esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Positioning Method of Steel Belt Anchor Holes Applied in Coal Mine Underground
## 6 Conclusions  
This paper introduces a method for identifying and positioning anchor holes in steel belts using an inductive probe. A detection platform was constructed based on functional requirements, and the software system was designed and developed accordingly. The induction of the head coil of the inductive probe was simulated and analyzed using ANSYS Electronics Desktop 2021 R1 software, and was experimentally validated to determine the most suitable vertical detection distance. The detection platform was used to position the holes in the steel belts, and the collected data were analyzed using an algorithm developed from Hough transform. The resulting anchor hole positioning error was within 5 mm. This study provides a solution for the automated positioning of anchor holes in coal mine tunnels, which is able to replace manual operations.  
Conceptualization, J.Z.; methodology, J.Z.; software, Y.W.; validation, Y.W.; formal analysis, Y.W.; investigation, H.W.; resources, H.W.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, G.L.; visualization, J.Z.; supervision, Y.W.; project administration, Y.W.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Support Program of Henan Province Industry-University-Research, grant number 17210/000008.  
Not applicable.  
The data present in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors express sincere gratitude for the support received from various project funds, which has been instrumental in facilitating the successful completion of this research endeavor. In addition, the authors wish to thank the reviewers for their useful and constructive comments.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c} \hline No. & Actual Coordinates/mm & Detecting Coordinates/mm & Error/mm \\ \hline
1 & (680, 380) & (683, 382) & (3, 2) \\
2 & (566, 400) & (565, 403) & (1, 3) \\
3 & (615, 395) & (617, 393) & (2, 2) \\
4 & (525, 370) & (528, 368) & (3, 2) \\
5 & (572, 360) & (570, 362) & (2, 2) \\
6 & (545, 375) & (548, 377) & (3, 2) \\
7 & (570, 365) & (567, 367) & (3, 2) \\
8 & (557, 374) & (559, 377) & (2, 3) \\ \hline \end{tabular}
\end{table}
Table 2: Anchor hole identification and positioning results at a vertical distance of 17 mm.  
\begin{table}
\begin{tabular}{c c c} \hline No. & Actual Coordinates/mm & Detecting Coordinates/mm & Error/mm \\ \hline
1 & (690, 370) & (687, 372) & (3, 2) \\
2 & (576, 405) & (579, 408) & (3, 3) \\
3 & (654, 390) & (650, 392) & (4, 2) \\
4 & (510, 330) & (512, 328) & (2, 2) \\
5 & (530, 365) & (532, 368) & (2, 3) \\
6 & (570, 375) & (567, 378) & (3, 3) \\
7 & (610, 382) & (613, 380) & (3, 2) \\
8 & (605, 395) & (602, 392) & (3, 3) \\ \hline \end{tabular}
\end{table}
Table 3: Anchor hole identification and positioning results at a vertical distance of 19 mm.
## 2 OCT-A Applications in Pediatric Population
### Applications of OCT-A in Children in Other Conditions  
With regard to the use of OCT-A in congenital alterations of the optic nerve, a dense peripapillar microvascular network has been shown in Morning Glory syndrome, a network that is absent both in colobomas and in optic disc pits [81]. With respect to papillary edema and papillitis, greater vascular tortuosity and dilatation have been described in the prelaminar capillary network [74], a fact which can be seen in Figure 2.  
Recently, OCT-A has also been used to assess the vascular density of the retinal capillary and radial peripapillary plexuses and FAZ in children with amblyopia. [104; 105]. A reduced macular vessel density in both the SCP and the DCP was found in amblyopic patients when compared to control eyes. The decreased vessel density that was found in amblyopic eyes has been hypothesized to be secondary to alterations in the retinal or choroid microvasculature due to underuse or to congenital or acquired retinal vascular capillary disorders associated with amblyopia [104; 105]. In addition, the absence of abnormalities in the papillary and peripapillary vessel density in amblyopic eyes found in the study of [PERSON] et al. [104] supported the findings of the Pediatric Eye Disease Investigator Group, in which amblyopia did not seem to be related with a pathologic condition of the optic nerve [106]. Finally, amblyopic eyes did not show differences in the dimensions of the FAZ in the SCP and DCP when compared to healthy controls in any of the studies [104; 105].
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 3 System Model
### Proposed Scheme-I(Key Recovery System Using Signcryption)
#### 3.2.1 Design Goals of Proposed Scheme-I  
The model of the key recovery system using signcryption is a key recovery system that is used when a device key is lost or corrupted as shown in Figure 2. The device requests key recovery from Med and sends KRF. The Med receiving the KRF verifies that it is a lawful device, it requests KRC to recover the key and sends KRF. After receiving KRF, KRA decrypts the KRF and sends the obtained KRF pieces to the M-KRA. Then, after receiving the pieces of KRF, M-KRA decrypts them and sends the session key pieces to KRC. It collects the session key pieces, generates a complete session key, and sends it to the device.
## 6 Experiment Evaluation
### Model Performance Evaluation  
To enhance the comparative experiment of the model, we used the MobileNet network as a comparison model. An adaptive moment estimation (Adam) optimizer was used during the experiment for parameter updates, and the input images were uniformly normalized. The initial learning rate was set to 0.01, and 60 training epochs were performed with batch sizes of 32. The loss function during training is shown in Figure 10, where the global epochs were trained 60 times. At approximately 40 epochs, the model stopped oscillating and began to converge. The model structure and weight parameters were saved using the Keras deep learning library model and the Save () method. The accuracy curve of the training and validation processes is shown in Figure 11, where train_acc represents the accuracy of the model during training, and val_acc represents the accuracy of the model on the validation set. Table 4 lists the model training parameters and performance configuration.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **GPU** & **Dataset Number** & **Training Time (h)** & **Epoch** & **Parameters** & **Accuracy (\%)** \\ \hline ShuffleNet.h5 & RTX A5000 & 40,737 & 9.6 & 60 & 971,659 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model configuration and performance analysis.  
Figure 10: Training and validation loss.  
To reduce the bias of the experiment, we repeat experiments for fifty times and plot the standard deviation of the accuracy of ShuffleNet. As can be seen from Figure 12, the proposed ShuffleNet model can maintain a relatively stable accuracy of around 70%. It should be note that this study mainly focuses on image aesthetic processing optimization after recognition and classification of the image, rather than the specific value of the accuracy. In other words, we can optimize image aesthetic processing with different accuracies as benchmarks.
# Airwave Noise Identification from Seismic Data Using YOLOv5
## 1 Introduction
Deep learning, particularly in the field of object detection, excels at this. Object detection techniques have been successfully applied across various domains, including plant protection [24; 25], wildlife conservation [26; 27], and urban surveillance [28]. Among the various deep learning models, YOLO (You Only Look Once) stands out as a widely used model for object detection. It is capable of simultaneously locating objects and identifying their categories in a single forward pass. Since its initial release in 2016, YOLO has gone through multiple iterations, each introducing notable improvements [29; 30; 31; 32; 33]. YOLOv5, developed in Python using the PyTorch (2.0.0) framework, is an open-source model that benefits from an active community. With a more lightweight architecture than its predecessors, YOLOv5 is known for its efficiency, accuracy, and ease of use, making it widely applicable in various real-world scenarios that require rapid object detection. These applications range from real-time monitoring and security systems to intelligent transportation, environmental perception, medical image analysis, and drone-based vision systems. This paper introduces the first implementation of rapid airwave noise prediction using YOLOv5, facilitating quick estimation of the location, extent, and confidence of airwave noise in raw seismic data. To the best of our knowledge, this is the first attempt at computer-based recognition of airwaves in seismic exploration. The proposed method provides a real-time seismic source quality monitoring method during seismic acquisition. The structure of this work is organized as follows. First, we outline the fundamental architecture of YOLOv5, including its input-output layer structure and loss function. Next, we detail the integration of a seismic dataset containing airwave noise for the network's secondary training and evaluate its performance on the test dataset. Finally, we apply the model to 2D seismic data from the Ningqiang area in Shaanxi Province and 3D seismic data from the Xiushui area in Sichuan Province to assess its performance and generalization capabilities. In addition, we compare the accuracy and efficiency of different YOLO models and discuss the necessity of image mapping from 2D seismic data.
## 3 Results
### Energy Expenditure  
There was a significant increase (\(p<0.001\)) in the energy excreted in the faeces of the treatment group that received 4% PRSE when compared to the control (Figure 4C). When the total energy intake is taken into account to calculate digestibility both the 2% and 4% treatment groups showed a significant decrease in digestibility (Figure 4D). However, faecal carbon content was significantly higher (\(p<0.001\)) in both 2% and 4% PRSE (Figure 4F). In terms of nitrogen excretion (Figure 4G) only the 4% PRSE treatment was significantly lower than the control (\(p<0.05\)).  
Figure 4: Energy expenditure and excretion after PRSE supplementation. In all graphs the mice of 2% PRSE dietary supplementation are represented by light bars and the group with 4% PRSE dietary supplementation by dark bars. (**A**) In the first week of study the metabolic rate of the mice was measured by indirect calorimetry. Data are expressed as mean (\(\pm\)SEM) 24-h energy expenditure. (**B**) Also in the first week, general locomotor activity was measured. Data are expressed as the mean (\(\pm\)SEM) of the amount of activity in 24 h. (**C**) In week 8 faecal matter was analysed by bomb calorimetry for the mean (\(\pm\)SEM) of daily excreted energy. (**D**) Calculation of the mean (\(\pm\)SEM) digestibility percentage (100 \(\times\) (total energy intake-total energy excreted)/(total energy intake); where total energy intake = diet energy \(\times\) total food intake; and total energy excretion = faecal energy \(\times\) total faecal excretion) of the diet consumed for mice in each of the experimental groups. Faecal matter was then analysed to determine mean percentage differences (\(\pm\)SEM) of (**E**) lipid, (**F**) carbon and (**G**) nitrogen levels. (**H**) Ratio of carbon to nitrogen in the faecal matter. Graphs with different superscripts (a, b, c) differ significantly \(p<0.05\).  
The significant differences in both the carbon and nitrogen excretion for 4% PRSE were associated with a significant difference also observed in the carbon-nitrogen ratio of the 4% PRSE treatment (Figure 4H).
# A Hierarchical Coordinative Control Strategy for Solid State Transformer Based DC Microgrids
## 2 Overview of the SST-Based DC Microgrid System
### Mathematical Model of the SST
Under single-phase-shift control, the transmission power of the CLLC resonant converter can be obtained as:  
\[P_{CLLCi}=\frac{8 nU_{dci}U_{out}}{\pi^{2}\left|Z_{qui}\right|}\cos(\varphi_{i} +\theta_{i}) \tag{2}\]  
where \(P_{CLLCi}\) is the transmission power of submodule i; \(\varphi_{i}\) is the phase shift angle between two H-bridges; and \(\left|Z_{qui}\right|\) and \(\theta_{i}\) are the magnitude and phase of the equivalent impedance \(Z_{qui}\), which can be calculated as:  
\[Z_{qui}=\frac{Z_{1}\left(Z_{m}+Z_{2}+\frac{8}{\pi^{2}}Z_{loadi}\right)+Z_{m} \left(Z_{2}+\frac{8}{\pi^{2}}Z_{loadi}\right)}{Z_{m}}=\left|Z_{qui}\right| \angle\theta_{i} \tag{3}\]  
where \(Z_{1}\), \(Z_{2}\), \(Z_{m}\), and \(Z_{loadi}\) represent the primary-side LC resonant impedance, secondary-side resonant impedance, the excitation impedance of the HFT, and load impedance (all equivalent to the primary side). From Equations (2) and (3), the input and output currents of the isolation stage can be calculated as:  
\[\left\{ \begin{array}{l}i_{1i}=\frac{8 nU_{out}}{\pi^{2}\left|Z_{qui}\right|} \cos(\varphi_{i}+\theta_{i})\\ i_{2i}=\frac{8 nU_{dci}}{\pi^{2}\left|Z_{qui}\right|}\cos(\varphi_{i}+\theta_{i}) \end{array} \right. \tag{4}\]  
and the dynamic equation of output voltage can be expressed as:  
\[NC_{L}\frac{dU_{out}}{dt}=\sum_{i=1}^{N}i_{2i}-i_{inv}-i_{mc}=i_{2}-i_{L} \tag{5}\]  
where \(i_{2}\) is the sum of the output currents of \(N\) CLLC submodules. Without loss of generality, the voltage and power balance control between the CLLC submodules are not considered in this paper, that is to say, the operating variables of \(N\) submodules are assumed to be the same, so the ISOP CLLC resonant converter can be equivalent to one submodule.
## 5 Conclusions  
In this research, we built the geological structure identification model based on a small dataset and the Inception-v3 model. The grayscale and color images datasets were both trained to construct different models. The two models had an accuracy of 91.0% and 92.6%, respectively. At the same time, we used 60 engineering images to test the model. The top-1 and top-3 accuracies were 83.3% and 90.0%, which showed the kernels and pooling layers in the Inception-v3 model could extract image features effectively. CNN models with different layers were built as well, while the model was overfitting in training even just with two convolutional layers and two fully connected layers. Three convolutional layers were adopted to establish the model in our study. The best parameters in CNN are hard to reach because it depends on experience. We also used OpenCV to build pixel feature based on origin pixel information and a pixel's histogram. However, the images features could not be extracted accurately in this way, which led to low accuracy in KNN, ANN, and XGBoost models. More feature engineering methods should be considered in the future. The retrained models based on Inception-v3 were trained using transfer learning method with color and grayscale datasets and had a small difference in accuracy, which indicated that color had little influence on geological structure identification.  
There are also some weaknesses in the model trained by a small dataset. Test data is small and overfitting still exists in the training process. Even though data augmentation was adopted, some patterns and features were not learned by the model. In this research, we proved the feasibility of transfer learning for geological structures classification. If the model is applied in practice in the future, more data should be added.  
Transfer learning based on the Inception-v3 model has strong adaptability for a small dataset. In the future, we are going to extend our dataset and combine the model with a UAV, which can be applied as a tool in geological surveys.  
[PERSON] wrote the code and the paper; [PERSON] gave professional geological guidance and the image data; [PERSON] provided the idea and edited the manuscript; [PERSON] collected and analysed the data.  
This research was funded by the Tianjin Science Foundation for Distinguished Young Scientists of China, Grant no. 17 JCJCJC44000 and the National Natural Science Foundation for Excellent Young Scientists of China, Grant no. 51622904.  
The authors declare no conflict of interest.
# The Circadian Effect Versus Mesopic Vision Effect in Road Lighting Applications
###### Abstract  
Several models on the circadian effect have been applied to indoor circadian lighting design, but applications in road lighting have not yet been clarified. Based on existing models and circadian research, we examined equivalent melanopic lux (EML), circadian light (CL\({}_{A}\)), and circadian stimulus (CS) representing the circadian effect and the S/P ratio representing the mesopic vision effect, among a dataset of light sources at photopic adaptation illuminance values of 1, 3, 10, 30, and 100 lux. The results show that the S/P ratio correlates with EML and CS (or CL\({}_{A}\)) much stronger than it correlates with color temperature. The EMLs of light sources are below 50 EML in mesopic vision, and the CSs of most light sources are below or around the threshold value of 0.05. We conclude that the circadian effect is not a significant issue in mesopic vision under most conditions and that optimization for mesopic efficiency is still a good strategy. There are quite a few light sources that may achieve both ideal mesopic efficiency and low CS. This work clarifies the circadian effect and mesopic vision effect performance of light sources in mesopic vision and will help guide choosing suitable light sources and optimization strategies for road lighting.  
S/P ratio; EML; CS; CCT; mesopic vision; photopic vision Article
## 5 Understanding Decentralised Identity Systems  
The existing model of identity management heavily relies on centralised systems, where the management and control is being regulated and governed by a single entity  
Figure 6: Sequence diagram of OAuth authentication.  
with the user having very little control over it. While decentralised systems aim to remove this central control, many still depend on trusted parties or governance frameworks to ensure system integrity, manage updates, and handle conflicts, indicating that complete independence from trusted intermediaries is not always achieved.  
Various studies have been conducted to analyse and demonstrate how blockchain and decentralised frameworks can be utilised to address the shortcomings in a CIMS. The DIMS model allows users to have full control over their identity, deciding whom to share it with, as well as managing its storage and security [47]. The use of blockchain's DLT in relation to the SSI model [48] makes the entire process more transparent. This transparency is achieved through SSI, which stores cryptographic proofs that are available at any time and are trusted by all parties [49].  
Each user in the SSI environment is given a unique DID and is connected to a key pair that the user can manage directly through a mobile or web application (depending on the product used). The user's public key is shared on the DLT and associated with an identifier, permitting users to independently verify identities [50]. Users can now prove their attributes with the help of VCs, which can be cryptographically confirmed by other parties without the need for a central authority. The verifying party uses publicly accessible data related to the identifier and credential issuer to confirm the validity of the credentials [51].
## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**The Trajectory Used for Sampling in k-Space** & **N** & **M** & **SD** & **t(99)** & \(p\)** \\ \hline The unaltered multi-blade k-space sampling technique & 100 & 29.50 & 0.01 & \(-1.770\) & 0.032 \\ \hline The proposed algorithm & 100 & 33.70 & 0.00 & 1.099 & 0.212 \\ \hline \hline \end{tabular}
\end{table} 
Table 20: The statistical data regarding the Peak Signal-to-Noise Ratio (PSNR) metrics for Figure 11. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**The Trajectory Used for Sampling in k-Space** & **N** & **M** & **SD** & **t(99)** & \(p\)** \\ \hline The unaltered multi-blade k-space sampling technique & 100 & 17.10 & 0.01 & \(-1.773\) & 0.021 \\ \hline The proposed algorithm & 100 & 13.36 & 0.00 & 1.139 & 0.233 \\ \hline \hline \end{tabular}
\end{table} 
Table 22: The statistical data regarding the IEM measures for Figure 11 are shown. \begin{table}
\begin{tabular}{l c c c c c} \hline
**SRR Procedure** & **N** & **M** & **MAE** & **SD** & **t** \\ \hline SR-no, MC-no & 100 & 29.10 & 0.01 & 1.612 & 0.101 \\ \hline SR-no, MC-yes & 100 & 25.01 & 0.01 & 0.411 & 0.264 \\ \hline cubic spline, MC-yes & 100 & 22.29 & 0.01 & 0.122 & 0.224 \\ \hline [PERSON] [50] & 100 & 16.00 & 0.01 & \(-\)0.354 & 0.433 \\ \hline [PERSON] [20] & 100 & 16.02 & 0.01 & \(-\)0.384 & 0.419 \\ \hline [PERSON] [51] & 100 & 16.29 & 0.01 & \(-\)0.416 & 0.431 \\ \hline [PERSON] no. 2 [43] & 100 & 15.57 & 0.01 & \(-\)0.265 & 0.534 \\ \hline [PERSON] et al. [52] & 100 & 14.44 & 0.04 & 0.135 & 0.604 \\ \hline [PERSON] et al. [53] & 100 & 15.26 & 0.02 & 0.111 & 0.109 \\ \hline [PERSON] et al. [54] & 100 & 16.41 & 0.03 & 0.721 & 0.421 \\ \hline [PERSON] et al. [55] et al. & 100 & 19.90 & 0.02 & 0.722 & 0.395 \\ \hline [PERSON] et al. [17] & 100 & 18.40 & 0.01 & \(-\)0.774 & 0.131 \\ \hline The presented algorithm & 100 & 13.01 & 0.01 & \(-\)1.012 & 0.101 \\ \hline \end{tabular}
\end{table} 
Table 24: The statistical data on the Peak Signal-to-Noise Ratio (PSNR) metrics for Figure 12. \begin{table}
\begin{tabular}{l c c c c c} \hline
**SRR Procedure** & **N** & **M** & **MAE** & **SD** & **t** \\ \hline SR-no, MC-no & 100 & 29.10 & 0.01 & 1.612 & 0.101 \\ \hline SR-no, MC-yes & 100 & 25.01 & 0.01 & 0.411 & 0.264 \\ \hline cubic spline, MC-yes & 100 & 22.29 & 0.01 & 0.122 & 0.224 \\ \hline [PERSON] [50] & 100 & 16.00 & 0.01 & \(-\)0.354 & 0.433 \\ \hline [PERSON] [20] & 100 & 16.02 & 0.01 & \(-\)0.384 & 0.419 \\ \hline [PERSON] [51] & 100 & 16.29 & 0.01 & \(-\)0.416 & 0.431 \\ \hline [PERSON] no. 2 [43] & 100 & 15.57 & 0.01 & \(-\)0.265 & 0.534 \\ \hline [PERSON] et al. [52] & 100 & 14.44 & 0.04 & 0.135 & 0.604 \\ \hline [PERSON] et al. [53] & 100 & 15.26 & 0.02 & 0.111 & 0.109 \\ \hline [PERSON] et al. [54] & 100 & 16.41 & 0.03 & 0.721 & 0.421 \\ \hline [PERSON] et al. [55] et al. & 100 & 19.90 & 0.02 & 0.722 & 0.395 \\ \hline [PERSON] et al. [17] & 100 & 18.40 & 0.01 & \(-\)0.774 & 0.131 \\ \hline The presented algorithm & 100 & 13.01 & 0.01 & \(-\)1.012 & 0.101 \\ \hline \end{tabular}
\end{table} 
Table 25: The statistical data on the Mean Absolute Error (MAE) metrics for Figure 12.