esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Objectives Optimization of the Frame Based on Interval Analysis Method
### Optimized Results  
There were five optimization objectives for this frame, and thus the non-dominated sorting genetic algorithm was utilized to search for the optimal solution in the all sample space [28; 29]. Before starting optimization design, the weight coefficient of every optimization objective had to ascertain firstly. The analytic hierarchy process method was used to calculate the coefficient. Its judgment matrix could be estimated by [30]:  
\[A=\left[\begin{array}{ccccc}\alpha_{11}&\alpha_{12}&\alpha_{13}&\alpha_{14}& \alpha_{15}\\ \alpha_{21}&\alpha_{22}&\alpha_{23}&\alpha_{24}&\alpha_{25}\\ \alpha_{31}&\alpha_{32}&\alpha_{33}&\alpha_{34}&\alpha_{35}\\ \alpha_{41}&\alpha_{42}&\alpha_{43}&\alpha_{44}&\alpha_{45}\\ \alpha_{51}&\alpha_{52}&\alpha_{53}&\alpha_{54}&\alpha_{55}\\ \end{array}\right] \tag{15}\]  
where \(\alpha_{ij}\) is the ratio of importance between two optimization objectives.  
According to the analyzed results mentioned above and actual engineering problem, the matrix \(A\) could be assigned by:  
\[A=\left[\begin{array}{ccccc}1&3/5&1&3/7&3/8\\ 5/3&1&6/5&5/7&4/7\\ 1&5/6&1&4/7&4/8\\ 7/3&7/5&7/4&1&7/8\\ 8/3&7/4&8/4&8/7&1\\ \end{array}\right] \tag{16}\]  
The matrix \(A\) was processed to be normalized vector:  
\[U=\left[\begin{array}{ccccc}0.117&0.178&0.142&0.261&0.302\\ \end{array}\right] \tag{17}\]  
Therefore, \(U_{1}\), \(U_{2}\), \(U_{3}\), \(U_{4}\) and \(U_{5}\) were successively weight coefficient of five optimization objective. Then, the optimization design was implemented by software ISIGHT(Engineeous, Los Angeles, CA, USA), based on the non-dominated sorting genetic algorithm. The number of populations was 200, and the number of iterations was equal to 1000. After calculating for 51 minutes, the final optimized results are listed in Table 9. Furthermore, the optimized objective values were also compared with the initial ones, Table 10. According to the optimized results, the thickness of four design variables  
Figure 17: Radius results between prediction and simulation.  
Figure 18: Midpoint results between prediction and simulation.  
increased, and the top longitudinal beam had the largest increase in thickness. Then, the optimization with respect to the stress related to dynamic horizontal bending, unexpected braking (fracturing) and torsion were decreased by 19.18%, 17.25% and 9.06%, respectively. The bending and torsional stiffness were improved by 21.53% and 15.88%. Obviously, the mechanical responses of this frame were enhanced with a slight increase in weight, which achieved the goal of optimization.
## 5 Time-Series Feature Prediction
### Direct Mode Prediction  
The direct mode prediction aims to study the correlation among input features, where eight features are used as the input attributes to predict the trend of input attributes. For instance, the kurtosis is made the target variable, while the other eight features serve as the input attributes to guide the predictive analytics. A schematic illustration of kurtosis feature prediction based on the PANFIS direct mode method is presented in Figure 6. This simulation aims to illustrate the descriptive power of input attributes to indicate the degradation of the slew bearing. Another goal is to model the characteristic of an input attribute based on its nonlinear relationship with the other eight input features. The 139 data points are partitioned into two groups: training and testing, where 108 samples are reserved for the training samples, while the rest are fed to test the model. In other words, prediction or testing is done for a one-month period. Figure 7 pictorially exhibits the predictive trend of PANFIS in modelling the kurtosis feature under the direct mode prediction. It is shown that the kurtosis feature characterizes a nonlinear, uncertain, and non-stationary nature; however, PANFIS is able to predict accurately. This scenario was carried out for all nine input attributes. PANFIS's performance is examined based on its generalization power for the testing samples. It is worth noting that under the direct mode, one can observe the generalization power of PANFIS because the model is fixed after 108 days.  
Figure 6: A schematic diagram of direct mode prediction.  
The evolving and adaptive characteristic of PANFIS is illustrated in Figure 8, where it shows the fuzzy rule evolution of PANFIS in the kurtosis feature prediction. PANFIS starts its learning process from no existing rules until the fuzzy rules are automatically created and pruned during the process in accordance with the novelty of data streams. PANFIS responds to changing characteristics of the system on a timely basis, where it introduces a new rule at \(t=88\) when there exists a \"spike\" in the kurtosis feature.  
Table 2 presents the numerical results of consolidated algorithms in direct mode prediction. It is evident from Table 2 that PANFIS outperforms the other three algorithms in terms of predictive quality. Although ANFIS is reported to have a lower RMSE value in a few cases, it experienced 10 epochs of the training process. In addition, it is found that ANFIS suffers from the curse of dimensionality, notably when the grid partitioning method is used. Hence, two numbers of rule is selected as a fixed number in our simulations.  
Figure 8: Fuzzy rule evolution of kurtosis feature.  
Figure 7: PANFIS prediction of kurtosis feature.
## 3 Results  
### Irradiance Sensor Tilt Response  
The tilt response of an irradiance sensor was evaluated using ground experiment data described in Section 2.5. The raw AIRS spectrometer data is equivalent to an output of an irradiance sensor without stabilization. Analyzing this data allows evaluation of how irradiance changes with sensor tilt (Figure 5). In the dataset with direct solar illumination, the irradiance reading from tilting sensor increases 2.2% for every degree tilted towards the sun. In the diffuse conditions, there was no such clear trend with tilt direction, but the measured irradiance decreased slightly when tilting toward any direction. However, this effect can be considered negligible, as the standard deviation of irradiance levels was only 0.4% at tilt angles less than <10\({}^{\circ}\). The AIRS tilt correction method employs bilinear interpolation of the photodiode data to calculate virtual irradiance sensor reading at desired orientations. The results show that in both illumination cases, the tilt response of the cosine corrected sensors is highly linear (Figure 5), justifying the bilinear interpolation as a feasible method within range 10\({}^{\circ}\) from zenith.  
Figure 5: Raw uncorrected irradiance reading vs the sensor tilt direction in the blue-sky (**a**) and overcast (**b**) conditions. Data is normalized to horizontal sensor position. The (**c**,**d**) show the same irradiance readings on the principal plane before and after the correction.  
The results show that applying the AIRS tilt correction method efficiently removes the effect of tilt from the irradiance reading at tilts within the photodiode sampling angles (10\({}^{\circ}\)). In the direct illumination dataset, the error in irradiiances tilt correction (relative root mean square error, RRMSE) was 0.79% at tilt angles less than 10\({}^{\circ}\), 1.19% at angles less than 15\({}^{\circ}\), and 1.96% at angles less than 20\({}^{\circ}\). In the diffuse dataset, similar RRMSE values were 0.30%, 0.67%, and 0.87%, respectively.
## 5 Preliminary Stability Analysis of the Landslide
### Calculation Results
The stability coefficient of the model was 1.065 in the natural state before drainage. After using the underground drainage corridor, the stability coefficient of the landslide increased to 1.347, and the stability coefficient increased by 26%. There were three reasons for this: first, the gravity of the sliding mass and the sliding force were reduced due to the decrease of the groundwater level. Second, the shear strength was improved due to the deformation of some slip zones from the saturated state to unsaturated state. Third, the underground drainage gallery was set into the bedrock, and the drainage pipe extended from the sliding bed through the sliding zone into the sliding mass, which was equivalent to adding anchor bolts to the landslide. Perhaps the stability coefficient of landslide would not have increased much simply considering the water level drop, but under the joint action of these three aspects, the stability coefficient of the landslide increased from 1.065 to 1.347. It can be seen from Figure 18 that the whole sliding mass was under the groundwater, except for some overburdens when no drainage tunnel was set for dewatering, and the seepage of water and the softening effect of water on the rock and soil mass greatly reduced the slope stability. When a drainage tunnel was set below the groundwater level, the phreatic line changed from a smooth curve to a broken line with the drainage tunnel as the break point. The deeper the drainage tunnel was buried, the greater the elevation of the break point of the phreatic line; the groundwater level will also decrease, and the sliding mass below the water level will also be smaller, so the stability coefficient of the slope will continue to rise. When the buried depth of the drainage tunnel exceeded 15 m, the slope changed from the critical state to the basically stable state. When the buried depth of the drainage tunnel reached 30 m, the deformation bodies, including the slip zone, were basically above the groundwater level, and the slope was basically stable. Figure 14: Drainage tunnel depth 15 m.  
Figure 15: Drainage tunnel depth 20 m.  
Figure 16: Drainage tunnel depth 25 m.  
Figure 17: Drainage tunnel depth 30 m.  
Figure 13:0 Underground water level map of the landslide at different depths for the drainage tunnel. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Depth of Drainage Tunnel** & **No Drainage Tunnel** & **15 m** & **20 m** & **25 m** & **30 m** \\ \hline Stability & 1.065 & 1.193 & 1.212 & 1.301 & 1.347 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Slope stability of the drainage tunnel at different depths.
## 1 Introduction
In order to optimize the process, the impact of energy recovery devices, membrane permeability, process configuration, brine management cost, pump efficiency, and frictional pressure drop on specific water cost under the constraints imposed by the osmotic pressure has been analyzed [11]. In another study, it was concluded that further reduction in specific water cost in RO is less likely to take place through the development of more permeable membranes, rather it should come from better fouling and scaling control, improved brine management, lower pretreatment cost, improved process control, and process optimization [12]. Improved control systems have been developed to incorporate the variation in feed water salinity, large set-point changes, and for the optimum management and operation of integrated wind-solar energy generation and RO desalination systems [12; 13; 14]. In addition to the abovementioned approaches, the use of new membrane operations offer promising solutions to these challenges. The use of the new processes in combination with the traditional ones can not only resolve the problem of waste handling but can also provide the opportunity to boost the economy of the process. An integrated approach takes into account energy savings (also production in certain cases), water rationalization, minimization of chemical utilization, resource recovery, and waste production [4]. Therefore, integrated systems can contribute significantly to the solution of strategic aspects of industrial productions. Different membrane operations can be coupled in integrated systems in order to approach the ambitious objective of \"zero liquid discharge\". A qualitative comparative analysis of various generations of desalination technology on the basis of different process intensification parameters has been explained in Figure 1. The first generation desalination plants (thermal) are bulky, consume huge amounts of energy, and have large environmental impacts in terms of carbon emissions and the use of various chemicals (e.g., anti-sealants). Due to high temperatures, the safety level of these plants is relatively weak. With the advent of RO technology, the situation has improved. However, energy consumption and environmental impact (in terms of carbon emission and brine disposal) of RO is still high. These drawbacks can be addressed by integrating novel membrane processes including pressure retarded osmosis (PRO), membrane distillation (MD), forward osmosis (FO), and reverse electrodialysis (RED). The details of these new processes have been explained in later sections.
# Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning
## 3 Methods
### Crowd-Counting Refined-Mapper Module  
By introducing the feature-extraction and density-estimator modules above, we can generate a coarse density map. In this section, we mainly focus on refining the density map and regressing the accurate number. Coarse maps are always produced in cross-domain crowd counting. The first training was based on the GCC dataset and transformed the density map from coarse to refined, before predicting crowd counting in other real-world domains. The Figure 5 shows the structure of the counting-map refiner.  
Given \(x^{S}\) and \(y^{S}\), we trained the model using supervised learning, and the counting-map refiner was trained to predict the refined density map. We trained the crowd-counting refined mapper with \(y^{S}\) and \(g(x^{S})\), as shown in Figure 5. Next, we used the trained model on the target domain to generate the pseudo-labels \(\hat{y}^{T}\) with \(x^{T}\). To reduce noise in the density estimation, we utilized a \(13\times 13\) kernel to obtain the receptive fields. Following the idea of a single-column network, we subsequently designed a five-layer network as a regression layer of the same size as the original input-density map.
## 2 Description of the Forward Model  
A forward model is developed to simulate the TOA radiance and polarization measurements and to provide the necessary Jacobians with respect to the state vector. This forward model is a combination of a linearized aerosol Mie-scattering model [44] and a vector linearized discrete ordinate radiative transfer model (VLIDORT). The optical processes taken into consideration are Rayleigh scattering, gas absorption, particle scattering and surface reflection. We discuss the components of the forward model in the following subsections.
## 2 Mathematical Models
### Forward Calculation of Phase Permeability Based on Digital Core  
The aforementioned two types of permeability models are mostly based on inverse modeling of relevant experimental data, which is the traditional approach to handling permeability data. In addition, digital rock core models can be established by scanning rock cores to obtain measurable data. This method primarily establishes models from geometric and calculus perspectives to describe the hysteresis phenomenon in relative permeability. Currently, this approach is mainly applied to the flow of two fluids.  
This method has been studied for a considerable amount of time. Nowadays, in addition to conventional CT scans, some scholars have attempted to introduce neural networks for computation with good results. [PERSON] et al. [46] visualized multiphase flow during core oil displacement experiments using X-ray microcomputer tomography. Quantify the obtained phase states using Minkowski Functionals and measure relative permeability using image-based methods, but only capture the effective permeability of the flow path and not the ganglion flow. [PERSON] et al. [47] Validated, using synchrotron X-ray computed tomography (\(\mu\)CT), that the geometric state function can represent the microscopic fluid configuration generated by a wide range of simulated flow conditions on an average basis, while considering the geometric description of capillary pressure. From the results, it can be seen that the geometric state function can serve as a closed relationship in macroscopic models to effectively remove hysteresis effects attributed to fluid arrangement in porous media. [PERSON] and [PERSON] [48] proposed a General Equation of State (EOS) method for relative permeability (kr) based on a set of geometric state parameters, normalized Euler properties (connectivity) and saturation, and verify that the new kr EoS matches experimental data better than the traditional Corey form. The fitting results are shown in Figure 3. The description of relative permeability hysteresis has been achieved in the two-phase flow of digital rock cores.  
In the latest research progress, [PERSON] et al. [49] developed an Artificial Neural Network (ANN) that relies on fundamental geometric relationships to determine the dissipation of mechanical energy during creeping immiscible two-fluid flow. The developed ANN is based on a set of physically insightful specific state variables. This model predicts the effective permeability of numerous unseen pore geometric states with strong correlation. The prediction results are shown in Figure 4.
## 4 Performance Evaluation
### Simulation Results
#### 4.2.2 Impact of Different Node Cooperation Thresholds on Transmission Performance  
Figures 6 and 7, respectively, show the impact of different cooperation thresholds on the delivery ratio and average delivery delay under varying proportions of selfish nodes. The simulation results indicate that the delivery ratio and average delivery delay in integrated UAV-ICV networks are closely related to the cooperation threshold, exhibiting a trend of first increasing and then decreasing. Specifically, when the cooperation threshold is low, selfish nodes receive a large number of message copies from non-cooperative nodes. This leads to an inundation of message copies within integrated UAV-ICV networks, which in turn reduces the delivery ratio. Conversely, when the cooperation threshold is high, selfish nodes may refuse to receive message copies not only from non-cooperative nodes but also from cooperative nodes, which also results in a decrease in the delivery ratio. From the results in Figures 6 and 7, it is evident that when the cooperation threshold is set at 0.6, the integrated UAV-ICV networks achieve a higher delivery ratio and a shorter average delivery delay.  
In integrated UAV-ICV networks, since data transmission relies on opportunistic communications, the optimal cooperation threshold is difficult to express using a closed-form solution. Additionally, given that the network conditions are dynamic, the optimal cooperation threshold is not a fixed value and may vary over time. To address this, we conducted Monte Carlo simulations to explore the impact of different cooperation thresholds on transmission performance. While we analyzed a set of thresholds in Figures 6 and 7, the optimal threshold may not be limited to just those values. Thus, the exploration of the optimal cooperation threshold is indeed an interesting topic for further investigation.
# Vibration Fatigue Life Reliability Cable Trough Assessment by Using Weibull Distribution
## 4 Numerical Application
### Random Vibration Stress Analysis  
To perform the Weibull stress analysis, it is required to have the principal \(\sigma_{1}\) and \(\sigma_{2}\) stress values. The stress is obtained as follows.  
Step1. Random vibration base input PSD.  
In this work, the product is regulated by the industrial standard GR-63. It does require that the product resist the random vibration profile PSD shown in Table 1 and Figure 3. It must be applied for 30 s, and it is applied in the three principal axes (x, y, z), respectively.  
Figure 2: (**a**) Cable trough, (**b**) static load, and (**c**) static load plus dynamic load applied.  
The sample size is determined by a reliability requirement of 0.95 and by using Equation (3).  
\[n=\frac{-1}{ln(0.95)}\approx 20\]  
Step 2. The vibration base input PSD is applied to the samples by using a shaker machine as shown in Figure 4.  
Once the vibration base input is applied to the cable trough straight section samples by using a shaker machine, the acceleration response is obtained.  
Step 3. The vibration and their acceleration responses are shown in Table 2 and Figure 5. Table 2 is shown the frequencies and their respective accelerations that most affect the cable trough straight sections by each principal axis.  
Step 4. Now, by using Equation (5), the dynamic factor is determined as follows.  
The stress concentration factor in the component is, \(K=1\), since there is no change in the component's geometry nor holes, the effective mass is, \(m_{e}=0.05\frac{\text{lb}-\text{sec}^{2}}{\text{ln}}\), the distance to the neutral axis is, \(C=0.0625\) in, the distance from the fixed point of the component to the point of application of the mass is, \(\hat{L}=L/2=72/2=36\) in, the constant gravity is, \(G=386\) in \(\frac{\text{ln}}{\text{sec}^{2}}\) and the moment of the inertia \(I=7.90\) in [4]. Now, we substitute those values in Equation (5), and the result is, \(\sigma_{dyn}=5.50\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency (Hz)** & **Acceleration (g)** & **Grms** \\ \hline
0.3 & 0.2 & \\ \hline
0.6 & 2.0 & \\ \hline
5.0 & 5.0 & \\ \hline
15.0 & 1.6 & \\ \hline
50.0 & 1.6 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Random vibration base input profile.  
Figure 4: Component under testing.  
Figure 3: Acceleration base input.  
Step 5. Thus, by substituting the acceleration response from step 3 and the dynamic factor from step 4 in Equation (8), the vibration stress \(\sigma_{vib}\) induced by the random vibration is obtained, and it is shown in the fourth column in Table 3. The stress values are obtained after the cable trough is exposed to the random vibration in the three principal axes (x, y, z).  
From the results of Table 3, the principal vibration stress values \(\sigma_{1}=1188.00\) and \(\sigma_{2}=330.00\) are obtained. With that data now, we can proceed to use the Weibull distribution with the objective to obtain its reliability and random behavior. Yet, it is required to determine the shape \(\beta\) and scale \(\eta\) Weibull parameters, respectively.