esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation
### Impact of Varying Network Radius on Network Lifetime  
The network lifetime of EEBET, EBET, BTM and UWDAR is shown in Figure 8. These simulation results are obtained for different network radii (\(R\)); ranging from 1 km to 5 km. From this figure it is clear that EEBET technique performs better than its counterpart techniques in terms of network lifetime. The UWDAR and BTM techniques show relatively shorter network lifetime at each network radius as compared to EBET and EEBET, respectively. BTM's short network lifetime is due to excessive transmit energy consumption of farthest nodes that directly transmit data to the sink. UWDAR has very shorter network lifetime at every network radius due to backward data transmissions. The sensor nodes nearer to sink, except the first hop-grade nodes, transmit data to far-away in-charge hop-grade nodes. Thus, the nodes at long distance from sink quickly deplete their energy and leading to network partitioning. The EBET technique has improved network lifetime when compared to BTM. The reason for its network lifetime improvement is the exclusion of one hop transmissions over long distance. However, this leads to increased number of hops in data transmission. Also, the energy consumption in both BTM and EBET is balanced between successive Ring Sectors, whereas, the overall network energy consumption is not balanced. The maximum difference of Energy Levels between successive Ring Sectors is one for small scale UWSNs in BTM and large scale UWSNs in EBET. The nodes nearer to the sink completely deplete their energy while the other nodes in the network are left with residual energy which leads to energy wastage. EEBET improves network lifetime due to two reasons; (i) minimized number of hops involved in data transmission; and (ii) reduced data load on nodes nearer to the data sink. When the distance between sensor nodes and sink decreases, their data load is managed. Therefore, overall its network lifetime is improved.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(R\) & 1–5 km \\ \(N\) & 80 \\ \(E_{0}\) & 300 J \\ \(f\) & 20 kHz \\ \(P_{r}\) & 0.2 \(\times\) 10\({}^{-4}\) J/bit \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
# Analysis of the Refraction Effect in Ultrasound Breast Tomography
## 2 Materials and Methods  
The refraction phenomenon occurs when an ultrasound wave falls on the border of two media with different ultrasound propagation velocities at an angle different from normal (oblique incidence). Then the direction of the wave changes, it is refracted at a certain angle. The angle of incidence and angle of refraction obey the well-known Snellius law, as in optics.
# Damping of Liquid Sloshing Using Hydrophobic Walls in the Off-Impulse Regime
###### Abstract  
In this experimental work, sloshing tests were performed with containers filled with water at 50% of their volume capacity. Two boundary conditions were considered: uncoated containers and containers with hydrophobic coated walls. In addition, several aspect ratios \(\lambda\) (container width over length) were tested. We characterized two regimes, the first when the container is periodically forced at a frequency lower than its resonant frequency and the second after the forcing is suddenly stopped. In each case, the amplitude of the waves was measured. Several surprising results were found. First, in the forced regime, the sloshing amplitude was lower in the hydrophobic containers than in the containers with the non-hydrophobic walls, despite the free-slip condition in the former case. Second, the damping after sudden stoppage was much higher in the containers with hydrophobic walls than in the uncoated containers. This behavior is explained by the collision of waves with oil-coated walls, which generates a lower load pressure. Finally, we found that the damping depends on the dimension of the container through \(\lambda\), and is greater when \(\lambda=1.00\). These experimental findings open the way for further innovative research.  
damping fluid; surface liquid; sloshing; hydrophobic walls; non-hydrophobic walls; damping time +
Footnote †: journal: Applied sciences
# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 2 Proposed Method
### MVFC Method
It uses deviations and characterizes feature differences. Therefore, it has a better accuracy and generalization compared to other methods. In terms of multi-channel LoG filtering extraction, considering the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database Criteria** & **DoG-ssim** & **diplQ** & **Deepsim** & **DeepFR** & **DIQA** & **Proposed** \\ \hline \multirow{2}{*}{TID2013} & SROCC & **0.9070** & **0.8940** & 0.8460 & **0.8760** & 0.8250 & 0.8606 \\  & PLCC & **0.9190** & 0.8770 & 0.8720 & **0.8940** & 0.8500 & **0.8822** \\ \hline \multirow{2}{*}{CSIQ} & SROCC & **0.9430** & 0.9300 & 0.9190 & **0.9600** & 0.8840 & **0.9570** \\  & PLCC & **0.9540** & 0.9490 & 0.9190 & **0.9660** & 0.9150 & **0.9611** \\ \hline \multirow{2}{*}{LIVE} & SROCC & 0.9610 & 0.9580 & 0.9740 & **0.9810** & 0.9750 & **0.9798** \\  & PLCC & 0.9630 & 0.9570 & 0.9680 & **0.9840** & **0.9770** & **0.9754** \\ \hline \multirow{2}{*}{Average} & SROCC & **0.9370** & 0.9273 & 0.9130 & **0.9390** & 0.8947 & **0.9325** \\  & PLCC & **0.9453** & 0.9277 & 0.9197 & **0.9480** & 0.9140 & **0.9396** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison with AI-based IQA methods on three databases. Figure 6: Scatter diagram of subjective MOS and method prediction scores on TID 2013 database: (**a**) FSIMc; (**b**) VSI; (**c**) GSM; (**d**) SSIM; (**e**) VIF; (**f**) MVFC. coordinated changes in luminance, as well as the impact of chroma components on image structure. The accuracy of LoG extraction is improved, and the image structure changes can be better characterized. ### Performance Comparison for Individual Distortion Types

The following analysis focuses on the predictive performance of the method under different distortion types. Since the distortion types in the TID2008 database overlap with those in the TID2013 database, they are not used repeatedly. The performance comparison data for individual distortion methods can be found in Table 5, where the PLCC value is used as the performance evaluation criterion. In each distortion type, the top three PLCC values are highlighted in bold, and the methods with the highest total count of marks are the MVFC method (21 times), VSI (16 times), and CVSS (12 times). Comparing the data in Table 5, the total count of marks for the MVFC method ranks first among the 11 methods and is significantly higher than VSI and CVSS. It also outperforms other methods in single-distortion evaluation results. The MVFC method performs well in the LIVE and CSIQ databases, with the highest PLCC value of 0.9912, which is significantly better than the computational methods that also use LoG (VSI and FSIMc). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Database** & \begin{tabular}{c} **Distortion** \\ **Type** \\ \end{tabular}
## 3 Results  
### Preparing Stretchable Substrate  
Using PDMS as a stretchable substrate results in high transmittance, but limited stretchability, whereas Ecoflex substrates provide better stretchability than PDMS but have very low transmittance. To overcome these challenges, an optimum ratio between the trade-off relation of transmittance and stretchability was determined by testing mixtures of Ecoflex/PDMS as a substrate at a weight ratio of 8:2 [26]. Figure 1 shows that at 550 nm, the transmittance values of PDMS, Ecoflex, and the mixed Ecoflex/PDMS substrates are 100%, 46.45%, and 86.54%, respectively. The PDMS and Ecoflex mixture at a weight ratio of 8:2 was confirmed to be suitable for use as a stretchable substrate with sufficient transmittance. OH- radicals were generated on the substrate surface by UV-O surface treatment for 30 min on the mixed PDMS and Ecoflex sample. The thickness of the fabricated substrate is 300 \(\upmu\)m.
## 1 Introduction
The main idea presented in the current study was opposite--the FE model had to be reduced while maintaining the highest possible accuracy in order not to involve very powerful computing resources for real-time simulations. Reducing the model is, however, not trivial, when accuracy must be maintained, and also usually requires a computational cost. Therefore, the following assumption was made: computational cost necessary for building the reduced model (offline--not in real-time) is of little importance as long as real-time computations during hybrid simulation can be performed effectively and efficiently. The efficiency of the computations is understood as the ability to test systems of a higher number of degrees of freedom while maintaining high accuracy, without increasing the time step. Effective simulation is a simulation that allows acquiring correct results while maintaining the time regime. The author researched a method for model order reduction in hybrid simulation-mode superposition [22; 23]. The method has proven the above assumptions, however, only for mechanical systems that meet certain requirements. The requirements are that the whole analytical subsystem is linear, the damping is proportional, and the physical subsystem is significantly smaller than the analytical. The method does not apply to other structures. In the present paper, a completely different approach was presented--the whole finite element model was replaced with an artificial neural network (ANN). ANN plays the role of a surrogate model that mimics the behavior of the FE model while being incomparably cheaper to evaluate. This approach does not have the abovementioned limitations of the mode superposition method. In [24], the authors have suggested the first time the idea of utilizing surrogate models in hybrid simulation; however, without any details of what form the surrogate model should have. Since then, after many tests, they came to the conclusion that ANNs are most suited in this role--are resistant to noise and distortion of signals, can represent complex issues, and detect significant connections between data. In the literature, there can be found other examples than a hybrid simulation of successful substituting FE models by ANNs [25; 26; 27]. Details of the proposed method are described in Section 2. Section 3 contains numerical and experimental examples. The results are discussed in Section 4.
# A Reversed-Phase HPLC Method for Determination of Osteopontin in Infant Formula
###### Abstract  
Osteopontin (OPN) is a multifunctional whey protein which has recently received much attention for possibly applications in fortifying infant milk formula (IMF) with its bioactivity. However, to date, there is no established high-performance liquid chromatography (HPLC) method to quantify this protein in milk or IMF. In this study, a rapid, simple, isocratic and reliable reversed-phase HPLC method was developed and validated to quantify the OPN in IMF. A C18 column (4.6 \(\times\) 150 mm \(\times\) 5 micron) was employed with 20% of 0.1% trifluoroacetic acid (TFA) and 80% of 60% acetonitrile in 0.1% TFA for 10 min detected at 214 nm. The flow rate was 0.3 mL/min with an injection volume of 10 \(\mu\)L. The column temperature was 40 \({}^{\circ}\)C, and the peak appeared after 4 min. The validation was based on the system suitability, linearity (\(t^{2}\) = 0.999), limit of detection (LOD) (0.14 mg/L), limit of quantitation (LOQ) (0.41 mg/L), precision (% relative standard deviation (RSD) \(<\) 0.2), recovery (% RSD \(<\) 3) and robustness. The results confirm that the method developed is suitable for OPN determination in IMF.  
Keywords:RP-HPLC; method validation; osteopontin; infant formula; milk +
Footnote †: journal: Applied Soft. 2019, 9, 3711; doi:10.3390/app9183711
## 3 Proposed Scheme
### Error-Detecting and -Correcting Scheme  
#### 3.2.1 Error Detecting  
As Figure 2 shows, after column-shifting processes, there are some columns with \(1\) at the end of the \(1\) sequence in the \(i\)th row, and at the same time, they are at the start of the \(1\) sequence of the \((i+1)\)th row. We denote the sequence formed by these columns as the Repeated Bits Area (RBA). And the rest of the \(1\) columns form the Unrepeated Bits Area (UBA), which helps to describe where errors occur.  
Figure 1: Scheme flow chart.  
Figure 2: The definition of UBA and RBA.  
The basic idea behind the proposed error-detecting and -correcting method is manipulating the bit-flipping based on the capacity or reliability of different channels. And, the proposed scheme is proposed mainly to correct one-bit errors and double-bit errors. However, the terms one-bit error and double-bit error mean that one bit error or two bit errors occurred in two consecutive columns, instead of the whole data block.  
The error-correcting process includes two steps:  
**Error Symptom Classification.** The error-correcting process is carried out only if errors occur. Consequently, the two consecutive columns always indicate that the first column has failed to pass the parity check equation. More specifically, for one bit error, if this happens in the RBA, the parity check equations of both consecutive columns are not equal to 0. On the contrary, if this happens in the UBA, the parity check of the first of the two consecutive columns is failed and the second parity check is passed: For double-bit errors, if both errors occur in the RBA, the parity check equations of both consecutive columns are not equal to 0, which is the same as a one-bit error happening in RBA. If both errors happen in the UBA in the first column, then this would have the same symptoms as one error occurring in the RBA. Moreover, if one error occurs in the RBA and one in the UBA in the same column or errors happen in different columns, both of the parity checks would be failed. Table 4 summarizes all these conditions.
## 3 SDWSNs and IEEE 802.15.4e TSCH
### IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH)
#### 3.2.1 Definition  
IEEE 802.15.4e time-slotted channel hopping (TSCH) [10] was designed for low-power and lossy networks (LLNs) to provide a reliable MAC layer. It uses time-slotted access to divide the time into several timeslots, clustered into one (or more) slotframe(s). Both the effects of congestion and collision are reduced via TSCH using the channel hopping feature since different channels (frequencies) are used to achieve the transmit and receive processes. Each TSCH node sends enhanced beacon (EB) messages periodically. When a node wants to join a TSCH network, it must listen to an EB. Thus, it randomly selects a channel and listens to it for a period before selecting another channel and performing the same operation. The process continues until the node receives an EB message and associates it with the TSCH network.
## 2 Challenges in Underwater Communications
### The Speed of Acoustic Waves  
The speed of an acoustic wave used in underwater communications varies with respect to salinity, temperature and depth of the water. The speed of acoustic waves in water is modeled by [13]:  
\[\begin{array}{lll}c&=&1449+4.591T-5.304\times 10^{-2}T^{2}\\ &&+2.374\times 10^{-4}T^{3}+1.34(S-35)\\ &&+1.63\times 10^{-2}D+1.675\times 10^{-7}D\\ &&+1.025\times 10^{-2}T(S-35)-7.139\times 10^{-3}TD^{3}\end{array} \tag{7}\]  
where the parameters \(c\), \(T\), \(S\) and \(D\) represent the speed of acoustic waves in m/s, temperature in \({}^{\circ}\)C (degrees Celsius), salinity factor in ppt (parts per thousand) and the depth of sea in meters, respectively. The above equation holds under the conditions 0 \({}^{\circ}\)C \(<T\leq\) 30 \({}^{\circ}\)C, 0 m \(\leq D\leq\) 8000 m and salinity in the range 30-40 ppt. The slower speed of acoustic waves than RF waves brings an inherently higher propagation delay in underwater communications than the terrestrial RF communications.  
Variations in the speed of an acoustic wave affect the delivery time of data during routing. This specifically challenges the design of routing protocols for time-critical underwater applications such as disaster prediction and prevention, military surveillance and rescue operations.
## 3 Results and Discussion
### Cake Studies
#### 3.2.2 Color Evaluation  
The color of a food product is one of the most important factors in determining how it will be evaluated by potential buyers. Hence, the colorimetric study was performed to evaluate the change in the color of cake crust and crumbs (Figure 7) upon replacing butter with oleogels. The \(L^{*}\) value for all the cake crusts appeared similar (\(p>0.05\)). Likewise, the crusts showed +ve value of \(a^{*}\) which was similar for all the samples. These +ve values are related to the red hue due to the browning of crusts. The butter replacement with the chosen oleogels has a similar crust browning effect. All the crusts displayed +ve values of \(b^{*}\), which indicated the yellowness of the crust. The cake crust of Sp-C showed a significant reduction in \(b^{*}\) values from the control. Similar to the crust, cake crumbs also appeared similar in their \(L^{*}\) values. However, the \(a^{*}\) values of cake crumbs were similar to batters and were \(-\)ve valued. The \(b^{*}\) values and derived \(YI\) of cake crumbs SaB-C, Sp-C and SpB-C were significantly higher than the control. The \(b^{*}\) values of batter and cake correlated quite well, suggesting a rise in the yellowness upon oleogel incorporation, possibly due to SO content in the oleogels. Similar \(b^{*}\) values have been reported for potato chips fried in SF-beeswax oleogels [43].
## Case Study  
### A Practical Distribution System with 173 Nodes  
#### 4.1.1 Case Descriptions of the 173 Nodes System  
To demonstrate the effectiveness, the proposed method EWOSM was tested on a practical distribution system with 173 nodes. The head of the system is slack bus, and it is in the low voltage side of a step-down substation from 220 kV to 110 kV. There are two lines of 110 kV connected from the slack bus to two step-down substations from 110 kV to 10 kV respectively, and the substations are connected by five medium-voltage lines of 10 kV, which are named by line A, line B, line C, line D and line E. The single-line diagram of the tested system is shown in Figure 5.  
In Figure 5, T1 and T2 are the main transformers in the substation, and the models are both SFZ11-12500/110 with the tap ranging from 0.9 p.u. to 1.1 p.u. by 17 steps. The load is connected to the medium-voltage distribution network by distribution transformer; take T3-T7 for example,  
Figure 5: Connection diagram of a practical distribution network with 173 nodes.  
the models are S11-400/10, S11-630/10, S11-630/10, S11-630/10, S11-630/10 and S11-800/10 respectively, and the taps are fixed to 1.0 p.u. There are thirteen nodes installed by shunt capacitors in the system, where Qc1 and Qc2 are the centralized compensations in the low-voltage side of substation, and Qc3-Qc13 are the feeder compensations. The capacity of capacitors is equal grouped and each group is 50 kvar. The configuration information of capacitors is listed in Table 1. The proposed method EWOSM was coded in MATLAB R2009a (MathWorks, Natick, MA, USA) and run on an Intel i5-3230M 2.6 GHz notebook with 4 GB RAM (Dell, Round Rock, TX, USA).  
The historical load data in the test system is from the actual historical database from year 2011 to 2015, and the corresponding reactive power control schemes are also read from the historical database. The missing part of control schemes are calculated by Sequential Quadratic Programming (SQP) based on literature [36], and then the historical database is completed.  
In Figure 5, the tie lines are represented by the red dashed lines, and S1-S6 are the tie switches on the corresponding tie lines. The network topology changes through the cooperation of the section switches and tie switches.
## 3 Calculations  
In steady state, the overall heat transfer coefficient expressed in terms of the external heat transfer area (\(U_{o}\)) was computed by Equation (1):  
\[\dot{m}Cp(T_{13}-T_{0})=U_{o}A_{o}\Delta T_{LMTD} \tag{1}\]  
where \(\dot{m}\) is the mass flow rate of the working fluid; \(T_{0}\) and \(T_{13}\) are the bulk fluid temperatures at the inlet and outlet of the test section (see Figure 1a), respectively, \(A_{o}\) is the external surface area of the test section in direct contact with the heating bath water, and \(\Delta T_{LMTD}\) is the logarithmic mean temperature difference, defined as:  
\[\Delta T_{LMTD}=\frac{\Delta T_{in}-\Delta T_{out}}{\ln(\frac{\Delta T_{in}}{ \Delta T_{out}})} \tag{2}\]  
where \(\Delta T_{in}=T_{b}-T_{0}\) and \(\Delta T_{out}=T_{b}-T_{13}\), and \(T_{b}\) is the temperature of the heating bath water.  
In the present CFI heat exchanger, the inverse of \(U_{o}\) was the sum of three thermal resistances: Two convective resistances (one at the outer surface and the other at the inner surface of the tube) and a conductive resistance at the tube wall. The formula is given by Equation (3) [34]:  
\[\frac{1}{U_{o}}=\frac{A_{o}}{A_{i}h_{i}}+\frac{A_{o}\ln(\frac{d_{o}}{d_{i}})} {2\pi k_{TS}L}+\frac{1}{h_{o}} \tag{3}\]  
where \(h_{o}\) and \(h_{i}\) are the outer and inner convective heat transfer coefficients, respectively; \(d_{o}\) and \(d_{i}\) are the outer and inner diameters of the tube, respectively, and \(k_{TS}\) and \(L\) are the thermal conductivity and the length of the tube in the test section, respectively.  
Under the present experimental conditions and setup, the outer convective thermal resistance was considered to remain constant throughout all trials. Thus, the \(h_{i}\) with deionized water as the working fluid was obtained at different inner flow rates by the widely accepted correlation of [PERSON] et al. [22], and was substituted in Equation (3). After computing \(U_{o}\) by Equation (1), \(h_{o}\) was obtained in each instance by rearranging Equation (3) and the average value was 2377 W m\({}^{-2}\)\({}^{\circ}\)C\({}^{-1}\). After determining \(h_{o}\), \(h_{i}\) with NF as the working fluid was determined by solving Equation (1) for \(U_{o}\) and substituting the result in Equation (3). This procedure was repeated for each inner flow rate, with at least one replicate.  
The internal Nusselt number is given by Equation (4):  
\[N_{Nu,i}=\frac{h_{i}d_{i}}{k} \tag{4}\]  
The accepted functional form of the Nusselt number for NF as the working fluid is [35]:  
\[N_{Nu}=CN_{Re}{}^{a}N_{Pr}{}^{b}\phi^{d} \tag{5}\]  
The values of the constant \(C\) and the exponents \(a\), \(b\), and \(d\) in Equation (5) were determined by least-squares regression analysis.