esa-sceva/satcom-chunk-collection huggingface dataset:
# Counter-Terrorism Video Analysis Using Hash-Based Algorithms
## 6 Implementation and Performance Study
### Pseudocode  
The pseudocode for the image rotation algorithm is shown in Algorithm 1. The pseudocode for the hash generation algorithm is also presented below as Algorithm 2.  
```
1:functionimageRotation(\(I_{0}\))
2:\(width=\text{getWidth}(I_{0})\)
3:\(height=\text{getHeight}(I_{0})\)
4:if\(width<height\)then
5:\(I_{0}=\text{rotate}(I_{0},90)\)\(\triangleright\) Longest image side over x-axis (landscape)
6:endif
7:\(nsI=I_{0}[0:width,0:height/2]\)\(\triangleright\) Get upper half of the image
8:\(ssI=I_{0}[0:width,height/2:height]\)\(\triangleright\) Get lower half of the image
9:if\(\overline{nsI}<\overline{ssI}\)then\(\triangleright\) Highest toality on top
10:\(rI=\text{rotate}(I_{0},180)\)
11:else
12:\(rI=I_{0}\)
13:endif
14:return\(rI\)
15:endfunction
```  
**Algorithm 1** Image rotation algorithm  
Where \(I_{0}\) is the original frame (input), and \(rI\) is the rotated image (output).  
```
1:functiongetImageHash(\(I_{0}\))
2:\(sI=\text{reduceSize}(I_{0},16,16)\)\(\triangleright\) Reduce size to 16 \(\times\) 16 pixels
3:\(gI=\text{greyScale}(sI)\)\(\triangleright\) Convert to greyscale
4:\(rI=\text{imageRotation}(gI)\)\(\triangleright\) Rotate as defined in Algorithm 1
5:\(mean=\text{calculateAverage}(rI)\)\(\triangleright\) Get the average pixel value
6:for each\(px\in rI\)do
7:if\(px>mean\)then\(\triangleright\) Compare every pixel against the mean
8:\(hash=hash+1\)
9:else
10:\(hash=hash+0\)
11:endif
12:endfor
13:\(hash=\text{convertHexadecimal}(hash)\)\(\triangleright\) Convert from binary to hexadecimal
14:return\(hash\)
15:endfunction
```  
**Algorithm 2** pHash-based algorithm  
Where \(I_{0}\) is the original frame (input), and \(hash\) is the resulting hexadecimal number (output).
# Improved Integral Inequalities for Stability Analysis of Interval Time-Delay Systems
## 4 Stability Analysis Criteria for Interval Time-Delay Systems
\right],\] \[\overline{V}_{2}(t)=\int_{t-d(t)}^{t}x^{T}(s)\overline{Q}_{1}x(s) ds+\int_{t-d_{2}}^{t}x^{T}(s)\overline{Q}_{2}x(s)ds,\] \[\overline{V}_{3}(t)=d_{2}\int_{t-d_{2}}^{t}\int_{\theta}^{t}\dot{x }^{T}(s)\overline{U}\dot{x}(s)dsd\theta,\] \[\overline{V}_{4}(t)=\int_{t-d_{2}}^{t}\int_{\sigma}^{t}\int_{ \theta}^{t}\dot{x}^{T}(s)\overline{W}_{1}\dot{x}(s)dsd\theta d\sigma+\int_{t-d _{2}}^{t}\int_{\tau-d_{2}}^{\sigma}\int_{\theta}^{t}\dot{x}^{T}(s)\overline{W} _{2}\dot{x}(s)dsd\theta d\sigma.\]  
The proof follows a similar fashion as that of Theorem 1, which ultimately leads to  
\[\dot{\overline{V}}(t)\leq\overline{v}^{T}(t)\left(\frac{d_{2}-d(t)}{d_{2}} \overline{\Pi}_{1}+\frac{d(t)}{d_{2}}\overline{\Pi}_{2}\right)\overline{v}(t), \tag{44}\]  
where  
\[\overline{v}(t)=col\{x(t),\ x(t-d_{2}),\ x(t-d(t)),\ \bar{x}_{1}, \cdots,\bar{x}_{6}\},\ \bar{x}_{1}=\frac{1}{d(t)}\int_{t-d(t)}^{t}x(s)ds,\ \bar{x}_{2}=\frac{1}{d_{2}-d(t)}\int_{t-d_{2}}^{t-d(t)}x(s)ds,\] \[\bar{x}_{3}=\frac{1}{(d(t))^{3}}\int_{t-d(t)}^{t}\int_{\theta}^{t} x(s)dsd\theta,\ \bar{x}_{4}=\frac{1}{(d_{2}-d(t))^{3}}\int_{t-d_{2}}^{t-d(t)}\int_{\theta}^{t-d( t)}x(s)dsd\theta,\ \bar{x}_{5}=\frac{1}{(d(t))^{3}}\int_{t-d(t)}^{t}\int_{\sigma}^{t}\dot{t}^{T}_{ \theta}x(s)dsd\theta d\sigma,\] \[\bar{x}_{6}=\frac{1}{(d_{2}-d(t))^{3}}\int_{t-d_{2}}^{t-d(t)} \int_{\sigma}^{t-d(t)}\int_{\theta}^{t-d(t)}x(s)dsd\theta d\sigma.\]  
Thus, if Equations (41)-(42) hold, the system (1) subject to (2) with \(d_{1}=0\) is asymptotically stable. This ends the proof. **Remark 7**. : Besides the number of decision variables, the dimension of the LMI-based condition is also considered as a key factor for computational complexity [29]. Compared to \(\Pi_{i}\ (i=1,2)\) in Theorem 1, the dimensions of \(\overline{\Pi}_{i}\ (i=1,2)\) in Corollary 2 are reduced from \(13n\) to \(9n\). Thus, Corollary 2 is more reasonable than Theorem 1 for systems with zero lower bounds.
## 3 Proposed Methodology
### Focal Node Identification Using Distributed Fuzzy Cognitive Map
#### 3.1.1 Initializing the State Vector and Weight Matrix  
The state vector comprises all the values of the concepts. Since the problem under consideration is the identification of focal nodes in a given network, the concept values should depict the degree of connections between the nodes in the network and consider the structural properties of the network. The betweenness centrality [59] measure was used for this purpose. Betweenness centrality was chosen to measure the degree of connections between nodes because it captures the flow of information by considering the number of shortest paths passing through a node. This provides insight into its role in facilitating communication within the network. Additionally, betweenness centrality takes into account the weights of the edges, assigning higher scores to nodes that lie on paths with greater weights. This aspect is especially pertinent in the context of fuzzy cognitive maps (FCMs), where the weights represent the causal relationships between nodes. Equation (1) determines the betweenness centrality measure, which quantifies the number of shortest routes passing through a specific vertex.  
\[g(v)=\sum_{s\
eq v\
eq t}\frac{\sigma_{st}(v)}{\sigma_{st}}. \tag{1}\]  
The overall number of shortest routes from the source to the target is equal to \(\sigma_{st}\), given in Equation (1), and the count of shortest routes that traverse through the vertex \(v\) can be determined by \(\sigma_{st}(v)\). High centrality ratings imply that a particular vertex is included in a significant percentage of the shortest routes that link pairs of vertices.  
For a weighted undirected graph, the weight matrix of the fuzzy cognitive map is initialized with the weight values of the connections between nodes of the network; when there is no connection between two nodes the weight value is set to 0. In the case of unweighted undirected networks, if a connection exists between two nodes, the corresponding weights are set to 1; otherwise, it is 0. A resilient distributed dataset is employed for the storage of both the weight matrix and the state vector. Figure 2 represents the initial state vector and weight matrix evaluated for a sample weighted network.
# Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model
###### Abstract  
Plant pests and diseases are important parts of insect disease control and the high-quality development of agriculture. Traditional methods for identifying plant diseases and pests suffer from low accuracy and slow speed, while the existing machine learning methods are constrained by environmental and technological factors, leading to low recognition efficiency. To address the issue of the above problems, this paper has proposed an intelligent recognition algorithm based on the improved YOLOv8 model, which has high recognition accuracy and speed. Firstly, in the Backbone network, the Global Attention Mechanism (GAM) is adopted to weigh the important feature information, thereby improving the accuracy of the model. Secondly, in the mixed feature part of the Neck network, the Receptive-Field Attention Convolutional (RFA Conv) operation is used instead of standard convolution operations to enhance the processing ability for feature information and to reduce computational complexity and costs, thus improving the network performance. After verifying the rice and cotton datasets, the accuracy indicator mean average precision (mAP) reaches 71.27% and 82.91%, respectively, in the two different datasets. Comparing these indices with those of the Faster R-CNN, YOLOv7, and the original YOLOv8 model, the results fully demonstrate the effectiveness and superiority of the improved model in terms of detection accuracy.  
plant pests and diseases recognition; YOLOv8 model; GAM; RFA Conv; disease identification
# Single-Shot Compressed Imaging via Random Phase Modulation
## 5 Conclusions  
In this paper, we have demonstrated the effectiveness of several different compressive imaging methods using random phase modulation. The principles of compressive sensing theory and random phase modulation are presented. A brief review of the implementation of compressive imaging with different optical architectures is described. Numerical experiments demonstrate that compressive imaging together with random phase modulation can help in recovering the image from real-valued measurements or complex-valued measurements.  
In summary, we have shown that random phase modulation can effectively enhance the performance of the compressive imaging method. We believe that random phase modulation combined with compressive sensing can further enhance the performance of three-dimensional imaging, video sensing, and phase retrieval, which will lead to new designs of optical setups and enable new applications.  
Methodology, C.Z.; writing--original draft preparation, R.Z.; writing--review and editing, Y.Z.; supervision, H.Y., C.S. and S.W. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subsampling Rate** & 0.1 & 0.2 & 0.3 & 0.4 & 0.5 \\ \hline
**SNR/dB** & 17.88 & 25.05 & 27.60 & 29.43 & 31.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance test of CI-DRPE method with different subsampling rate.  
Figure 9: Numerical experiments of CI-DRPE. Numerical experiments of CI-DRPE. (**a**) Input Cameraman image. (**b**) RPM1. (**c**) RPM2. (**d**) The amplitude of measurements \(\mathbf{y}\). (**e**) The phase of measurements \(\mathbf{y}\). (**f**) Reconstructed image using CS-DRPE method.  
**Funding:** The Natural Science Foundation of Anhui Province (No. 2008085 MF209), the Major Natural Science Foundation of Higher Education Institutions of Anhui Province (Nos. KJ2019 ZD04, KJ2020 ZD02), and Open Research Fund of Advanced Laser Tech-nology Laboratory of Anhui Province (AHL2020 KF05).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Kinematic Analysis of Daily Activity of Touching Lateral Shoulder for Normal Subjects
## 2 Methods
### Data Analysis  
After the recording process was completed, each marker was identified by a technician using Nexus software, and data were captured using Vicon, which aims to ensure that each marker could be detected properly during the data capture process. In some recordings, markers were sometimes hidden or overlapped with other markers and could be detected automatically. In each analysis, a minimum of three recordings of 90% success were used for analysis of every subject. From three successful recordings, the mean was calculated as a measurement for each subject. For this activity, data movement was divided into three phases (lifting, resting and returning) based on movement data and timing for the activity. The definition of each phase is described in detail in the next section.  
Our goal was to gather as much potentially useful information and data as possible to determine and define the parameters that proved clinically useful for the task of touching the contralateral shoulder using a camera system for various different patient age groups  
Figure 1: Original position.  
for the next study. After the graphical plots for the kinematic data recordings were analysed, the following variables were obtained for analysis in more detail:  
1. Time movement. The total movement time (total duration of the entire movement) was based on the definition phase for each predetermined phase.
2. Joint angle. The aim of this study was to calculate the angle of each joint. Joint angles were obtained from digitised data. The shoulder angle was determined using a marker on the hips, shoulder and elbow joints. An elbow angle was ascertained by markers on the shoulder, elbow, and wrist joints. The angle of the wrist involved markers on the elbow, wrist and index finger. Examination of the experimental data for joint angle in the frontal planes was performed.
3. Peak velocity. The velocities during the lifting phase (from three phases, namely lifting, resting and returning). It is known as tangential velocity (when a body is moving in a circular path, this velocity is measured at any tangent point). The angular velocity and the radius of the segment are closely related to tangential velocity. The angular velocity of the upper-limb joint (\(\dot{\theta_{s}}\)) during performance of touching contralateral shoulder was multiplied by the length of upper-arm segment (\(l_{ua}\)) to obtain a tangential velocity (\(\dot{\theta_{t}}\)).
4. Time to reach peak velocity and percentage of time with peak velocity. These values were calculated only for the lifting phases.
5. Interjoint coordination. This coordination was calculated using the correlation coefficient (Pearson product moment correlation) during the movement of the shoulder and elbow joints for lifting and returning phases.
# Fault Location in Double Circuit Medium Power Distribution Networks Using an Impedance-Based Method
## 4 Simulation Results
### Numerical Results
#### 4.2.3 The Effect of the Fault's Inception Angle  
To determine how sensitive the proposed method is to the fault's inception angle, various simulations were done. Table 4 shows the obtained results at fault inception angles of 0, 30, 45, and 90 degrees for single-phase to the ground faults with a resistance of 50 \(\Omega\). The simulation results show that the proposed method was not sensitive to the fault's inception angle.  
Figure 6: The recorded faulty phase current at the beginning of the feeder (in 21.06 km).  
Figure 7: Error percentage of the proposed method for various resistances and different locations.
# A Lithology Recognition Network Based on Attention and Feature Brownian Distance Covariance
## 4 Conclusions  
Addressing the challenges posed by the insufficient tunnel rock face image samples and the limited accuracy of lithology recognition networks, this study enhances the efficiency and precision of rock recognition. Our approach involves the design of an improved Brownian distance covariance module to augment the attention to sample features and the mitigation of background interference through an enhanced CBAM attention mechanism. Numerous comparative experiments conducted on a rock tunnel rock face dataset collected from the field reveal that the FC-Res network yields the most favorable results in lithology recognition tasks. The proposed FC-Res lithology recognition network significantly enhances accuracy, precision, recall, and F1 score, surpassing the performance of current mainstream lithology recognition neural networks.  
In future research endeavors, we aim to integrate the structural characteristics of rocks in various types of tunnel rock face images into the lithology recognition network design. This integration, coupled with an extensive dataset, will facilitate a more profound exploration of lithology recognition models that strike a balance between efficiency and precision.  
Conceptualization, D.Z., B.G. and S.L.; methodology, B.G. and D.Z.; software, B.G.; validation, D.Z. and Y.C.; formal analysis, S.L. and Y.C.; investigation, D.Z.; resources, B.G.; data curation, B.G.; writing--original draft preparation, D.Z.; writing--review and editing, D.Z., Y.C. and B.G.; visualization, S.L. and B.G.; supervision, B.G.; project administration, D.Z.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data obtained in this study are openly available in [[https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) BdT-K-NICXPRKaeCypEzcQ?pwd=tofh]([https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) BdT-K-NICXPRKaeCypEzcQ?pwd=tofh) (accessed on 9 February 2024).  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Parameters/M** & **ACC/\%** & **P/\%** & **R/\%** & **F1/\%** \\ \hline ResNet18 & 117.45 & 91.70 & 89.94 & 88.71 & 89.32 \\ ResNet50 & 235.47 & 93.57 & 93.75 & 93.33 & 93.54 \\ EfficientNet-B0 & 78.28 & 89.16 & 87.28 & 88.23 & 87.75 \\ SENet & 267.91 & 92.63 & 92.86 & 92.16 & 92.51 \\ MobileNet-V3 & 34.82 & 89.23 & 86.89 & 86.11 & 86.50 \\
**Ours** & **135.15** & **97.60** & **97.43** & **97.46** & **97.44** \\ \hline \hline \end{tabular}  
* Text in bold indicates optimal indicators.  
\end{table}
Table 7: Comparison of rock recognition performance of different network models.
## 2 Materials and Methods
### Data Source and Processing  
This study primarily used multi-period remote sensing imagery and socioeconomic data. Land-cover data for the Dongting Lake region were sourced from the Resource and Environment Science Data Center of the Chinese Academy of Sciences ([[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)), accessed on 12 March 2024), spanning four periods (2005, 2010, 2015, and 2020) with a 30 m spatial resolution [13]. Boundary vector data for 2020 were obtained from the National Geomatics Center of China ([[http://ngcc.sbsm.gov.cn](http://ngcc.sbsm.gov.cn)]([http://ngcc.sbsm.gov.cn](http://ngcc.sbsm.gov.cn)), accessed on 12 March 2024). Socioeconomic data were obtained from the China City Statistical Yearbook (2005-2020), the China Urban Construction Statistical Yearbook, prefecture-level statistical yearbooks, and environmental status bulletins. For satellite image processing, Landsat TM/ETM imagery from four periods was analyzed using ENVI 15.6, applying a human-computer interactive interpretation method to extract land-use and land-cover information. Data with varying  
Figure 1: Overview of the study area. (**a**) shows the location in China; (**b**) shows the names of municipal areas; (**c**) shows the types of land use).  
statistical criteria were standardized, and minor missing values were filled using linear interpolation or trend extrapolation.
# Reviews Based on the Reconfigurable Intelligent Surface Technical Issues
## 3 RIS Field Trials
### RIS Performance Evaluation Parameters [28]
#### 3.1.5 Secrecy Rate  
Secrecy rates play an important role in physical layer security. It is a metric that defines the rate at which a transmitted signal reaches a legitimate receiver without being intercepted by an eavesdropper. One definition of the secrecy rate is the non-negative difference between the capacity of the intended receiver and the capacity of the eavesdropper. Without precise knowledge of the eavesdropper's identity and channel, the secrecy rate for each receiver can be estimated by considering all legitimate but unintended terminals in the network as potential eavesdroppers.  
Latency, reliability, and overhead can also be used as performance parameters for the RIS systems. In practice, there are several methods and techniques to measure or evaluate the performance metrics of RISs. The first is to use a System Level Simulator, a software tool that can model the behavior and performance of an RIS with different parameters and configurations in realistic scenarios such as urban or rural environments [149; 150; 151]. The second is to measure parameters that can characterize the characteristics and capabilities of the RIS, such as the reflection coefficient, phase shift, polarization, and scattering of the RIS. The third is to analyze the integration performance using a mathematical framework that can derive analytical expressions for the performance metrics of RISs in different channel models [152; 153; 154; 155].
## 5 Conclusions  
Though the WSN based AST system presented in this paper is proven to be successful for static testing, the system design will be much more complicated when the number of testing points to be measured increases greatly. Furthermore, fatigue testing for full-scale structure requires higher data transmission rates, data synchronization and data buffer processing capacity. Therefore, hardware capabilities for the WSN based AST systems should be improved in the further research. Networking and routing protocols should be also studied deeply to solve these problems.
# Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification
## 4 Experiments
### Ablation Studies  
#### 4.4.1 Parameter Setting on Focal Loss  
For focal loss, there are two vital parameters, namely \(\alpha\) and \(\gamma\). They are similar to hyperparameters such as learning rate, which need to be preset before training. As focal loss aims to alleviate the degree of class imbalance, we first study the influence of class imbalance on setting these parameters.  
We refer to the setting of \(\gamma\) with corresponding optima \(\alpha\) in [6], conducting experiments on two subsets, SIXray10 and SIXray100. The performance results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gun** & **Knife** & **Wrench** & **Pliers** & **Scissors** & **Hammer** & **Total** \\ \hline
3131 & 1943 & 2199 & 3961 & 983 & 60 & 8929 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Class distribution of positive samples in SIXray dataset. As class “hammer” contains only 60 sample images, we deprecate it in later experiments.  
The experimental results show that the adoption of the focus loss can improve the classification performance of the model for class imbalanced datasets. It has a significant effect on both SIXray10 and SIXray100 subsets. The optimal result has increased by 7.19 points compared with baseline on SIXray10, while 20.31 points on SIXray100. As the results show, the improvement on SIXray100 is far greater than SIXray10. This implies that, for a deeper class imbalance degree, focal loss with appropriate parameter setting can handle the scenario better, due to balancing the importance of negative and positive samples.  
As for the value of focus loss parameter on optimal performance, the optimal result of the SIXray10 occurs at \(\gamma=0\), \(\alpha=0.5\). Meanwhile, with the continuous increase in \(\gamma\), model performance continues to decline. For the focal loss function, when the value of \(\gamma\) is 0, it degrades into a weighted CE loss function. It also shows that the modulating factor of focal loss is not effective in this subset. It is possibly prompted by inadequate difficult samples in SIXray10 when the model is on predicting. As for the SIXray100 subset, the optimal result occurs at \(\gamma=0.2\), \(\alpha=0.75\). As the level of class imbalance further deepens, difficult samples gradually increase. Henceforth, the impact of modulating factor begins to gradually rise, further validating the effectiveness of the loss function setting in this paper.
# The Concurrent Validity of Mobile Application for Tracking Tennis Performance
## 1 Introduction
SV enables impact monitoring and video analysis of technical and tactical performance in real time. Given that the number of repetitions of a particular element is extremely important in the training process, by applying video analysis, it is possible to correct the technical performance and create a detailed training plan and program. Additionally, the transfer of information from the training process to the competitive match represents an important segment of the development of every athlete. Using the application simplifies the collection and processing of data and thus can more effectively affect the development process of players. Given that numerous applications have emerged in the market, it is necessary to determine their metric characteristics or whether their application in practice is justified. The SV application, which is used by tennis players at recreational and professional levels around the world, has not yet been the subject of scientific research for the purpose of testing metric characteristics. In the near future, the innovators of this application will announce the possibility of its use on other mobile systems, as well as in other racket sports such as badminton, pickleball, and padel. Therefore, the aim of this research is to determine the validity of the SwingVision mobile application for monitoring speed, rotation, and placement parameters when performing serve and other strokes.