esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, under different geological conditions, the support effect of the two support schemes was investigated by changing the support spacing. The following main conclusions were drawn:  
1. The numerical simulation results show that no matter what the geological condition, the support installation could effectively reduce the tunnel wall deformation and stabilize the tunnel structure. This effect largely depended on the support spacing.
2. Under the same support spacing, the performance of the two support schemes was evaluated. The results showed that the effect of the non-radial support was lower than that of the radial support. This comparison was also carried out in the other two geological conditions, and the results suggest that the non-radial support scheme had a better performance for controlling the surrounding rock deformation with a worse geological condition.
3. By analyzing the variation of the growth rate of the support effect with the changing support spacings, the law of the interaction of the support was found. The enhancement of the support effect originated from the interlapping of the support influence domain. According to this law, the appropriate support spacing was specified to be smaller than 50% of the support influence domain length.  
Conceptualization, Y.G. and F.H.; methodology, Y.G. and Y.L.; software, F.D.; formal analysis, M.L.; writing--original draft preparation, F.D.; writing--review and editing, Y.G., F.G. and M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National key research and development program (2022 YFC2904100), the National Natural Science Foundation of China (no. 52078477 and 51827901), Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization (2020-3), and the Key Laboratory of Deep Earth Science and Engineering (Sichuan University) of the Ministry of Education (DESE202106).  
Not applicable.  
The data used to support the findings of this study are available from the first author upon request.  
The authors declare no conflict of interest.
# The Long and Winding Road to Cardiac Regeneration
## 2 Stem Cell Therapy
### Pluripotent Stem Cells
#### 2.2.1 Embryonic Stem Cell  
ESCs are derived from the inner cell mass of early-stage embryos and possess remarkable pluripotent properties [134]. They have the ability to self-renew indefinitely and differentiate into virtually any cell type in the human body, including cardiomyocytes. This capacity to generate cardiac cells makes ESCs a compelling candidate for cardiac tissue regeneration [135].  
The advantages of ESCs in cardiac regeneration lie in their exceptional regenerative potential and the ability to create large quantities of functional cardiomyocytes [63]. ESCs offer a renewable source of cells that can be expanded in culture, providing a virtually unlimited supply for transplantation. When introduced into damaged cardiac tissue, ESC-derived cardiomyocytes can integrate within the host tissue and potentially restore heart function [135; 136].  
However, the use of ESCs in cardiac regeneration also faces significant limitations. One of the primary concerns is the ethical dilemma associated with their acquisition, as ESCs are typically obtained from discarded embryos or through the destruction of viable embryos. This raises ethical considerations and has led to substantial controversy surrounding their use [137]. Additionally, ESCs may elicit immune responses when transplanted into a recipient, necessitating the use of immunosuppressive drugs that carry their own risks [138; 139].  
Furthermore, the possible tumorigen poses a significant challenge. ESCs tend to form teratomas, which are nonfunctional masses of differentiated cells. Ensuring the differentiation of ESCs into specific cardiac cell types before transplantation is critical to minimize the risk of tumor formation [140].  
Ethical concerns, immune rejection, and tumorigenicity remain major hurdles that must be addressed before their widespread clinical application can be realized [141]. Continued research and development are essential to overcome these limitations and unlock the full potential of ESCs in cardiac regenerative medicine.
## 5 Deep Learning
To quantify the reliability of each estimated keypoint, the system computes a confidence score based on the difference between the predicted and correct keypoint coordinates. This score helps to assess how certain the model is about its estimation. The confidence score is derived using the Mean Squared Error (MSE), which measures the average squared difference between the predicted coordinates \(Y_{\text{predicted}}\) and the reference coordinates \(Y_{\text{correct}}\):  
\[\text{MSE}=\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}Y_{\text{correct},i}-Y_{\text{ predicted},j}\Bigr{)}^{2}\]  
where  
* \(N\) = total number of coordinate points considered (e.g., all keypoint coordinates);
* \(Y_{\text{correct},i}\) = the \(i\)-th coordinate of the correct/reference keypoint;
* \(Y_{\text{predicted},i}\) = the \(i\)-th coordinate of the predicted keypoint by the model. The Root Mean Squared Error (RMSE) is then calculated as the square root of the MSE:  
\[\text{RMSE}=\sqrt{\text{MSE}}\]  
The RMSE provides a metric in units similar to the original data, making it easier to interpret the average magnitude of the estimation error. In this system, the confidence score is derived from the RMSE value and normalized to lie between 0 and 1, where a higher score indicates higher confidence in the keypoint estimation. A threshold of 0.8 is established: if the confidence score exceeds this value, the estimated movement is considered reliable and can be evaluated further. If the confidence score falls below this threshold, the system displays a warning to the user, indicating that the estimation is uncertain and that the feedback provided may not be accurate. This mechanism ensures that the system only processes and provides feedback on movements when the pose estimation is sufficiently precise, enhancing the robustness and trustworthiness of the rehabilitative monitoring.
## 7 Results and Discussion
### Effect of Deborah Numbers (\(\beta_{1}\)) and (\(\beta_{2}\))  
A Deborah number is a dimensionless number that is frequently used in rheology to describe the fluidity of substances under particular flow conditions. Material behavior enters a non-Newtonian phase with significant Deborah numbers when elasticity progressively dominates and solid-like behavior is observed. Figure 3 illustrates the velocity of both nanofluids with varying \(\beta_{1}\) values (for basic fluid). The velocity of the flow for both kinds of nanofluids is reduces as the value of \(\beta_{1}\) is increased because the basic fluid is easily influenced by \(\beta_{1}\). However, according to observation, MWCNT-CMC has a higher velocity than that of SWCNT-CMC.  
Therefore, the momentum boundary layer thickness of MWCNT-CMC is increased. The consequence of reduced velocity is shown in Figure 4. Figure 4 shows the effect of changes in \(\beta_{1}\) on the temperature profile for both nanofluids. The thermal distribution  
Figure 3: Velocity variation versus \(\beta_{1}\).  
increased with increased \(\beta_{1}\) values. This anomaly occurs due to the slowing down of the movement of the nanomolecules in the liquid, resulting in reduced heat transfer from the shallow end in the direction of the movement, which eventually increased the heat transfer temperature. The temperature of MWCNT-CMC is slightly higher than that of SWCNT-CMC because SWCNT is a better heat conductor than MWCNT.  
Figure 5 shows the velocity distribution when the third-grade parameter, \(\beta_{2}\) (for nanoparticles), is reinforced, indicating a dramatic increase in fluid velocity. This phenomenon lead an increased width of the momentum boundary layer. Nonetheless, MWCNT-CMC has a higher velocity than that of SWCNT-CMC. However, the viscosity does not affect MWCNT-CMC under these conditions. The fluid thickness can be reduced by adjusting the third-grade parameter, which is well-known for this purpose.  
Figure 6 depicts the varying patterns of heat dispersion. An increase in temperature causes a reduction in the diffusion of thermal energy, resulting in a reduction in the width of the thermal boundary layer. These results demonstrate that temperature from the  
Figure 4: Temperature variation versus \(\beta_{1}\).  
Figure 5: Velocity variation versus \(\beta_{2}\).  
shallow velocity is rapidly transferred to the fluid and emitted into the distant field. This phenomenon occurs because the fluid's velocity increases as it moves through the system. SWCNT-CMC has a lower temperature relative to that of other nanofluids because SWCNT is a good heat conductor. Similarly, the velocity for the third-grade parameter, \(\mathbf{\beta_{3}}\), increases as the parameter increases, as illustrated in Figure 7. The figure shows that MWCNT-CMC has a higher velocity than that of SWCNT-CMC. The fluid boundary thickness is reduced when SWCNT-CMC is used as a nanofluid, which acceptable because MWCNT-CMC has a more negligible effect on the third-grade parameter, \(\mathbf{\beta_{3}}\).
###### Abstract  
Accurate PM2.5 concentration prediction is crucial for protecting public health and improving air quality. As a popular deep learning model, deep belief network (DBN) for PM2.5 concentration prediction has received increasing attention due to its effectiveness. However, the DBN structure parameters that have a significant impact on prediction accuracy and computation time are hard to be determined. To address this issue, a modified grey wolf optimization (MGWO) algorithm is proposed to optimize the DBN structure parameters containing number of hidden nodes, learning rate, and momentum coefficient. The methodology modifies the basic grey wolf optimization (GWO) algorithm using the nonlinear convergence and position update strategies, and then utilizes the training error of the DBN to calculate the fitness function of the MGWO algorithm. Through the multiple iterations, the optimal structure parameters are obtained, and a suitable predictor is finally generated. The proposed prediction model is validated on a real application case. Compared with the other prediction models, experimental results show that the proposed model has a simpler structure but higher prediction accuracy.  
Keywords:PM2.5; deep belief network; grey wolf optimization algorithm; concentration prediction; air pollution +
Footnote †: journal: Applied
## 1 Introduction
### Related Work
This scheme exploited pilot tones and data tones instead of reserved tones which is different from traditional clipping methods. It provides more accurate UWA channel characteristics for estimating the clipping noise than traditional methods such as Least square (LS) and measuring mean squared error (MMSE). The proposed algorithm is named FFB, which detects nonlinear distortion at the receiver side. Our method is based on research [14]. The method used in [33] is an extension approach of the literature [14]. The proposed PA model is called a memory device whose output depends upon the OFDM symbols. The channel estimation was performed using pilot symbols. In this process, only a few carriers were active. It makes sure that the value of the PAPR is low, and the PA mostly operates in a linear region. In this paper, we have used the approach used in [14]; we named this method FFB. The FFB model is used to mitigate the nonlinearity of the PAPR. Firstly, the PAPR is reduced in an OFDM system with a clipping technique. Secondly, the unnecessary distortion caused by high PAPRs is reduced at the receiver side by using a modern neural network. This model is trained with a machine learning algorithm with a proper set of data. Collectively, the channel coefficient fits best to the maximum likelihood model, and the performance is improved in terms of the BER and higher efficiency.
###### Abstract  
A key component in the quantitative assessment of the risk posed to spacecraft by the micrometeoroid and orbital debris (MMOD) environment is frequently referred to as a ballistic limit equation (BLE). A frequently used BLE for dual-wall configurations (which are commonly used on spacecraft to protect them against the MMOD environment) is the New Non-Optimum, or \"NNO\", BLE. In design applications where a BLE is needed for a new structural system that has not yet been tested, but resembles to a fair degree a dual-wall system, it is common practice to equivalence the materials, thicknesses, etc., of the new system to the materials, thicknesses, etc., of a dual-wall system. In this manner, the NNO BLE can be used to estimate the failure / non-failure response characteristics for the new system. One such structural wall system for which a BLE does not yet exist is a dual-wall system that is stuffed with a lightweight polymer-based foam material. In this paper we demonstrate that the NNO BLE, in its original form, frequently over- or under-predicts the response of such a system. However, when the NNO BLE is modified to more properly include the effects of the presence of the foam as well as the actual material properties of the walls and the impacting projectile, there is a marked improvement in its predictive abilities.  
ballistic limit equation; dual-wall system; foam-filled; hypervelocity impact; space debris Article
## 4 Results
### Comparison with Other Methods  
To further verify the performance of the proposed method, a series of comparison experiments was carried out. First, Random Forest was utilized to classify the input image without fractions to verify whether the inclusion of fraction information can improve the classification accuracy. Next, maximum likelihood classifier (MLC) was incorporated as a benchmark to classify both the input image, with and without fraction information. Also, the same training and validating data were used during all the classification to ensure comparability. Finally, the traditional NDWI thresholding method was utilized to extract the flooded areas. A trial and error approach was employed to determine the optimal threshold of NDWI and the final threshold was set to be -0.18. The results of the above comparison experiments are listed in Table 7.  
Figure 9: Importance of input variables.  
Table 7 indicates that the proposed method in this study yields the highest accuracy. The inclusion of fraction information increases the overall accuracy by 2.5% when comparing MESMA\(+\)RF to RF-only. Similarly, when using MLC, the overall accuracy rises by 3% after the addition of fraction bands, indicating that the inclusion of sub-pixel fractions can improve the classification accuracy regardless of the type of classifiers. Besides, RF outperformed MLC with an increase of 2% and 2.5% in classifying the images with and without fractions bands, respectively. The traditional NDWI thresholding method shows the lowest accuracy of 82.3%, indicating that it is inferior to these classification methods. Although the inclusion of MESMA based fraction images only increased the classification accuracy by 2.5% for RF classifier, this was derived from the classification results of only one study area during the flooding period. However, when considering the classification results of the pre-flood image, the inclusion of MESMA improved the accuracy by 4.7% of RF, which was higher than that of the flood image. Therefore, future studies should include more study cases to further verify the role of MESMA in the improvement of classification accuracy.
# Optimal Estimation Retrieval of Aerosol Fine-Mode Fraction from Ground-Based Sky Light Measurements
## 5 Discussion  
### The Limit of the Algorithm  
The OE-based algorithm takes advantage of the effective information in longer NIR band observations to enhance the constraints of coarse-mode aerosol inversion. Thus, the FMF parameter can be retrieved from single-viewing observation. However, after introducing 5% random noise on the simulation data, the mean relative retrieval error was about 4.5%, which revealed the relative low ability of error resistance for the single-viewing observation.  
The real part of the refractive index and the effective radius are two decisive parameters in the optical scattering characteristics of aerosol, and they further affect the retrieval of FMF. In this study, we use the historical statistical data from AERONET products in the experimental area as the a priori estimates of aerosol model parameters. However, for practical applications, different aerosol types need to be considered.
###### Abstract  
This paper presents a dynamic health intelligent evaluation model proposed to analyze the health deterioration of satellites under time-varying and extreme thermal loads. New definitions such as health degree and failure factor and new topological system considering the reliability relationship are proposed to characterize the dynamic performance of health deterioration. The dynamic health intelligent evaluation model used the thermal network method (TNM) and fuzzy reasoning to solve the problem of model missing and non-quantization between temperature and failure probability, and it can quickly evaluate and analyze the dynamic health of satellite through the collaborative processing of continuous event and discrete event. In addition, the temperature controller in the thermal control subsystem (TCM) is the target of thermal damage, and the effects of different heat load amplitude, duty ratio, and cycle on its health deterioration are compared and analyzed.  
satellite; dynamic health evaluation; fuzzy reasoning +
Footnote †: journal:  
Article
## 4 Applications in Optimization
### Self-Organizing Migrating Algorithm (SOMA)
#### 4.3.2 Performance Comparison of PID Controller Using DE and SOMA Versus PSO  
The comparison of PSO performance with DE (see Section 4.2.4) was extended to SOMA by Davendra et al. (2016) for the problem of a PID controller (see Section 4.1.2) using three transfer functions:  
* A third-order system \[G(s)=\frac{0.1}{s(3s+1)(0.8s+1)},\] (61)
* A fourth-order system comparable with the Ball and Hoop system (40),
* A third-order system for an electric DC Motor (35). For all these problems, four unique optimization specifications (IAE (37), ISE (38), ITAE (36), and MSE (39)) were utilized. The obtained values were then used to calculate the following three performance indices:  
* Rise time: the time it takes for the process output to achieve the new desired value. One-third of the dominant process time constant is typical. * Settling time: the time it takes for the process output to die to between, say, \(\pm 5\%\) of the setpoint. * Peak overshoot: the magnitude by which the controlled \"variable swings\" past the setpoint \(\pm 5\%\)-\(10\%\) overshoot is normally acceptable for most loops. The Lozi map was selected as the chaotic map of choice due to its superior performance in previous research (Kurz and B  
et al. [88], who based it on the Lozi Map within the scope of Multiobjective Transformer Design Optimization. Ant lions, or doodlebugs, belong to the Myrmeleontidae family and Neuroptera order (net-winged insects). The lifecycle of antlions includes two main phases: larvae and adult. A natural total lifespan can be up to three years, which mostly occurs in larvae (only 3-5 weeks for adulthood). Antlions undergo metamorphosis in a cocoon to become adults. They mostly hunt as larvae, with the adulthood period used for reproduction [87]. Their name originates from their unique hunting behaviour and favourite prey. An antilon larva digs a cone-shaped pit in sand by moving along a circular path and throwing out sand with its massive jaws. After digging the trap, the larva hides underneath the bottom of the cone and waits for insects (preferably ants) to become trapped in the pit. When the ant lion realizes that prey is in the trap, it tries to catch it. However, insects are usually not caught immediately, and try to escape the trap. In this case, the ant lion intelligently throws sand towards to edge of the pit in order to slide the prey to the bottom of the pit. When prey is caught in the ant lion's jaws, the prey is pulled under the soil and consumed. After consuming the prey, the ant lions throws the remains outside the pit and repairs the pit for the next hunt. The ant lion algorithm mimics the interaction between ant lions and ants in the trap. To model this interactions, ants are required to move over the search space and ant lions are expected to hunt them and become fitter using their traps. Because ants move stochastically in nature when searching for food, in the original ALO algorithm a random walk is chosen to model the ants' movement.
# Electromagnetic Interference Shielding Anisotropy of Unidirectional CFRP Composites
## 3 Results
### Skin Depth of Unidirectional CFRP Composite  
When the EM wave is incident on the surface of the material, the displacement current of the wave is coupled to it and generates the magnetic field at a right angle, which can create a back electromotive force to cause a force named the \"skin effect\" [42]. As the propagation depth increase, more EM waves are attenuated. When the strength of the EM wave is reduced to 1/e of the incident strength, the distance is known as the skin depth [43], and can be calculated by the following equation [44]:  
\[\delta=1/\sqrt{\pi\mu f\sigma} \tag{4}\]  
where \(\delta\) is the skin depth, \(f\) is the frequency, and \(\sigma\) is the electrical conductivity of the material. The magnetic permeability \(\mu=\mu_{0}\mu_{r}\), where the magnetic permeability of vacuum (\(\mu_{0}\)) is 4\(\pi\)\(\times\) 10\({}^{-7}\) H/m, and the relative magnetic permeability (\(\mu_{r}\)) of CFRP is about 1 [28; 45].  
Figure 4 shows the skin depth of unidirectional CFRP as a function of carbon fiber orientation angles at different frequencies. The skin depth of the composite decreased with an increase in the orientation angle. This is mainly due to the decrease in conductivity as the carbon fiber orientation angle increases, as shown in the conductivity results in Figure 3. The skin depth also decreases as the frequency increases. At a frequency of 15 GHz, the  
Figure 3: Electrical conductivity of the CFRP at different carbon fiber orientation angles.  
CFRP had the lowest skin depth value of 0.035 mm in the 0\({}^{\circ}\) direction, which refers to the fiber direction. At 5 GHz, CFRP obtained a maximum value of 1.633 mm in 90\({}^{\circ}\) direction.  
The thickness of the fabricated unidirectional CFRP was about 0.55 mm. According to the definition of skin depth, when the frequency is 15 GHz, and the electric field of the EM wave is consistent with the fiber direction, the electric field intensity would be less than 1/e of the incident electric field intensity when the EM waves penetrate through the CFRP composite. At 5 GHz, when the electric field is perpendicular to the fiber direction, the electric field intensity would be greater than 1/e of the incident electric field intensity when the EM waves pass through the CFRP. Consequently, the skin depth of materials plays a key role in the attenuation of EM waves, and due to the dependence of the skin depth on the carbon fiber orientation angle, the EMI shielding performance of CFRP would be different at various carbon fiber directions.