esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 4 Prospects for WBAN and Suggestions for Routing Design
### Prospects for Current Routing Design  
* In the analysis of the above routing methods, both MHRP [18] and HDPR [33] protocols, stipulate that the relay node only has the function of receiving and forwarding, and the biological node only collects and transmits information, without forwarding other information. This method can reduce the energy consumption of the biological node and prolong the network life [38]. However, the problems are--how to determine the number and location of additional relay nodes? Will the connectivity of the network topology be guaranteed after its death? Whether the added nodes will affect people's comfort and so on. WBAN is a technology applied to human body, the above issues need to be carefully considered.
* In the routing design, the hottest research at present is about the next hop selection method [39; 40; 41; 42], which has both optimization and innovations. For example, the literature [22; 23; 30] uses the cost function to solve the next hop selection. In recent years, the cost function has been developed from a single parameter to multi-parameters, and the weighting factor has been used to weigh  
Figure 14: Node independent multipath routing.  
the multi-parameters, which achieves a better performance than a single parameter. It is believed that the cost function plays a more important role in the design of energy efficient routing in the future.
* The cross-layer method can achieve a better performance than a single-layer, and can achieve unexpected results in energy efficiency. Cross-layer is a comprehensive design of performance by coupling multiple protocol stacks, such as combining network layer, MAC layer, and physical layer, planning appropriate transmission power in physical layer, efficient slot allocation technology in MAC layer, and reliable routing design in network layer, which can theoretically achieve better network performance, reduce energy consumption of nodes and realize energy efficiency of the whole network.
* Postures-based methods can make full use of mobility, in terms of energy efficiency. If the various postures of human body can be skillfully utilized, then the delay and energy consumption can be greatly reduced in theory. Posture prediction can be used to select routing quickly and reduce energy waste in the routing process, but the premise is to solve the problem of human posture recognition, which requires joint efforts in many fields.
## 4 Case Study
### Monitoring Data Collection and Processing  
The data relating to risk factors in this study were obtained from the excavation process of the project, the DFP itself, the tunnel, and the surrounding environmental system deformation monitoring including settlement class, lateral shift class, stress class, and other monitoring data. In this study, 143 sets of sample data containing the risk factors and pit safety status were collected, with 102 sets of high safety, 36 sets of medium safety, and five sets of low safety. Among these data, 123 sets of data were used to train the Bayesian network risk-coupled dynamic model, and the other 20 sets were used to test the model. Table 1 shows the sample data used for model training.  
The collected data related to the risk factors of the DFPs adjacent to the existing underpass tunnels were processed for risk classification. Moreover, these data are either numerical or textual. Hence, they were discretized according to _the Code for monitoring measurement of urban rail transit engineering_ (GB 50911-2013), as shown in Table 2, wherein low, medium, and high represent the safety degree. The lower the safety level, the more unsafe and dangerous the risk factor.
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 4 Experiment
### Choosing and Justifying Comparative Experimental Models  
We attempted to use traditional dynamic numerical methods for drift prediction but found that they were not applicable to the dataset we had obtained. It is difficult to obtain accurate initial conditions for drift trajectory prediction in the ocean, and the accuracy of physical models established with current meteorological data and environmental factors is very low. Even if a specific ocean environment is simulated for prediction, the choice of parameters is a major issue, and the model may not have good adaptability to the drift trajectories of floating objects in other environments. Our goal is to create a low-cost, simple, and highly adaptable model that can be applied to a variety of ocean climates and floating objects. Therefore, we chose to use neural networks for prediction.  
The neural networks selected for comparison with models are CNN, CNN-LSTM, and QCNN models. The decision to improve simple CNN and CNN-LSTM networks, rather than choosing more complex neural networks, is often based on a balance between model performance and computational complexity. Although more complex neural networks may achieve higher accuracy, they typically require more computational resources and may be more difficult to train and interpret. On the other hand, these two networks are relatively simple and mature architectures that have been proven effective in a wide range of applications. Adapting these two networks to the quantum model may be an effective strategy to achieve high accuracy while minimizing computational complexity and training time. They also allow us to leverage the advantages of these two architectures and create a highly suitable model for a wide range of applications.  
The QCNN model provides us with a neural network for quantum-inspired improvement, but the QCNN model is good at capturing spatial features, while LSTM is very suitable for processing time-series data and handling long-term dependencies. By combining these two architectures, we can create a model that can capture both spatial and temporal features, making it highly suitable for a wide range of applications.
# Automatic Tortuosity Estimation of Nerve Fibers and Retinal Vessels in Ophthalmic Images
## 4 Experimental Results
### The Effectiveness of Curvilinear Structure Enhancement for Fiber Segmentation  
In this experiment, the effectiveness of different enhancement methods on the tortuosity assessment is also evaluated on our group internal and public segmentation method, respectively. For the segmentation methods, IPACHR [13] is the traditional enhancement mehtod which is proposed by our group. U-Net [36] is a popular deep learning based segmentation method which is widely used in medical image segmentation. For the enhancement methods, We compare the Weighted Symmetry Filter (WSF) [13], the [PERSON] proposed multi-scale vesselness filtering [8], and the proposed curvilinear structure enhancement method for fiber segmentation.  
In order to evaluate different enhancement methods for different segmentation scenario, two segmentation methods are compared: one is the previous IPACHR method, another is the U-Net. In medical image processing domain, U-Net is a deep convolutional neural network for segmentation task, which classify every pixel of a medical image into different semantic label. Here, we verify the U-Net for nerve fibers segmentation with and without our enhancement methods separately, and the IPACHR model is also evaluated in this two cases. Because the U-Net is a trainable model, we randomly select 80% images as the training set in CCM-A, and the left 20% as the testing set. The IPACHR model is also validated on the same testing dataset.  
We compare different enhancement methods from two evaluation criterias: the false discovery rate (FDR) and the sensitivity (Sen). The calculation of these two evaluation criteria is obtained by comparing the centerline predicted by the segmentation model with the ground truth centerline. Because the segmentation method only extracts one pixel wide centerline, the pixels of the extracted centerline are considered as true positive as long as it is within the range of three pixels around the ground truth centerline.  
The segmentation performance is illustrated in Table 5. Our proposed method improve the Sen and decrease the FDR in both IPACHR and U-Net segmentation methods. Compared with the segmentation results of the raw image without enhancement, our method increase about 0.022 in Sen,  
Figure 6: Comparison results of the exponential curvature estimation (**b**,**c**) and (**e**,**f**) from original image (**a**) and our enhanced image (**d**).  
and decrease about 0.027 in FDR by the IPACHR. For U-Net segmentation model, the Sen increase 0.056, and the FDR decrease 0.090. Compared with the results of WSF, the proposed method also demonstrates its promising result.  
The last two images of Figure 7 demonstrate the fiber segmentation results obtained by U-Net over the original and enhanced image, respectively. In the red circle regions, we notice that our enhanced method can improve the segmentation performance. When comparing with the results from the raw image, our method can detect the fibers with noise matter much better, and it's also more sensitive to some small fibers.
## 4 Algorithms
**Algorithm:**_Concept_  
```
0:receivingData /* the data received by Collector */
0:resource nodes and network information, a request message, job data, or job execution result {If (receivingData = *.rrd \(\parallel\) *.log) /* receives resource nodes' status and network status from Ganglia, i.e., *.rrd file, and NWS, i.e., *.log file, every 15 s which is the default data access frequency of the Ganglia */ {Transform *.rrd to *.xml with a rrttool;  Send *.xml // *.log to Calculator; /* to update the performance table */}
0:Else if (receivingData = requestMessage) /* a message sent by a request node */  Send requestMessage to Scheduler via Mobile Agent;  Else if (receivingData = jobData) /* the data issued by a request node for a job */  Send jobData to the allocated resource node; Else /* receivingData = executionResult */  Send the result to the corresponding request node; /* job completion */}
```  
**Algorithm Front Unit**  
**Input:**_receivingData_ /* the data received by Front Unit */  
**Output:**_PerforTable[][]_  
**If** (receivingData = SchedulerMessage) /* issued by Scheduler */ {Load _PerforTable[][][]_ from Data Center;  Deliver _PerforTable[][][]_ to Scheduler via Mobile Agent;}
0:Else if (receivingData = CalculatorRequest) /* issued by Calculator */ {Load _PerforTable[][][]_ from Data Center;  Send _PerforTable[][][]_ to Calculator via Mobile Agent;}
0:Else if (receivingData = PerforTable[][][]) /* PerforTable[][][] has been newly updated by Calculator */  Send _PerforTable[][][]_ to Data Center via Mobile Agent;
0:Else if (receivingData = CerforTable[][][]) /* Performance table sent by a child MCI server */ {Concatenate _CerforTable[][][]_ and its own _perforTable[][]_ as a new _perforTable[][][]_;  Store _perforTable[][][]_ in Data Center; Send _perforTable[][][]_ to the underlying MCI server and the MCI server's parent MCI server;}
0:Else _/* receivingData =_ a performance-table-newly-updated message */  Send the message to Scheduler;  
Figure 4: The algorithm performed by Collector on receiving a message or data from other components. Figure 5: The algorithm performed by Front Unit.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 3 Biomarkers in Diabetics with Known HF
### Biomechanical Stress Biomarkers
#### 3.1.1 Natriuretic Peptides
NPs are substrates of neprilysin and thereby B-type of NP (BNP) concentrations rises with neprilysin inhibition, whereas NT-proBNP will continue to decrease with improvement in the functional class of HF [66; 67]. At the same time, GLP-1 agonists were able to increase urinary sodium excretion and diuresis independent of NP changes [68]. Moreover, the EXAMINE (Examination of Cardiovascular Outcomes with Alogliptin versus Standard of Care) trial has shown that T2 DM patients with persistently high levels of serum NT-proBNP or an increase in NT-proBNP levels over 6 months while taking GLP-1 agonists or dipeptidyl peptidase 4 (DPP-4) inhibitors were at a significantly higher risk of CV mortality and HF occurrence when compared with those who had low levels of NT-proBNP at baseline or during the observation period [69]. SGLT-2 inhibitors have also demonstrated cardiac and renal protective effects in association with improving oxidative stress, LV diastolic filling, endothelial function, and decreasing BNP serum levels [70]. Thus, NPs are a powerful diagnostic and predictive tool for patients with T2 DM with established HF and those at higher risk of HF occurrence using different phenotypes [71; 72].
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 3 Experimental Results
### Results
#### 3.3.2 Localization of DR Regions  
For localization of the DR signs (i.e., hemorrhages, microaneurysms, hard and soft exudates), the affected regions are considered a positive example while other portion and background are considered a negative example. The overlapped region is labeled through the threshold value IoU, less than 0.3, considered the region as background. Similarly, IoU value greater than 0.7 the regions are considered as affected regions.  
We adopted the FRCNN method for DR lesions localization. The localization outcome of FRCNN as shown Figure 3 having 16 test retinal images with a confidence score. The test results show a higher score which is greater than 0.89 and up to 0.99.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technique** & **mAP** \\ \hline SPPnet [44] & 0.85 \\ RCNN [56] & 0.89 \\ FRCNN (Proposed) & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the presented technique with other approaches.
###### Abstract  
Preservation of spectral and spatial information is an important requirement for most quantitative remote sensing applications. In this study, we use image quality metrics to evaluate the performance of several image fusion techniques to assess the spectral and spatial quality of pansharpened images. We evaluated twelve pansharpening algorithms in this study; the Local Mean and Variance Matching (IMVM) algorithm was the best in terms of spectral consistency and synthesis followed by the ratio component substitution (RCS) algorithm. Whereas the IMVM and RCS image fusion techniques showed better results compared to other pansharpening methods, it is pertinent to highlight that our study also showed the credibility of other pansharpening algorithms in terms of spatial and spectral consistency as shown by the high correlation coefficients achieved in all methods. We noted that the algorithms that ranked higher in terms of spectral consistency and synthesis were outperformed by other competing algorithms in terms of spatial consistency. The study, therefore, concludes that the selection of image fusion techniques is driven by the requirements of remote sensing application and a careful trade-off is necessary to account for the impact of scene radiometry, image sharpness, spatial and spectral consistency, and computational overhead.  
pansharpening; image fusion; image quality; Satellite Pour l'Observation de la Terre (SPOT) 6; spectral consistency; spatial consistency; synthesis +
Footnote †: journal: Medical Image Fusion
## 3 Methodology and Proposed Innovation
### Latency  
Some NG applications, like ad hoc ACV media sharing or communication over UAV-assisted PSNs, cannot be accurately designed, and delay factors are near to impossible to be accounted for. As a result, ensuring network performance under such circumstances can become a serious challenge. Network latency (L), in the form of RTT delay, is incorporated in this study. The impact of both fixed and variable delay components is taken into account on transited packets across the examined network. As in the case of \(PJ\), \(L_{l}\) is defined as the statistical normalized values of \(RTT_{l}\), \(\forall l\in E\).  
\[L_{l}=\frac{RTT_{l}}{\sum_{l\in E}RTT}\left\{\begin{matrix}\forall l\in E\\ PJ_{l}\,\ RTT_{l}\in\mathbb{R}^{+}\end{matrix}\right. \tag{4}\]  
where \(RTT_{l}\) is the always-positive transmission delay of link \(l\) and \(\sum_{l\in E}RTT\) the summation of all delay values of edges belonging to \(E\). In realistic networking environments, \(\sum_{l\in E}RTT\) would never be equal to zero; hence, the value domain of the \(\varphi\) coefficient of the unusable path is always empty in case a path exists that satisfies the demands of an incoming VNR.  
\[\varphi\left\{\begin{matrix}=\{\varnothing\}&iff\exists l\in E:RTT_{l}^{SN} \leq RTT_{l^{\prime}}^{VNR}\\ =\omega(l)&iff\exists l\in E:RTT_{l}^{SN}>RTT_{l^{\prime}}^{VNR}\end{matrix}\right. \tag{5}\]  
When the \(i\)th path is formed, the \(L_{i}\) notation is defined as the summation of the corresponding delay values of each concatenated link \(l\in i\).  
\[L_{i}=\sum_{l\in i}\frac{RTT_{l}}{\sum_{l\in E}RTT}\left\{\begin{matrix}\forall l \in i,\forall i\in P\\ L_{i}\,\ RTT_{l}\in\mathbb{R}^{+}\end{matrix}\right. \tag{6}\]
## 2 Methodology
### Thermodynamic Modeling  
The energy and exergy modeling of the SORC, RORC, and DORC configurations as the waste-heat recovery bottoming cycle was developed [10]. Cyclohexane was selected as the working fluid, considering the cost-effective performance in this system integrated with the gas engine. The parameters considered to study the systems in the configurations were isentropic efficiency turbines (80%) [28], isentropic efficiency pumps (75%) [28], cooling-water temperature in stream T1A (50\({}^{\circ}\)C), pinch-point condenser (15 \({}^{\circ}\)C), pinch point evaporators ITC2 and ITC4 (35 \({}^{\circ}\)C) and effectiveness in the recuperator (85%) [28]. Pressure ratios in B1 of (2.5) and B2 (30) were considered in the SORC and RORC systems, while in the DORC the pressure ratios were B1 (11.09), B2 (20) and B3 (9).  
All the equipment of the systems was studied in steady-state condition according to the mass conservation law, as shown in Equation (1), and the energy balance shown in Equation (2).  
Figure 2: Physical structure of the simple ORC (SORC), ORC with recuperator (RORC), and double-pressure ORC (DORC) configurations.  
The thermodynamic model of the WHR systems was developed applying the mass balance (Equation (1)) and the energy balance (Equation (2)) to each component in a steady-state condition.  
\[\sum\dot{m}_{in}-\sum\dot{m}_{out}=0 \tag{1}\]  
\[\sum\dot{m}_{in}\cdot h_{in}-\sum\dot{m}_{out}\cdot h_{out}-\sum\dot{Q}+\sum\dot {W}=0 \tag{2}\]  
where \(\dot{m}\) is the mass flow rate, \(h\) the working fluid specific enthalpy, \(\dot{Q}\) is the energy transfer by heat and \(\dot{W}\) is the power rate.  
The specific physical exergy for all the stream was calculated by the mean of the Equation (3), which the change of kinetic and potential energy was not considered, while the chemical exergy (Equation (4)) was only evaluated in the exhaust gases line from the natural gas engine (stream 10).  
\[ex=(h-h_{0})-T_{0}\cdot(s-s_{0}) \tag{3}\]  
where enthalpy \(h_{0}\), and entropy \(s_{0}\) were evaluated at the reference temperature (298.15 K) and pressure (101.3 kPa) conditions.  
\[ex_{\mathrm{G}}^{\mathrm{ch}}=\sum_{\mathrm{i=1}}^{\mathrm{n}}X_{\mathrm{i}} \cdot ex^{\mathrm{ch_{i}}}+\mathrm{R}\cdot T_{0}\cdot\sum_{\mathrm{i=1}}^{ \mathrm{n}}X_{\mathrm{i}}\cdot\ln X_{\mathrm{i}} \tag{4}\]  
R represents the universal gas constant, and \(X_{i}\) the gas molar fraction. From the exergies calculated for all thermodynamic states, the exergy balance (Equation (5)) to each equipment of the WHR systems was conducted.  
\[\sum\dot{m}_{in}ex_{in}-\sum\dot{m}_{out}\cdot ex_{out}+\dot{Q}\left(1-\frac{T _{0}}{T}\right)-\dot{W}-\dot{E}x_{D}=0 \tag{5}\]  
where \(\dot{m}_{int}\cdot ex_{int}\) is the stream exergy at the inlet, \(\dot{m}_{out}ex_{out}\) the stream exergy at the outlet and \(\dot{E}x_{D}\) is the exergy destruction of each component.
## 2 Experimental Apparatus and Results  
Figure 1 shows the aluminum cylindrical container used in the experimental test. Its height is 25.0 cm and internal radius is 6.9 cm. Furthermore, it is partially filled with 2.4 kg of a commercial bio-based phase change material, whose characteristics are shown in Table 1.  
Temperature measurements inside the phase change material are all done on the same horizontal section, at a distance of 9 cm from the container bottom, by five T-type thermocouples of class 1. One measuring point is located on the container axis, while the other four points are located at a distance of 3.45 cm from the axis. These are arranged to form a cross as shown in Figure 1b. Temperature data acquisition is done with a sample time of 1 s, by means the of the National Instruments NI 9213 module, using the NI cRIO 9066 controller. Figure 2 shows the climatic chamber used to realize the experimental test.  
The container is put in the climatic chamber on a 2-cm thick rigid sheet of polyurethane foam for the thermal insulation of the container bottom side, with all the PCM in the liquid state, and at a  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Property** & **Value** \\ \hline Melting point (\({}^{\circ}\)C) & 15 \\ Latent heat (kJ kg\({}^{-1}\)) & 182 \\ \hline Thermal conductivity (W m\({}^{-1}\) K\({}^{-1}\)) & \\ \hline Solid & 0.25 \\ Liquid & 0.15 \\ \hline Density (kg m\({}^{-3}\)) & \\ \hline Solid & 950 \\ Liquid & 860 \\ \hline Specific heat (J kg\({}^{-1}\) K\({}^{-1}\)) & \\ \hline Solid & 2250 \\ Liquid & 2560 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermo-physical characteristics the PCM.  
Figure 1: Pictures of the cylindrical aluminum container: (**a**) Liquid PCM at room temperature inside the container; and (**b**) Thermocouples arrangement.  
Figure 2: Picture of the climatic chamber used for the experimental test.  
uniform temperature equal to 23.8 \({}^{\circ}\)C (room temperature). Then, the following four steps are applied sequentially:  
1. one-hour temperature ramp is applied to bring the internal temperature of the climatic chamber to the cooling energy charge temperature Tc = 7 \({}^{\circ}\)C;
2. the climatic chamber internal temperature is kept at Tc for 72 h;
3. one-hour temperature ramp is applied to bring the internal temperature of the climatic chamber to the cooling energy discharge temperature Tc = 23 \({}^{\circ}\)C;
4. the temperature inside the climatic chamber is kept at Tc until all the measured temperatures inside the PCM are well above the phase change temperature (15 \({}^{\circ}\)C).  
As it can be argued, the main contributions to the cooling energy charge and discharge are represented by steps 2 and 4, respectively. Nonetheless, in the numerical simulation of the PCM thermal behavior, steps 1 and 3 are also simulated.