esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification
## 8 Conclusions  
In this study, we developed an advanced deep-learning framework for automated sperm head classification, integrating efficient segmentation, pose correction, and morphological classification. By leveraging EdgeSAM for precise segmentation, we achieved high accuracy with minimal computational requirements. The inclusion of a Sperm Head Pose Correction Network standardized the orientation and position of sperm heads, enhancing the consistency and reliability of downstream classification tasks. Furthermore, the use of a flip feature fusion module improved the model's ability to capture the inherent symmetries in abnormal sperm morphology, ultimately increasing classification accuracy. This robust, computationally efficient approach provides a clinically viable solution for automated sperm morphology analysis.  
Conceptualization, Y.G.; Methodology, Y.G.; Software, K.H.; Validation, Y.G. and T.L.; Investigation, Y.G. and W.Z.; Resources, Y.G. and J.L.; Writing--original draft, Y.G.; Writing--review & editing, Y.G., J.L., K.H., B.W., Y.L. and L.W.; Project administration, Y.G., J.L. and L.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by The High-tech industrialization special project in cooperation between Jilin Province and the Chinese Academy of Sciences under Grant 2024 SYHZ0045 for the project \"AI-Based Automated Analysis System for Human Chromosomal Structural Abnormalities\".  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
We thank [PERSON] and [PERSON] for their support in software development and data processing. Their expertise and dedication were instrumental in enhancing the quality and efficiency of our work.  
Figure 11: Typical misclassified images; the image source IDs are Amorphous_004 and Amorphousimage_011. The image on the left and its augmented image are misclassified as normal or pyriform, while the image on the right and its augmented image are misclassified only as pyriform.
## 3 Results and Discussion
As a result of further analyses, the obtained results and transformations, a generalized notation of the correction factor was obtained according to the following relationship proposed in (2):  
\[F_{\text{Ic}}=\left(\left(-5\cdot 10^{-6}\cdot s-0.0015\right)\cdot\left(\frac{\rho_{ \text{g}}}{\rho_{\text{z}}}\right)^{2}+0.0169\cdot\left(\frac{\rho_{\text{g}}} {\rho_{\text{z}}}\right)-0.015\right)\cdot z+1 \tag{2}\]  
Taking into account the scope of the simulations performed by the authors and the related limitations, the application of Formula (2) is possible for the following values of the considered parameters:  
* cross-section of cable cores: \(s\in\langle 35;240\rangle\), [mm\({}^{2}\)];
* native soil thermal resistivity: \(\rho_{\text{g}}\in\langle 1.0;2.0\rangle\), [(K\(\cdot\)m)/W];
* stabilized backfill thermal resistivity: \(\rho_{\text{z}}\in\langle 0.75;1.0\rangle\), [(K\(\cdot\)m)/W];
* thermal resistivity ratio: \(\rho_{\text{g}}/\rho_{\text{z}}\in\langle 1.0;2.7\rangle\), [-1];
* stabilized backfill dimension: \(z\in\langle 0;30\rangle\), [cm]. By employing (1) and (2) provided herein, one can ascertain the ampacity of a power cable line situated within a stabilized backfill medium. This is achieved by multiplying the correction factor \(F_{\text{Ic}}\) by the ampacity \(I_{\text{A}}\) of a power cable line placed directly in the ground, devoid of stabilized backfill (e.g., as delineated by line 4 in Table 2). Table 4 offers a comparative analysis, demonstrating the calculated ampacity for a power cable line  
Figure 7:0 Dependence of the slope coefficient of a family of straight lines (for a given type of power cable lines) depending on the ratio (\(r\)) of thermal parameters of the ground \(\rho_{\text{g}}\) to stabilized backfill \(\rho_{\text{z}}\). Figure 7:1 Dependence of the value of the first term of the polynomial depending on the cross-sectional area (s) of the considered lines. featuring a cross-sectional area of 240 mm\({}^{2}\) and positioned within a thermally stabilized medium, and the parameters of stabilized backfill. The fourth column of Table 4 (\(I_{\rm A}\)) contains a repetition of the results of the numerical calculations for the analyzed cases involving stabilized backfill, which are included in Figure 7:2 Column 5 of Table 4 contains results for the same cases, but multiplying the ampacity included in Table 2 (for the same values of thermal resistivity of the native soil) by the value of the correction factor \(F_{\rm Ic}\) from (2). As we can observe, the accuracy of determining ampacity by using the correction factor \(F_{\rm Ic}\) is high, and the average error of the obtained comparison is 2.4% (arithmetic mean of the results contained in the last column of Table 4).
## 3 Results and Discussion
### Test of Photon Counting Imaging in Space
If the dark count rates were to approach the system's maximum or drop drastically, it would be necessary to reduce or turn off the high-voltage power supply of the detection system and pause operations while the  
Figure 6: Dark background images before and after the Wide-angle Aurora Imager passed through the SAA. (**a**) The image was captured by the Doris instrument aboard the Jason\(-\)1 satellite and shows the South Atlantic Anomaly (SAA)—a region close to Earth where the planet’s magnetic field is relatively weaker compared to an idealized Earth-centered dipole field. As a result, this region experiences an elevated flux of energetic particles, which exposes orbiting satellites to higher levels of radiation than usual. The colors on the image, ranging from green to red, represent the increasing flux of charged particles in the SAA, as the Earth’s magnetic field weakens in this region; (**b**,**c**) exposure numbered 50 instances, with each exposure time 0.34 s; (**d**,**e**) exposure numbered one instance, with a time of 0.34 s; (**f**,**g**) exposure numbered 50 instances, with each exposure time 0.34 s; (**h**,**i**) dark count rates of the two detectors corresponding to the trajectory in Figure 6a. The counting rate curves of detector one and detector two overlap completely, as the counting rate statistics of the instrument are based on a fixed set of blocks. The curves accurately represent the continuous measurements made in orbit and are based on real data points. instrument enters the SAA. This is only a makeshift measure though, and to properly tackle this issue, more effective shielding measures against charged particles need to be implemented. Unlike electronics, instantaneous radiation has a significant effect on detectors. In addition, the quantum efficiency and spectral response characteristic of the photocathode is altered due to changes in its chemical properties as the total radiation dose increases, and the electron gain of the microchannel plate is also reduced. In the long-term, both detectors and electronic devices will suffer a decrease in their lifetimes because of radiation.
# Link Budget Analysis with Laser Energy for Time Transfer Using the Ajisai Satellite
## 3 Simulation Results
### Simulation Parameters
#### 3.1.2 Parameters of SLR Stations in the Target Network  
We selected 4 SLR stations from among 10 ILRS (International Laser Ranging Service) sites in the East Asia region--Sejong, Geochang, Beijing and Kogane--as the target network to analyze the link budget in terms of number of received photons for TLTT using the Ajisai  
\begin{table}
\begin{tabular}{c c} Number of mirrors & 318 pieces \\ Curvature of mirrors & 8.35–8.7 m (8.5 m for simulation) \\ Reflectivity & 0.85\(\sim\)0.92 (0.85 for simulation) \\ Duration of light flash & 5 msec \\ Rate of flashing & 1.25 Hz (2 Hz at initial) \\ Spin rate & 25 rpm (40 rpm at initial) \\ \end{tabular}
\end{table}
Table 1: Key parameters of Ajisai for TLTT simulation.  
satellite. All the parameters of the four SLR stations used in this simulation, including the geographical position, are shown in Table 2.  
The Sejong SLR station was chosen as a laser transmitting station for the TLTT simulation because it has the smallest transmitting telescope aperture (0.1 m) and highest repetition rate of laser pulse (5 kHz at maximum). The key parameters of the Sejong SLR station affected to the laser link budget of Equation (8) are the laser pulse energy (\(E_{t}\)) of 2.5 mJ, transmit optic efficiency (\(\eta_{t}\)) of 92.3%, beam divergence half angle (\(\theta_{d}\)) of 5 arcsec, and beam pointing error (\(\theta_{p}\)) of 5 arcsec [12].  
The other three SLR stations were selected as receiving stations for TLTT implementation in the target network, with three combinations of one transmitting and one receiving station, as shown in Figure 2; these stations are located around the Sejong station at distances of 117.3 km (Geochang), 1056 km (Beijing) and 1098.7 km (Koganei).
## Appendix A.3.1 Generator Complexity  
The generator is designed using U-Net architecture, which includes multiple down-sampling (encoder) blocks and up-sampling (decoder) blocks. Each down-sampling block employs a 2D convolution operation, and each up-sampling block uses a transposed convolution operation. Additionally, skip connections link the corresponding encoder and decoder layers, ensuring efficient feature reuse.  
For the down-sampling path, the computational complexity of the \(l\)th block is expressed as  
\[\text{FLOPs}_{\text{Conv2D}}^{(l)}=H_{l}\cdot W_{l}\cdot\mathcal{C}_{\text{in}}^ {(l)}\cdot\mathcal{C}_{\text{out}}^{(l)}\cdot K^{2}. \tag{12}\]  
Here, \(H_{l}\) and \(W_{l}\) represent the height and width of the feature map at the \(l\)th layer, \(\mathcal{C}_{\text{in}}^{(l)}\) and \(\mathcal{C}_{\text{out}}^{(l)}\) are the input and output channel dimensions, and \(K\) is the kernel size. In our implementation, \(K=4\). The feature map dimensions \(H_{l}\) and \(W_{l}\) are halved at each subsequent layer, leading to a gradual reduction in spatial resolution. The total complexity of all down-sampling layers is obtained by summing the contributions from all 8 blocks:  
\[\text{FLOPs}_{\text{Down}}=\sum_{l=1}^{L}H_{l}\cdot W_{l}\cdot\mathcal{C}_{ \text{in}}^{(l)}\cdot\mathcal{C}_{\text{out}}^{(l)}\cdot K^{2}, \tag{13}\]  
where \(L\) is the total number of down-sampling layers.  
Similarly, in the up-sampling path, the computational complexity of the \(l\)th block is given by  
\[\text{FLOPs}_{\text{ConvTranspose2D}}^{(l)}=H_{l}\cdot W_{l}\cdot\mathcal{C}_{ \text{in}}^{(l)}\cdot\mathcal{C}_{\text{out}}^{(l)}\cdot K^{2}. \tag{14}\]  
The up-sampling blocks restore the spatial resolution of the feature maps, with the total complexity across all up-sampling layers calculated as  
\[\text{FLOPs}_{\text{Up}}=\sum_{l=1}^{L}H_{l}\cdot W_{l}\cdot\mathcal{C}_{ \text{in}}^{(l)}\cdot\mathcal{C}_{\text{out}}^{(l)}\cdot K^{2}. \tag{15}\]  
By combining the complexities of the down-sampling and up-sampling paths, the overall computational complexity of the generator is determined as  
\[\text{FLOPs}_{\text{Generator}}=\text{FLOPs}_{\text{Down}}+\text{FLOPs}_{ \text{Up}}. \tag{16}\]
## 4 Our Modeling Approach
### Timing Considerations  
In CPN, every token carries some properties. When considering time, one of these properties has to be the time at which the tokens were generated. Usually, these times follow the Poisson distribution. Let us assume that each token is of a specific class and carries some values (which describe their properties, one ID to distinguish from other tokens of the same class and a timing value:  
* _Id_ : Token Id.
* _V_: A value or set of values that describe the properties of each token (entity).
* _T_: The time this token has been generated or an updated time following one activity. Then, we can consider the following cases: 1. _A token resides in a place and it suffices to activate a transition_\(t\): Then, transition \(t\) can fire at time \(T\) and the result of this firing will be available to the system at time \(T+\delta\), where \(\delta\) is the time required for the corresponding event to occur. 2. _Two or more tokens in different places activate a transition:_ In this case, the transition will fire by the time indicated by the last generated token. Clearly, before that time, it is impossible for all the transitions to be active. For example, see Figure 5a. Transition \(X\) is active at time \(2.5\) (the maximum of the two tokens) and its completion is \(2.5+\delta\). The value of \(\delta\) is randomly chosen between \([0,\ldots,3]\) (meaning that the duration of \(X\) is from \(0\) to \(3\) time units). In this example, \(\delta=2\), thus \(X\) is completed at time \(4.5\). 3. _Two or more transitions are active:_ To decide the timing sequence, we use the following procedure:
* We find the time each transaction becomes active based on the input tokens.
* We choose the one that has been activated earlier. For example, see Figure 5b. Event \(X\) is active at time \(2.5\) (the first black token has been generated at time \(2.0\) and the red at time \(2.5\)). Event \(Y\) is active at time \(4.0\) (the first black token has been generated at time \(2.0\) and the green at time \(4.0\)). Thus, \(X\) will fire first and its effects will be available at time \(2.5+2.0=4.5\). Then, \(Y\) is still active (by using the second black token generated at time \(5.0\)). Then, it will fire at time \(5.0\) and its effects will be available at time \(5.0+2.0=7.0\) (in both cases, we have assumed that \(\delta\) has been randomly chosen to be \(2.0\)). The timing considerations described are generally applicable and can be disregarded in cases our model includes guardian expressions that demand to do so. These expressions are shown in the next subsection.  
Figure 5: (**a**) Two or more tokens in different places activate a transition, (**b**) two or more transitions are active.
# Design of a Sweet Potato Transplanter Based on a Robot Arm
## 3 Mathematical Model of Transplanting Robot Arm  
As shown in Figure 2a, in the proposed design, the sliding table driving motor drives the seedling claws to move up and down along the sliding platform module. Meanwhile, the rotating motor drives the seedling claws to rotate on the transplanting plane. As shown in Figure 2b, proximity switches are installed on the side of the moving platform and the sliding platform module. The proximity switches indicate the initial position of the seedling claw rotation angle and the movement of the moving platform. The signal returned by the controller through the proximity switch can be used to adjust the initial rotation angle of the seedling claw for each transplant. In this way, deviations will not be accumulated in process of transplanting sweet potatoes. The seedling claw motor adjusts the push rod through the eccentric wheel to achieve the opening and closing of the seedling claw. A proximity switch is installed above the eccentric wheel. When the eccentric wheel rotates, the controller will receive the seedling claw status signal returned by the proximity switch to prevent the seedling claws from damaging the potato seedlings. Figure 2c is the assembled transplanting robotic arm.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Value** \\ \hline Length \(\times\) width \(\times\) height/(mm \(\times\) mm \(\times\) mm) & 1600 \(\times\) 1200 \(\times\) 1400 \\ Working width/(mm) & 900 \\ Matching power/(KW) & 5 \\ Most suitable row spacing/(mm) & 900 \\ Ridge height/(mm) & \(>\)300 \\ Distance between hills/(mm) & \(>\)200 \\ Forward speed/(m\(\cdot\)s\({}^{-1}\)) & 0.1–0.4 \\ \hline \end{tabular}
\end{table}
Table 1: Structural parameters of sweet potato transplanter.  
Figure 1: Structure of the entire machine. (**a**) Transplanting instructions. (**b**) Rear view. (1) Transplanting conveyor belt; (2) gasoline generator; (3) working table; (4) tracked chassis; (5) designated location; (6) transplanting robot arm; (7) transplanting conveyor belt drive motor; (8) sweet potato seedlings; (9) brush; (10) groove; (11) replanting conveyor belt; (12) replanting robot arm; (13) chassis drive motor; (14) control box.  
The transplanting robot arm can be simplified to the linkage mechanism shown in Figure 3.  
\[T_{0}^{3}=\left[\begin{array}{ccc}\cos(\varphi(t))&-\sin(\varphi(t))&l\cos( \varphi(t))+x_{1}(t)\\ \sin(\varphi(t))&\cos(\varphi(t))&l\sin(\varphi(t))+y_{1}(t)\\ 0&0&1\end{array}\right] \tag{1}\]  
where \(x_{1}(t)\), \(y_{1}(t)\) is the coordinate of joint 1 at time \(t\); \(\varphi(t)\) is the angle of joint 2 at time \(t\), and \(l\) is the length of link 2.
# Two-Dimensional Histogram Shifting-Based Reversible Data Hiding for H.264/AVC Video
## 4 Experimental Results
### File Size
It can be seen from Figures 9 and 10 that whether the QP is 16 or 28, the increase in file size of the marked video generated by our proposed scheme is basically smaller than that of [29, 37] for all six videos. Moreover, as the payload increases, the differences between the file size increase of our scheme and those of the related schemes become more obvious. The reason is that the scheme proposed in [37] not only uses more points with lower embedding efficiency, but also needs to modify both values of many points without embedding any data. Moreover, due to the low embedding efficiency in [29], there are more coefficient pairs will be modified under the same payload, resulting  
Figure 8: The increase in the file size of marked video generated by different schemes using the \((0,0)\) coefficient pairs when QP is 28. Figure 7: The increase in the file size of marked video generated by different schemes using the \((0,0)\) coefficient pairs when QP is 16.  
distortion, secret data is only embedded into the DCT coefficients with middle and high frequencies. The experimental results demonstrated that our proposed schemes can achieve better visual quality and smaller increase in the file size of marked video compared with the related schemes. Conceptualization, [PERSON] and Y.X. ; methodology, Y.X. ; software, Y.X. ; validation, Y.X. and J.H. ; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, Y.X. ; writing-original draft preparation, [PERSON]; writing-review and editing, [PERSON]; visualization, Y.X. ; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the Guangdong Natural Science Foundation under grant number 2019A1515011231, Guangdong Province Key Area R&D Program of China under grant number 2019B010137004. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results
### Effect of UPM on Viability and NO Secretion of MH-S Macrophages  
To determine the cytotoxicity of UPM in MH-S lung macrophages, cells were incubated with four different concentrations prepared from UPM. As shown in Figure 2a, cell viability decreased with the increasing concentrations of UPM, which differed significantly from the control group after the 31.3 ug/mL.  
Figure 2: Protective effect of 3-hydroxy-5,6-epoxy-ß-ionone (HEBI) against urban fine dust (UPM)-activated inflammatory responses in MH-S lung macrophages. UPM-induced toxicity (**a**) and NO production (**b**) in MH-S macrophages. MH-S macrophages were incubated with HEBI (**c**) for 24 h without UPM stimulation. Cytoprotective effect of (**d**), NO inhibition (**e**), and PGE2 (**f**) in HEBI co-treated and UPM stimulated MH-S macrophages were compared using MTT, Geriss, and ELISA assays, respectively. Data are expressed as mean \(\pm\) S.D. from three independent experiments. * \(p\) < 0.05 or **_p_ < 0.01 compared with UPM-stimulated group and * \(p\) < 0.05 or **_p_ < 0.01 compared to control group.  
In parallel to the cell viability results, NO secretion increased with UPM concentrations up to 31.3 \(\upmu\)g/mL and then declined (Figure 2b). The reduction of NO levels in concentrations higher than 31.3 \(\upmu\)g/mL might be associated with reduced cell viability. Previously, several studies reported the exposure of macrophages to PM cause to induce chronic inflammatory responses and activate apoptosis signal cascades induce cell death in healthy tissues. With the electron microscope analysis, a considerable amount of UPM contained less than 2.5 \(\upmu\)m diameter PM. It has been reported that particles smaller than 2.5 \(\upmu\)m can easily go through the throat and nose to reach the alveolus (25); thus, UPM can potentially induce adverse health consequences in the respiratory system via altering inflammatory signals. With these observations and previous evidence, we decided to use a 31.3 \(\upmu\)g/mL concentration of UPM for further studies.
## 3 Numerical Simulation  
### Description of the FE Model  
Using the finite element method, a numerical model was developed to simulate the interaction between the strata, support, and structure of the project. This three-dimensional model, as depicted in Figure 3, spans 380 m in length, 200 m in width, and 50 m in height, conforming to the construction scheme and boundary effects. The existing tunnel's right line is located 3.9 m north of the foundation pit and at a depth of 9.3 m. The model features increased mesh density in deformation-sensitive areas such as tunnel structures, retaining walls, and the surrounding strata. The displacement boundary conditions, as illustrated in Figure 3, include no horizontal displacement along all vertical mesh boundaries (in order to simulate a real-world scenario of only settlement deformation without lateral soil movement), no vertical or horizontal displacement at the bottom boundary of the mesh  
Figure 1: Plane position of foundation pit and tunnel.  
Figure 2: Cross-section of the pit and tunnel with monitoring points and staged excavation depths.  
(which mimics a stable bedrock scenario), as well as freedom for displacement along the top boundary. Figure 4 details the structural features of the support system and the specific placement of supports in sections a-a and b-b.  
Figure 4: Layout of support structure.  
Figure 3: Finite element model.  
The numerical simulation proceeded as follows: Initially, the geo-stress was balanced, and then the model was rebalanced following the sequential construction of tunnel segments. Subsequently, the retaining structure, which includes the diaphragm wall, grid columns, and pile foundations, was built. The soil was then excavated using the birth-and-death element method and segmented into six layers, as depicted in Figure 2. The excavation continued progressively with support until the bottom of the pit was reached. Throughout the simulation, deformations at the tunnel vault and haunch positions were continuously monitored.
## 1 Introduction
### Contributions  
The core contributions of this paper can be summarized as follows:  
* We construct an energy evaluation model, which considers the effect of energy harvesting, energy consumption and energy classification, to determine the energy state of the node. Furthermore, we also formulate a channel feature (Rayleigh fading) based communication range judgment model. Through this model, the neighbor nodes set, forward transmission nodes set and candidate nodes set of the current node can be identified.
* Gathering the above two models, we design a reception state adjustment mechanism. Through considering the buffer occupancy and IEEE 802.11b protocol hidden terminal problem, this mechanism can adjust the data reception state of nodes dynamically.
* On this basis, we propose a greedy strategy-based relay node selection algorithm, which adopts the greedy strategy to select the best next hop. At the same time, we also give a solution for the extreme case.
* We analyze the correctness and computational complexity of our algorithm.
* We conduct extensive simulation experiments to validate the superior performance of our proposed algorithm.