esa-sceva/satcom-chunk-collection huggingface dataset:
# Chaotic Characteristic Analysis of Spillway Radial Gate Vibration under Discharge Excitation
## 3 SRG Analysis of the Prototype Test Data
### Signal Denoising
A comparison of different thresholding functions is shown in Figure 7.  
Whether it was a hard, soft, or improved threshold function, the choice of threshold, \(\lambda\), is very important. A fixed threshold is widely used in engineering, and the effect of  
Figure 7: Comparison of different thresholding functions. noise reduction is obvious when there is a lot of noise. The natural frequency of an ambient vibration of the gate was relatively low, and the fixed threshold criterion was more effective for denoising purposes. At present, the selection of \(\lambda\) is adopted for the fixed threshold (sqtwolog) mainly based on the following formula:  
\[\lambda= \sqrt{2 lnN} \tag{22}\]  
where \(N\) is the length of the signal sequence. The power values of the effective and noise signals were unknown and the signal-to-noise ratio could not be used to judge the noise reduction effect in the actual project. Therefore, this paper introduced the denoise signal-to-noise ratio (dnSNR) to judge the noise reduction quality [27]. The calculation formula is as follows:  
\[dnSNR=10 lg(P_{s}/P_{g}) \tag{23}\]  
where \(Ps\) is the power of the noise signal and \(Pg\) is the power of the denoised signal. The lower the \(dnSNR\) value, the more significant the noise reduction effect. The combined denoising method of CEEMDAN-MPE-IWTD was used to process the original signal. The realization flowchart of the CEEMDAN-MPE-IWTD method is shown in Figure 8. For the measuring points on the main beams, point 3# in the \(x\)-direction is located on the lower main beam, which is closest to the high-speed flow at the bottom edge of the gate, and the \(y\)-direction on the lower main beam indicates the direction of the downstream flow. Therefore, point 3# has certain representativeness. The noise reduction effect at point 3# in the \(x\)-direction is shown in Figure 9. Figure 9b shows the normalized power spectrum (NPS) curves, where the normalization method is both two power spectral density curves divided by the maximum power spectral density of the original signal. At the same time, the noise reduction effects of the soft, hard, and improved wavelet thresholds were compared, as shown in Table 2. It can be observed from the table that the noise reduction error ratio of the CEEMDAN-MPE-IWTD algorithm is the smallest, indicating that the algorithm has the best noise reduction effect. Figure 8: Flowchart of the CEEMDAN-MPE-IWTD method. Note: \(Z_{\text{MPE}}\) denotes the multi-scale permutation entropy (MPE) of each intrinsic mode function (IMF) component.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
With respect to quantitative requirements, the application should not suffer from any significant impairment in terms of, for example, experiencing latency or throughput. In addition, SRv6 simplifies the network protocol stack in 5G scenarios. Finally, SRv6 supports the centralized control plane [15], allowing an easy integration with the 5G-MEC environment. Let us consider the scenario of Figure 1, where MEC Hosts are deployed in proximity to the gNBs to run latency-constrained services. A UE can move and access the network through a different gNB. After the handover, the MEC system may decide to relocate the edge service associated with the moved user to a different MEC Host. Subsequently, a network reconfiguration is required to allow the UE to seamlessly reach the edge service in its new location. In this context, we propose a solution for lossless workload migration, based on buffering implemented as a VNF that is dynamically allocated in the network and by the network. Our approach relies on the integration between MEC and 5G, using an SRv6-based data plane, and on the strategic implementation of SRv6 Network Programming. Leveraging the MEC environment enables the allocation of edge services and their dynamic relocation to accommodate user mobility. The SRv6 Network Programming paradigm provides the capabilities required to ensure service continuity within the ETSI MEC framework through dynamic reconfiguration of the network, by means of an SDN controller, and through the integration of buffering as a VNF within the packet-processing pipeline. A preliminary version of this work was presented in [16]. This work extends it by integrating our SRv6-based, service continuity solution into the 5 GC data plane and the ETSI MEC architecture. Specifically, this work makes the following four-fold contribution:  
1. It presents a converged MEC-based architecture for service continuity in 5G with an SRv6 programmable data plane. 2. It defines an SRv6 End.Buffer behavior for guaranteeing lossless workload migration in SRv6-compliant 5G networks. 3. To ensure compatibility with non-SRv6 5G network components, it introduces a new End.M.GTP6.D.Buffer behavior acting as a VNF at the border between such components and the SRv6 5 GC network. 4. It describes a Proof-of-Concept (PoC) implementation of the proposed system based on P4 devices. We evaluated our solution over a limited-scale test environment and compared the performance against a non-SRv6 alternative that uses IPv6 routing. The remainder of this paper is organized as follows. Section 2 introduces background content on SRv6 Network Programming. Next, Section 3 reports the related work in the field. Then, Section 4 presents our converged 5G and MEC architecture and related procedures, whereas Section 5 describes our SRv6-based migration solution that is integrated in the above 5G-MEC context. In Section 6, we detail the experimental analysis of the proposed solution. Finally, Section 7 concludes the paper.
# Characteristics of Resonant Tunneling in Nanostructures with Spacer Layers
## 3 Results and Discussion
Thus, the functional dependences of the peak current and valley current on the thickness of the emitter spacer and the thickness of the collector spacer were consistent with the experimental data presented in [18; 19; 20; 21; 22]. The values of \(V_{p}\) and \(V_{v}\) increased monotonically with the increasing thickness of the collector spacer. Figure 7: The dependence of the peak current on the thickness of the emitter spacer for the considered resonant tunneling nanostructures. As shown in [10], the maximum oscillator output power of the RTD can be estimated through the product \(\Delta I\cdot\Delta V\), \(\Delta I=I_{p}-I_{p}\), \(\Delta V=V_{v}-V_{p}\). From the data presented in Tables 2 and 3, it can be seen that \(\Delta I\) weakly depends on the thickness of the collector spacer and decreased with the increasing thickness of the emitter spacer for all RTD structures considered. Moreover, \(\Delta V\) depends on both the thickness of the emitter spacer and the thickness of the collector spacer, as well as on the RTD structure. Figure 10 shows the dependences of \(\Delta I\) and \(\Delta V\) on \(L_{SE}\) for Structure #1 at \(L_{\rm SC}=3\) nm. It can be seen that \(\Delta I\) decreased with the increasing thickness of the emitter spacer, while \(\Delta V\) increased. Thus, the product \(\Delta I\cdot\Delta V\) can have a maximum value at a certain value of the emitter spacer (in the case considered in Figure 10, the optimal value of the emitter spacer is 8 nm), which will depend on both the structure of the RTD and the chosen thickness of the collector spacer. Figure 8: **(Left)** The potential of the bottom of the conduction band (right \(y\)-axis, solid curve) and electron density (left \(y\)-axis, dotted curve) depending on the coordinate for the resonant tunneling Nanostructure #1 at the applied voltage \(V_{dc}=0.29\) V. The position of the Fermi level is shown by a green fill. **(Right)** The potential of the bottom of the conduction band for Nanostructure #1, maximized in the emitter spacer region, for several values of the spacer thickness. Figure 9: Current-voltage characteristics of the RTD Structure #2 at various emitter spacer thicknesses: \(L_{SE}=2\) nm—black lines, \(L_{SE}=4\) nm—blue lines, \(L_{SE}=6\) nm—red lines, \(L_{SE}=8\) nm—green lines. Solid lines correspond to \(L_{SC}=10\) nm, and dashed lines correspond to \(L_{SC}=20\) nm. This significantly complicates the analysis and search for general patterns of behavior of the value \(\Delta I\cdot\Delta V\) for different RTD structures and requires the consideration of large intervals of spacer thickness variation in smaller increments. One of the important parameters of the static current-voltage characteristic of resonant tunneling nanostructures is the peak-to-valley-current ratio (PVCR). In the range of thicknesses of the emitter spacer and collector spacer considered, for Structure #1, the PVCR is of the order of 4; for Structure #2, the PCVR is in the range of 6.9-8.2; for Structure #3, the PVCR is in the range of 7.2-8.7.
# Optimization of Storage Medium for Hematological Reference Samples in External Quality Assessment
## 3 Results  
### Evaluation of RBC Durability  
Hemolysis was monitored to assess the durability of three types of RBCs. Results showed that unfixed human RBCs (control) did not hemolyze in 0.60% and 0.58% NaCl, but started to hemolyze at a concentration of 0.56% (191 mOsmol/kg) and severely broke down at 0.46% (157 mOsmol/kg), evidenced by the increase in color intensity towards lower concentrations. On the other hand, fixed RBCs exhibited significantly higher durability, starting to hemolyze at a lower concentration of NaCl, 0.48% (164 mOsmol/kg), and being severely destroyed at 0.42% (144 mOsmol/kg). The data suggested that the durability of fixed human RBCs was higher than unfixed. In fact, the fixed human RBC samples withstand the osmolality at a concentration of 0.46% NaCl (157 mOsmol/kg), higher than that of unfixed red blood cells, at 0.58% (198 mOsmol/kg).  
Results on fixed goose RBCs (pseudo-leucocytes) and goat RBCs (pseudo-platelets) showed that no hemolysis was observed in all 18 concentrations of NaCl. It can be explained that the membrane structures of both cell types were stabilized by the fixation reagents, and therefore unaffected by the external environmental conditions. In the study of [PERSON] et al., avian and goat RBCs were completely hemolyzed at 100 mOsmol/kg and 200 mOsmol/kg, respectively. This proved that fixed avian and goat RBCs were more durable than unfixed RBCs. In conclusion, the blood cell durability tests showed that fixed cells were more durable than unfixed ones.  
The result of defining the stability of RBCs showed that fixed RBCs are more durable than the unfixed ones. Fixed human RBCs were durable in the osmolality from 157 to 205 mOsmol/kg while goat and goose RBCs could withstand the osmolality at 89 to 205 mOsmol/kg. Values of HGB and MCV in goose and goat fixed RBCs were recorded by an ABX analyzer (Table 1). The results showed that HGB in goose and goat fixed RBCs was almost zero. This proved that there is no an increase in the value of HGB and MCV in the case of combination.
## 4 Analysis of Different Parameters
### Methods of Increasing Structural Stiffness  
It is necessary to analyze the fundamental frequency changes to evaluate the sensitivity of structural stiffness changes to different structural parameters. The structural parameters were consistent with the engineering examples analyzed in Section 2.1. The change of fundamental structural frequency under various conditions was analyzed by adjusting the track wall thickness and the column wall thickness, height, and diameter. The changing trend of fundamental structural frequency under different structural parameters is shown in Figure 15.  
Through analysis, the fundamental frequency of the structure changed obviously with the structural parameters. If we adjusted the structural parameters slightly within a reasonable range, there were differences in the change range of the fundamental frequency of the structure. The fundamental frequency changed the fastest with the diameter of the column, the wall thickness of the column changed the second-fastest, and the change of speed with the height of the column was the slowest. That is, the structural stiffness was the most sensitive to the change in the diameter of the column, and the sensitivity to the height of the column was relatively weak. In design, when the structural rigidity does not meet the requirements, we suggest first considering adjusting the diameter of the column.  
Figure 14: \(RMS_{\text{max}}\) at different frequency.
and monitoring [1]. Thus, sensors can now be deployed and utilised to monitor patients, allowing for timely and accurate diagnoses and immediate healthcare actions. It is important to note that this progress in monitoring has drastically improved the way patients can be cared for within the healthcare domain and, in turn, enhances their Quality of Life (QoL) because such a system allows live monitoring and provides scope for making decisions and actions in real-time.
# An Improved YOLOv5 Algorithm for Drowning Detection in the Indoor Swimming Pool
## 3 Methods
### Self-Made Dataset  
A deficiency exists in the availability of pertinent public datasets addressing drowning incidents. To assess the accuracy of the enhanced YOLOv5 algorithm, this study created a self-made dataset via simulated drowning scenarios, as illustrated in Figure 3. Utilizing drones, the project recorded videos and extracted images at predefined intervals, culmi  
Figure 2: Improved YOLOv5 structure diagram.  
nating in the formation of the self-made dataset. Drowning behavior is delineated by non-voluntary controlled movements, encompassing lateral arm extension and struggle at the water's surface [36].  
Four healthy college students (mean age \(\pm\) standard deviation: 21.2 \(\pm\) 1.0 years; BMI: 22.1 \(\pm\) 1.2) willingly participated in the study to simulate drowning (refer to Figure 3a). This study was approved by the Human Research Ethics Committee for Non-Clinical Faculties of the School of Aeronautics and Astronautics, Tiangong University (registration number 2022024). The experimental procedures and precautions were verbally communicated to all subjects the day before the experiment, and they provided informed consent by signing a consent form. Participants abstained from alcohol, coffee, or vigorous exercise within 24 h before the experiment. All subjects actively contributed to the design, execution, reporting, or dissemination plans of our research. To ensure dataset diversity, various postures, including treading water, breaststroke, backstroke, freestyle, and group configurations with two, three, and four individuals, were included (refer to Figure 3b-h). The study took place in a swimming pool under normal lighting conditions, utilizing the DJI Mini3 pro drone manufactured by Shenzhen Dajiang Innovation Technology Co., Ltd. in China (Shenzhen, China) (refer to Figure 3i).  
Subsequently, our self-made dataset was established using videos captured by drones. The Labellmg tool, a graphical image annotation tool developed in Python, was employed to select one image every five frames. This self-made dataset comprises 8572 labeled images representing drowning, treading water, and swimming. To assess the performance of our improved YOLOv5 algorithm, 7000 images were randomly chosen for the training set, and 1572 images were allocated to the validation set. In the self-made dataset, according to the distribution of positional proportions and the aspect ratios of the labeled targets (as shown in Figure 4), it is evident that the labeled targets are widely and mainly distributed in the middle region of the picture, with aspect ratios mainly falling within 0.3 of the input image size.  
Figure 3: Images of the self-made dataset and the drone. (**a**) drowning; (**b**–**h**) diverse swimming postures, such as tread water, breaststroke, backstroke, freestyle, and group configurations featuring two, three, and four individuals; (**i**) the DJI Mini3 pro.
# Parametric Selection of Optimized Epicyclic Gearbox Layouts for Wind Power Plant Applications
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**No.** & **S/V** & \(t_{\mathbf{I}}\) & \(t_{\mathbf{II}}\) & \(i\) & \(m_{\mathbf{z}}\), \(\mathbf{kg}\) & \(\eta\) & \(d_{\mathbf{3 max}}\), \(\mathbf{mm}\) & \(d_{\mathbf{3 max}}\)/\(d_{\mathbf{3 min}}\) & \(m_{\mathbf{I}}\), \(\mathbf{mm}\) & \(m_{\mathbf{IL}}\), \(\mathbf{mm}\) \\ \hline
1 & S26 NW(E) & 2.6667 & 2.6667 & \(-\)0.08036 & 2026.302 & 0.9687 & 960 & 1.42857 & 14 & 20 \\ \hline
2 & S16 EW(N) & 3 & 3.1667 & \(-\)0.08 & 1997.356 & 0.96503 & 1026 & 1.58333 & 12 & 18 \\ \hline
3 & S55 EN(W) & 2.1667 & 2.8333 & \(-\)0.08027 & 4154.823 & 0.84418 & 1053 & 1.03235 & 27 & 20 \\ \hline
4 & S55 WN(E) & 2.8333 & 2.1667 & \(-\)0.08027 & 4154.823 & 0.84418 & 1053 & 1.03235 & 20 & 27 \\ \hline
5 & S23 EW(N) & 3.6667 & 2.6667 & \(-\)0.08036 & 2649.395 & 0.96453 & 1056 & 1.33333 & 12 & 22 \\ \hline
6 & S13 NW(E) & 4.5 & 3 & \(-\)0.08 & 2930.026 & 0.95737 & 1080 & 1.11111 & 12 & 20 \\ \hline
7 & S66 EN(W) & 3.5 & 3.1667 & \(-\)0.08 & 3537.87 & 0.58702 & 1134 & 1.10526 & 18 & 18 \\ \hline
8 & S66 WN(E) & 3.1667 & 3.5 & \(-\)0.08 & 3537.87 & 0.58702 & 1134 & 1.10526 & 18 & 18 \\ \hline
9 & S44 WN(E) & 2.1667 & 2.8333 & \(-\)0.08027 & 5629.575 & 0.84418 & 1170 & 1.04278 & 30 & 22 \\ \hline
10 & S44 EN(W) & 2.8333 & 2.1667 & \(-\)0.08027 & 5629.575 & 0.84418 & 1170 & 1.04278 & 22 & 30 \\ \hline
11 & S33 WN(E) & 4.1667 & 4.5 & \(-\)0.08 & 6220.158 & 0.4654 & 1458 & 1.08 & 18 & 18 \\ \hline
12 & S33 EN(W) & 4.5 & 4.1667 & \(-\)0.08 & 6220.158 & 0.4654 & 1458 & 1.08 & 18 & 18 \\ \hline
13 & S22 EN(W) & 3.1667 & 3.5 & \(-\)0.08 & 11,940.31 & 0.58702 & 1701 & 1.10526 & 27 & 27 \\ \hline
14 & S22 WN(E) & 3.5 & 3.1667 & \(-\)0.08 & 11,940.31 & 0.58702 & 1701 & 1.10526 & 27 & 27 \\ \hline
15 & S11 EN(W) & 4.1667 & 4.5 & \(-\)0.08 & 20,993.04 & 0.4654 & 2187 & 1.08 & 27 & 27 \\ \hline
16 & S11 WN(E) & 4.5 & 4.1667 & \(-\)0.08 & 20,993.04 & 0.4654 & 2187 & 1.08 & 27 & 27 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Gear trains with nominal \(i=-0.08\) ranked according to minimal larger ring gear reference diameter.
# ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks
## 5 Conclusions  
We confirmed that regardless of the depth of the layer, the SE-ResNet model had better prediction performance than the baseline model, ResNet. Especially, our SE-ResNet model with 152 layers surpassed the F1 scores for the baseline model, ResNet with 152 layers, by + 1.40% (the difference between 97.05% for the 152-layer SE-ResNet and 95.65% for the 152-layer ResNet) for the seven-class ECG signal classifications. These results indicate that, with only one ECG signal, instead of the 12-lead ECG measured by the hospital, our SE-ResNet multi-classification model classifies the ECG signals sufficiently correctly. In addition, our model is expected to be a tool to provide arrhythmia patients and the general public with information about arrhythmia, and to enable doctors to immediately perform the necessary treatment in the medical field.  
Conceptualization, J.P., J.-k.K., H.S.S. (corresponding author); methodology, J.P., J.-k.K.; validation, J.P., J.-k.K., S.J., Y.G., H.S.S.; formal analysis, J.P., S.J., Y.G., H.S.S.; data acquisition, S.J., Y.G., J.-IC.; writing, J.P., H.S.S.; supervision, Y.G., J.-IC., H.S.S.; fund acquisition, H.S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare that there is no conflict of interest regarding this publication.
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 3 Network Structure
### Hierarchical  
In Hierarchical subcategory, a WSN is divided in clusters with an elected cluster head for each one. Cluster heads are used for high level communication with the BS, while cluster nodes monitor the environment and exchange data between neighboring nodes and cluster head. They are further divided into three subcategories depending on how they route data or how they divide the network area. These subcategories are Hierarchy based, Zone based, and Fuzzy logic based.
# Event-Based Path-Planning and Path-Following
## 2 Problem Formulation
### Path Representation  
Throughout the paper, a path \(\mathcal{P}\) is represented as a sequence of line and arc segments as  
\[\langle P_{j-1},P_{j},R_{j}\rangle,\quad j=\overline{1,N_{p}},\]  
where \(P_{j}\), \(P_{j+1}\) are the start and end points of the segment, \(R_{j}\) is the radius of the segment connecting these points (for line segments \(R_{j}=\infty\)), and \(N_{p}\) is the number of segments. We assume that \(R_{j}<0\) corresponds to the left turn and \(R_{j}>0\) to the right one. It is natural to require that every two adjacent segments of a path are mutually agreed to be smooth. Moreover, all paths, it does not matter whether they are predefined or constructed using path-planning algorithms, should satisfy the kinematic constraints that are given by the minimum turning radius \(R_{\min}\), \(R_{j}>R_{\min}\).
## 3 Results
### Evaluation of Matrix Content  
The efficiency of decellularization was validated through a number of different methods. As an initial step, the simple H&E staining method demonstrated the removal of cells, while preserving the 3D ECM. The preservation of collagen fibers and clearance of nuclear content from the decellularized scaffolds was highlighted using [PERSON]'s trichrome staining. Orcein staining and argentic impregnation visualized the elastic fibers and the reticulin fibers, respectively. The scanning electron microscopy (SEM) of the 3D cardiac framework strengthened the results of the optical microscopy (OM), showing reserved collagen fibers with no cellular components remaining. The evaluation of the matrix content using microscopy showed no significant differences between the scaffolds generated by the two decellularization protocols (Figure 5a-j).  
Figure 4: Coronary vessel tree integrity. Perfusion of decellularized ECM with trypan blue solution to visualize the intact coronary vasculature. (**a–d**) Control group of decellularized hearts (30 s); (**e–h**) Electric field group of decellularized hearts (30 s).  
Figure 5: Extracellular matrix evaluation. (**a,f**) H&E staining of thin sections from the rat hearts perfused with 1% SDS (Ob 40\(\times\)) showed a uniform ECM, with no intact cells, nuclei or basophilic structures. (**b,g**) Decellularized myocardial ECM scaffold stained with [PERSON]’s trichrome staining protocol (Ob 40\(\times\)) showed the absence of black nuclei; however, collagen was still present, indicated by the blue staining; in (**g**) the arrows indicate two blood vessels, identified by the thickness of the wall. (**c,h**) Argentic impregnation (Ob 40\(\times\)) visualized preserved reticulin fibers. (**d,i**) Orcein staining (Ob 40\(\times\)) of the ECM showed parallel elastic fibers. (**e,j**) SEM of the ECM showed that cells throughout tissue layers were removed, preserving the collagen fiber organization and structure.  
Comparing decellularization in the control group with decellularization in the electric field group regarding DNA and protein concentrations, the latter proved to be time-saving, taking about 8 h for the process to be completed. In the control group, the amount of time needed for the process was about 14 h. The final moment of decellularization could be settled, considered as the time when the concentration of the two determined analytes (DNA and proteins) became constant (Figure 6a,b). The results provide evidence that alternative electric field decellularization is more efficient, and the matrix integrity is completely preserved. The total number of decellularized hearts (\(n=20\)) was statistically significant, providing reliable results.