esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 5 Performance Evaluation  
### Simulation Environment  
Our simulations are performed under Matlab R2014b simulator [32] and the parameters used in this simulation are listed in Table 4.  
In this simulation, sensor nodes are deployed in a 400 \(\times\) 400 m\({}^{2}\) region randomly and independently. The random mobility mode has two stages: mobile and stationary, and the mobile sink can independently determine the direction and speed when moving, and it can decide the dwell time when keeping stationary. The duration for a data collection period varies and the mobile sink can change it in different collection tasks, but in our experiments, we fix this value to compare the impact of the other parameters on the performance of the algorithm. For classical data transmission schemes, in ExOR [33], by taking advantages of the broadcast nature of the wireless medium and allowing multiple neighbors that overhear the transmission to participate in forwarding data, it can improve the reliability of data forwarding. In Probabilistic and Opportunistic Flooding Algorithm (POFA) [34], by adopting the controlled transmissions, it can achieve a target reliability on each hop of data forwarding. In Reference [35], a data transmission schemes named Reliable Proliferation Routing with low Duty Cycle (RPRDC) is proposed, by integrates three core concepts namely reliable path finder, a randomized dispersity, and forwarding, it improves the success rate of packet forwarding. In addition, both of them consider unreliable wireless links when making the routing decision. In this work, the performance of the proposed scheme is compared with that of ExOR, POFA and RPRDC in terms of the determined performance metrics, (i) Packet Delivery Ratio (PDR), (ii) Energy Consumption (EC), and (iii) Network Lifetime (NL).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Network Size & 400 \(\times\) 400 m\({}^{2}\) \\ Number of the Mobile Sink & 1 \\ Mobility Pattern & Randomly \\ Duration for a Data Collection Period & 600 s \\ Communication Range for Sensor Nodes & 20 m \\ Data Packet Size & 1024 bits \\ Probe Message Size & 120 bits \\ Target Reliability in Probabilistic and Opportunistic Flooding Algorithm (POFA) & 0.6 \\ Initial Energy of Nodes & 2 J \\ Buffer Size of Nodes & 1024 bits \\ \(E_{dec}\) & 20 \(\times\) 10\({}^{-7}\) J/bit \\ \(\epsilon_{amp}\) & 10 \(\times\) 10\({}^{-9}\) J/bit/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
Figure 8: The process of a source node \(v_{\text{s}}\) forwards its sensing data to the mobile sink along the optimal path on the spanning tree.
###### Abstract  
Objectives: This paper studies the patellar tendon release's effect on the movement characteristics of the artificial patellofemoral joint squat to provide reference data for knee joint surgery. Methods: Firstly, the dynamic finite element model of the human knee joint under squatting was established. Secondly, in the above no-release models, the release of 30% of the attachment area at the upper end, the lower end, or both ends of the patellar tendon were conducted, respectively. Then the simulations of all above four models were conducted. Finally, the results of the simulation were compared and analyzed. Results: The simulation results show that, after releasing the patellar tendon (compared with the no-release simulation's results), the relative flexion, medial-lateral rotation, medial-lateral tilt, and superior-inferior shift of the patella relative to the femur increased; the medial-lateral shift and anterior-posterior shift of the patella relative to the femur decreased. Conclusion: In this paper, the maximum flexion angle of the patella increased after the patellar tendon being released (compared with the no-release model), which indicated that the mobility of knee joint was improved after the patellar tendon release. The simulation data in this paper can provide technical reference for total knee arthroplasty.  
Keywords:total knee arthroplasty; patellar tendon; patellofemoral joint; squat movement; dynamic finite element analysis +
Footnote †: journal: Applied
# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 5 Conclusions  
This paper presents an algorithm to detect and segment hemorrhages. The previous experiments performed by the research community suggest that two categories of hemorrhages are difficult to segment. The first category of hemorrhages is located at the retinal border blended with the black background. The second category of hemorrhages is attached to the blood vessels. The proposed algorithm segments both categories satisfactorily. The proposed algorithm preprocesses, segments, and classifies hemorrhages from retinal fundus images. Two well-known datasets (DIARETDB1 and DIARETDB0) are used in this research. State-of-the-art methods are used for benchmarking, while quality evaluation criteria are used to report the results. The SWAT algorithm (proposed method) segments hemorrhages efficiently and accurately. Our results suggest that the proposed method performs better than other methods in terms of quantitative and visual inspection (see Tables 1-3 and Figures 6-8). The preprocessing and enhancement techniques are used in the detection phase, while the SWAT algorithm isolates the hemorrhages from other pathological features and non-hemorrhage regions.  
Finally, since the SWAT algorithm is adaptive and has better segmentation characteristics, it could be helpful in other ophthalmological conditions as well. The proposed model is expected to be useful in clinical medicine, such as surgery navigation and diagnosis. It can be promising to extend the proposed method by using information from the original image as a guide for preprocessing and segmentation. In the future, we plan to extend and combine the proposed method with a deep learning framework.  
T.A.: investigation, methodology, software, writing--original draft, conceptualization, formal analysis. A.E.I.: resources, visualization, writing--original draft, validation, methodology, conceptualization. C.C.: funding acquisition, project administration, supervision,  
Figure 11: ROC curves of various kernels.  
writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
Thammasat University Research Fund, SIIT Research Fund.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.it.lut.fi/project/imageret/diaretdb0/](https://www.it.lut.fi/project/imageret/diaretdb0/)]([https://www.it.lut.fi/project/imageret/diaretdb0/](https://www.it.lut.fi/project/imageret/diaretdb0/)), [[https://www.it.lut.fi/project/imageret/diaretdb1/index.html](https://www.it.lut.fi/project/imageret/diaretdb1/index.html)]([https://www.it.lut.fi/project/imageret/diaretdb1/index.html](https://www.it.lut.fi/project/imageret/diaretdb1/index.html)).  
All authors in this paper have no potential conflict of interest.
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 3 Applications of Smart Drug Delivery Devices
### Implantable Smart Drug Delivery Devices
#### 3.2.2 Implantable Drug-Eluting Stents  
These are stents that are implanted in blood vessels to deliver drugs, such as anti-inflammatory or anti-proliferative agents, directly to the site of action. The stents can be controlled wirelessly using a smartphone or other device, allowing healthcare providers to adjust the delivery rate as needed. A list of some examples of implantable drug-eluting stents is given in Table 6.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Ref.** & **Brand Name** & **Device Name** & **Key Features** & **Drug Delivered** & **Limitations** \\ \hline
[38] & Abbott & Xience Sierra(tm) & Implantable stent with drug coating and remote monitoring for artery treatment. & Everolimus, an immunosuppressivement medication, for the prevention of restenosis after coronary artery angioplasty & Complications and potential re-treatment needed. \\ \hline
[39] & Medtronic & Resolute Onyx(tm) & Resolute Onyx(tm) & Implantable stent with polymer coating and remote monitoring to prevent re-narrowing. & Zotarolimus, an immunosuppressivement medication, for the prevention of restenosis after coronary artery angioplasty & Expertise required, risk of complications with some medications. \\ \hline
[40] & Boston Scientific & Promus PREMIER(tm) & Remote monitoring, drug-eluting, artery stent for preventing re-narrowing with app. & Everolimus, an immunosuppressivement medication, for the prevention of restenosis after coronary artery angioplasty & Late complications, limited options. \\ \hline \end{tabular}
\end{table}
Table 6: List of implantable drug-eluting stent.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Ref.** & **Brand Name** & **Device Name** & **Key Features** & **Drug Delivered** & **Limitations** \\ \hline
[36] & Abbott & Infinity(tm) Deep Brain Stimulation (DBS) & Implantable infusion pump for deep brain stimulation therapy in Parkinson’s. & Various medications, such as levodopa for the treatment of Parkinson’s disease & Invasive procedure, possible neurological side effects, frequent follow-up. \\ \hline
[37] & B. Braun Medical & Infusomat Space & Implantable pump delivers medication to specific body areas for various conditions. & Various medications, such as opioids for pain management & Compatibility limitations, requires specialised training, susceptible to error. \\ \hline \end{tabular}
\end{table}
Table 5: Implantable sensor-based infusion pumps for drug delivery.
## 4 Additional Clarification on Computational Complexity  
In Section 3.3, we discussed the computational complexity of the proposed hybrid algorithm. Here, we provide a focused comparison of complexity with respect to the original dandelion optimizer (DO) and JADE, addressing whether the hybrid approach is more expensive, cheaper, or roughly the same in terms of computational cost.
# Vibration Energy Harvesting from Plates by Means of Piezoelectric Dynamic Vibration Absorbers
## 2 Mathematical Model  
In this study, a flat rectangular aluminum plate with two opposite free sides and that is clamped on the other two sides is considered. The system and the main dimensions are represented in Figure 1. The plate lies on the \(x\)-\(y\) plane, and vibrations occur in the \(z\) direction. The cantilever piezoelectric dynamic vibration absorber (CPDVA) is composed of a cantilever beam with its axis parallel to the plate and fixed to the plate through one of its extremities. A tip mass is attached to the free end of the cantilever for tuning. Concentrated and distributed loads in the \(z\) direction are considered since they excite the vibrations of the plate in the transversal direction. The vibrations are then transferred to the CPDVA, which harvests energy through the piezoelectric patch attached to the cantilever.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 2 Related Work  
Blind image deblurring is a classic problem of image degradation. In the process of image generation, various types of blur will be produced due to different degradation factors. Among these, motion blur is particularly common in real-life scenarios. Therefore, this study focuses on blind deblurring for motion blur, which is typically modeled as follows:  
\[y=k*u+n \tag{1}\]  
where \(y\) and \(u\) are the blurred image and recovered image respectively, \(k\) is the blur kernel, \(*\) is the convolution operator, and \(n\) is random noise, usually Gaussian noise.  
In blind deblurring, the blur kernel is estimated in the gradient domain, which has several advantages over the image domain. A primary benefit is the use of edge information for estimation, allowing the gradient operator to filter out non-informative areas effectively. The gradient domain's smoother characteristics yield a higher signal-to-noise ratio, reducing noise interference and improving estimation accuracy. Additionally, it facilitates the incorporation of constraints, such as blur kernel sparsity and prior shape information, which helps prevent overfitting and ensures a more plausible estimation of the blur kernel. Since the gradient operator \(\
abla\) can be exchanged with the convolution operator, the derivation of both sides of Equation (1) can be expressed as follows:  
\[\
abla y=k*\
abla u+n^{\prime} \tag{2}\]  
where \(n^{\prime}=\
abla\)n is Gaussian noise.
## 5 Results and Discussion
The training occurs together with the simulations in batch mode (batch size 100) for 1,000 epochs. Figure 10 shows the comparison between the data and the ANN output for both the training and the validation sets in case 1: when \(k^{*}<0.5\), the cell is classified as soft; when \(k^{*}>0.5\), the cell is classified as rigid. When the difference in stiffness of the two populations is one order of magnitude (case 1 in Table 1), the ANN correctly identifies all the cells with 100% accuracy. In case 2, the stiffness of the rigid cells is only 2.5 times higher than that of the soft cells (Figure 11). The difference is lower than the previous case, but the model is still capable of distinguishing between the two populations with 100% accuracy. In case 3, the stiffness of the two populations only differs by 10%, and the stiffness of the two populations partially overlaps. The output of the ANN is reasonably accurate, and it fails to correctly classify only 11% of the cells. After the neural component of the deep multiphysics model is trained, the model acquires the ability to separate cells with unknown stiffness (Figure 12). As a new cell moves into the device,  
Figure 11: Comparison between data and the ANN results for both the validation sets in case 2. Figure 10: (**a**): Comparison between data and the ANN results for both the training and (**b**): The validation sets in case 1. In both cases, the first 50 simulations refer to cells from the soft population and the other 50 from the rigid population. the ANN reads the'stress fingerprint' and classifies it as rigid or solid. In the first case, it opens the lower gate so the cell goes in the lower chamber; in the second case, it opens the upper gate, and the cell goes in the upper chamber. More details are given in the supplementary material: see the videos rigid_cell.avi and soft_cell.avi. It is interesting to compare the performance of the model with respect to visual observation. Figure 12 shows two cells from case 1, where the stiffness difference is one order of magnitude. The different behaviour of the two cells can be clearly identified by visual observation. In case 3, in contrast, the cells stiffness is very close. As Figure 13 shows, visually the difference is minimal. However, the ANN is able to correctly separate the two cells in 89% of the cases.
# Preliminary Propulsion and Power System Design of a Tandem-Wing Long-Range eVTOL Aircraft
## 2 Propulsion System Design Methodology
### Analysis of the Blades in Off-Design Conditions  
The resulting geometry is optimised for a chosen design condition, however, due to the nature of eVTOL operations, the propellers encounter a very wide range of operating conditions at different required thrust levels. Thus, the propellers need to be analysed in off-design conditions to verify whether they can achieve enough thrust during all flight phases, including hover. The paper by [PERSON] and [PERSON], in which the design procedure is presented, also features an analysis procedure for off-design conditions using BEMT [5]. This procedure takes into account the fully defined propeller from which the radius, number of blades, and blade geometry are known, together with the flight conditions and rpm.  
The thrust and torque per unit radius can be defined as Equations (7) and (8), respectively [5]:  
\[T^{\prime}=C_{y}\frac{1}{2}\rho w^{2}Bc \tag{7}\]  
\[Q^{\prime}/r=C_{x}\frac{1}{2}\rho w^{2}Bc \tag{8}\]  
These equations make use of the coefficients \(C_{y}\) and \(C_{x}\), which are related to the lift and drag coefficients of the blade sections, as shown in Equations (9) and (10). A visual representation of the definition of these coefficients on the blade is shown in Figure 4:  
Figure 5: Definition of the fuselage–propeller and propeller–propeller clearances used in the lateral positioning of the propellers.  
\[C_{y} =C_{l}\;\cos(\Phi)-C_{d}\;\sin(\Phi) \tag{9}\] \[C_{x} =C_{l}\;\sin(\Phi)+C_{d}\;\cos(\Phi) \tag{10}\]  
\(B\) is the number of blades, \(c\) is the chord of the section, \(\rho\) is the air density, and \(w\) is the local effective flow velocity at the section. The first three parameters are known inputs to the analysis, but the local flow velocity needs to be calculated using Equation (11). The airspeed \(V\), rotational speed \(\Omega\) and local radius are known inputs, but the axial and radial interference factors, \(a\) and \(a^{\prime}\), need to be iteratively calculated. This iterative process is also detailed in the original paper [5]--and hence not detailed here:  
\[w=\sqrt{\left(V(1+a)\right)^{2}+\left(\Omega r(1-a^{\prime})\right)^{2}} \tag{11}\]
## 3 LOS Path Planning
### Camera Planning Angles Calculation  
The vector \(\overrightarrow{v_{\mathit{LOS}_{N}}}\) represents the LOS direction in the navigation coordinate system without attitude disturbances. However, during aircraft flight, external disturbances cause variations in the attitude angles, necessitating the transformation of this LOS vector into the aircraft coordinate system for direction control. The LOS direction vector \(\overrightarrow{v_{\mathit{LOS}_{N}}}\) in the navigation coordinate system is transformed to the aircraft coordinate system using the rotation matrix \(R_{N}^{B}\) as follows:  
\[\overrightarrow{v_{\mathit{LOS}_{B}}}=R_{N}^{B}\cdot\overrightarrow{v_{ \mathit{LOS}_{N}}}=R_{\mathit{N}x}(\phi_{\mathit{pos}})R_{\mathit{N}y}(\theta _{\mathit{pos}})R_{\mathit{N}z}(\psi_{\mathit{pos}})\cdot\overrightarrow{v_{ \mathit{LOS}_{N}}} \tag{6}\]  
Since \(\overrightarrow{v_{\mathit{LOS}_{N}}}\) is a unit vector and the rotation matrix \(R_{N}^{B}\) is an orthogonal matrix, the transformed vector \(\overrightarrow{v_{\mathit{LOS}_{B}}}\) remains a unit vector. According to Equation (2), \(\overrightarrow{v_{\mathit{LOS}_{B}}}\) can be expressed in terms of the camera roll angle \(\phi c\) and the camera pitch angle \(\theta c\):  
\[\overrightarrow{v_{\mathit{LOS}_{B}}}=\begin{bmatrix}\sin\theta_{\mathrm{C} }\\ -\cos\theta_{\mathrm{C}}\sin\phi_{\mathrm{C}}\\ \cos\theta_{\mathrm{C}}\cos\phi_{\mathrm{C}}\end{bmatrix} \tag{7}\]  
Substituting Equations (5) and (7) into Equation (6) and solving the matrix equation, we obtain the following results:  
\[\theta_{\mathrm{C}} =\arcsin\bigl{(}R_{N}^{B}(1,1)\sin\theta_{N}-R_{N}^{B}(1,2)\cos \theta_{N}\sin\phi_{N}+R_{N}^{B}(1,3)\cos\theta_{N}\cos\phi_{N}\bigr{)} \tag{8}\] \[\phi_{\mathrm{C}} =\arctan\biggl{(}\frac{R_{N}^{B}(2,1)\sin\theta_{N}-R_{N}^{B}(2, 2)\cos\theta_{N}\sin\phi_{N}+R_{N}^{B}(2,3)\cos\theta_{N}\cos\phi_{N}}{R_{N}^ {B}(3,1)\sin\theta_{N}-R_{N}^{B}(3,2)\cos\theta_{N}\sin\phi_{N}+R_{N}^{B}(3,3) \cos\theta_{N}\cos\phi_{N}}\biggr{)}\]  
Since \(\phi_{N}\) and \(\theta_{N}\) vary over time, and the aircraft's three-axis attitude angles are continuously updated, real-time calculations can determine the target roll and pitch angles required for the camera system. These target values are then applied to the roll scanning motor and pitch mirror to achieve stable LOS pointing.
## 3 Evolution of the Plastic Zone under Osmotic Pressure
### Effect of Poisson's Ratio on the Plastic Zone Radius  
Figures 3-5 illustrate the distribution of \(r\) and \(v\) for osmotic pressures 0 MPa, 6 MPa, and 18 MPa. The results indicate that for a constant fracture dip angle, \(r\) increases as \(v\) increases. The impact of Poisson's ratio on the radius of the plastic zone varies significantly under low and high osmotic pressure. When the osmotic pressure is 0 MPa, 6 MPa, and 18 MPa, and the fracture dip angle is less than 30\({}^{\circ}\), the radius \(r\) of the fractured rock remains relatively constant for different Poisson's ratios. On the other hand, increasing Poisson's ratio results in a considerable difference in the plastic zone radius as \(\theta\) increases from 0\({}^{\circ}\) to 45\({}^{\circ}\). In Figure 4, for instance, when [PERSON]'s ratio was increased from 0.20 to 0.28, the peak value of the plastic zone radius increased from 0.23 mm to 0.25 mm under a 6 MPa osmotic pressure (low osmotic pressure). Moreover, under an 18 MPa osmotic pressure (high osmotic pressure), the peak \(r\) increased from 1.17 mm to 1.29 mm, indicating a 0.12 mm increment.  
The analysis indicates that the Poisson's ratio of fractured rock has a more significant substantial impact on the plastic zone extension at the fracture tip under high osmotic pressure compared to low osmotic pressure. This is because when the fracture angle is less than 45\({}^{\circ}\), the fracture tip is primarily subject to a shear type II fracture, and the variation in \(v\) has a less notable impact on the plastic zone of the fracture. Nevertheless, when the fracture angle exceeds 45\({}^{\circ}\), the fracture tip experiences a shear type I fracture, and variations in Poisson's ratio affect the rock mechanical parameters, thereby affecting the radius \(r\) of the fracture.  
Figure 4: The distribution of \(r\) and \(\theta\) for different \(v\) (\(P\) = 6 MPa).  
Figure 3: The distribution of \(r\) and \(\theta\) for various \(v\) (\(P\) = 0 MPa).
###### Abstract  
Monitoring the main environmental conditions during storage and transportation of perishable foods is necessary to predict quality losses throughout shelf life. By far, temperature is the main factor affecting quality and shelf life, but there are other variables that would greatly affect quality losses such us relative humidity, O\({}_{2}\), CO\({}_{2}\), ethylene, etc. Thus, the real-time knowledge of the evolution of these parameters during the whole supply chain allows suppliers to prevent for food losses. This paper deeply describes the design of a flexible monitoring system with real-time communication to be used in the supply chain of perishable commodities, using Wi-Fi wireless communication as collaborative networks between different measurement points. Aspects such as consumption, performance and feasibility of the system are described in detail to check the adaptability of its use.  
shelf life; temperature monitoring; wireless sensor networks; commodities quality +
Footnote †: journal: Applied Sciences  
Article  
Design of a Distributed Wireless Sensor Platform for Monitoring and Real-Time Communication of the Environmental Variables during the Supply Chain of Perishable Commodities