esa-sceva/satcom-chunk-collection huggingface dataset:
# Robotic Manipulation of Cumulus-Oocyte Complexes for Cumulus Cell Removal
## 4 Experimental Results
### Cumulus Cell Detection  
During the cumulus cell removal process, COC images were captured outside the micropipette and resized to 224 \(\times\) 224 as the input of the improved Yolov5s. A total of 2200 COC images were captured, with 1320 used for training, 440 for validation, and 440 for testing. The COC images were zoomed in, and cumulus cells were labeled with rectangular bounding boxes by the experienced embryologists using the LabelImg software (version: 1.8.1). Both the improved Yolov5s and the traditional Yolov5s were trained on the same dataset, and the validation loss curves are shown in Figure 10. As the number of epochs increased, the loss values decreased rapidly within the first 60 epochs and finally converged at 300 epochs. Compared with the traditional Yolov5s, the improved Yolov5s showed a lower convergence value and a faster convergence speed (loss: 0.015 vs. 0.031, epochs: 238 vs. 273).  
Figure 10: Validation loss of the improved Yolov5s and the traditional Yolov5s.  
Figure 9: Control performance of aspirating a single COC into the micropipette by the adaptive controller, LQR controller, and PD controller. The initial position of the COC is 140 \(\upmu\)m from the micropipette tip.  
To further verify the effectiveness of different modules in cumulus cell detection, the ablation experiments were conducted, and the Yolov5s networks integrated with different modules were tested on the same dataset. As shown in Table 1, the experimental results showed that the backbone, neck, and head in the Yolov5s all contributed positively to improving the performance of the network. Adding the Res2 Net-COT module to the backbone increased the mAP and recall by 1.5% and 1.1%, respectively. When the BiFPN module was added to the neck of the traditional Yolov5s, the mAP and recall were increased by 3.9% and 2.6%, respectively. The inception module added to the head also contributed to the time cost of the Yolov5s. Although the recall remained the same and mAP was increased by 0.7%, the FPS was significantly increased by 29.6%. After adding the Res2 Net-COT module, the BiFPN module, and the inception module to the traditional Yolov5s, the improved Yolov5s achieved the best performance, with the mAP increased by 4.7%, the recall increased by 5.9%, and the FPS increased by 12.0%.
## 1 Introduction
Compared with the previous studies on MIMO detection [13; 14; 15; 16; 17; 18; 19; 23; 24; 25; 26; 27] that focus mainly on the transmission power required, this work provides a new perspective for study the detectors in realistic scenarios under the EE criterion. Our work also differs from the previous studies on EE [3; 5; 12; 33] where the capacity, i.e., the upper bound of the achievable rate under Gaussian signaling, is considered, as the actual power consumption for practical modulation, signal processing schemes and reliability requirement is evaluated in our studies. The rest of this paper is organized as follows. We introduce the model of uplink large-scale massive MIMO systems with low-precision quantization in Section 2. Three MIMO detectors, i.e., MMSE, LR-SIC and a modified multi-branch LR-SIC, are then discussed in Section 3. A system-level power consumption model is introduced in Section 4, based on which the EE is analyzed. The numerical results are presented in Section 5. The conclusion is given in Section 6. _Notation:_ The superscripts \((\cdot)^{-1}\) and \((\cdot)^{H}\) represent matrix inversion and transpose, respectively. \(\mathbf{I}_{N}\) denotes an \(N\times N\) identity matrix; \(\mathbf{0}_{K}\) denotes an all-zero vector of dimension \(K\times 1\). \(\mathbb{E}[\cdot]\) denotes expectation \(det(\mathbf{A})\) the determinant of \(\mathbf{A}\). \(\mathbb{C}\) is the set of complex numbers. The notations appearing in the paper are listed in Table 1. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \(K\) & number of UEs & \(N\) & number of receiver antennas & \(b\) & number of quantization bits \\ \hline \(\Delta\) & quantization step & \(\rho_{\pi}\) & scaling factor for the \(n\)-th antenna & \(d_{k}\) & distance between UE-k and the BS \\ \hline \(\varphi_{k}\) & large-scale fading & \(\sigma^{2}\) & noise variance & \(\lambda\) & number of coherence blocks \\ \hline
**H** & channel matrix & **P** & power of the UEs & **n** & additive white Gaussian noise (AWGN) \\ \hline
**x** & transmitted signal vector & \(\overline{\
u}\) & scaling matrix & \(\textbf{n}_{q}\) & quantization noise \\ \hline \(\overline{\textbf{y}}_{q}\) & quantized received signal & \(\textbf{R}_{n_{q}}\) & covariance matrix of the quantization noise & **W** & weight matrix of detector \\ \hline \(\Pi\) & permutation matrix & **R** & received signal after QR decomposition & \(\xi\) & noise variance after QR decomposition \\ \hline
**y** & received signal & **G** & channel matrix after QR decomposition & **y** & scaled received signal \\ \hline
**G** & extended channel matrix & **T** & transformation matrix & **G** & lattice-reduced channel matrix \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation.
###### Abstract  
Velocity is one of the most important parameters to evaluate the damaging potential of a mass movement, but its assessment, especially for extremely rapid landslides, is a complex task. In the literature, several models to assess mass movement velocity exist, but they usually require many detailed parameters, and therefore, they are applicable only to a single slope and not usable for regional-scale analyses. This study aims to propose a simple morphometric methodology, based on the spatialisation of the Energy Line method, to determine the velocity of shallow landslides and rockfalls at a regional scale. The proposed method requires a limited amount of input data (landslide perimeters and a digital elevation model), and its application can be carried out using GIS software and a Matlab code. The test area of this work is the Valle d'Aosta Region (Northern Italy), selected due to its peculiar geological and geomorphological setting that makes this region susceptible to the occurrence of both shallow landslides and rockfalls. Since measured velocity values for rockfalls and shallow landslides were not available, the results obtained with the proposed method have been validated through the implementation of a model in the literature, namely the Gravitational Process Path (GPP) model, for some selected landslides.  
shallow landslides; rockfalls; landslides velocity; Heim method; GPP
## 2 Background and Theory
### Feature Extraction Techniques
#### 2.2.1 Katz  
The Katz fractal dimension (KFD) [51] quantifies a signal's complexity by analyzing how its structure expands across different scales. This approach assesses deviations from a smooth trajectory, taking into account both the signal's overall length and the spatial extent it occupies, as shown in Equation (6):  
\[FD_{\text{Katz}}=\frac{\ln(n)}{\ln(n)+\ln(\frac{d}{L})} \tag{6}\]  
In this context, In indicates the natural logarithm, \(n\) represents the total number of data points in the time series, \(L\) denotes the cumulative length of the signal path, and \(d\) signifies the maximum distance between the initial point and any subsequent point within the signal.
# Numerical Design and Laboratory Testing of Encapsulated PCM Panels for PCM-Air Heat Exchangers
## 3 Results and Analysis  
### Numerical Design  
#### 3.1.1 3D Simulations  
Nine 3D panels with different configurations at Re = 18,736 plus the existing, dimpled and teardrop panel were generated. The results of Nusselt number, pressure drop and the ratio between the existing and smooth panels are presented in Figure 7.  
Figure 6: (a) Diagram of the test rig for pressure drop test. (b) Test configuration of 1, 2 and 3 TB for existing and new panel.  
Figure 7 (up left) shows that all panels present a Nusselt number higher than the existing panel with the dimpled panel being the highest for a smaller increase in the pressure drop. Shape 6 presents a higher pressure drop (10 times higher than the existing panel) which makes it unfeasible; for the same reason, shape 3 was discarded. Shapes 1 and 2 also have a good heat transfer but the lozenges along its width doubles the cost of production and makes it unfeasible; for the same reason, the dimpled and the teardrop panel was discarded. Shape 9 is the panel with simplest design (ridges along its width through the whole panel) and capable of increasing the Nusselt number three times, with a pressure drop five times higher compared to the existing panel (Figure 8). These ratios can be seen in Figure 7 (right) where Nusselt number and pressure drop with existing and smooth panel are presented. Figure 7 (bottom) shows that shape 2, teardrop and dimpled panel, has an efficiency higher than 1, which means that the gains in heat transfer are higher than the gains in pressure drop, while shapes 6, 1, 9 and 3 increased the pressure drop more than the heat transfer.  
Figure 8: Views of Shape 9.  
Figure 7: Nusselt number and pressure drop ratio for existing and smooth panel (**A**) and Nusselt number and pressure drop (**B**) at \(\text{Re}=18\),736 for 3D simulations (**C**).  
Based on these results and due to the simplicity of its design and manufacturing cost, shape 9 (Figure 8) was chosen for additional refinements. Moreover, a panel uniform along its width gives rigidity to the panel.
# GPU-Accelerated Eclipse-Aware Routing for SpaceWire-Based OBC in Low-Earth-Orbit Satellite Networks
## 3 Proposed Method
### BFS-Based K-Shortest Path Algorithm
Each thread locates the adjacent nodes of the last node in its assigned path using the CSR format, then generates new candidate paths accordingly. The CSR format optimizes memory access and prevents cycle formation by ensuring that only nodes not already included in the path are added. Since multiple threads modify the candidate path list simultaneously, atomic

Figure 5: Block diagram of GPU-accelerated Eclipse-Aware Routing algorithm. operations are applied to maintain data consistency. This prevents synchronization issues when adding new paths, ensuring safe and efficient search execution. Once the search is complete, the destination list is copied back to the CPU, where the shortest paths are sorted, and the top K paths are selected and returned. ```
1 graph_offsets[], graph_edges[]
2 src, dst, k num_nodes
3 Output k_paths[]
4
5 Copy graph data to GPU memory
6
7 Initialize device candidate_paths with {src}
8
9 Initialize device destination_paths as empty list
10
11 Initialize device new_candidates as empty list
12
13
14 While candidate_paths is not empty AND size(destination_paths) < k
15 Set new_candidate_count = 0
16
17 Launch CUDA kernel:
18
19 For each path in candidate_paths (Parallelized by threadIdx.x)
20 last_node = last node in path
21 start_idx = graph_offsets[last_node]
22 end_idx = graph_offsets[last_node + 1]
23
24
25 For i = start_idx to end_idx
26 neighbor = graph_edges[i]
27
28 If neighbor is NOT in path (prevent cycles)
29 new_path = path + neighbor
30 new_path_cost = path.cost + edge_cost(last_node, neighbor)
31
32
33 If neighbor == dst
34 AtomicAdd(destination_paths_count)
35 Store new_path in destination_paths
36 Else
37 AtomicAdd(new_candidate_count)
38 Store new_path in new_candidates
39 End
31 End
32 End
33
34 Swap candidate_paths with new_candidates
35 End
36
37 Copy destination_paths to CPU memory
38 Sort destination_paths by cost
39 Return K paths from destination_pathsFigure 6 clearly illustrates the difference between CPU-based and CUDA-based search methods. As shown in Figure 3, the conventional CPU-based BFS search expands nodes sequentially, processing each node one at a time. Specifically, in level 0 (starting node), nodes in level 1 (e.g., 27, 37, 39) are explored one by one, followed by the expansion of level 2 nodes in the same manner. In contrast, the proposed CUDA-based BFS search expands all nodes in level 1 simultaneously, followed by a parallel expansion of all level 2 nodes. Since each level's exploration is performed in parallel, the search time is significantly reduced, allowing the K shortest paths to be found much faster. This confirms that CUDA-based KSP search operates more efficiently, particularly in large-scale graph search problems.
## 5 Weather Forecasting Models
### The Next Leap
The varying impacts of environmental moisture on storm intensification are governed by the relative locations of moisture perturbations and their interactions with the storm's Lagrangian structure\"._ [28]This demonstrates that a precipitation model continuously updated with near-real-time data driven by high satellite revisit rates can significantly improve the ability to predict severe weather events and issue timely warnings to decision-makers. Continuous tracking of atmospheric moisture properties will enhance forecasting accuracy--one of many applications that, over time, will become a practical daily reality thanks to initiatives like Tomorrow.io's satellite constellation.
## 3 Proposed Method
### Satisfying Separation-of-Duty Constraints
#### 3.1.3 Construction of t-t SMER Constraints  
However, many RSSOD constraints might become nonenforceable in some cases because of the restrictive 2-2 SMER constraints. Next, we present an approach for implementing RSSOD with \(t\)-\(t\) SMER constraints (\(t>2\)), which is less restrictive as compared with only using 2-2 SMER constraints. It is necessary to find a maximum \(t\) for the given RSSOD constraints in Algorithm 3.  
```
0:\(UA\), \(CR\), set E of \(k\)-\(n\) RSSOD constraints
0: set C of \(t\)-\(t\) SMER constraints
1: C=\(\emptyset\);
2:for each \(\mathrm{e_{i}}\) in E do
3:\(P(e_{i})\) denotes the permissions covered by \(e_{i}\);
4:\(\exists(S=(r_{1},r_{2},\ldots))\subseteq CR\):\((\forall r\in S:\mathit{Permissions}(r)\cap\ P(e_{i})\
eq\varnothing)\)
5:if\(|S|\)<\(k\)then
6:\(e_{i}\) is nonenforceable;
7: continue;
8:elseif\(\exists r\in S:\mathit{Permissions}(r)\supseteq P(e_{i})\)then
9:\(e_{i}\) is nonenforceable;
10: continue;
11:elseif\(\exists\left(r_{i},r_{j}\right)\in S:\left(r_{i},r_{j}\right)\in RH\) or \(\left(r_{j},r_{i}\right)\in RH\)then
12:\(e_{i}\) is nonenforceable;
13: continue;
14:else
15:for\(t\)=3 to \(\left\lfloor\frac{|S|-1}{k-1}\right\rfloor\)+1 do
16:for any set \(\left\{r_{1}^{{}^{\prime}},r_{2}^{{}^{\prime}},\ldots\ r_{t}^{{}^{\prime}}\right\}\) in \(S\)do
17:if UA satisfies \(\mathit{smer}\)<\(\left\{r_{1}^{{}^{\prime}},r_{2}^{{}^{\prime}},\ldots\ r_{t}^{{}^{\prime}}\right\}\),\(t\)> then
18:\(C\)=\(\mathit{smer}\)<\(\left\{r_{1}^{{}^{\prime}},r_{2}^{{}^{\prime}},\ldots\ r_{t}^{{}^{\prime}}\right\}\),\(t\)>\(\cup\)\(C\);
19:else
20:\(e_{i}\) is nonenforceable with \(t\)-\(t\) SMER constraints;
21:endif
22:endfor
23:endfor
24:endif
25:endfor
```  
**Algorithm 3** Construction of \(t\)-\(t\) SMER constraints  
To construct \(t\)-\(t\) SMER constraints for an RSSOD \(e_{i}\), like in Algorithm 2, we should find set \(S\) at first. Next, we determine whether \(e_{i}\) is enforceable or not. In Algorithm 3, line 4 determines set \(S\) from \(CR\). Lines 5-6 indicate that if the number of roles in set \(S\) is less than \(k\) in \(e_{i}\), it cannot be enforced. Lines 8-9 indicate that if any role in set \(S\) has all the permissions in \(e_{i}\), it cannot be enforced. Lines 11-12 show that if \(RH\) relationships exist in \(S\), \(\mathrm{e_{i}}\) cannot be enforced. Lines 15-23 determine whether the \(t\)-\(t\) SMER constraints are satisfied in matrix \(UA\) and insert them into set \(C\).  
Computational complexity: if the number of roles in \(S\) is \(m\), the time for determining whether a constraint is enforceable or not is \(O(m)\). Constructing \(t\)-\(t\) SMER constraints depends on the double loops. The execution number of the first loop is \(\left\lfloor\frac{|S|-1}{k-1}\right\rfloor\)-2; in the second loop, for the particular t, it is necessary to obtain any \(t\) roles from \(S\), and the execution time is \(O(C_{m}^{t})\). Thus, the total time complexity is \(O(m)\)+\(O(m\)\(\cdot C_{m}^{t})\)= O(\(m\)\(\cdot C_{m}^{t}\)). If m is small, the efficiency of the algorithm is acceptable.
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
## 4 Scheme Description
### Set-Up Stage
Based on these messages, the base station first calculates \(T_{trutch}\), the residual working time, as a cluster head for each ordinary node. Subsequently, an ordinary node with the maximum \(T_{trutch}\) in a cluster is selected as the cluster head. Finally, the base station broadcasts information on the cluster heads and members of a cluster to all the sensor nodes. The information consists of the locations of the sensor nodes in a cluster; thus, the nodes can adjust their transmission power to fit the transmission distance within a cluster.
## 3 Results and Discussion
### Evaluation of Process Parameters
#### 3.2.3 Correlation Between the Contamination Index and the S\({}_{\rm r}\)  
The correlation between the mean contamination index of the coupling glass and S\({}_{\rm r}\) is redrawn from the data in Figures 9 and 10, and is shown in Figure 12. For all measured data, the S\({}_{\rm r}\) shows an inverse linearity with the contamination index. The Pearson correlation coefficient for both parameters was \(-0.99\), which means a strong negative linear correlation. The S\({}_{\rm r}\) of 90% or more was maintained when the contamination index of the coupling glass was 7.3% or less.  
In conclusion, we proposed a contamination index that was quantified using a spectrum transmission meter. We experimentally confirmed that it was an effective and accurate index that could be used to represent the contamination of the coupling glass and stability of welding penetration.
## 2 Related Work
### Comparison with This Work  
This work and the mentioned studies share a focus on using ML and clinical scoring systems to improve the diagnosis of pediatric appendicitis, but differ in methodology and dataset size.  
[PERSON] et al. combine multiple ML algorithms but are limited by a single-center dataset and the need for external validation. This work, with its larger, multicenter dataset, offers more generalizability. Similarly, [PERSON] et al. developed a Clinical Decision Support System (CDSS), but their reliance on a smaller, single-center dataset limits its applicability compared with the broader approach taken in this work. [PERSON] et al. focus on reducing negative appendectomies using a highly sensitive ML model. While both studies aim to improve diagnostic accuracy, [PERSON] et al. prioritize sensitivity, whereas this work provides a more comprehensive comparison of scoring systems, broadening its clinical impact. [PERSON] et al. also developed a CDSS but use a small dataset and single-center data, limiting reproducibility. This work surpasses these limitations by leveraging a multicenter dataset, offering more robust results. [PERSON] et al. focus on the Appendicitis Inflammatory Response (AIR) score, showing high sensitivity and specificity, but are limited by single-center data. This work incorporates multiple scoring systems and a broader dataset, offering a more comprehensive diagnostic analysis. [PERSON] et al. evaluate predictive scoring systems in a multicenter setting, similar to this work, but this study's rigorous comparison of scoring systems provides deeper insights into their performance across various clinical populations.  
While each study contributes to improving pediatric appendicitis diagnosis, this work stands out for its multicenter design, broad applicability, and detailed evaluation of multiple scoring systems, offering stronger validation than the other studies.
## 4 Conclusions  
In this paper, a non-contact test system was presented to obtain the displacement-time curves of the granary and the displacement vector map and displacement cloud map of soil around the granary under different working conditions. On this basis, the influences of relative density of the soil on the displacement of granary and the soil around the granary were analyzed. Finally, the soil pressure theory regarding retaining walls was used to discuss the granary-soil interaction under the action of groundwater. The following conclusions were obtained.  
(1) The presented non-contact experiment system can overcome the shortcomings of traditional experiment methods, i.e., strain gauge cannot be accurately fixed, strain gauge failure, single measurement direction, and so on. It provides a new method for the study of soil-structure interaction under groundwater buoyancy.  
Figure 11: Characteristic diagram of silo displacement and soil deformation at different stages: (**a**) Time 1; (**b**) Time 2; (**c**) Table 3; (**d**) Time 4; (**e**) Time 5; (**f**) Time 6; (**g**) Time 7; (**h**) Time 8.  
(2) Under the action of groundwater buoyancy, the vertical displacement is large, and the horizontal displacement is small. With the increase of relative compactness of the soil around the granary, the uplift water level of the granary increased by 10.77%, and the vertical displacement of the granary at the critical point of the unstable failure decreased by 17%. Due to the soil around the granary from loose state to dense state, the mutual embedded force between soil particles increases, which reduces the relative motion and anti-slip ability between particles, resulting in reducing the deformation and disturbance range of soil around the granary.  
(3) For the stability of the underground granary, its displacement subjected to water buoyancy is an important factor causing soil deformation and soil pressure to change. The earth pressure acting on the granary cannot exist independently from the granary and the surrounding soil, and they interact with, restrict, and influence each other. In engineering, the influence of the soil around the granary on the anti-floating property of the underground granary and the stability of the granary-soil system cannot be ignored.  
Conceptualization, Z.X. and H.Y.; methodology, Z.X.; software, H.Y.; validation, Z.X. and H.Y.; formal analysis, Z.X.; investigation, Z.X. and H.Y.; resources, Z.X.; data curation, H.Y.; writing--original draft preparation, Z.X.; writing--review and editing, Z.X. and H.Y.; visualization, Z.X.; supervision, Z.X.; project administration, Z.X.; funding acquisition, Z.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51978247).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.