esa-sceva/satcom-chunk-collection huggingface dataset:
# Meta-Analysis of the Performance of Previous Concrete with Cement and Aggregate Replacements
## 3 Statistical Analysis  
A fixed-effect meta-analysis (FEMA) statistical approach was employed in this study. It is a quantitative approach designed to establish a relationship between dependent and independent variables based on different parameters under consideration [23]. FEMA was used because the changes in the performance of PC are based on the variation of a single factor at a given time. In this study, only the properties that had been examined in more than five relevant articles were considered in the meta-analysis. The statistical parameter considered is the effect size, which differentiates between the dependent and independent variables [23,24]. A positive effect size signifies that the cement and aggregate replacement mixes perform better than the control mixes and vice versa. The dependent variables are the replacement mixes, while the independent variables are the control mixes. The factors considered are the mechanical, hydraulic, and durability properties. The steps followed for the meta-analysis are as follows:  
1. Calculate the pooled standard deviation (\(S_{p}\)), also known as pooled \(d\), using Equation (1). \[S_{p}=\sqrt{\frac{(Nr\ -\ 1)s_{r}^{2}+(Nc\ -\ 1)s_{\xi}^{2}}{Nr+Nc\ -\ 2}}\] (1)
2. Calculate the Effect size (\(E\)) following Equation (2). \[E=\frac{Mr\ -\ Mc}{Sp}\] (2)
3. Find the Variance (\(\vartheta\)) using Equation (3). \[\vartheta= \frac{1}{Nr}+\frac{1}{Nc}+\frac{E}{2(Nr+Nc)}\] (3)
4. Determine the value of corrected \(J\) by multiplying Equations (2) and (3) to obtain the modified Effect sizes and Variance, respectively, as per Equation (4). \[J=1+ \frac{3}{4(Nr+Nc\ -\ 2)-1}\] (4)
5. The value of the modified Variance (\(\lambda\)) is inversed and multiplied by the modified Effect sizes (\(\ell\)) to obtain the fixed pooled d using Equation (5). \[G=\frac{\lambda}{\ell}\] (5)
6. The fixed effect (F.E.) is then calculated at the upper and lower 95% confidence interval, as per Equation (6). \[\text{F.E.}=G\ \pm\ 1.96\ \times\sqrt{\delta}\] (6)  
where \(Nr\) = number of data points based on the cement replacement; \(Nc\) = number of data points based on control mixes; \(Sr\) = standard deviation based on the cement replacement; \(Sc\) = standard deviation based on control mixes; \(Sp\) = Pooled standard deviation; \(Mr\) = mean for data based on the cement replacement; \(Mc\) = mean for data based on control mixes; \(G\) = inverse of the product of effect size (\(E\)) and corrected \(J\).
# The Challenges of Advanced CMOS Process from 2D to 3D
## 3 Process Integration of New Transistor Architecture
### Precise and Uniform Fin Formation  
Electrical characteristics of FinFETs are closely correlated to the fin profile (width, height, and verticality) [25]. To handle more current, longer fins are needed which results in more challenges to manufacturing. Smaller fin width is favorable to channel electrostatic control, but this design cannot go too far, because of mobility degradation, random doping fluctuation (RDF) from the S/D gradients, and variation in off-state current.  
Fins are usually defined by SADP [26]. Fin etching in bulk silicon has to be controlled by a timer process. In the most cases, the fins at the edges of a cluster suffer a higher variability than those in the middle. To achieve uniform fin width and height in a cluster, dummy fins are required [27]. Some dummy fins need to be cut at the pitch. As fin pitch shrinks and approaches the overlay limit, cutting fins becomes more difficult. Fin isolation by STI and channel-stopper doping step are also challenging, because of the tighter pitch, difficulty to control on STI depth, and doping variation.  
To preserve structural integrity of fins with a high aspect ratio is also another challenge. The Si surface of narrow fins appears differently than Si bulk [28], and excessive Si loss was observed after the usual wet cleaning. Thus, wet process needs optimization with a diluted concentration and a lower temperature. Similarly, the oxidation is also faster at corner and tip of fins. Furthermore, the dry etching of fins is more stringent due to the 3D topography, therefore, a plasma pulsing scheme may be viable for minimizing Si loss [25]. The growth of defect-free alternated fin materials for high mobility channel application is also a challenge due to differences in thermal budget and lattice matching [27].
## 3 Results and Discussion
### In Situ Biological Field Test Results
#### 3.2.3. TCE and Derivatives' Concentration Variations
The _cis_-1,2-DCE and VC concentrations of each monitoring well during the in situ biological field tests are presented in Figures 8 and 9, respectively. The results revealed that, although substantial TCE degradation was observed to begin on day 60 at OW1, no apparent accumulation of _cis_-1,2-DCE and VC concentrations was observed. On day 160, the _cis_-1,2-DCE concentration of OW1 increased from 0.012 mg/L to 0.124 mg/L, exhibiting no considerable accumulation. VC concentrations at OW1 were lower than the detection limit (<0.01 mg/L). At OW2, _cis_-1,2-DCE concentrations varied similarly to the TCE concentration variations observed in the well. The concentrations clearly began to decrease on day 90. The _cis_-1,2-DCE concentration at OW2 decreased from 2.63 mg/L to 0.742 mg/L on day 160, reaching a removal rate of 72%. The VC concentration at OW2 increased from below the detection limit (<0.01 mg/L) to 0.112 mg/L. The OW1 monitoring results revealed that, although the TCE concentration decreased considerably, the metabolites did not accumulate in a notable manner. DCE concentration even decreased rapidly after day 90. These results suggested that adding FL in emulsions accelerated metabolite degradation and decreased metabolite accumulation. However, as the distance from the IW increased, the FL concentration became insufficient and caused the accumulation of metabolites such as VC, as observed in OW2. [PERSON] [32] compared PCE and TCE degradation effectiveness in microcosm tests in 2015 using two substrates--a general emulsified oil substrate (EOS) and a long-lasting emulsified colloidal substrate (LECS), a combination of EOS and nanoscale zero-valent iron (nZVI). The test results indicated that the VC concentration was about 0.5 mg/L 130 days after the EOS application. However, VC concentration met Taiwan Groundwater Control Standards 130 days after the LECS application. It was verified that nZVI of LECS did not result in the cumulation of metabolic byproducts compared with EOS. Ferrous lactate used in the test had similar effectiveness as in [PERSON]'s tests. Figure 7: TCE concentration variation of each monitoring well in the in situ field testing.
## 4 Discussion
When the VIs were used, we observed an essential collinearity between them, and the correlation with the yield was similar or even lower than the bands. The VIs should be interpreted together with their band components, seeking the collinearities with the bands when this collinearity is the basis for interpreting the index [101]. In using the NDVI, the lack of correlation between NIR and the red band would represent a distinct interpretability to the most common use since the fundament is the correlation between a high NIR and low red reflectance value [102]. At PI and GF, the individual use of VIs would hamper the knowing of a low correlation between visible bands and the yield. The NIR region could better inform us about the yield variability, according to the variability of NIR around the Valencia and Seville land rice crop area (Figure S2). The recommendation should be to perform different interpretations for B04 (red reflectance) and B08 (NIR reflectance) variability. The strong influence of chlorophyll in the visible bands, reported in the literature [39], may have caused the low R. The NIR region, influenced by the cell structure, rather than the chlorophyll [30], also showed a different behavior in our results. Therefore, future work is presented to study why the cell structure might better predict yield than chlorophyll content [103]. The new VIs observed a slight improvement in comparison with the NDVI that it would obtain the exact estimation of yield variability using any of the other indices, except LSWI. The vast number of publications about the NDVI and its increasing use by the rice industry prompted us to consider this index as the best-normalized alternative for analyzing yield variability at the commercial level. Based on the correlations for each year and location, the best moment to use a normalized VI would be MT; however, for the use of a band, the best moment would be PI; in any case, at MT, the use of indices did not significantly improve the use of bands. The use of VIs should be adequate if the biophysical basis is previously confirmed. Thus, at MT, the increase in crop greenness and cell structures in a similar way would also result in similar correlations with the yield for spectral bands and VIs [104; 105]. However, at PI and GF, the interpretability of VIs would be confusing, and it would not be the best option for studying yield variability. This study has been focused on detailed research of direct correlations between the yield and spectral bands or VIs. [PERSON]'s and [PERSON]'s tests are simple methods that limit the current scope for yield estimations; however, the novel database of commercial yield maps requires first exploring simple statistical outputs. The present work can be useful in continuing the research to develop advanced yield forecasting models. Moreover, PA practices can be directly modified, especially when using VI or spectral bands at different growth stages. The work is considered a first step to ensure data interpretability, providing key information for various research gaps, such as yield estimations, PA practices, and rice physiology.
## 2 Materials and Methods
### Multi-Elemental Analysis by ICP-OES  
The analysis of seven elements of interest (Ca, Fe, K, Mg, Na, P, and Zn) was carried out using an Iris Intrepid ER/S Thermo-Elemental (ThermoScientific, Waltham, MA, USA) ICP-OES spectrometer equipped with an Echelle grating optical system and a charge injection device (CID) solid-state detector on the digested samples (after transfer into polypropylene vials). A Timberline II (ThermoScientific) autosampler collected the samples with a flow rate of 1.85 mL/min, controlled by a peristaltic pump. Each sample was conveyed in a concentric pneumatic nebulizer, where it was nebulized (nebulizer gas flow of 0.6 mL/min) into the argon plasma, connected to a cyclonic spray chamber and eventually analyzed in a radial torch-reading configuration using the operating conditions recommended by the manufacturer, i.e., radiofrequency power of 1.200 kW, coolant gas flow of 12 L min\({}^{-1}\), and auxiliary gas flow of 0.5 L min\({}^{-1}\). Ultra-High Purity 5.0 Grade Argon (99.999%) was used in the spectrometric analysis. The emission lines with the best signal-to-noise ratio and fewest spectral interferences were chosen, and the emission intensity underwent a manual background correction. The measurements on standards and digested samples were performed in four replicates and results averaged. A 200 \(\upmu\)g/L Yttrium solution as the Internal Standard (line 324.228 nm) was used to monitor matrix effects, possible non-spectral interferences, and instrumentation drift. Cleanliness of the introduction system and an absence of memory effects were controlled by the analysis of one standard solution (0.06 \(\upmu\)g/mL for Fe and Zn, 10 \(\upmu\)g/mL for Mg, 20 \(\upmu\)g/mL for K, and 30 \(\upmu\)g/mL for Ca, Na, and P) followed by a blank every six samples.
## 1 Introduction
### Contributions  
The contributions obtained with the proposed method are given below:  
* For the first time, an approach is presented that optimizes dual-adaptive PV structures using GA under partial shading conditions. Therefore, the proposed method provides more connection possibilities compared to single-adaptive structures and provides higher power from the array.
* In the proposed method, it is shown that a real-time applicable solution is provided in the optimization process thanks to the use of GA, which is a metaheuristic approach, and the effects of the exponential increase in the solution space are minimized. Thus, it is demonstrated that the reconfiguration provides scalability with a metaheuristic method, i.e., it can be used for larger sized PV arrays.
* Since the method uses only the short-circuit currents of the adaptive panels and each row of the fixed part, it is overcome with a minimum number of sensors.
* In the study, the proposed model is simulated under different shading scenarios and the performance increase in the PV array is shown.
* The matching mechanism between adaptive panels and fixed panels enhances energy efficiency. The dual-adaptive structure provides a broader matching space.
* In the proposed method, the mutation, crossover and fitness value optimization processes of the GA are specially designed for dual-adaptive PV arrays. Real-time optimization is achieved by eliminating the inappropriate connections in the permutation space.
# A Digital Twin-Based State Monitoring Method of Gear Test Bench
## 2 Construction of the State Monitoring System of the Gear Test Bench Based on a Digital Twin Model  
The state monitoring system of the gear test bench based on a digital twin model is composed as follows: physical information acquisition system and digital twin monitoring system, as shown in Figure 1. Here, the physical information acquisition system is composed of the gear test bench, sensors (i.e., temperature sensor, torque sensor, noise sensor, and acceleration sensor), working condition machine, data acquisition interfaces, etc. The digital twin monitoring system consists of different modules, such as data driving, operation management, human-machine interaction, digital twin model, signal processing, running state monitoring, and data analysis.  
Under the normal running state, in real time, the physical information acquisition system can collect the test bench operation data and transform it into real physical quantities through sensors during the normal operation of the gear test bench. At the same time, it connects with the equipment condition machine to obtain the real-time running state of the gear test bench equipment. The physical quantities are converted into digital data by means of the data acquisition interfaces. RS-232 and other interfaces are used to collect and convert these physical data, such as temperature, noise, vibration, acceleration, etc. The operation of the digital twin monitoring system is based on the Windows operating system and developed jointly through MATLAB, SolidWorks, ADAMS, Visual C++, and other software. The management program can access the data acquisition interface through the driver in the monitoring system, so as to obtain the real-time signal of the running state of the gear test bench. The signal processing module then conducts the acquired raw signals and maps the processed data to a digital twin model. In the end, the data analysis module can analyze the collected data and extract the data features.
# Aircraft Wake Recognition Based on Improved ParNet Convolutional Neural Network
## 4 Aircraft Wake Identification Results and Discussion
### Comparison Experiments of Different Attention Modules  
To compare the difference between the CBAM and other attention modules in improving the wake recognition performance of the model, four attention modules of the ECA, SE,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Value (\%)** & **MFLOPs** \\ \hline ParNet & 96.88 & 98.07 & 95.63 & 96.84 & 1.79 \\ DSC-ParNet & 97.34 & 98.10 & 96.56 & 97.32 & 0.84 \\ CBAM-ParNet & 97.81 & 97.51 & 98.12 & 97.82 & 1.70 \\ DSC-CBAM-ParNet & 98.91 & 99.06 & 98.90 & 98.90 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recognition effect of ablation experiment.  
Figure 11: Verification accuracy and loss curves of ablation experiment. (**a**) verification accuracy; (**b**) verification loss.  
CA, and SSE were introduced into the recognition model [24; 25; 26]. During the training, the changes in verification accuracy are shown in Figure 12.  
Figure 12 shows that the attention modules of the SE and SSE with only channel attention had the worst effect. The CA module had a certain spatial representation capability, which improved its verification accuracy better than the original model. The ECA module had good lightweight characteristics, which resulted in a good balance between the calculation amount and the feature extraction capability of the model. The verification accuracy of the ECA and CBAM was similar. In this study, the CBAM was applied to the aircraft wake recognition model. Because of the special spatial attention of the CBAM, the verification accuracy increased significantly, achieving the highest verification accuracy. The performance was better than the other attention modules. In this study, the CBAM was used in the improved ParNet to effectively improve the wake recognition performance.  
The effects of wake recognition using the improved models with different attention modules are shown in Table 3. When the four evaluation indicators of the CBAM adopted in this study reached the optimal value, the recognition effect was the best.
## 2 Related Works  
### Weighted-Fusion-Based Matching Cost Function  
The main task of matching cost function is to quantify the matching cost of corresponding points between stereo image pairs. Classical matching cost computation methods can be divided into three kinds: pixel-based methods, window-based methods and non-parametric methods. Each kind of method has their own disadvantages. Hence, the commonly used form of matching cost function is the weighted-fusion of several different methods.  
[PERSON] et al. [17] effectively combined the absolute differences (AD) and Census transformation [18], known as AD-Census. It provided more accurate matching result than individual method. [PERSON] et al. [19] used the pixel-based truncated absolute difference of AD and gradient as the matching cost, which was robust to illumination changes. [PERSON] et al. [20] proposed a new matching cost function that combined three different similarity measures including AD, gradient and Census transformation. It could reduce the radiometric distortions and the effect of illumination variations. More recently, [PERSON] et al. [21] proposed a weighted-fusion matching cost function that included AD, square difference (SD), Census and Rank transformations. In addition, they considered the stereo matching as an optimization problem and used an evolutionary algorithm to automatically optimize the parameters in the cost function.  
The mentioned above weighted-fusion-based matching cost functions were all designed for passive binocular stereo vision, so they only considered the spatial feature and could not utilize the temporal feature brought by multiple speckle stereo image pairs.
## 3 System Model  
### LEO Edge Computing  
The LEO satellite network consists of a constellation of LEO satellites positioned in low Earth orbits. These LEO satellites act as access points and routers. These satellites work together to ensure continuous coverage and connectivity to all the regions of the world. The low orbital altitude of LEO satellites results in reduced signal latency, making it suitable for relatively low-delay communication. The constellation's global coverage allows it to reach even remote and sparsely populated areas, providing Internet access to a wider population.  
LEO satellite networks typically use mesh-like topology, in which there are several paths between every satellite and another satellite. The main advantages of this topology are flexibility and fault tolerance: data can be transmitted through different paths, ensuring system reliability and continuity even if a satellite fails. Figure 1 illustrates a typical edge-computing scenario in an LEO satellite network. UAVs or user terminals offload inference tasks to the LEO network via a user link, and these tasks are distributed among several satellites within the LEO network. The satellites are interconnected in full-mesh topology, which provides path redundancy and seamless coverage. For the sake of visual clarity, not all inter-satellite links are shown in the figure.  
Due to the satellites being distributed around the Earth's orbit, there may be significant differences in communication delay between different satellites. The communication delay between satellites depends on the hop count, ISL bandwidth and signal propagation distance.  
Figure 1: Edge computing in the LEO satellite network.  
In order to provide a global edge-computing service, an important idea is to integrate edge computing into the LEO network [22]. Specifically, the edge-computing platform should be deployed on the LEO satellite. Additionally, the edge-computing platforms on every satellite should work collaboratively to form a distributed satellite-based edge cloud with abundant computing resources.  
DNN inference is an important type of algorithm that will be run on the LEO satellite-based edge-computing platform. However, the computing power of an LEO satellite is less than that of an edge-computing node in the terrestrial network, so it is necessary to use multiple satellites' computing resources in order to complete massive inference tasks in a short time.
## 3 Experiment 1: Frequency Dependence of Vibration-Weight Illusion
### Results  
#### 3.5.1 Grip Force at Different Frequencies (Task 1-1)  
Figure 2 indicates the means and standard errors of the time-series grip force among all the participants. For all types of vibratory stimuli, the grip force began to increase within 0.1 s after the onset of the vibration. After a rapid and large increase in the grip force, the excessive grip force was adjusted to potentially minimum levels to hold the activated voice coil motor during 0-0.5 s. Similar changes in the grip force have also been observed in other studies investigating its adjustment [34,37,38].  
Figure 2: Grip force over time during Experiment 1. The red lines and dashed black lines indicate the means and range of standard errors, respectively. Stimuli started at 0 s and lasted 3 s. For the analysis of the change in grip force, means of 0.5 s before the stimulus (\(-\)0.5–0 s) and after the stimulus (1-1.5 s), which are surrounded by the black and green dashed lines, respectively, were used.  
The means and standard errors of the increase in the grip force for each vibratory stimulus are shown in Figure 3A. The lower the frequency, the greater the increase in the grip force. There was a negative correlation between the frequency and increase in the grip force (\(\rho=-0.30\), \(p=0.027\)). The presentation of the 30 Hz (\(t(10)=4.03\), \(p=0.002\), two-tailed \(t\)-test) and 100 Hz (\(t(10)=2.31\), \(p=0.043\)) vibrations significantly increased the grip force. For the 60 Hz vibratory stimuli, the grip force tended to increase but not significantly (\(t(10)=2.21\), \(p=0.051\)). The mean grip force before the presentation of the stimuli was 58.7 g, and the 30-100 Hz vibratory stimulus increased the grip force by 9.8 g (17%) on average.
## 3 Results and Discussion
### Impact of Drilling Fluid
#### 3.2.4 Impact of Density
Collectively, increasing drilling fluid density is an effective technical means for reducing frictional torque and associated energy losses in purely horizontal drilling. However, in practical applications, an excessively high drilling fluid density will significantly increase the circulating pump pressure. Therefore, a balance must be struck, and density should be increased judiciously, possibly in conjunction with appropriate amounts of drag reducers or lubricants, to maximize the potential for friction and energy consumption reduction while effectively controlling the pump pressure.