esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved YOLOv7 for Small Object Detection Algorithm Based on Attention and Dynamic Convolution
## 4 Experiments
### Experimental Results and Comparative Analysis  
In this section, the efficacy of introducing both the attention mechanism and the dynamic convolutional module is rigorously verified through ablation tests. These tests served to assess the impact of the individual modules on the algorithm's efficiency, all conducted under identical conditions. The outcomes of these ablation tests are meticulously documented in Table 2. Notably, the incorporation of the improved SPPFCSPC module yielded a significant boost in the efficiency of the enhanced algorithm.  
Table 2 offers insightful comparative data, demonstrating the performance improvements achieved through various attention mechanisms. Notably, the Coordinate Attention (CA) module emerged as the most-impactful, yielding an evident enhancement of 2.9% when contrasted with the SE and CBAM modules. The introduction of the improved Spatial Pyramid Pooling-Fast, Cross-Stage Partial Channel (SPPFCSPC) module contributed to a noteworthy increase of 3.0% in the mean Average Precision (mAP). This underlines the efficacy of the suggested feature structure amalgamation, especially in terms of facilitating the identification and recognition of small targets.  
Furthermore, the incorporation of the dynamic convolutional module, as presented in this study, significantly bolstered the original model's adaptability to targets of varying sizes. This adaptation culminated in a substantial accuracy increase of 3.5%. Ultimately, the enhanced YOLOv7-CA Dynamic algorithm translated to a remarkable 5.2% enhancement in the mAP. Remarkably, this improvement was realized with a mere 9% increment in the processing power, reinforcing the algorithm's efficacy through experimental comparisons with the original counterpart.  
Figure 9 visually demonstrates the thermal image comparison between the baseline YOLOv7 model and its variations after integrating distinct attention mechanisms. Upon the observation of the figure, a notable trend emerges. Upon incorporating the Coordinate Attention (CA) mechanism, the model exhibited a heightened emphasis on the target under consideration. Consequently, it adeptly captured precise positional information, thereby enhancing both the localization and identification accuracy for small targets. This effect was distinctly valuable in bolstering the overall model accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **Params (MB)** & **FLOPs (GB)** & **mAP (\%)** & **F1-Score (\%)** \\ \hline YOLOv7 & 37.6 & 106.5 & 75.9 & 74.2 \\ +SE & 37.6 & 106.5 & 77.6 & 74.9 \\ +CBAM & 37.6 & 106.5 & 77.5 & 76.7 \\ +CA & 37.7 & 106.7 & 78.8 & 77.3 \\ +SPPFCSPC & 37.6 & 106.5 & 78.9 & 77.9 \\ +Dynamic & 51.4 & 117.0 & 79.4 & 78.6 \\ +CA+Dynamic & 51.5 & 117.2 & 80.2 & 79.4 \\ +CA+SPPFCSPC & 51.5 & 117.2 & 79.5 & 78.3 \\ +Dynamic+SPPFCSPC & 51.5 & 117.2 & 80.5 & 78.9 \\ YOLOv7-CA Dynamic & 51.5 & 117.2 & 81.1 & 79.5 \\ \hline \end{tabular}
\end{table}
Table 2: Ablation experiments.
## 2 Determination of the RVE of the Calculated Strength of Yellow River Ice
### Method for Determining the RVE of the Calculated Strength  
For Yellow River ice, it is impracticable to predict the strength of the river ice through analytical expressions due to the complexity of its microstructure. At the same time, experimental observations are limited by the experimental conditions and cannot correlate the microstructure with the macromechanical experimental results. Thus, numerical simulation has become a more effective means in RVE research due to its independence from experimental instruments, ability to conduct repeated experiments, and ability to track changes in the microstructure during loading. In this article, numerical methods were used to analyze the strength of river ice in different conditions, and then the RVE size of the calculated strength of the Yellow River ice was determined.  
According to the definition of the RVE above, the RVE of the calculated strength of the Yellow River ice mainly considered the simplified uniaxial compression condition for analysis. A numerical microscopic model of river ice was established, the strength changes of river ice were studied in different working conditions at a microscopic scale, and the results were systematically analyzed; ultimately, the RVE of the calculated strength that could reflect the overall sample strength was determined.  
In the given load conditions, for any given size \(L\), the fluctuation in macroscopic strength caused by an increase in sample size cannot exceed the allowable error. The calculation is shown in Formula (1).  
\[\left|\frac{f_{L+\Delta L}-f_{L}}{f_{L}}\right|<\varepsilon \tag{1}\]  
where \(f\) is the average strength of the river ice; the subscript \(L\) represents the random volume element size; the subscript \(\Delta L\) represents the size increment; and \(\varepsilon\) is the allowable error, taken as 5%.  
The specific steps for determining the RVE of calculated strength are:  
1. Generate an overall sample of given conditions.
2. Cut subsamples of the same size from different positions as calculation samples.
3. Calculate the strength value in a given load and analyze the discreteness of the strength value in this load.
4. Increase the size of the subsamples.
5. Determine the stable scale \(L_{1}\) of the strength with sample randomness at this load based on the analysis results of the strength discreteness of different sample sizes.
6. Determine the stable scale \(L_{2}\) of the strength variation with size at this load based on the relative error results of the strength for different sample sizes.  
Take the larger of \(L_{1}\) and \(L_{2}\) as the RVE size for calculating the strength of river ice in this working condition.  
Because the properties of river ice fluctuate with sample size, to ensure that the performance of river ice tends to be stable, it is required to meet the accuracy requirements for at least two consecutive dimensions. The larger stable scale is taken as the representative size.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 9 Analysis and Discussion
### Discussion  
The protection of location privacy in case of vehicles moving at high speed and lower-traffic conditions is a challenging one. In the existing schemes [10; 18], it is difficult to provide a high level of location privacy in diverse vehicle traffic, negatively impact on road network applications and the privacy is provided only inside the communication range of an RSU. We have analyzed the proposed scheme DGVP regarding privacy protection, quality of service, impact on VANET applications, and computation cost. Based on the simulation results, DGVP has improved location privacy protection results compared with [10; 18]. The time complexity requires the time taken by components of the proposed scheme such as vehicle grouping, pseudonym update process, and position-mixing algorithm to apply privacy protection mechanism [52]. We calculated the time complexity of the proposed algorithm that shows the running time of these algorithms. The provision of security creates some cost [53]. Our proposed scheme, DGVP, has a slightly increased cost of computation. This increase in the cost of the DGVP is due to the communication and computation of the pseudonym update process and cooperation among neighboring vehicles. Still, overall, the proposed scheme achieves a higher level of vehicle location privacy protection in diverse vehicle traffic. The quality of the VANET applications is not compromised in the case of DGVP because there is no distortion of location data in the proposed scheme. The existing schemes [10; 18] utilize a silent period that affects road safety applications.
# Vessel Velocity Estimation and Docking Analysis: A Computer Vision Approach
## 2 Theoretical Foundation
### Structural Similarity Index Measure  
Digital images can exhibit various types of distortions. Therefore, in many applications where images are intended for human viewing, human subjective evaluation is considered the most accurate approach [7]. However, this subjective evaluation method is time-consuming and requires the involvement of multiple observers [10]. In response to these limitations, researchers have proposed objective approaches that can automatically estimate the visual quality of images without the need for human intervention [11]. These objective methods aim to provide efficient and reliable metrics for image quality assessment.  
One of the earliest objective quality metrics used to assess image quality is the Peak Signal-to-Noise Ratio (PSNR) between a base image and a compared image [12]. The PSNR metric is commonly used in image processing literature as a converted Mean Squared Error (MSE) measure. While the PSNR/MSE metric is widely employed and has historically been used to optimize various signal processing applications, it does not always align with human perception. For example, experiments have shown that images with different types of noise can have the same PSNR value, despite one being visually superior based on human perception [13].  
The limitations of the PSNR metric have led researchers to propose alternative objective quality metrics that aim to better align with human perception and capture the visual quality of images more accurately [14]. The Structural Similarity Index (SSIM) is an objective quality metric used in image processing. It quantifies the similarity between two images by considering their structural information, luminance, and contrast. The SSIM considers the visual system's characteristics and perception, which makes it more reliable in assessing image quality than traditional metrics [7].  
Equation (1) shows the SSIM equation, where \(x\) and \(y\) are the input images that are being compared, \(u_{i}\) are the means, \(C_{1}\) and \(C_{2}\) are constants added to avoid division by zero, \(\sigma_{i}^{2}\) are the variances, and \(\sigma_{xy}\) is the covariance between the two compared images.  
\[\text{SSIM}(x,y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{ 2}+\mu_{y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{1}\]  
The SSIM scores are more coherent than other MSE scores when considering human visual perception. It is observed that degradation in intensity and contrast does not significantly degrade the image structure. On the other hand, noise contamination is considered a process that affects the image structure. Developing a metric that considers these factors helps construct models that closely align with human perception [13].
# An Efficient Task Autonomous Planning Method for Small Satellites
## 3 Methods
### Algorithm Design
#### 3.2.2 Selection Operator  
In this paper, we use grouped roulette operators. The main steps are as follows:  
* Step 1: Determine the number of population groups (\(G_{num}\)) and the number of all of the individuals (\(IN_{num}\)).
* Step 2: List all of the individuals in descending order of task profit.
* Step 3: Divide the sorted individuals into \(G_{num}\) groups.
* Step 4: The group number of the \(i\)-th individual is as follows: \[g_{i}=\left[i/\left(\frac{IN_{num}}{G_{num}}\right)\right]\] (7)
* Step 5: The probability that the \(i\)-th group is selected equals to the ratio of the total profit of the current group to the total population profit. Calculate the cumulative selection probability for each group to form a roulette.
* Step 6: Generate a random number within [0, 1], if the random number is greater than or equal to the cumulative probability of the (\(j-1\))-th group and is less than the cumulative probability of the \(i\)-th group, randomly choose an individual from the \(j\)-th group as the returned individual.  
The grouped roulette operator can be regarded as a trade-off between a traditional roulette operator and random selection. If \(G_{num}\) equals the number of individuals in the population, it is the  
Figure 5: A diagram of coding method.  
traditional roulette operator, and if \(G_{num}\) is equal to \(1\), it is random selection. When applied, \(G_{num}\) can be adjusted according to the actual problems, so that the grouped roulette operator can be continuous between the traditional roulette and random selection.
# Overlay-ML: Unioning Memory and Storage Space for On-Device AI on Mobile Devices
## 3 The Design of Overlay-ML
### Overlay-Write  
Figure 3 shows the overall architecture and flows of Overlay-ML on embedded devices when on-device ML runs for image processing and pattern recognition; Overlay-ML is designed to work in the application space. As mentioned before, Android OS does not allow for an application to utilize memory space over the pre-defined threshold (i.e., 256 MB or 512 MB). To avoid unexpected termination of an application by Android OS, Overlay-ML provides two write paths: a _fast write path_ and a _slow write path_. Also, Overlay-ML monitors how much memory space an application running on-device ML is using to determine which write path runs. In the data-load step, the fast write path follows the traditional store procedure of on-device ML, thus it is always triggered first. If the memory usage of the monitored application is lower than the threshold of Android OS, Overlay-ML keeps using the fast write path to store data in the main memory space (denoted by ). If the memory usage of the application exceeds the threshold, Overlay-ML starts to store data on the underlying storage device via the slow write path (denoted by ). Now, data generated by on-device ML can be stored whether in the main memory or the underlying storage device space. Therefore, Overlay-ML tracks and manages the location where data is stored by using a mapping table. In other words, data generated by on-device ML before the memory threshold is stored in the main memory space, and data beyond the threshold is stored in the underlying storage device space.  
Figure 3: The overall flows of the overlay-write operation in Overlay-ML.  
On the other hand, since two different applications using on-device ML can run concurrently, Overlay-ML should isolate data generated by one application from others. To support this isolation, Overlay-ML stores data in the form of a dedicated file that is generated per application.
## 5 Conclusions  
A general solution to the anti-plane problem of an arbitrarily shaped hole reinforced with an FG layer in a homogenous plate is provided. Using the method of piece-wise homogeneous layers and the conformal mapping technique, the FG layer in which the material properties change continuously along the normal direction of the hole is reduced into \(N\) homogeneous concentric circular rings. The complex potentials in each circular ring and the plate are solved based on the continuous conditions at the interfaces by following the Muskhelishvili approach. The numerical examples of shear stress around some typical shaped holes are presented for different Young's modulus. Four conclusions can be drawn as follows.  
1. The maximum shear stress of ellipse and rectangle shaped holes is usually larger than that of circular and square shaped holes, respectively, and the FG layer around the triangular hole has the largest magnitude of concentrations of all the five different shaped holes.
2. When the hole is reinforced by an FG layer, the maximum shear stresses at the hole surface get smaller, and they decrease further for all shaped holes when the gradient exponent of Young's modulus increases.
3. As the gradient exponent \(n\) increases, the shear stresses decrease obviously in the FG layer, while they increase slightly in the plate. So, the position of maximum stress changes from the hole to the interface after \(n\) increases to some value.
4. The SCF of various shaped holes can be reduced significantly by using the optimum design of the material variations in the layer, and the most obvious one is the triangular hole whose SCF can be decreased by more than 50%.  
[PERSON] conceived the research ideas; [PERSON] derived the equations and analyzed the numerical results; both authors participated in the writing of this article.  
The work is supported by the Project of \"Six Talent Peaks\" in Jiangsu Province (No. GDZB-014) and the Project for the Integration of Industry, Education and Research of Jiangsu Province (No. BY2018243).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Analysis  
We measured task performance by standardized task completion time (ST) ratios and standardized task accuracy (SA) ratios. If the ST ratio is greater than 1, the automation is considered as having lengthened the task completion time; and if it is less than or equal to 1, the automation is considered as having accommodated the task completion time. Similarly, if a SA ratio is greater than 1, the automation improves the task completion accuracy; and if it is lower than or equal to 1, the automation causes more errors and deteriorates the task accuracy.  
A repeated two-way analysis of variance (ANOVA) was performed to test for differences between the values of the measures, i.e., task completion time and accuracy, under different automated conditions. Prior to the analyses, all dependent variables were examined to check if the assumptions of ANOVA were met. This was achieved by checking normality using [PERSON]'s criteria of skewness (<2) and kurtosis (<7) [24], and homogeneity of variance across groups [25]. The alpha criterion of 0.05 was used to assess statistical significance. Furthermore, the Friedman test was performed as a post-hoc test using the Bonferroni method for testing both main and interaction effects. All analyses were performed using IBM SPSS Statistics version 24.  
For a subjective workload measure, NASA TLX was administered post-trial, which required the participants to rate each question item. Five NASA TLX subscales including mental demand, temporal demand, effort, frustration, and perceived performance were rated on a 20-point scale (0-low, 20-high) [22]. Physical demand, one of subscales, was excluded considering task features. The \"raw TLX\" approach was applied without the pairwise comparison between the subscales in the NASA TLX data analysis, which is validated by many researchers [26]. The raw TLX approach is simpler to apply; the ratings are averaged or added to create an estimate of the overall workload, and an overall estimate of the subjective workload by each automation case was computed by averaging the scores describing each of the five subscales. In addition, a question to indicate a variation from perceived trust on automation, reported as \"expected,\" \"less than expected,\" or \"more than expected,\" was asked to participants.
## 3 Materials and Methods
### Base Model  
In this study, instead of using pre-built CNN architectures, the Sequential model was employed to create the basic model. One of the main reasons for this choice is the small size of the dataset, which contains only two classes. In such cases, using more complex architectures like VggNet, DenseNet, or ResNet can lead to unnecessary computational overhead and an increased risk of overfitting. Additionally, the Sequential model's advantages in rapid prototyping and development proved highly beneficial for achieving results within a short timeframe. Its straightforward and understandable structure accelerated the development process, enabling faster testing and verification. The structure of the Sequential model, which incorporates both classification and feature extraction stages, is illustrated in Figure 2.  
Conv2 DThese layers are used to extract features from the input data. The first Conv2D layer uses 128 filters with a 3 \(\times\) 3 kernel. The ReLU activation function is applied in this layer, which reduces negative values to zero, provides a non-linear transformation, and increases the model's learning capacity. To ensure that the output size remains the same as the input size after the convolution process, padding ='same' is used. The second Conv2D layer uses 64 filters and also employs ReLU activation. This layer processes the features extracted from the previous layer at a higher level.  
Figure 1: MRI image samples of PCa dataset: (**a**) benign and (**b**) malignant.  
Figure 2: The architecture of the Keras Sequential model.  
MaxPooling2D: This layer reduces the dimensionality and complexity of the model by selecting the maximum value within a specified window size. One MaxPooling2D layer is added after each convolution layer. These layers represent the feature maps at a lower resolution, thereby reducing the computational load and minimizing the risk of overfitting.  
Flatten: This layer converts the multidimensional outputs from the convolutional layers into a one-dimensional vector. This step is essential for fully connected layers, as they require flattened inputs to operate.  
Dense: The model contains three dense layers. The first dense layer has 128 neurons and uses the ReLU activation function, providing non-linear processing of the features extracted by the convolutional layers. The second dense layer has 32 neurons, again using ReLU activation. The final dense layer is the output layer, which consists of 2 neurons and employs the sigmoid activation function. This layer is used for binary classification and outputs a probability value for each class.  
The Adam optimization algorithm was used to train the Sequential-based model, with the training process carried out over 10 epochs.
## 3 Results
### Clinical Results  
3.2.1. AOFAS Score  
The average AOFAS score was 90 points with a range from a minimum of 83 points to a maximum of 98 points.  
AOFAS scores did not show a significant correlation with sex (U-test \(p\) value = 0.84), AO/OTA classification (\(p\) = 0.57), and surgical approach to posterior malleolus (\(p\) = 0.22).  
The graphical representation of data regarding the association between AOFAS Score and articular step-off appears to suggest that lower scores are more frequently associated with the presence of articular step-off (Figure 5a), but the statistical significance for this relationship was not reached (\(p\) = 0.07). Similar considerations can be made regarding the association between AOFAS scores and a reduction in ROM \(>15^{\circ}\) compared to the contralateral ankle (Figure 5b), in this case with a \(p\)-value of 0.08.  
Bartonicek classification showed a correlation with AOFAS scores: the median score for type 2 was 93 points, for type 3 it was 90 points, and for type 4 it was 86 points, a significant difference according to the statistical test used (\(p\) < 0.05).  
Age emerged as the only variable exhibiting a statistically significant correlation with worse AOFAS scores. In this case, the correlation is negative, meaning that as age increases, the score decreases, as indicated by [PERSON]'s rank correlation coefficient of \(-0.56\), which is significantly different from zero with a \(p\)-value \(<0.001\).  
\begin{table}
\begin{tabular}{l l l l} \hline  & & **N** & **\%** \\ \hline \hline
**Sex** & Male & 24 & 44.4 \\  & Female & 30 & 55.6 \\ \hline \hline  & 44 B 3.2 & 30 & 55.6 \\ AO/OTA & 44 B 3.3 & 18 & 33.3 \\  & 44 C 1.3 & 6 & 11.1 \\ \hline \hline  & 2 & 28 & 51.9 \\ Bartonicek & 3 & 16 & 29.6 \\  & 4 & 10 & 18.5 \\ \hline \hline  & A-P Screw & 34 & 63 \\ Surgical Technique & P-L Plate & 20 & 37 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data distribution of the variables observed in the sample.  
Figure 5: AOFAS score correlation with articular step-off (**a**) and ROM reduction (**b**).