esa-sceva/satcom-chunk-collection huggingface dataset:
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 7 Relays Selection
```
1:procedure:NODE-INITIALIZATION
2:MinorAxis \(\gets\) b
3:MajorAxis \(\leftarrow\) 2b
4:CentralPoints \(\leftarrow\) x\({}_{0}\), y\({}_{0}\)
5:Energy(MemberNodes) \(\leftarrow\) \(\mu\)]
6:return TRUE
7:endprocedure
8:procedureCATEWAY-NODE-SELECTION
9:GWs_MedicalIsInTRangeOfSinkNode
10:ifNode.GWs = TRUE
11:E(GWsNode) \(\leftarrow\) E(MemberNodes) + \(\zeta\)
12:return TRUE
13:endprocedure
14:procedureNEIGHBOR-FINDING-MODULE
15:ifd(i) \(>\frac{1}{2}(LengthOfNetwork)\) and d(i) \(>\) d(j)
16:\(\varphi\gets d(i)-d(i)\)
17:elseif\(d(i)\ <\frac{1}{2}(LengthOfNetwork)\) and d(j) \(>\) d(i)
18:\(\varphi\gets d(i)-d(i)\)
19:endif
20:if\(\varphi\leq TxRange\)
21:NeighborFound = TRUE
22:return TRUE
23:endprocedure
24:procedure:NEXT-HOP-FINALIZATION
25:\(\theta\) = and (Neighbor, GWsNode)
26:ifSumOf(\(\theta\)) \(\geq\) 1
27:NextHop = MinimumOfDownNeighbors (\(\theta\))
28:elseifSumOf(\(\theta\)) \(\leq\) 1
29:NextHop \(\leftarrow\) MinimumDepthNeighbor
30:elseif\(\theta\) = FALSE and NodelsGWsNode
31:NextHop \(\leftarrow\) Sink
32:endif
33:return TRUE
34:endprocedure
35:procedure:BIT-ERROR-CHECK
36:ifBERO/Packet \(\leq\) ThresholdBER
37:PacketAccepted
38:elseifBERO/Packet \(\geq\) ThresholdBER
39:PacketDisearaled
40:return TRUE
41:endprocedure
42:procedure:PACKET-RESEND-REQUEST
43:ifBERO/Packet \(\geq\) ThresholdBER
44:ifPacketResendRequest = 0 or 1
45:SendPacketResendRequest
46:PacketResendRequest = PacketResendRequest + 1
47:endif
48:endif
49:endprocedure
```  
**Algorithm 1** RE-AEDG  
The source nodes broadcast sensed data within their transmission ranges. The receiver node checks the BER of the packet; if it exceeds the threshold BER (0.5 out of 1) then it drops the packet immediately and asks the sender node for re-transmission. If it is not the case then the cooperative nodes apply the AF mechanism and re-broadcast the packet. As soon as the destination nodesreceive the required copies of packet, these apply the MRC technique. After the required processing, the destination node re forwards the packet. This process continues till data reaches the AUV.
## 5 Conclusions  
We propose an automated cone cell identification method that uses TV-L1 optical flow registration and \(K\)-means clustering identification on AO-SLO images. The proposed method successfully achieves denoising based on TV-L1 optical flow registration, bias field correction, identification based on \(K\)-means clustering, and merging of closed identified cone cells, as verified experimentally. To evaluate the performance of the proposed method, we compared its automated cone cell identification with manual labeling. The proposed method achieves precision, recall, and F1 scores of 93.10%, 94.97%, and 94.03%, respectively. Furthermore, the proposed method exhibits high-performance cone cell identification on AO-SLO images of eyes with low cone cell density, diabetic retinopathy, and acute zonal occult outer retinopathy.  
Conceptualization, Y.C.; methodology, Y.C., and Y.H.; software, Y.C.; validation, Y.C.; formal analysis, Y.C.; investigation, Y.C.; resources, Y.H., G.S.; data curation, Y.H.; writing--original draft preparation, Y.C.; writing--review and editing, J.W., W.L., X.Z., and Y.H.; visualization, Y.C.; supervision, Y.C.; project administration, L.X. and X.Z.; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Jiangsu Province (BK20200214); National Key R&D Program of China (2017 YFB0403701); Jiangsu Province Key R&D Program (BE2019682, BE2018667); National Natural Science Foundation of China (61605210, 61675226, 61378090); Youth Innovation Promotion Association of Chinese Academy of Sciences (2019320); Frontier Science Research Project of the Chinese Academy of Sciences (QYZDB-SSW-JSC03); Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0206000).  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences.  
Informed consent was obtained from all subjects involved in the study.  
The data are not publicly available due to them containing information that could compromise research participant privacy.  
The authors wish to thank the anonymous reviewers for their valuable suggestions.  
The authors declare no conflict of interest.
## 4 Ad Hoc Routing: Configuration, Scenarios, and Results
### Scenarios and Results
#### 4.2.2 Scenario 2: Forwarding through One Intermediate Hop  
As the user is mobile, there will be a time when it gets away from the server's range and the established path will not be available anymore. The user now needs other nodes to forward its packets to the server. The user, however, is still quite close to the server--therefore, just one hop will be sufficient for the communication to take place, as presented in Figure 8.  
Figure 6: List of all available connections through the wlan0 interface.  
Figure 7: One-to-One TCP communication between the user and Control Room.  
When we trace the route of a packet toward the server with a MAC ID \(78:32:1b:ad:e4:11\), we see that it takes one hop to an intermediate node (with a MAC ID of \((40:9b:cd:00:5a:85)\) in around \(10.78\) milliseconds, and then travels for another \(5.87\) milliseconds to reach the server. The total time for the packet to complete its journey is then roughly \(16.65\) milliseconds, as depicted in Figure 9.
# Bounding the Likelihood of Exceeding Ward Capacity in Stochastic Surgery Scheduling
## Appendix A Notations  
\begin{tabular}{p{128.0 pt} p{128.0 pt}}
**Sets and Indices** \\ \(o\in O\) & Operators within a surgical speciality. \\ \(d\in D\) & Days in the planning horizon. \\ \(r\in R\) & Available operating rooms. \\ \(a\in A\) & Available beds in the ward. \\ \(i\in I\) & All patients. \\ \(i\in I_{o}\) & Patients of operator \(o\). \\ \(p\in P\) & Operating room day schedule (ORDS). \\ \(p\in P_{i}\) & ORDS including patient \(i\). \\ \(p\in P_{o}\) & ORDS including operator \(o\). \\ \((d,p,r)\in DPR\) & ORDS \(p\) for days \(d\) and rooms \(r\) for which patients and operators are available. \\ \(l\in\mathcal{L}\) & Combinations of ward admission probabilities (ward combinations). \\ \(k\in\mathcal{K}\) & Groups of length of stay probabilities. \\ \(k\in\mathcal{K}^{\prime}\) & Groups of length of stay probabilities, excluding extremes (first and last). \\ \(j\in J\) & Days of stay in the ward after surgery. \\ \end{tabular}  
\begin{tabular}{p{128.0 pt} p{128.0 pt}}
**Parameters** \\ \(M^{P}\) & Upper bound on the number of patients assigned to an ORDS. \\ \(M^{ICU}\) & Upper bound on the number of surgical procedures assigned to an ORDS and requiring ICU admission. \\ \(\bar{M}^{ICU}\) & Upper bound on the number of surgical procedures per day requiring ICU admission. \\ \(M^{W}\) & Upper bound on the number of days a patient stays in the ward. \\ \(M^{A}\) & Upper bound on the number of available beds in the ward. \\ \(C_{d,r}\) & Available surgery time on day \(d\) in room \(r\). \\ \(F_{l,a}\) & Feasibility of ward combination \(l\) when there are \(a\) beds available in the ward. \\ \(g_{i}\) & 1 if patient \(i\) requires ICU admission following surgery, otherwise 0. \\ \(n_{p}^{ICU}\) & Number of patients in ORDS \(p\) that require ICU admission following surgery. \\ \(\rho_{k}\) & Probability of ward admission for probability group \(k\). \\ \(n_{k}\) & Number of patients that belong to probability ward group \(k\). \\ \(\rho_{i,d}^{r}\) & Probability of a patient \(i\) being in ward on the day \(d\). \\ \(Q_{j,k,p}\) & Number of patients on day \(j\) after surgery belonging to probability group \(k\) and ORDS \(p\). \\ \(n_{d,k}\) & Number of patients operated in the previous planning period that occupy the ward on day \(d\) and belong to probability group \(k\). \\ \(\delta\) & Limit on the probability that an ORDS exceeds \(C_{d,r}\). \\ \(\Omega\) & Limit on the likelihood that a ward combination exceeds the number of available beds in the ward. \\ \end{tabular}
# Instability Study of Magnetic Journal Bearing under S-CO\({}_{2}\) Condition
## 3 Experiments with Magnetic Bearings Under S-CO2 Conditions
### Data Analysis Method  
The relationships between the unstable shaft rotation and the forces on the shaft were analyzed. The experimental results were analyzed from the shaft trajectory data and the electromagnet current data. The trajectory data were dynamically utilized to calculate the net force, \(F_{net}\), on the shaft. Three major forces formed \(F_{net}\): force from the lubricating fluid, \(F_{LUIB}\); magnetic force from the bearing, \(F_{MB}\); and centrifugal force due to rotation of the unbalanced mass, \(F_{UB}\). The free body diagram for those forces is displayed in Figure 15.  
To measure \(F_{net}\), the mass center of the shaft was obtained. The shaft had an unbalanced mass center; therefore, the shaft's mass center has phase, \(\beta\) and distance, \(d\), as shown in Figure 16.  
Figure 16: Description of the unbalanced mass center.  
Figure 15: Free body diagram of the rotating shaft under AMB control.  
\(\beta\) can be obtained by using Equation (14) [22].  
\[\beta=\tan^{-1}\frac{2\xi f}{1-f^{2}} \tag{14}\]  
Frequency, \(f\), was a known value. The damping ratio, \(\xi\), was iteratively defined with \(F_{net}\) as the termination criterion. The \(d\) from the cross-section center could be obtained from the shaft design information. From the shaft design information, force due to unbalance (\(F_{UB}\)) was the same as the weight of the shaft when it operated at 37,000 rpm, as described in Equation (15). \(d\) can be obtained from this relationship.  
\[F_{UB}=m*d*\omega_{37,000~{}RPM}^{2}=m*g, \tag{15}\]  
To analyze the source of instability, other forces were calculated. The \(F_{MB}\) was calculated as a sum of forces from each electromagnet. The magnitude of each force, \(f\), was calculated with Equation (16). The permeability of CO\({}_{2}\), \(\mu\), barely changed from \(\mu_{0}\), even if CO\({}_{2}\) was flowing with high speed or had a large density variation. Thus, the magnetic field's affection to the hydrodynamic field was reasonably smaller than the shaft friction. Therefore, the interaction between the magnetic field and hydrodynamic field was ignored in this paper.  
\[f=\frac{B^{2}A_{g}}{2\mu_{0}}=\frac{\mu^{2}N^{2}l^{2}A_{g}}{2\mu_{0}l_{g}^{2}}, \tag{16}\]  
\(F_{MB}\) had several losses; therefore, the \(F_{MB}\) in Equation (16) was calibrated under the vacuum test. The vacuum test created an environment to nullify \(F_{LUB}\), leaving only the gravitational force. Finally, \(F_{LUB}\) was measured from experiments and it was compared to the calculated values from the lubrication model results to verify the model. \(F_{LUB}\) was obtained by subtracting the calibrated \(F_{MB}\), \(F_{UB}\), and gravity from the measured \(F_{net}\) obtained from the shaft trajectory data.
## Results and Discussion
### Proteomic Analysis of pDMYO and pDSIS
There are only a few studies on proteomic analysis of decellularized myocardial matrix, especially for that derived from porcine. [PERSON] et al. [25] identified at least 14 kinds of protein components in porcine decellularized myocardial matrix. [PERSON] et al. [26] detected 28 distinct peptides in porcine decellularized myocardial matrix, which uniquely represent ECM and ECM-associated proteins. [PERSON] et al. [27] identified more than 200 proteins in the human decellularized myocardial matrix and also found unique proteins including agrin and alectin-1. [PERSON] et al. [28] found 387 proteins in decellularized ventricle samples of zebrafish hearts and Cathepsin D was found in them. In our study, we found pDMYO contained 944 proteins. More importantly, we found some unique proteins in pDMYO, including annexin-6 (ANXA6) [29], agrin (AGRN) [30], cathepsin D (CTSD) [31] and galectin-1 (LGALS1) [32]. Studies have shown that ANXA6 physically interacts with sarcomeric \(\alpha\)-actinin and alters cardiomyocyte contractility, suggesting that ANXA6 may play an important role for the excitation and contraction of the cardiomyocytes [29]. Agrin has been proved to be able to promote cardiac regeneration in adult mice after myocardial infarction [30]. Myocardial CTSD upregulation induced by myocardial infarction protects against cardiac remodeling and malfunction [31]. Galectin-1 has emerged as key modulator of inflammatory processes, and recombinant Galectin-1 administration emerges as the most attractive approach for treatment of cardiovascular diseases, particularly acute myocardial infarction [32]. Galectin-1 in the pDMYO-gel may regulate the inflammatory response after myocardial infarction, and promote the repair of injured heart. This may explain the reason why cardiomyocytes can adhere, grow, spread, and maintain normal beating on pDMYO-gel. The proteomic analysis approach used this study can be utilized as a means for comparing between complex natural biomaterials derived from different tissue sources or species, provides insight into protein composition of different ECM, and advances the field progress.
# Influence of the Quality of Consumer Headphones in the Perception of Spatial Audio
## 1 Introduction
### Hypothesis and Planning of the Study  
Different factors can affect the perception of the spatial sound image. Our hypothesis states that three main factors are responsible for this degradation. Some of these degrading factors could be the frequency response, the distortion and the disparity between the left-right transducers, especially in low cost headphones. To determine this, we propose a series of perceptual tests [5] to particularly study these factors.  
Section 2 describes the methodology, the headphones employed in the study, as well as the technique used to measure and simulate them. Sections 3-6 explain a series of perceptual tests that constitute the bulk of this research. Firstly, Section 3 presents a perceptual test carried out to study the influence of the sensitivity disparity between left and right transducers and to establish the degree to which perception of the sound source position in the azimuth is affected. Although in high quality headphones, manufacturers match transducers with similar sensibilities, these low cost headphones have different sensibilities due to broader manufacturing tolerances. Another second subjective perceptual test described in Section 4 was conducted to evaluate the effect of the frequency response in the perception of quality and spatial impression with headphones. As frequency response is the factor that varies most among different headphones due to their quality, this test is of particular interest to better understand how frequency response affects the spatial sound impression. Section 5 outlines the third perceptual test planned to evaluate the effect of harmonic distortion in listening with headphones. Distortion can be considerable if high dynamic sound and high reproduction levels are employed. Section 6 explains the fourth and last test, which studies the relation of the frequency response with the accuracy of localization in the horizontal plane. The capacity of a headphone to generate a good spatial immersion can be different from its capacity to generate precise locations. To explore this point, azimuth localization is tested here for different kinds of headphones. The discussion and conclusions of these experiments are presented in Section 7.
# Aerodynamic Performance Assessment of Distributed Electric Propulsion after the Wing Trailing Edge
## 5 Conclusions  
This paper proposed a pusher DEP configuration to study the aerodynamic performance of the mutual interference between the propeller and wing with wind tunnel tests and numerical simulations. Conclusions are as follows:  
1. The propeller has no significant effect on the lift of the wing when the AoA is less than 16\({}^{\circ}\). However, the lift coefficient is much higher than the baseline (isolated wing), and the lift of the pusher DEP system is significantly increased up to 73-87% without any decrease in L/D. In contrast, the drag coefficient is also higher than that of the isolated wing. With a higher AoA, it is interesting to note that the lift for the pusher configuration was also improved.
2. The wing of the pusher structure stalled at 16\({}^{\circ}\) AoA. This may be caused by the acceleration of the axial airflow affected by the propeller and the interference from the inflow and downwash caused by the slipstream. Accordingly, it increased the drag coefficient, especially for the DEP with a smaller horizontal distance between the propeller and the trailing edge of the wing. Also, a delay in stall and an increase in lift at a high AoA were observed when the pusher props were placed above the wing chord line.
3. The trailing edge vortex of the wing for the pusher DEP structure interacted with the flow field of the propeller, and it showed an increase in lift and drag increase. L/D variation was within the experimental uncertainty limits of the baseline case (isolated wing), and no significant difference was found. The propeller in the pusher DEP was subjected to the blocking effect of the wing or the eddy current of the trailing edge of the wing, and the lift was significantly increased.  
[PERSON] carried out experiments; [PERSON] wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 52275095) and Fujian Provincial Industrial Robot Basic Components Technology Research and Development Center (2014H2004).  
Not applicable.  
All data are already included in the manuscript.  
Figure 10: Lift distribution.  
**Acknowledgments:** The authors thank the Key Laboratory of Fluid Power and Intelligent Electro-Hydraulic Control (Fuzhou University), Fujian Province University, and Fuzhou University Jinjiang Science and Education Park for applying the experimental field.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 4 Algorithm
### Complexity Analysis  
The task processing model proposed in this paper combines static path planning with dynamic adjustment optimization, and the algorithm's time complexity can be divided into two phases. In the static path planning phase, the K-means clustering algorithm is used to perform the initial task allocation, with a time complexity of \(O(k\cdot n\cdot t)\), where \(k\) represents the number of clusters, \(n\) is the number of task points, and \(t\) is the number of iterations. The vehicle path planning is based on a variant of the Traveling Salesman Problem (TSP), with a time complexity of approximately \(O(n^{2})\). The global optimization is achieved through the Hybrid Genetic Algorithm (HGA), with a complexity after multiple iterations  
Figure 4: KGTSA.  
of \(O(m\cdot n\cdot\log n)\), where \(m\) is the number of iterations. In the dynamic adjustment phase, Tabu Search (TS) is used to reallocate and optimize paths based on the real-time distribution of task points and the UAV status, with the complexity of UAV path optimization being \(O(k\cdot p)\), where \(k\) is the number of UAVs and \(p\) is the number of task points. Overall analysis shows that the static path planning phase has a higher complexity and, as the number of task points increases, it becomes the dominant factor affecting the model's performance.  
In real-world applications, especially under dynamic weather conditions, the complexity of the KGTSA can significantly impact real-time path optimization. The static path planning phase of the algorithm, particularly when the number of task points increases, has a high time complexity that may lead to delays in the path planning process, affecting the system's responsiveness. In real-world environments, weather changes can cause rapid fluctuations in task point distribution and UAV status, requiring the system to adjust promptly. However, the complexity of static path planning makes it difficult for the system to quickly adapt to these changes. Specifically, when the number of task points increases significantly or when clusters change dramatically, the time required for recalculating the optimal path may affect real-time decision-making. To improve the algorithm's real-time performance in practical applications, future work could explore parallel computing or adaptive simplifications of the path planning process to reduce its time complexity, ensuring that the algorithm maintains good responsiveness even in dynamic and complex environments.
# A Neural-Network-Based Watermarking Method Approximating JPEG Quantization
## 2 Preliminary: JPEG Quantization  
JPEG compression is a lossy compression that reduces the amount of information in an image to reduce the file size. In this kind of compression, an image is divided into \(8\times 8\)-pixel blocks. Then, in each block, the processes of the DCT, quantization, and entropy coding are sequentially performed. In JPEG compression, the process of reducing the amount of information is the quantization process of the DCT coefficients. We focus on the quantization of the DCT coefficients of the luminance component in an image because the watermark is embedded in these coefficients of the image. Figure 1 shows the quantization process in JPEG compression for DCT coefficients [16]. The process consists of three steps: (1) the creation of the quantization table \(T_{Q}\), (2) the quantization process, and (3) the dequantization process.  
During the quantization process, the DCT coefficients are quantized based on a default basic table or a self-defined basic table. The default basic table \(B\) is defined as  
\[\mathbf{B}=\begin{bmatrix}16&11&10&16&24&40&51&61\\ 12&12&14&19&26&58&60&55\\ 14&13&16&24&40&57&69&56\\ 14&17&22&29&51&87&80&62\\ 18&22&37&56&68&109&103&77\\ 24&35&55&64&81&104&113&92\\ 49&64&78&87&103&121&120&101\\ 72&92&95&98&112&100&103&99\end{bmatrix}. \tag{1}\]  
The quantization table is then determined using the quality factor (\(Q\)) and the basic table \(B\). The quantization table \(T_{Q}(u,v)\) for the quantization level \(Q\) at coordinates \((u,v),u=0,1\cdots,7,v=0,1\cdots,7\) is defined as  
Figure 1: JPEG compression quantization for luminance components. (1) Creation of the quantization table \(T_{Q}\), (2) the quantization process, and (3) the dequantization process.  
\[T_{Q}(u,v)=\bigg{\lfloor}\frac{B(u,v)s(Q)+50}{100}\bigg{\rfloor}, \tag{2}\]  
where \(\lfloor\cdot\rfloor\) is the floor function and where \(B(u,v)\) is the \((u,v)\) component of the basic table \(B\). Also, the scaling factor \(s(Q)\) is given by  
\[s(Q)=\begin{cases}\frac{5000}{Q}&(Q<50)\\ 200-2Q&(Q\geq 50)\end{cases}. \tag{3}\]  
The quantization process is performed using the quantization table \(T_{Q}\). Let \(y(u,v)\) be the quantized data, and let \(x(u,v)\) be the DCT coefficients in an \(8\times 8\)-pixel block. The quantization process is performed as  
\[y(u,v)=\text{round}\bigg{(}\frac{x(u,v)}{T_{Q}(u,v)}\bigg{)}, \tag{4}\]  
where  
\[\text{round}(a)=\begin{cases}\lfloor a+0.5\rfloor&(a\geq 0)\\ -\lfloor-a+0.5\rfloor&(a<0)\end{cases}. \tag{5}\]  
Let \(z(u,v)\) be the quantized DCT coefficients; then, the dequantization process is performed as  
\[z(u,v)=y(u,v)T_{Q}(u,v). \tag{6}\]