esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
An experimental study has been conducted to investigate the time-average streamwise flow characteristics along a 1:20 scale articulated lorry model with and without using the linear AC-DBD plasma actuation at the rear end of a square-back trailer. Force balance was used to measure the drag force acting on the articulated lorry model with and without the implementation of the plasma actuation. Force balance data showed that the AC-DBD plasma actuation exerts a negligible effect in altering the drag coefficient of the lorry model. In addition, the two-component particle image velocimetry measurements were conducted to obtain the time-averaged flow velocity, turbulence and vorticity information along the centreline of the wake region. Results about the time-average velocity field show that using the linear AC-DBD plasma actuation at the trailer rear end could neither alter the size nor the flow velocity in the x-direction along the wake region of the trailer model. However, it did show that the shear layer is deflected more downwards due to the effect of the AC-DBD plasma actuation at 8 and 9 kHz actuation frequencies. In addition, the flow velocity in the y-direction along the wake region could be altered by the effects of the linear AC-DBD plasma actuation. Turbulence kinetic energy was used to assess the flow turbulence levels along the wake region of the trailer model. Results show that higher levels of turbulence kinetic energy were generated in those trailers with the linear AC-DBD plasma actuation being implemented. It was observed that the highest levels of the turbulence kinetic energy appeared along the upper shear layer when the actuation frequency is 9 kHz. In addition, it was concluded that using the linear AC-DBD plasma actuation increased the levels of turbulence kinetic energy and the flow vorticity fluctuation along the wake region but did not show any significant effect in changing the frequency response of the flow in the wake region. Finally, it was proposed that the AC-DBD plasma actuation could not generate any significant impact to affect the flow characteristics in the wake region and hence, no flow control effect can be observed by using this active flow control technique in the present study.  
Experimental work was jointly conducted by K.H.L. and R.S. Data analysis and manuscript preparation were jointly done by K.H.L., R.S. and K.K.  
This research received no external funding.  
The author would like to thank the technical support provided by the University of Glasgow for the manufacturing of the false floor used in the present study.
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 6 Industry 4.0 Applications
In this case, there exist \(2^{31}=2.1475\times 10^{9}\) different combinations for performing the assembly task. As can be seen from Figure 5, the first two bits determine which bolts are used to fasten nut #1, the bits numbers 3 and 4 are used to determine which bolts are used for nuts number 2, the fifth bit is for nut number 3. Since we have four \(M10\times 20\) mm bolts, the remaining bolt goes to nut number 4. Bit number 6 determines which \(M8\times 20\) mm is used to fasten nut number 5, the other one is used to fasten bolt number 6. The bit number 7 is used for bolts numbers 7 and 8. However, since the order of performing these tasks effects the cost function, it is required to determine the order of performing the tasks using the next 24 bits. All 3 of them making an integer value from the interval of \([0,\ 7]\). In this case, if the \(i\)th value after converting to decimal value is equal to \(j\), the \(i\)th and \(j\)th task swap their position. This results in avoiding forbidden conditions which is difficult to avoid otherwise. The cost function to be optimized is the total distance travelled by robot with its initial conditions being at point \((10,\ 30,\ 10)\). The evolution of total distance travelled by the robot is depicted in Figure 6. The total number of particles in this optimization is chosen to be 10 with the maximum number of iterations being equal to 200. As can be seen from this figure, the best random initialization for the total distance travelled by robot is 328.0 cm which decreases to 273.2 cm after optimization, resulting in a 16.7% improvement. The optimum solution of the problem is represented in Table 4 and the assembly process done by UR5 is illustrated in Figure 7. The total assembly time is 116 seconds. Where for our laboratory safety reasons, the joint speed of the robot is limited to 90/s. However, as the UR5 is capable of working with joint speed equal to 180/s, the total assembly time could be reduced if it works under full speed. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Action** & **Bolt** & **Nut** \\ \hline Step 1 & \#8 & \#8 \\ \hline Step 2 & \#7 & \#7 \\ \hline Step 3 & \#4 & \#1 \\ \hline Step 4 & \#5 & \#5 \\ \hline Step 5 & \#3 & \#4 \\ \hline Step 6 & \#6 & \#6 \\ \hline Step 7 & \#2 & \#3 \\ \hline Step 8 & \#1 & \#2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal assembly solution obtained from the optimization algorithm. Figure 5: Encoding assembly sequence to a particle.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Relationships between Variables**} & \multicolumn{5}{c}{**R.W.**} & \multicolumn{2}{c}{**S.R.W.**} \\ \cline{3-8}  & & **Estimate** & E.E. & **C.R.** & \(p\) & **EST** \\ \hline SC & \(\leftarrow\) & SD & \(0.038\) & \(0.016\) & \(2.454\) & ** & \(0.270\) \\ SC & \(\leftarrow\) & PA & \(0.002\) & \(0.000\) & \(8.396\) & *** & \(0.441\) \\ MD & \(\leftarrow\) & SD & \(0.111\) & \(0.095\) & \(1.172\) & \(0.241\) & \(0.036\) \\ PUVG & \(\leftarrow\) & SD & \(-0.164\) & \(0.015\) & \(-10.587\) & *** & \(-0.485\) \\ MD & \(\leftarrow\) & SC & \(0.340\) & \(0.178\) & \(1.912\) & \(*\) & \(0.258\) \\ PUVG & \(\leftarrow\) & SC & \(-0.309\) & \(0.029\) & \(-10.655\) & *** & \(-0.394\) \\ MD & \(\leftarrow\) & PA & \(-0.001\) & \(0.001\) & \(-1.142\) & \(0.254\) & \(-0.034\) \\ PUVG & \(\leftarrow\) & PA & \(0.000\) & \(0.000\) & \(2.316\) & ** & \(-0.264\) \\ SD & \(\leftrightarrow\) & PA & \(2.639\) & \(0.821\) & \(3.214\) & *** & \(0.295\) \\ \hline \hline \end{tabular} Note 1: Sleep Duration (hours per day) (SD); Self-Concept (SC); Physical Activity (minutes per day) (PA); Mediterranean Diet (MD); Problematic Use of Videogames (PUVG). Note 2: Regression Weights (R.W.); Standardized Regression Weights (S.R.W.); Error Estimation (E.E.); Critical Ratio (C.R.); Estimations (EST). Note 3: Statistically significant relationship between variables at the level \(0.001\) (***); Statistically significant relationship between variables at the level \(0.01\) (*); Statistically significant relationship between variables at the level \(0.05\) (*).  
\end{table} 
Table 2: Regression weights and standardized weights of the theoretical structural equation model. Figure 3: Structural equation for the males. Note: Sleep Duration (hours per day) (SD); Self-Concept (SC); Physical Activity (minutes per day) (PA); Mediterranean Diet (MD); Problematic Use of Videogames (PUVG).
###### Abstract  
Modern smart grids are built based on top of advanced computing and networking technologies, where condition monitoring relies on secure cyberphysical connectivity. Over the network infrastructure, transported data containing confidential information, must be protected as smart grids are vulnerable and subject to various cyberattacks. Various machine learning based classifiers were proposed for intrusion detection in smart grids. However, each of them has respective advantage and disadvantages. Aiming to improve the performance of existing machine learning based classifiers, this paper proposes an adaptive deep learning algorithm with a data pre-processing module, a neural network pre-training module and a classifier module, which work together classify intrusion data types using their high-dimensional data features. The proposed Adaptive Deep Learning (ADL) algorithm obtains the number of layers and the number of neurons per layer by determining the characteristic dimension of the network traffic. With transfer learning, the proposed ADL algorithm can extract the original data dimensions and obtain new abstract features. By combining deep learning models with traditional machine learning-based classification models, the performance of classification of network traffic data is significantly improved. By using the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset, experimental results show that the proposed ADL algorithm improves the effectiveness of existing intrusion detection methods and reduces the training time, indicating a promising candidate to enhance network security in smart grids.  
deep learning; machine learning; intrusion detection; smart grid; neural networks
# Content Management Based on Content Popularity Ranking in Information-Centric Networks
## 5 Concluding Remarks  
The in-network caching policy is recognized as one of the primary keys for developing fast and efficient communications and networking technologies. The schemes used for content caching and cache removal play a vital role in determining the efficiency of the in-network caching policy. An inefficient caching policy may produce unnecessary duplicates of the content, causing regular cache overflow. Thus, cache misses may occur, and delivery times for the requested content may increase. The CCN paradigm can alleviate some of the drawbacks of the host-based Internet architecture; however, the schemes used by the original CCN architecture for content caching and cache removal are simple in concept and can be enhanced. This paper proposes a content popularity ranking (CPR) mechanism, content caching scheme, and content removal scheme for the ICN-based networks. The CPR mechanism takes into consideration several aspects of the requested content and ranks them in terms of content popularity among the existing content of the server nodes. The proposed CPR mechanism and the proposed schemes for content caching and cache removal are described in detail. The main objectives of the proposed schemes were to identify the more popular content and cache them in the server nodes. The objectives were also to select the less popular content and remove them from the repositories when there is a lack of storage space. The proposed schemes were compared to existing schemes for content caching such as Leave Copy Everywhere (LCE) and Leave Copy Down (LCD) in terms of Average Hop Count, cache removal such as Least Recently Used (LRU) and Least Frequently Used (LFU) in terms of the Cache Hit Ratio, and finally, the CCN paradigm incorporating LCE and LRU schemes and the host-based Internet architecture in terms of Content Delivery Time. Graphical presentations of the performance results show that the proposed CPR-based schemes for content caching and cache removal provide better performance in terms of the mentioned performance criteria than the host-based Internet and the original CCN utilizing LCE and LRU schemes.  
Conceptualization, N.A.N. and S.-H.J.; methodology, N.A.N.; software, N.A.N.; validation, N.A.N. and S.-H.J.; formal analysis, N.A.N. and S.-H.J.; investigation, N.A.N. and S.-H.J.; writing--original draft preparation, N.A.N.; writing--review and editing, N.A.N. and S.-H.J.;supervision, S.-H.J.; project administration, S.-H.J.; funding acquisition, S.-H.J. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Ministry of Trade, Industry, and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&D program. This work was partly supported by the ICT R&D program of MSIT/IITP. This work was supported by Hankuk University of Foreign Studies Research Fund of 2020.  
Not applicable.  
The authors declare no conflict of interest.
## 4 The Global SAC and Grid Systems
### The Tetrahedron Version of the Global Grid System
#### 4.1.2 The Tetrahedron Version of the Global Grid System  
When a coordinate system is established on a surface (sphere/plane), some lines or curves must exist that can be represented by a constant value function. In a Cartesian coordinate system, \(y=1\) can represent a horizontal line in the Euclidean plane, and if we choose a different value, the function will represent another horizontal line. When we choose some constant value as the step length \(\Delta\), a serial of functions (\(y=i\Delta,i\in\mathbb{Z}\)) represents a set of horizontal lines. Similar to these horizontal lines, functions (\(x=i\Delta,i\in\mathbb{Z}\)) represent a set of vertical lines. These vertical and horizontal lines construct a coordinate grid at a certain resolution. In the geographic coordinate system, a set of parallels and meridians construct a coordinate grid on the Earth. Since we established the coordinate system on the sphere, there is a series of coordinate lines named the _ultraviolet (UV)-isoline_. In Figure 8, the green lines are the UV-isoline, and the expressions of these lines are \(\lambda_{1}=0.5;\lambda_{2}=0.5;\lambda_{3}=0.5\). The black lines are the great circle arc that connects the midpoint of the reference triangle (red edges). The sub-regions that are subdivided by these two kinds of lines (UV-isoline and great circle arc) have different properties. The UV-isoline has smaller area and shape distortions.  
Using these coordinates, one can easily construct the global grid by choosing a particular coordinate step length (\(\Delta\lambda_{i},i=0\ldots 3\)), and the corresponding UV-isoline composes the network. We call this kind of global grid a _UV-isogrid_. In Figure 9, the values of \(\Delta\lambda\) are 1/2, 1/3, and 1/5. Figure 10 shows the 4-fold DGGSs based on SACs at levels 2, 3, and 4.  
Figure 8: Ultraviolet (UV)-isoline and great circle arc.  
Figure 9: The UV-isogrid \(\Delta\lambda=1/2,1/3,1/5\).
## 3 Proposed Method
### Receptive Field Expansion Module  
The challenge of detecting multi-scale objects lies in the inability of the feature map's receptive field to align with the scales of these objects. To deal with this problem, we propose the Receptive Field Expansion Module (RFEM), which enlarges the receptive field to accommodate multiple scales of objects. The specific implementation process is depicted in Figure 5.  
The motivation for this module derives from the architecture of the E-ELAN of YOLOv7. Through the expansion of the receptive field, we integrate features from different layers to gather more comprehensive gradient flow information, allowing the network to achieve improved accuracy while maintaining reasonable processing time. To achieve the objective of enhancing the receptive field, we employ stacked convolution layers in the main branch. Through skip connections, the feature layers with varying receptive fields are concatenated together, thereby enhancing the diversity of multi-receptive field features.  
In our approach, we employ \(3\times 3\) convolutions for all convolution layers, and each layer generates an output with C/4 channels. These convolution blocks are stacked in a cascaded manner, allowing subsequent layers to reuse computations from the preceding  
Figure 5: The architecture of the proposed Receptive Field Expansion Module (RFEM).  
layers. This strategy not only reduces computational overhead but also effectively expands the receptive field. The incorporation of an ELAN-like structure ensures the accuracy and efficiency of our model while further enhancing the feature extraction capabilities for multi-scale objects. The output of the RFEM structure is  
\[y_{\text{cat}}=\text{Concat}\{\text{CBL}(x),\text{CBL}(\text{CBL}(x)),\ldots\} \tag{4}\]  
To selectively emphasize important channels and suppress less important ones, we introduce a Squeeze-and-Excitation (SE) branch, which begins by conducting global average pooling on the feature map layer, which yields the global feature vector \(G\) with dimensions \(1\times 1\times C\). Subsequently, the global feature \(G\) is passed through a two-layer fully connected bottleneck structure. This compression step reduces the number of channels to 14 of the original count, followed by an expansion step that restores the number of channels back to the original value. The resulting weights represent the importance of each channel in the feature maps, and each channel is multiplied by its corresponding weight. The process of the SE module can be summarized as follows:  
\[w=\text{Sigmoid}(\text{Liner}(\text{Liner}(\text{AvgPool}(x)))) \tag{5}\]  
The entire module is positioned in front of the detection heads for medium-scale and large-scale objects, and the output through the RFEM is  
\[y_{out}=w\cdot y_{cat} \tag{6}\]
# Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20
## 4 Experimental Results
### SF-PPP Performance Employing Reliable Stochastic Model
These findings demonstrate the positive impact of employing a reliable stochastic model in this kinematic experiment. Several studies have focused on investigating the performance of PPP smartphone positioning using different weighting models. Some examples include [19; 33; 35; 36; 37; 38]. [PERSON] et al. [19] utilized a weighting approach that combined the elevation angle and C/N0 for smartphone-based GNSS PPP. The proposed method was then assessed through GNSS kinematic experiments using the Xiaomi M18 device. The RMS values of the PPP errors in the east, north, and up components were 1.00 m, 0.62 m, and 2.22 m, respectively, when utilizing the combined weighting scenario. This represents an improvement compared to using either the elevation-angle-only weighting or the C/N0-only weighting scenarios. [PERSON] et al. developed their own CSV to RINEX converter in [33] and applied PPP to GNSS observations collected from a Xiaomi M18 smartphone based on a C/N0-dependent stochastic model. They found that the 50 th percentile horizontal error was 0.330 m in a kinematic test conducted in an open-sky environment. In [35], [PERSON] et al. used GNSS observations from the Xiaomi M18 and Samsung S20 smartphones and achieved sub-meter PPP accuracy at the 50 th percentile horizontal error. However, no details were provided regarding the employed stochastic model. [PERSON] et al. [36] used dual-frequency GPS (L1/L5) and Galileo (E1/E5a) observations from a Xiaomi M18 smartphone. By utilizing  
Figure 11: Cumulative distribution error plot of horizontal positioning error: (**left**) nominal stochastic model and (**right**) reliable stochastic model. Figure 10: Horizontal positioning error in two cases: (**left**) nominal stochastic model and (**right**) reliable stochastic model. an elevation-dependent stochastic model, they demonstrated that the PPP algorithm's positioning accuracy was at the meter level in kinematic mode, which is comparable to the accuracy of our solution. [PERSON] et al. [37] proposed a modified single-frequency PPP algorithm that estimates separate clock biases for pseudorange and carrier phase observations. They used a Xiaomi Mi8 smartphone and found that the average horizontal and vertical RMS errors based on an elevation-dependent stochastic model were 0.81 m and 1.65 m, respectively. [PERSON] et al. [38] also utilized a Huawei Mate 30 smartphone to carry out PPP testing in a kinematic mode, employing the C/N0-dependent stochastic model. They could achieve a positioning accuracy of 0.93 m, 0.62 m, and 2.17 m in the east, north, and up directions, respectively. The findings showed that the accuracy was improved by approximately 26.2%, 20.5%, and 20.4% in comparison to the elevation-dependent stochastic model results. Although differences in the measurement environment and employed mathematical models could explain variations in the obtained accuracy, the overall performance of PPP remains consistent across different studies.
## 4 Experimental Results and Discussion
### The Experiments in the Sea
In Figure 6, the red circle indicates the target position; we can see that the SNR of many clutters is higher than that of the target. In this case, if a preset threshold higher than the target SNR is applied, the target echo may not be detected. For this reason, a weak target echo should be classified regardless of the threshold. Figure 5: Examples of the experimental ownship and target paths. Figure 6: One example of a sea trial matched filter output.
# Identification of Key Performance Indicators for T20--A Novel Hybrid Analytical Approach
###### Abstract  
Cricket is a dynamic sport, making the selection of key performance indicators (KPIs) challenging. **Objective:** The study aims to identify KPIs in Twenty-20 (T20) cricket affecting match outcomes. **Methods:** Cricket performance data was analysed from three seasons of male T20 matches, identifying 136 performance indicators (PIs). The random forest algorithm and lasso logistic regression were used to develop a model to predict match outcomes. **Results:** The hybrid model achieved 85.9% accuracy with leave-one-out cross-validation statistical analyses. Sixteen KPIs were identified and ranked by importance including wickets lost in the last six overs, two or more wickets in the second innings, run rate in the last six overs, wickets by seam and spin bowling, batting strike rate, singles percentage in the second innings, sixes in the first innings, overs bowled by seam, runs in last six overs, sixes in middle overs, total catches in second innings, dot ball percentage, opening partnership runs, dot balls in the opening six, and singles in the last six. **Conclusions:** Cricket match performance in the final overs, especially bowling strike rate and scoring runs, were crucial for successful match outcomes. These KPIs offer insights into team strategy, player selection, and match performance evaluation in T20 cricket.  
T20 cricket; key performance indicators; sport performance analysis; random forest algorithm; lasso logistic regression Article
## 2 Materials and Methods
### Methodology
#### 2.2.3 Digital Image Correlation Investigation  
The application of the DIC method involves ensuring that the whole surface has unique, random, and high-contrast identifiers to allow the correspondence problem to be solved [36]. A common practice is that the sample's surface must be coated with a white background and black speckled paint layer, or use other synthetic identifiers such as markers [37].  
The specimens were prepared for the DIC investigation via manually spraying acrylic paint (Corro Protect) with two hours of drying between the white background layer and the black-coloured speckles. The spraying distance and intensity were adjusted to obtain a black-coloured speckle size of approximately 5 pixels. The specimens were tested 19 h after the final coat application and stored at 60% RH and 23 \({}^{\circ}\)C to reach approximately 12% EMC.  
The DIC acquisition setup (Figure 1C) was based on a single DMK 33 GX183 camera (2D-DIC analysis) equipped with a Fujinon CF25 ZA-1S 23 MP 25 mm focal length lens at f/1.8. The illumination source was a single-ring SVL R80 light. The image resolution was 26.5 pixels/mm. The image acquisition was performed at two-second intervals until failure detection.  
The two-dimensional augmented Lagrangian digital image correlation (2D-ALDIC) algorithm was used for the DIC analysis [37]. The algorithm is based on local and global subset displacement convergence, resulting in a low cost and high precision [38; 39]. The deformation gradients and strains were computed using the finite difference method for the Eulerian strain type [39]. For the DIC parameters, the subset size was 20 pixels and the subset step was 10 pixels.