esa-sceva/satcom-chunk-collection huggingface dataset:
# Imputing Covariance for Meta-Analysis in the Presence of Interaction
## 3 Meta Analysis for the Main Effect and Interaction Effect
### Random-Effects Meta-Analysis  
For the random-effects meta-analysis, the overall main effect, \(\beta_{1,\text{overall}}\), can be calculated as  
\[\beta_{1,\text{overall}}=\frac{\sum_{i=1}^{k}w_{1i}^{*}\hat{\beta}_{1i}}{\sum_ {i=1}^{k}w_{1i}^{*}} \tag{6}\]  
where  
\[w_{1i}^{*}=\frac{1}{var(\hat{\beta}_{1i})+\tau^{2}}\]  
The overall variance of the main effect, \(\beta_{1,\text{overall}}\), from the random-effects model can be calculated as  
\[V_{1,\text{overall}}=\frac{1}{\sum_{i=1}^{k}w_{1i}^{*}} \tag{7}\]  
The between-study variance, \(\tau^{2}\), for the random-effects meta-analysis is calculated in the following steps:  
Step 1: Calculate the Q statistic  
\[Q=\sum_{i=1}^{k}w_{i}\beta_{i}^{2}-\frac{(\sum_{i=1}^{k}w_{i}\beta_{i})^{2}}{ \sum_{i=1}^{k}w_{i}} \tag{8}\]  
Step 2: Calculate \(c\)  
\[c=\sum_{i=1}^{k}w_{i}-\frac{\sum_{i=1}^{k}w_{i}^{2}}{\sum_{i=1}^{k}w_{i}} \tag{9}\]  
Step 3: Calculate \(\tau^{2}\)\[\tau^{2}=\left\{\begin{array}{cc}\frac{Q-df}{c}&\text{if}\ Q>df\\ 0&\text{if}\ Q<df\end{array}\right. \tag{10}\]  
Here, \(df\) represents the degrees of freedom, which is equal to the number of studies minus one, i.e., \(k-1\).  
Similarly, for the interaction effect, \(\beta_{2i}\), the overall effect, \(\beta_{2,\text{overall}}\), and its variance, \(V_{2,\text{overall}}\), can be calculated using the same approach as for the main effect.  
After calculating the Q statistic, the \(I^{2}\)-statistic, which quantifies the percentage of variation across studies due to heterogeneity, can be determined using  
\[I^{2}=\left\{\begin{array}{cc}\frac{Q-df}{Q}*100\%&\text{if}\ Q>df\\ 0&\text{if}\ Q<df\end{array}\right. \tag{11}\]
# The Thermal Analysis and Heat Dissipation Structure Optimization of a Propeller Driver System
## 3 Thermal Analysis and Optimization of Heat Sink Based on Sealed Cabin
### The Influences of Base Thickness on Thermal Performance  
The total number of fins is set as 18. The fin thickness is set as 2 mm. The fin length is set as 16 mm. Then, the impacts of base thickness on the driver's maximum temperature are analyzed. Five kinds of thicknesses are discussed here: 5 mm, 10 mm, 15 mm, 20 mm, and 60 mm. Figure 15 shows the temperatures of monitoring points on the U1, U2, U3, and U4 at various base thicknesses. When the thickness of the base increases from 5 mm to 60 mm, the temperature curve of the device U1 shows a trend of decreasing, increasing, decreasing, and increasing with the increase in the base thickness. After the thickness of the pedestal increases, the temperature curves of devices U2, U3, and U4 show a trend of first decreasing and then flattening. In addition, as the thickness of the base increases, the cross-sectional area of the thermal conductivity channel from base to fin inside the heat sink increases, which can strengthen the heat dissipation of the driver through conduction. However, when the thickness of the base increases, the distance between the device and the fins decreases (especially device U1); the heat will transfer back from the hot fin to the low-temperature parts of the driver via radiation and convection. Under the combined influences of two ways of heat transfer above, the temperature of device U1 decreases and then increases twice as the base thickness increases. When the thickness of the base reaches 40 mm, increasing the thickness of the base cannot improve the heat dissipation of the system. Therefore, the optimal base thickness is 40 mm, and the temperatures of devices U1, U2, U3, and U4 are reduced by 3%, 1.1%, 1.5%, and 2.9%, respectively. At this time, the temperatures of U1, U2, U3, and U4 monitoring points are 39.36 \({}^{\circ}\)C, 43.22 \({}^{\circ}\)C, 48.67 \({}^{\circ}\)C, and 50.77 \({}^{\circ}\)C.
## 4 Experimental Results and Analysis
### Dynamic Occurrence Law of Oil and Gas in Depletion Stage
#### 4.1.3 Evolution Process of Gas Phase  
Through the simulation of the depletion development of the porous carbonate reservoir and the fractured porous carbonate reservoir, it is found that gas in dissolved crude oil undergoes a series of changes, such as nucleation, growth and the merger of bubbles in the porous media, as shown in Figure 19.  
Nucleation: when the pressure is lower than the bubble point pressure, the dissolved gas begins to separate from the oil phase, forming a bubble core. The bubble is bound to the pore wall and does not flow with the oil flow. Expansion: the pressure continues to reduce, the solubility of the gas becomes less and less, through diffusion into the formed bubble core, so that the bubble continues to expand. Coalescence: in the process of bubble expansion, multiple adjacent bubbles begin to contact and merge to form large bubbles. Migration: Large bubbles continue to coalesce and eventually form a continuously flowing gas phase.  
From the evolution of bubbles, it can be seen that bubbles in porous media need to go through a series of processes from generation to migration, and the evolution time is greatly affected by the size of the pore throat and the fractures in the porous media. At the same time, in the evolution process of the gas phase, nucleation is not only affected by pressure, oil-dissolved gas is just an internal cause of degassing: whether or not the oil will degas still depends on the external conditions, the degassing of the external cause. This  
Figure 19: Evolution process of gas phase. (**a**) Oil saturation; (**b**) Bubble nucleation; (**c**) Bubble expansion; (**d**) Bubble coalescence; (**e**) Continuous gas phase migration.  
Figure 18: Threshold segmentation of the fracture porosity model depletion development.  
is because the oil-dissolved gas can also exist in liquid oil, and the gas migration process without a degassing area can also be used as the external cause of bubble nucleation, which can be used to gather the dissolved gas molecules in the undergassed region.
# Computational Modeling of Passive and Active Cooling Methods to Improve PV Panels Efficiency
## 2 Modeling and Computational Details  
This section presents the modeling and meshing process of the PV panel for the three cooling methods, as well as the physical processes considered in the investigation. The simulations of the passive, active, and hybrid panels were implemented with the COMSOL Multiphysics software. This software was used because it has an intuitive interface characterized by ease of use and flexibility, which allows the modeling of complex physical problems quickly and accurately. Thus, it was possible to model the phenomena of heat transfer, turbulent flow, radiation in participating media, and the dynamics of water in the solar panels.
## 3 Energy Efficiency Problem Model for SONs  
To address the issue of energy efficiency in edge-AI-driven SONs, this section begins with an analysis of the global traffic model based on the gravity model. It proceeds to explore the energy efficiency problem model and concludes with the GreenRP algorithm design. The primary objective of this section is to propose and establish the designed energy-efficient algorithm model, aimed at optimizing the energy efficiency and operational costs of SONs for edge AI sustainability.
## 5 Results
### Comparison of Predictive Performance of Models Using Dataset-1
#### 5.1.1 Experiments Using fastText  
At first, the models are trained on fastText word embedding, and the results are presented in Table 3. All machine learning models show almost similar results and achieve an 81% F1 score using fastText for the sentiment analysis of in-text citations. SGD achieves 86.89% accuracy, which is the highest among all machine learning models. RF achieves the highest precision score, 79%, which is 3% greater than the precision achieved by other models. SGD, LR, and VC (LR + SGD) achieve 87% recall. For deep learning models, LSTM achieved the lowest result, with 86% accuracy, 70% precision, 74% recall, and 72% F1 score. However, the customized CNN achieves the best result, with the highest accuracy (89%), precision (87%), recall (85%), and F1 score (86%) for citation sentiment analysis.
# Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
## 3 Experimental Results and Discussion
### Discussion  
To facilitate the discussion, three models for ablation comparison are set up in the experiments for exploring the fault diagnosis performance of the proposed models, and the details of differentiating them are shown in Table 9.  
1. The first model is without embedding the CBAM attention mechanism inside the residual block and without using the Meta ACON-C activation function, named Base.
2. The second model is to embed the CBAM attention mechanism inside the residual block, but without using the Meta ACON-C activation function, named Base + A.
3. The third model is to use the Meta ACON-C activation function in the second feature extraction layer of the model, but without embedding the CBAM attention mechanism in the residual block, named Base + B.
4. The proposed model is named Base + A + B.
# A Feasibility Study of the Drive-By Method for Damage Detection in Railway Bridges
## 3 Plan of the Simulations
### Data Processing Techniques
#### 3.4.2 Root Mean Square (RMS) on the Moving Window  
The second approach that is proposed is intended to look for an effect on the dynamic response of the vehicle, caused by the presence of damage on the bridge, seen as a local disturbance. In principle, indeed, the presence of a defective section can generate a variation in the amplitudes of frequency components of bogie andaxlebox accelerations, not only in the quasi-static frequency range, but also in the natural frequency related to bogie motion, and in the frequency related to the bridge first flexural mode, when excited. Figure 14 shows the accelerations of point number 3 (see Figure 9) of the leading bogie, corresponding to bridge #2, train (TSR), damage entity (D40), with a train speed of 120 km/h. In the time signals, the difference in the acceleration recorded for the healthy and damage bridge starts slightly before the faulty section and continues several meters after it. In the frequency domain, an increase in the PSD component related to the bridge natural frequency is observed for the damaged bridge.  
To catch the variations in the dynamic behavior of the system, and to precisely localize the position where this variation is maximum, an RMS with a moving average window is performed with length T = 0.4 s, and a high overlap level of 95%. (The time-based window length of 0.4 s corresponds to 13 m at 120 km/h). Before calculating the RMS, signals are filtered with a cut-off frequency of 30 Hz, since no relevant differences were detected in the acceleration peaks that are related to the wheelset crossing of the sleepers. RMS corresponding to the healthy and damage conditions are then subtracted, and the RMS damage component is obtained. The result of the analysis is reported in Figure 15, highlighting that the maximum of the RMS damage component corresponds to the location of the defect.  
Figure 14: Comparison between bogie accelerations resulting from the damaged and healthy bridges. Bridge type #2, TSR train type, speed 120 km/h, damage level D40. (**a**) Time signals. (**b**) Power spectral density.
## 7 Results and Discussion
### MRE-EHMM  
Table 3 shows the performance of the introduced MRE-EHMM in comparison to PCA-EHMM at \(k=9\). From the table, MRE-EHMM and PCA-EHMM exhibited approximately the same \(\mathcal{S}_{e}\), \(\mathcal{S}_{p}\), \(\mathcal{P}\), and \(\mathcal{F}1\) performances, even as \(\chi\) increases. However, one should keep in mind that MRE-EHMM is five-dimensional while PCA-EHMM is nine-dimensional. However, the computational time complexity of EHMM is proportional to the dimension size and increases as the dimension grows. Thus, when compared to the PCA approach, the MRE approach requires significantly less processing time from EHMM. As a result, the MRE approach can be used as a more efficient performance alternative to the PCA approach for real-time credit fraud prediction systems.
## 2 Materials and Methods
### Methods
#### 2.3.2 Physical Extraction Methods  
There has been rapid development and wide utilization of the double logistic function (DLF) method for extracting phenological indices on a site or regional scale [30; 70]. It is a common nonlinear function consisting of two logistic functions, with each function having an inflection point and a curvature parameter. The following logistic function was used to extract values for the start of the growing season (SOS) and the end of the growing season (EOS). The parameters for the DLF are adjustable to fit diverse ecosystem responses, making it highly adaptable to different ecosystem types and environmental conditions.  
\[Y(t)=\alpha_{1}+\frac{\alpha_{2}}{1+e^{-\beta_{1}(t-\beta_{1})}}-\frac{\alpha_ {3}}{1+e^{-\beta_{2}(t-\beta_{2})}} \tag{4}\]  
\[SOS=\beta_{1} \tag{5}\]  
\[EOS=\beta_{2} \tag{6}\]  
where \(Y(t)\) is the observed data value at time \(t\) for DOY (day of year). The value during the dormant winter period is represented by \(\alpha_{1}\). The amplitude between the winter dormant period and the spring and early summer peak is given by \(\alpha_{2}\)-\(\alpha_{1}\), while the amplitude between the winter dormant period and the late summer and autumn peak is \(\alpha_{3}\)-\(\alpha_{1}\). Curvature metrics \(\alpha_{1}\) and \(\alpha_{2}\) are utilized to adjust the slope of the logistic function. \(\beta_{1}\) and \(\beta_{2}\) are the inflection points of the DOY corresponding to the transitions of the vegetation green-up and senescence stages. The fitted parameters of \(\beta_{1}\) and \(\beta_{2}\) signify SOS and EOS, respectively. The length of the growing season is calculated as the difference between \(\beta_{1}\) and \(\beta_{2}\).
## 5 Results and Discussion  
In the training process, verification is taken at the end of every epoch, and the final verification accuracy reaches over 99.99%. The training progress with accuracy and loss results is shown in Figure 9.  
When the network training is successful, then the test process is carried out. Using the method of generating training data mentioned above, the random modulated data but with a different random seed is generated. Another 4000 test frames with random turbulence strength and AWGN with random SNR are generated for each format.  
The trained network is used for recognition, and the test accuracy reaches over 99.99%. Three confusion matrix figures for the test data for three gamma-gamma turbulence strengths are shown in Figure 10. Figure 10a depicts the integrated test accuracy under different SNR (10-30 dB) in weak turbulent channels, and the test accuracy can reach 100%. Similarly, Figure 10b,c depict the moderate and strong turbulent channel circumstances, and the test accuracy can reach 99.9864% and 99.9818%, respectively. Moreover, Figure 11 shows the same meaning more intuitively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Small batch size & 256 \\ Maximum number of rounds & 6 \\ Initial learning rate & 0.3 \\ Reducing period & 4 \\ Learning rate drop factor & 0.3 \\ Dropout rate & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network training parameters.  
Figure 9: Training progress with accuracy and loss results.
# Application of a MEMS-Based TRNG in a Chaotic Stream Cipher
## 5 Cryptanalysis
### Reconstruction of the Map  
A possible attack for some chaotic stream ciphers consists of trying to reconstruct the map by finding the initial parameters via analyzing the output sequence. For example, if an attacker knows the value of \(x_{i}\) and \(x_{i+1}\), he could use Equation (1) for \(f(x_{i-1})\) and \(f(x_{i})\) to calculate the values of \(\gamma\) and \(x_{i-1}\).  
This method, however, seems to be very difficult to be carry out successfully in our system. First, we are only transmitting eight bits from each \(x_{i}\), so the information that a possible attacker has is very limited. Therefore, if an attacker could somehow manage to obtain the last eight bits of \(x_{i}\) and \(x_{i+1}\), there would still be \(\left(2^{56}\right)^{2}=2^{112}\) possible values of \(x_{i}\) and \(x_{i+1}\) that would have to be tried. Furthermore, after each iteration, the LSB of each \(x_{i}\) is XORed with a bit generated by an LFSR, which makes the reconstruction even more difficult to accomplish.  
Therefore, we can conclude that it is nearly impossible to reconstruct the chaotic map in a reasonable amount of time.
# Effects of Polypropylene Fiber on the Liquefaction Resistance of Saturated Sand in Ring Shear Tests
## 2 Materials and Methods  
### Materials  
Silica sand No. 6 (s6), made from grinding of silica sandstone, was used in this study. Based on the geotechnical test results, the used sand was characterized with specific gravity (\(G_{\mathrm{s}}\)) of 2.64, maximum dry density (\(\rho_{\mathrm{max}}\)) of 1.58 g/cm\({}^{3}\) and minimum dry density (\(\rho_{\mathrm{min}}\)) of 1.25 g/cm\({}^{3}\). Its particles had a mean grain size (\(D_{\mathrm{50}}\)) of 0.30 mm, gradation coefficient (\(C_{\mathrm{g}}\)) of 1.34 and uniformity coefficient (\(C_{\mathrm{u}}\)) of 2.29. The grain size distribution of sand is shown in Figure 1.  
Figure 1: The grain size distribution of used sand.  
With excellent properties including lightweight, high strength, good elasticity and dispersity, and economical cost (about $1.10/kg), polypropylene fiber is the most widely used inclusion in laboratory testing of soil reinforcement. The specification of indoor-studied polypropylene fiber is concentrated in 6-50 mm [32]; grain size was incorporated into reinforced soil and conventional, short polypropylene fiber was used in this study. The selected short polypropylene fiber is pictured in Figure 2. It has a length 12 mm, diameter 0.034 mm, specific gravity (\(G_{\mathrm{s}}\)) of 0.91, tensile strength of 350 MPa, elasticity modulus of 3500 MPa, fusion point at 165 \({}^{\circ}\)C, burning point at 590 \({}^{\circ}\)C and good dispersibility. In this study, the amount of fiber used was calculated as the percentage to dry sand ratio, as shown in Equation (1):  
\[P_{f}\ =\ \frac{W_{f}}{W_{\mathrm{s}}}\cdot 100\% \tag{1}\]  
where \(W_{f}\) (g) = the weight of used fiber and \(W_{\mathrm{s}}\) (g) = the weight of used dry sand.
## 1 Introduction
The research subject includes four focal companies of an industrial SC, two from the agrifood industry and two from the metalworking industry, all located in Southern Brazil. This study contributes to SCM studies by encompassing four SC meta-strategies, four competitive priorities, and an entropy-based measure in a single study. The subsequent sections are structured to provide a comprehensive review of SC complexity, delineate the methodology, present and discuss the results, and draw conclusions from the findings. This systematic approach ensures a thorough exploration and understanding of the intricate dynamics surrounding complexity in industrial SCs.
# An Experimental Study on Alkali Silica Reaction of Concrete Specim Using Steel Slag as Aggregate
## 3 Results and Discussion
### Evaluation Expansion Properties of Concrete with Steel Slag
#### 3.3.2 Evaluation of Crack Propagation in Concrete Specimens Due to ASR  
Cracks caused by ASR are typical non-structural cracks and are generally different from cracks caused by thermal stress or drying shrinkage. Besides, the ASR crack pattern occurs primarily in the horizontal direction [32]. In this study, we evaluated the pattern of cracks caused by ASR in the concrete specimens by drawing crack maps during the test period. The concrete specimens using natural aggregates did not exhibit any cracks due to ASR. Consequently, on the crack characteristics of the concrete specimens using steel slag were evaluated.  
The typical crack progression patterns recorded during the test are shown in Figure 5. As shown, all concrete specimens, regardless of the type of binder, exhibited ASR cracking. Moreover, the number of cracks and their thickness increased during the test period, growing horizontally. Figure 5a shows the crack map of the concrete specimens at the time of crack initiation. Although the expansion rate was still below the permitted limit, all the concrete specimens exhibited ASR cracking. The specimen with the OPC binder experienced crack initiation earlier than the specimens with mineral admixtures, exhibiting a trend that is similar to be measured expansion rate. The expansion values of the specimens with FA20 and BFS30 were similar after 3 days, however, crack initiation in the concrete with FA20 occurred earlier than that in the concrete with BFS30. Thus, BFS30 offers the highest resistance against ASR cracking in concrete specimens using steel slag aggregate. Figure 5b shows the crack patterns after the first 7 days of the test period. In the specimens using OPC binder, most of the cracks after 7 days were due to expansion beyond the permitted limit. The crack patterns and expansion in the specimens with the value of FA20 and BFS30 were similar.  
Figure 4: Results of ASR expansion (concrete specimens).  
Figure 5: Crack progression patterns of concrete specimen. (**a**) Test period: crack initiation; (**b**) Test period: 7 days; (**c**) Test period: 14 days.  
Figure 5c shows the crack patterns after 14 days of the test period, when all specimens had experienced expansion beyond the permitted limit. The cracks in the surface of the specimens developed suddenly and the number of cracks increased dramatically. The existing cracks joined the newer cracks, increasing their length. In particular, the specimens with OPC showed a typical concrete surface with developed ASR cracking, wherein cracks tend to occur in a polygonal pattern.
###### Abstract
The objective is to present and discuss a conceptual model and a horizontal (multi-domain) End-To-End (E2E) dynamic multi-provider Orchestrator architecture approach to reduce regulatory barriers to the expansion and improvement of the performance of 5G and future communication systems, as well as to preliminarily prove its efficiency in a V2X scenario under a higher degree of automation for an imminent collision scenario. The remainder of this paper is organized as follows: Section 2 contextualizes the problem in question; Section 3 exposes the related work; Section 4 presents the proposed Orchestrator architecture by reporting, analyzing, and discussing it; Section 5 presents the experimental tests conducted; Section 6 reveals the results and discusses them. Finally, Section 7 concludes the paper and outlines the future perspectives from this work.