esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 4 Results and Discussion
### Evaluation Metrics
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Custom** & **Size (Pixels)** & **Epochs** & **Batch Size** & **Param (m)** & **Optimizer** & **mAP-50** \\ \hline \multirow{3}{*}{Hand} & \multirow{3}{*}{640 \(\times\) 640} & \multirow{3}{*}{1000} & \multirow{3}{*}{32} & \multirow{3}{*}{25.9} & Auto & 0.637 \\  & & & & Adam & 0.692 \\ \cline{1-1}  & & & & AdamW & 0.561 \\ \cline{1-1}  & & & & SGD & 0.711 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Validation results for the hand on the FracAtlas dataset using the YOLOv8m model with the HA mechanism and comparing various optimizers. Research from Ultralytics suggests that training YOLOv8 models typically necessitates around 500 epochs. However, leveraging pre-trained models allowed us to set the total number of epochs to 1000, with an early stopping mechanism that would terminate training if no significant improvement was detected after 200 epochs. Our experimental results showed that the optimal performance for all models occurred within the first 100 epochs, mainly between the 50 th and 70 th epochs.
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 4 Methodology
### MVT Model Architecture  
The mixed vision transformer model builds upon the classical vision transformer framework and adapts it specifically for malware classification based on visual representations of binary files. The architecture is composed of several functional components, each contributing critically to the model's effectiveness:  
* Input encoding: Raw binary files are first transformed into grayscale byteplot images, where each byte is mapped to a pixel value. This transformation preserves the structural and sequential characteristics of the file, allowing the model to exploit visual cues such as repeated patterns, padding zones, or anomalous header structures often associated with malicious behavior.
* Patch extraction and positional encoding: The byteplot image is divided into non-overlapping fixed-size patches (e.g., \(16\times 16\)). Each patch is flattened and linearly projected into a latent embedding space. Sinusoidal positional encodings are added to each patch vector to retain spatial order and location information, which is crucial to preserving the sequential logic of the original binary layout.
* Transformer encoder blocks: The sequence of encoded patches is passed through multiple stacked transformer encoder blocks, each consisting of multi-head self-attention mechanisms and feed-forward neural networks with residual connections and layer normalization. This configuration enables the model to capture long-range dependencies across distant regions in the byteplot, allowing for the identification of complex and distributed patterns characteristic of obfuscated or polymorphic malware.
* Classification layer: The aggregated output (via a class token or average pooling) is fed into a fully connected neural network, which produces a binary prediction indicating whether the file is benign or malicious. A binary cross-entropy loss function is used for optimization, with regularization via dropout to enhance generalization.  
Each component contributes synergistically to the model's overall performance. The multi-head attention mechanism facilitates the modeling of inter-patch relationships, while positional encoding ensures that the sequential and spatial semantics of binary data are preserved throughout the pipeline. As demonstrated in the ablation study removing or altering any of these modules results in a significant performance drop, confirming their essential role in achieving reliable detection.
## 3 Test Results and Analysis
### Relationship between Vertical Displacement of Pile Head and Moment  
Figure 12 shows the relationship between the vertical displacement and moment of a batter pile under vertical load according to depth. Here, depth means the depth measured along the pile axis exposed above the ground, not the vertical depth of the buried pile.  
Figure 12: _Cont_.  
The measured value of moment (\(M\)) was calculated with the compressive strain of the strain gauge attached to the pile, and the applied formula is represented as Equation (1) ([PERSON] et al., 1998) [17].  
\[M=\frac{E\cdot I\cdot e}{d}, \tag{1}\]  
\(e\): Compressive strain  
\(E\), \(I\): Bending rigidity of model pile  
\(d\): Radius of model pile  
In Figure 12, the maximum bending moment was greater when the pile slope increased, or the relative density of the ground increased. In addition, the maximum bending moment occurred increasingly at a lower point when the vertical displacement of the pile head increased more. Additionally, the rate of increase of the moment ratio became higher with  
Figure 12: Relation of pile head displacement and bending moment.  
the increase in the batter angle of the pipe in the case with a high relative density of ground (72%) rather than the case with a low relative density of ground (31%). This is because a greater bending moment occurred by the vertical load on the pile as the ground was tamped more if the relative density increased more. Moreover, if vertical load was put on the pile in low-density ground, the fixation effect at the front end of the pile decreased, and the load was transferred to a deeper point by the compression of the ground. However, the fixation effect at the front end of the pile increased in high-density ground by the greater tamping effect. Therefore, it was thought that the bending moment became larger due to the increased subgrade reaction against the vertical load of the pile.
## 5 Conclusions  
In the actual application scenario of spoofing, it is difficult for the spoofer to obtain the accurate position of the antenna phase center of target receiver due to the limitation of observation conditions. Generally, the method of suppressing interference first to make the receiver lose lock and then performing spoofing is widely adopted. Under this application background, in this paper, a spoofing signal power control strategy is proposed for the receiver in the acquisition phase and subsequent control under the receiver power constraint is proposed. The actual practical experiments show that, compared with the conventional spoofing signal high power control strategy, when the newly designed spoofing signal power control strategy is adopted, the Doppler frequency fluctuation duration of the received signal is shortened by 72.2%, and the range is reduced by 75.9%, the \(C/N_{0}\) of the received signal is less than the threshold value of \(C/N_{0}\). During the process of spoofing signal taking over the receiver, ECEF is significantly reduced, which indicates that the newly designed spoofing signal power control strategy can make the spoofing behavior more concealed and will make it more difficult for the target receiver to detect the spoofing behavior. The designed power control strategy can covertly take over the receiver with the assistance of suppressing interference and then pull off the positioning results. It has good feasibility and effectiveness. After accurately mastering the receiver parameters, the more covert spoofing of the receiver can be realized by changing the constraint amount.  
Methodology, Y.G.; writing--original draft preparation, Y.G.; writing--review and editing, Y.G. and G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by State Key Laboratory of Geo-Information Engineering, grant number SKLGIE2020-Z-2-1.  
Not applicable.  
Figure 17: Changes in ECEF three dimensional coordinates of receiver in experiment 2.
# The Analysis of the Applications of Crop Seed Tape Sowing Technology and Equipment: A Review
## 5 Discussions and Prospection of Seed Tape Sowing
### Fundamental Issues in Seed Tape Sowing That Still Need Further Discussion
#### 5.1.3 Crop-Seed Tape-Soil Coupling Interaction  
Theoretically, any crop can be seeded by seed tape. However, some researches showed that the emergence rate of the same crop was inhibited and uninhibited in the seed tape sowing experiment and people may believe that it is risky to sow with seed tape. Therefore, the agronomic requirements (such as hill distance, sowing depth and other parameters) of different crops when seeding with seed tape are not clear. The effects of seed tape sowing on the root zone environment are not clear. The most suitable seed tape materials and technological parameters for different crops need to be further explored. As a result, we should strengthen the agronomic basic research of seed tape sowing technology. Reveal the suitable agronomic parameters for different crops sowed with seed tape such as hill distance, row spacing, temperature and humidity of the soil.
## 5 Discussion
\tag{12}\]  
Table 6 shows the percentage changes in silt and clay fraction as a consequence of bar projectile impact during the SHPB test compared to the reference sample, depending on the configuration of the variables--soil sample moisture content \(w_{i}\) and initial shot pressure \(p_{i}\) (impact velocity \(v_{i}\)). For the first type of soil sample \(f_{Si+C1,1}=15.14\%\), the bar projectile impact load was most significant at a launching pressure \(p_{2}=1.8\) bar for all moisture content variants \(w_{1}\div w_{4}\) (particularly prominent values are for samples with moisture content \(w_{1}\) and \(w_{2}\) lower than the optimum moisture content \(w_{opt}\)--for moisture content \(w_{3}\) and \(w_{4}\) higher than the optimum moisture content \(w_{opt}\), there is a phenomenon of \"damping\" of the impact through the reaction of water in the soil pores). At this pressure \(p_{2}\), the greatest percentage change in granulometric composition occurred through an increase in the value of the silt and clay fraction in relation to the reference sample \(f_{Si+C1,1}\):  
\[ \begin{array}{l}\bullet\quad\quad f_{Si+C1,1}=15.14\%\to f_{Si+C1,1-III}=25.10 \%\text{ (increase of 9.96\%);}\\ \bullet\quad\quad f_{Si+C1,1}=15.14\%\to f_{Si+C1,1-VI}=26.22\%\text{ (increase of 11.08\%);}\\ \bullet\quad\quad f_{Si+C1,1}=15.14\%\to f_{Si+C1,1-IX}=22.96\%\text{ (increase of 7.82\%);}\\ \bullet\quad\quad f_{Si+C1,1}=15.14\%\to f_{Si+C1,1-XI}=21.24\%\text{ (increase of 6.10\%).}\end{array} \]  
However, for the second soil sample type \(f_{Si+C1,2}=20.48\%\), the most significant was the bar projectile impact at SHPB pneumatic launcher pressure \(p_{3}=2.4\) bar for all moisture contents \(w_{1}\div w_{4}\) (analogous to the first sample type, values were particularly noticeable for samples with moisture contents \(w_{1}\) and \(w_{2}\) lower than the optimum moisture content \(w_{opt}\)--for moisture contents \(w_{3}\) and \(w_{4}\) higher than the optimum moisture content \(w_{opt}\), a phenomenon of \"damping\" of the impact occurred through the reaction of water in soil pores). At this pressure \(p_{3}\), the greatest percentage change in granulometric composition occurred through an increase in the value of the silt and clay fraction in relation to the reference sample \(f_{Si+C1,2}\):  
\[ \begin{array}{l}\bullet\quad\quad f_{Si+C1,2}=20.48\%\to f_{Si+C1,2-III}=32.87 \%\text{ (increase of 12.39\%);}\\ \bullet\quad\quad f_{Si+C1,2}=20.48\%\to f_{Si+C1,2-VI}=35.65\%\text{ (increase of 15.17\%);}\\ \bullet\quad\quad f_{Si+C1,2}=20.48\%\to f_{Si+C1,2-IX}=25.79\%\text{ (increase of 5.31\%);}\\ \bullet\quad\quad f_{Si+C1,2}=20.48\%\to f_{Si+C1,2-XII}=25.35\%\text{ (increase of 4.87\%).}\end{array} \]
# Performance Analysis of Quantum Key Distribution Technology for Power Business
## 4 Structure of Power QKD Network
### QKD-Based Electricity Information Collection  
The electricity information collection system is one of the important components of the smart grid. It provides timely, complete and accurate feedback of user power consumption information to provide a reference for power control. Since the power consumption information contains a large amount of private data, if the data are manipulated by a third party, it will not only affect the daily power consumption of users, but also cause the leakage of sensitive data and private information [33]. In order to ensure the safe collection of a large number of electricity consumption information, it is necessary to build a highly secure and reliable transmission network. Therefore, QKD technology is used to increase the update frequency of the encryption key for the electricity information collection  
Figure 6: Dispatching automation system based on QKD.  
Figure 7: Distribution automation architecture based on QKD.  
service, and to ensure the secure data transmission of the electricity information collection system. Figure 8 shows the structure of QKD-based electricity information collection.
## 5 Conclusions  
In this paper, the performance of using the presented wavelet packet features and support vector machine algorithm for radio fuze anti-frequency sweeping jamming signals is investigated. The conceptual framework of this approach is based on a combination of wavelet packet transform entropy features and machine-learning technologies. To extensively verify the performance of the presented approach, a variety of samples were collected under the microwave darkroom laboratory environment, including target signals, noise AM frequency sweeping jamming signals, sine AM frequency sweeping jamming signals, and square AM frequency sweeping jamming signals. The significance of differences in targets and jamming signals for classification is analyzed using PDFs and the Kruskal-Wallis test. In addition, PCA is used for reducing the dimensionality of wavelet packet entropy features, so that the computational cost can be minimized without compromising the accuracy. In order to fully evaluate the methodology proposed in this paper, the four indicators of 'Accuracy', 'Precision', 'Recall', and 'FalseAlarm' were used to evaluate the experiment. The experimental results show that the highest 'Accuracy' is 98.81% \(\pm\) 0.0037 when the kernel function is Gaussian function, the highest 'Precision' is 99.43% \(\pm\) 0.0043 when the kernel function is polynomial function, the highest 'Recall' is 99.21% \(\pm\) 0.0030 when the kernel function is Gaussian function, and the lowest 'FalseAlarm' is 0.57% \(\pm\) 0.0043 when the kernel function is polynomial function. In summary, the classification and recognition accuracy of the proposed method is higher than 98% and the false alarm rate is lower than 2%, which can provide guidance for the design of radio fuze anti-jamming.  
Figure 8: Histogram of experimental results. (**a**) Accuracy, precision, and recall experiment results. (**b**) FalseAlarm experiment results.  
**Author Contributions:** Conceptualization, B.L. and X.H.; methodology, B.L.; software, B.L.; validation, B.L. and X.H.; formal analysis, B.L.; investigation, B.L.; resources, B.L.; data curation, B.L.; writing--original draft preparation, B.L.; writing--review and editing, B.L.; visualization, B.L.; supervision, X.H.; project administration, X.H.; funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 6187414 and partly by the Basic Strengthening Project of Military Science and Technology Commission 2019-JCJQ-ZD-324.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Robot Calibration Principle
### Base Coordinate Calibration of Multi Robots
\(p_{1(G)}\), \(p_{2(G)}\) and \(p_{3(G)}\) are the positions of the calibration points \(p_{1}\), \(p_{2}\), \(p_{3}\) relative to the tool base coordinate system \(\{G\}\). From Formulas (3) and (6), \(H_{B_{i}}^{G}\) can be denoted as  
\[H_{B_{i}}^{G}= \begin{bmatrix}n_{11}&n_{12}&n_{13}&p_{G(B_{i}X)}\\ n_{21}&n_{22}&n_{23}&p_{G(B_{i}Y)}\\ n_{31}&n_{32}&n_{33}&p_{G(B_{i}Z)}\\ 0&0&0&1\end{bmatrix} = \begin{bmatrix}\hat{X}_{G(B_{i})}&p_{G(B_{i}X)}\\ \hat{Y}_{G(B_{i})}&p_{G(B_{i}Y)}\\ \hat{Z}_{G(B_{i})}&p_{G(B_{i}Z)}\\ 0_{1\times 3}&1\end{bmatrix}  \tag{8}\]  
In Formula (8), \(p_{G(B_{i}M)}(M=X,Y,Z)\) is the value of the calibration tool base coordinate system origin \(p_{G}\) projected to the M direction of the robot base coordinate system \(\{B_{i}\}\). Figure 5: The pose relationship diagram: (**a**) \(\{G\}\) relative to \(\{B_{i}\}\); (**b**) calibration points \(p_{1}\), \(p_{2}\), \(p_{3}\) relative to \(\{G\}\). From Formulas (3), (5), (7) and (8), can be obtained:  
\[\left\{ \begin{array}{l}p_{G(B_{i}X)}=\frac{Q}{W}\\ p_{G(B_{i}Y)}=p_{3(B_{i}Y)}-\frac{\left(p_{3(B_{i}X)}-p_{G(B_{i}X)}\right)r_{21} }{r_{11}}\\ p_{G(B_{i}Z)}=p_{3(B_{i}Z)}-\frac{\left(p_{3(B_{i}X)}-p_{G(B_{i}X)}\right)r_{31 }}{r_{11}}\end{array} \right. \tag{9}\]  
In Formula (9), \(W=r_{13}r_{21}-r_{11}r_{23}\), \(Q=\left(p_{1(B_{i}Y)}-p_{3(B_{i}Y)}\right)r_{11}r_{13}-p_{1(B_{i}X)}r_{11}r_{23 }+p_{3(B_{i}X)}r_{13}r_{21}\). \(H_{B_{i}}^{G}\) can be obtained, and Formula (4) can be combined to obtain the transformation matrix \(H_{B_{2}}^{B_{1}}\) of the base coordinate \(\{B_{1}\}\) of robot 1 relative to the base coordinate \(\{B_{2}\}\) of robot 2.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 5 Parametric Study
### Influence of Material Strength  
Figure 23 demonstrates the influence of concrete strength on the pull-out performance of notched T-perfobond connectors. As concrete strength escalated from 80 MPa to 95 MPa, 110 MPa, 130 MPa, and 140 MPa, the corresponding increases in pull-out strength were 8.87%, 22.79%, 25.31%, and 25.29%, respectively. The data indicate that the pull-out resistance of these connectors increases significantly with rising concrete strength, likely due to the strong anchorage effect of the flange. The predominant mode of specimen damage is concrete punching, and the pull-out resistance correlates linearly with concrete strength. Therefore, utilizing high-strength concrete can enhance the pull-out resistance of notched T-perfobond connectors. However, beyond a concrete strength of 110 MPa, there is a tendency for the pull-out resistance to plateau. This saturation may occur because, at certain strength levels, the concrete's anchoring effect on the flange surpasses the strength of the notched perfobond rib, making the rib itself the limiting factor.  
Figure 24 illustrates the impact of rebar strength on the pull-out performance of notched T-perfobond shear connectors. Notably, the pull-out resistance remained essentially constant as rebar strength increased from 235 MPa to 335 MPa, 400 MPa, and 500 MPa. The findings indicate that variations in rebar strength have a negligible effect on the pull-out resistance of the specimens. This outcome is likely due to the overwhelming anchorage effect of the flange and the notches, which diminishes the influence of rebar strength changes on pull-out resistance.  
Figure 22: Influence of rebar diameter. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.  
Figure 25 demonstrates the impact of the perfobond rib's strength on the pull-out performance of notched T-perfobond connectors. As the strength of the perfobond rib increased from 235 MPa to 345 MPa, 390 MPa, 420 MPa, and 460 MPa, the pull-out strength correspondingly rose by 41.65%, 41.82%, 41.29%, and 52.61%, respectively. The results indicate that the pull-out resistance of the notched T-perfobond connectors increases significantly with the strength of the notched perfobond ribs. This substantial increase may be  
Figure 23: Influence of concrete strength. (**a**) Load–separation curves; (**b**) influence analysis; (c) failure mode.  
Figure 24: Influence of rebar strength. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.  
attributed to the linear relationship between pull-out resistance and the strength of the per-fobond rib, facilitated by the exceptionally strong anchorage provided by the bottom flange of the notched perfobond rib, which is relatively weaker than other restraining elements.
## 4 Experiments and Results
### Parametric Analysis
#### 4.3.1 The Effect of Iterations  
Taking BMP2 as an example, we conduct a qualitative analysis of the loss during dictionary learning. At this time, the number of dictionary atoms is 40, and the factors \(\lambda_{1}\) and \(\lambda_{2}\) are set to 0.1 and 0.01, respectively. As shown in Figure 5, when \(iteration>30\), the value of loss almost does not change. Specifically, when \(iteration=30\) and \(iteration=60\), the loss values are 66.07 and 65.79, respectively. The difference between the two loss values is only 0.28, which is very subtle. We think the model converged when \(iteration=30\). In addition, the change trend of the loss curve in other class-specific dictionaries is basically consistent with that in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **BMP2** & **BTR70** & **T72** & **BTR60** & **2S1** & **BRDM2** & **D7** & **T62** & **ZIL131** & **ZSU234** & **Overall** \\ \hline Joint & & & & & & & & & & \\ Train- & 0.9897 & 1.0000 & 1.0000 & 1.0000 & 0.9818 & 0.9891 & 1.0000 & 0.9963 & 0.9964 & 1.0000 & 0.9951 \\ \multicolumn{1}{c}{} & & & & & & & & & & \\ Replay & 0.8821 & 0.9694 & 0.9592 & 0.9436 & 0.9891 & 0.8650 & 0.9891 & 0.9817 & 0.9891 & 1.0000 & 0.9592 \\ iCaRL & 0.9282 & 0.9694 & 0.9796 & 0.8462 & 0.9635 & 0.9124 & 0.9781 & 0.9963 & 0.9964 & 1.0000 & 0.9604 \\ Wa & 0.9641 & 0.9694 & 0.9643 & 0.9026 & 0.9343 & 0.8942 & 0.9380 & 0.9853 & 1.0000 & 1.0000 & 0.9559 \\ Podnet & 0.9744 & 0.9694 & 0.9898 & 0.9538 & 0.9489 & 0.9088 & 0.9270 & 0.9707 & 1.000 Quantitatively, we train these class-specific dictionaries when the iterations are 5, 10, 15, 30, 45 and 60, respectively. Then, the recognition accuracy under different iterations is measured. Taking Scenario_A as an example, the accuracy is shown in Figure 6. It is obvious that the change in accuracy is more obvious when the iterations are 5 and 10. However, when the iterations are 15, 30, 45 and 60, the accuracy fluctuates only slightly. This phenomenon indirectly indicates that the model converged when \(iteration>30\). Therefore, we take \(iteration=45\) in our experiments.
# A Deep Learning Technique for Optical Inspection of Color Contact Lenses
## 3 Contact Lens Defect Detection with a CNN Model
### GoogLeNet Structure  
Figure 3 shows a block diagram that simplifies the GoogLeNet structure. Based on the input data, each block was divided into three parts: the stem network, inception modules, and classifier output. After the training data were input, the first six layers were referred to as the stem network, which is a stack of convolution and max-pooling layers. The stem network structure is shown in Figure 4a. The inception module block, which is the core element of GoogLeNet, is a stack of inception modules whose structure is shown in Figure 4b. It extracts various training data features using convolution layers of different sizes and pooling. Further, it can have a deep neural network structure while reducing the number of parameters via the application of a \(1\times 1\) convolution layer. Finally, the classifier output comprised the collection of layers shown in Figure 4c, and the final classification was performed based on global average pooling (GAP).  
The existing GoogLeNet, pretrained with ImageNet, can classify input images into 1000 object categories, such as keyboards, mice, and various animals. In this study, only seven categories (one good and six defects) were needed for contact lens defect classification. Consequently, the last three layers of classifier output layers (i.e., full connection, softmax, and output) were modified accordingly.