different parameters is plotted as shown in Figure 5. As can be observed from Figure 5, under the same damping coefficient, the smaller \(v\) is, the larger \(a\) and \(r\) are, and the higher the peak of the signal-noise ratio curve will be. This indicates that a lower potential barrier in the potential function favors particle transitions. When the system parameters remain constant, the value of the steady-state output SNR first increases and then decreases with the increase in the damping coefficient, showing that there is not a monotonic relationship between the damping coefficient and the SNR value. Instead, there exists an optimal damping coefficient that optimizes the value of the steady-state output SNR.
## 3 Results
### Bone Density Scan (DEXA)  
All patients included in the study performed the DEXA scans preoperatively and 4 weeks postoperatively. From the total of 45 patients, eleven patients did not undergo the second postoperative DEXA scan at 8 weeks (five patients in Group I, three patients in Group II, and three patients in Group III).  
The preoperative values of the DEXA scan according to modified Gruen zones are available in Figure 3a. The highest BMD values in all of the three groups were found in G3 and G5 zones. No statistically significant differences were observed between groups in any Gruen zone.  
The postoperative DEXA scan at one month of the affected hip according to Gruen zones is available in Figure 3b. The alendronate group had the highest bone mineral density values at G2, G3, G4, G5, G6, and G7. Simvastatin showed higher values compared to control at G1, G2, G5, and G6. The only statistically significant differences were between alendronate and control at G5 and G6 (\(p\) = 0.042; \(p\) = 0.039).  
The postoperative DEXA scan at two months of the affected hip according to Gruen zones is available in Figure 3c. The only statistically significant differences were between alendronate and simvastatin at G6 (\(p\) = 0.032).  
The overall bone mineral density data are available in Figure 4. Alendronate showed an increase of 1.5% in bone mineral density at one month postoperatively, while simvastatin and the control group showed a decrease (12.8% and 11.6%, respectively). The only statistically significant result was between alendronate and control at one month postoperatively (\(p\) = 0.043).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Group I (Alendronate)** & **Group II (Simvastatin)** & **Group III (Control)** \\ \hline Sex & & & \\ Male & 2 & 8 & 11 \\ Female & 13 & 6 & 4 \\ Age (years) & \(67.5\pm 3.2\) & \(68.9\pm 4.8\) & \(65.6\pm 5.3\) \\ BMI & \(22.5\pm 1.2\) & \(24.3\pm 0.7\) & \(21.3\pm 1.1\) \\ Hypertension & 9 & 7 & 11 \\ Diabetes & 3 & 3 & 2 \\ Smoking & 2 & 1 & 1 \\ Hyperlipidemia & 4 & 15 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clinical data of patients according to group distribution.  
Figure 3: Preoperative DEXA scan (**a**); postoperative DEXA scan at one month (**b**); postoperative DEXA scan at two months (**c**).  
The changes in BMD for each Gruen zone at 1 month postoperatively compared to preoperatively are available in Figure 5. The highest periprosthetic bone loss was in Gruen zone 7 (an average of 54% for all groups), followed by zone 1 (an average of 38% for all groups) and zone 6 (an average of 34% for all groups). On the other hand, the least periprosthetic bone loss was found in zone 2 (an average of 4% for all groups) and zone 3 (an average of 8% for all groups).
## 2 Ground Motion Intensity Measures  
With the increasing availability of strong ground motion data, new IMs are suggested to improve existing EDP prediction models. However, the efficiency of a particular IM depends on how well it correlates with the EDPs. An IM with stronger correlation with a particular EDP indicates that it is a good predictor. For example, spectral measures such as spectral acceleration (_Sa(T)_), spectral velocity (_Sv(T)_) and spectral displacement (_Sd(T)_) at the fundamental period of the structure are commonly found as efficient IMs based on correlation between EDP and IM [10; 11; 12]. However, these IMs do not account for higher vibration modes of the structure and non-linear effects, such as elongation of natural period [5; 13; 14; 15; 16; 17]. To address this concern, several contemporary IMs have been proposed to accommodate the characteristics of both the ground motion and the structure [7; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34].
## 3 System Description
### Methodology
#### 3.1.1 Iterative Placement Analysis  
This part considered eleven different iterative lamp arrangements to determine the optimum transmitter locations based on uniformity and general lighting conditions. Even though the iterations could yield several placement combinations, the eleven candidate scenarios were obtained by starting at the conventional placement locations that are defined by Cases I and VII in Figure 3 for four and five transmitters, respectively, and then moving symmetrically along the room diagonals with a resolution of 0.25 m to generate further cases until the ends of the diagonals were reached. The placements that fit the uniformity and lighting criteria were then used to calculate illuminance and communication parameters. Subsequently, the case with minimum \(SNR\) fluctuations was compared with the conventional technique.