esa-sceva/satcom-chunk-collection huggingface dataset:
# Concerto: Dynamic Processor Scaling for Distributed Data Systems with Replication
## 2 Background
### Dvfs
#### 2.1.1 Power Management of Intel Processors  
With the rapid and continual increase in the popularity of both resource-limited embedded devices and energy-hungry data centers, the power consumption becomes an important concern in the design of computers. Intel processors provide two types of power management states: P-states and C-states [13]. P-states use different levels of voltages and frequencies of the CPU to allow optimization of the power consumption during runtime. Because lower voltage means the less power consumption, processors with P-states can save energy by throttling down the speed when a running task not in a computation-intensive state. Such states include the time waiting for the next chunk of data to process or the client's responses. In contrast to P-states, C-states are designed to reduce power consumption in an idle mode. The C-state divides the sleeping modes into several levels. It determines the depth of the sleep mode appropriately while taking into account both the power consumption and the wake-up delay.  
The proposed Concerto technique is associated with the runtime power management and is, thus, based on P-states. Table 2 shows the P-states of the Intel(R) Xeon Silver 4114 processor, which is the target processor here. The available frequencies are obtained using the cpupower frequency-info command in linux but the corresponding voltages are not provided; we estimated them for the purpose of this study using publicly available P-state examples [18] with the linear regression model implemented in scikit-learn [19].
# Ambulance Locations in a Tiered Emergency Medical System in a City
## 3 Results
### Results for the Street Demand Model
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Indicator** & \begin{tabular}{c} **Current** \\ **Locations** \\ \end{tabular} & \begin{tabular}{c} **\(p\)MP** \\ **Binary** \\ \end{tabular} & \begin{tabular}{c} **MEXCLP** \\ **Integer** \\ \end{tabular} & \begin{tabular}{c} **MEXCLP** \\ **ERTM** \\ \end{tabular} & \
\begin{tabular}{c} **ERTM** \\ \end{tabular} &
\begin{tabular}{c} **MCLP** \\ **MCLP** \\ \end{tabular} \\ \hline Average response time & 9.60 & 8.97 & **8.90** & 8.99 & 8.95 & 8.99 \\ to all patients (min) & (9.51; 9.69) & (8.92; 9.02) & (8.85; 8.96) & (8.92; 9.06) & (8.86; 9.04) & (8.91; 9.06) \\ \hline \% of calls responded to & 83.37 & 85.18 & 85.13 & 85.01 & **85.24** & 84.92 \\ within 15 min & & & & & \\ \hline Average response time & 9.43 & 8.75 & 8.72 & 8.83 & **8.60** & 8.67 \\ to high-priority & (9.31; 9.55) & (8.63; 8.88) & (8.58; 8.87) & (8.73; 8.92) & (8.48; 8.73) & (8.52; 8.82) \\ \hline \% of high-priority calls & & & & & & \\ responded to within & 51.00 & 53.48 & **54.44** & 53.70 & 54.23 & 53.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance indicators for different distributions of the EMS stations in the district of Presöv, street model of demand. Figure 7: Ambulance locations using the binary MEXCLP model with the street demand model.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 2 Damage Identification Method Based on Additional Virtual Mass and Damage Sparsity
### Damage Identification Method Based on Sparsity  
Because of the local damage in actually damaged structures, the damage-factor variation \(\Delta\mathbf{\mu}\) should exhibit strong sparsity. Although the additional virtual mass method expands the amount of structural modal data, the expanded data are not independent but have some correlation and the objective function is transformed into an overdetermined Equation. Moreover, the noise error still exists when the modal data are identified from the experiment, leading to the insignificant sparsity of optimized damage-factor and a significant deviation from the actual structure damage-factor.
## Nomenclature  
### Abbreviations \begin{tabular}{l l l l}
**Abbreviations** & & & & \\ BRTGS & Bundled renewable energy and thermal & & & \\  & generation system. & & & \\ CNY & Chinese Yuan. & & & \\ PDF & Probability density function. & & & \\ RPR & Regular peak regulation. & & & \\ VPP & Virtual power plant. & & & \\ WT & Wind turbine. & & & \\
**Indices** & & & \\ \(h\) & Index of pollutant type. & & & \\ \(t\) & Index time-step. & & & \\ \(u\) & Index TPUs. & & & \\
**Variables** & & & \\ \(D_{u,t}^{\text{TPU,on}}\) & Continuous duration of operation for a TPU. (h) & & \\ \(F^{\text{A}}\) & Annual total revenues. (CNY) & & & \\ \(F^{\text{PV,GI}}\) & Annual total generation revenues of PV (CNY) & & \\ \(F^{\text{TPU,ON}}\) & Annual total on-off costs of TPUs. (CNY) & & \\ \(F^{\text{TPU,OP}}\) & Annual total operation costs of TPUs. (CNY) & & \\ \(P_{t}^{\text{GO}}\) & Surplus generation of power sources. (MW) & & \\ \(p_{t}^{\text{L,P}}/p_{t}^{\text{L,F}}\) & Predicted value and real-time fluctuation of & & \\  & load demand. (MW) & & \\ \(p_{t}^{\text{PV,P}}/p_{t}^{\text{PV,F}}\) & Predicted value and real-time fluctuation of & & \\ \(p_{t}^{\text{PV,P}}/p_{t}^{\text{PV,F}}\) & PV. (MW) & & \\ \(p_{u,t}^{\text{TPU}}\) & Actual output of TPUs. (MW) & & \\ \(p_{t}^{\text{WT,P}}/p_{t}^{\text{WT,F}}\) & Predicted value and real-time fluctuation of & & \\ \(\beta_{u,t}^{\text{RPR}}/\beta_{u,t}^{\text{DPR}}\) & Binary variables for the state of a TPU either & & \\  & in RPR state or DPR state. & & \\
**Parameters** & & & \\  & Coefficients for TPU DPR loss derived from & & \\  & fitting. & & \\ \(C_{0}/C_{1}/C_{2}\) & Unit generation cost of TPUs in different & & \\  & compensation standards. (CNY/MW) & & \\ \(C_{h}^{\text{E}}\) & Unit cost for pollutants treatment. (CNY/kg) & & \\ \(C_{u}^{\text{GN}}\) & Cost of a single off to on transition for & & \\  & TPU. (CNY) & & \\ \(C^{\text{WT}}\) & Unit generation revenue of WT. (CNY/MW) & & \\ \(D_{u}^{\text{TPU,off,min}}\) & Minimum operation time. (h) & & \\ \(P_{u}^{\text{TPU,R,min}}\) & Minimum output of TPU in RPR states. (MW) & & \\ \(P_{t}^{\text{C,max}}/P_{t}^{\text{C,min}}\) & Limits of the power grid's capability to & & \\  & accommodate RES fluctuations. (MW) & & \\ \(\tau_{u}^{\text{TPU}}\) & Flexible reservation ratio of TPUs. & & \\  & Different output rates for DPR service & & \\ \(\mu_{1}/\mu_{2}\) & compensation standards. & & \\ \(\epsilon_{h}^{\text{E}}\) & Equivalent mass of the pollutant per unit & & \\  & power generation. (kg/MW) & & \\ \end{tabular}
## 3 Results and Discussion
### Extraction Kinetics and Temperature Effects
\(E_{\mathrm{a}}\) thus estimated for the extraction with \(\beta\)-CD, HP-\(\beta\)-CD, and m-\(\beta\)-CD were 7.18, 9.50, and 5.64 kJ mol\({}^{-1}\), respectively. This finding did confirm that the extraction with m-\(\beta\)-CD was the least energy-demanding process. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(T\) (\({}^{\circ}\)C) & \multicolumn{3}{c}{**Kinetic Parameters**} \\ \hline  & \(k\) (\(\times\)10\({}^{-3}\)) & \(h\) (mg g\({}^{-1}\) min\({}^{-1}\)) & \(\mathbf{Y_{TP(s)}}\) (mg GAE g\({}^{-1}\)) \\  & (g mg\({}^{-1}\) min\({}^{-1}\)) & \(h\) (mg g\({}^{-1}\) min\({}^{-1}\)) & \(\mathbf{Y_{TP(s)}}\) (mg GAE g\({}^{-1}\)) \\ \hline \(\beta\)-CD & & & \\
40 & 4.95 & 11.76 & 48.74 \\
50 & 5.59 & 14.45 & 50.86 \\
60 & 5.83 & 21.51 & 60.75 \\
70 & 7.53 & 33.70 & 66.89 \\
80 & 8.46 & 48.68 & 75.85 \\ \hline HP-\(\beta\)-CD & & & \\
40 & 2.43 & 8.91 & 60.53 \\
50 & 2.71 & 11.09 & 63.92 \\
60 & 3.02 & 16.30 & 73.41 \\
70 & 4.40 & 28.84 & 80.92 \\
80 & 6.92 & 46.47 & 81.93 \\ \hline m-\(\beta\)-CD & & & \\
40 & 2.55 & 14.66 & 68.99 \\
50 & 2.64 & 17.99 & 82.56 \\
60 & 2.90 & 21.79 & 86.71 \\
70 & 2.95 & 25.97 & 93.89 \\
80 & 3.28 & 31.76 & 98.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Kinetic parameters determined for the extraction of _S. frutiosa_ polyphenols with the CDs tested. Extractions were accomplished under optimal \(C_{\mathrm{CD}}\), R\({}_{\mathrm{I}/\mathrm{S}}\), and pH. Figure 5: Plot showing \(\mathrm{Y_{TP}}\) achieved using each of the CDs tested, under optimized conditions, at 80 \({}^{\circ}\)C, after 180 min. Figure 6: Diagram illustrating the \(\mathrm{A_{AR}}\) and \(\mathrm{P_{R}}\) of the extracts produced using each of the CDs tested, under optimized conditions, after 180 min, at 80 \({}^{\circ}\)C. Assignments: m-\(\beta\)-CD, methyl \(\beta\)-cyclodextrinrin; HP-\(\beta\)-CD, hydroxypropyl \(\beta\)-cyclodextrinrin; \(\beta\)-CD, \(\beta\)-cyclodextrinrin.
# Hybrid Deep Reinforcement Learning for Pairs Trading
## 2 Background  
### Pairs Trading  
Pairs trading is a popular market-neutral trading strategy that uses highly correlated and cointegrated stock pairs. If two non-stationary time series (i.e., stock prices) are cointegrated, we can combine them into one stationary time series (i.e., a spread) according to Equation (1). The augmented Dickey-Fuller test [8] is widely used to analyze whether a time series is stationary. In Equation (1), \(P_{A}\) and \(P_{B}\) are the prices of stocks \(A\) and \(B\), respectively, and \(\beta\) is the cointegration factor (or hedge ratio).  
\[Spread=\log P_{A}-\beta*\log P_{B} \tag{1}\]  
The hedge ratio \(\beta\) indicates that pairs traders longs (or shorts) one share of stock \(A\) and shorts (or longs) \(\beta\) shares of stock \(B\) for hedging. Linear regression methods, such as ordinary least squares (OLS) and total least squares (TLS), can be used to calculate \(\beta\). OLS minimizes the sum of squared errors, while TLS minimizes the sum of the squared orthogonal distances from data points to the regression line [1].
# Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing
## 2 Materials and Methods
### Performance Test of the NFC Potentiostat  
To evaluate the performance of the NFC potentiostat, a commercial screen-printed carbon electrode consisting of a 3 mm diameter carbon working electrode, carbon counter electrode and an Ag/AgCl reference electrode was used to perform cyclic voltammetry and chronoamperometry. The experiments on the NFC potentiostat were compared to a commercial laboratory potentiostat (CHI 611E, Texas Instruments Inc., Austin, TX, USA) used as a standard instrument. For each experiment, 100 uL of potassium ferricyanide in 0.1 M KCl as a supporting electrolyte was applied on a screen-printed carbon electrode. The solution of potassium ferricyanide is a common analyte to test the performance of a potentiostat, because it exhibits a reversible electrochemical behavior and rapid one-electron process [45; 46]. Cyclic voltammetry was carried out over the potential range from \(-\)300 to 600 mV at a scan rate of 5-200 mV/s. For chronoamperometric detection, the fixed potential of \(-\)100 mV was applied to the working electrode and the current period of 0-30 s was recorded.
## 4 Results and Discussion
### Comparison of Different Methods  
The extraction ratios of the 6u, 6m and 6l coalbeds obtained by the measured flow method are compared with the results determined by isotope geochemistry, as shown in Figure 11.  
It can be observed from Figure 11 that, when boreholes are adopted for CBM extraction from the 6u + 6m + 6l coalbeds, the extraction ratio from the 6m coalbeds is dominant, and the extraction ratios from the adjacent 6u and 6l coalbeds are both relatively small.  
For the 6u, 6m and 6l coalbeds, the arithmetic means of the extraction ratios determined by measured flow methods are 12.47%-20.21%, 57.19%-61.35% and 25.63%-27.05%, respectively. The extraction ratios determined by isotope geochemistry are 10.82%-24.54%, 57.81%-69.58% and 28.06%-31.37%, respectively. It can be obviously seen that the extraction ratios of the 6u, 6m and 6l coalbeds are basically consistent.  
On the 90 th day of CBM extraction, the deviation of extraction ratio from the 6m seam determined by the above two methods is 6.4%. The test results may be affected by the sampling operation or test accuracy, also may be affected by multiple factors such as coalbed thickness, original gas content, gas content of rock strata and interlayer spacing. However, this deviation is within the range of error influence, which also indicates that the results of the above two methods can be comprehensively employed in determining the extraction ratio of a target coabled, when conducting CBM extraction from multi-seams in the Xiaotun Coal Mine.  
Based on the research results in this paper, the extraction ratio of M6-3, M7-2, M8 and M11 coalbeds, in the Fengchun Coal Mine in Chongqing, have also been determined by isotope geochemistry [38].  
The results of the isotope geochemistry show certain differences compared with the results of the theoretical method; this is because the theoretical method mainly assumes that coalbeds deposited in different geological ages have similar properties. However, when the parameters of coalbed thickness, extraction radius, original gas content and unit weight are considered, differences of pore structure and permeability of coal body will be caused by different deposition conditions. Compared with the theoretical method, the results of isotope geochemistry are more consistent with the actual situation of CBM extraction. Therefore, this paper further verifies the conclusion of isotope geochemistry by using measured flow methods.  
Figure 11: Comparison results of extraction ratio.
## 3 Analysis of Simulation Results
### Radiation Pattern and SAR  
The simulated radiation patterns of the antenna in the three-layer body tissue model are displayed in Figure 9. The antenna peak gains are \(-20.85\) dBi and \(-20.65\) dBi at 1.4 GHz  
Figure 8: Current distributions on antenna radiation patch and GND: (**a**) 1.4 GHz; (**b**) 2.45 GHz.  
Figure 7: The simulated AR when some key parameters are changed. (**a**) The length (L\({}_{1}\)) and width (W\({}_{2}\)) of the cross-slot at different sizes, and (**b**) the rotation angle of the cross groove.  
and 2.45 GHz and maintain good omnidirectionality at the two working frequency points, meeting the operational requirements for an implantable medical monitoring system.  
The simulated SAR distribution of the designed antenna within the three-layer body tissue model is shown in Figure 10. Assuming a transmitting power of 1 W, the maximum 1 g average SAR values are 376 W/Kg and 318 W/Kg at 1.4 GHz and 2.45 GHz. Therefore, the maximum transmit power ought to be less than 4.26 mW and 5.03 mW to satisfy the requirements of the IEEE C95.1-1999 standard [30]. The maximum power allowed here is far less than the transmitter power of commercially available transmitters, so in this case, the SAR meets safety considerations for humans.  
Figure 10: SAR distribution in skin model (1 g) at 1.4 GHz (**a**) and 2.45 GHz (**b**).  
Figure 9: The simulation results of the radiation patterns: (**a**) 1.4 GHz and (**b**) 2.45 GHz.
## 5 Example Simulation and Analysis
### Results Analysis
#### 5.4.2 The Effect of Uncertainty on Scheduling Results  
This paper mentions the uncertainty model of wind power, photovoltaic power, system load, and demand response. These uncertainties' influence on system optimization results is analyzed under five cases, and the maximum prediction error of wind power, photovoltaic power and system load is increased by 30%. The prediction error of the virtual response unit (including day-ahead PDR and intraday IDR) increases by 30%, and other conditions remain the same. The average adjustment cost and total system cost under each scene can be simulated, as shown in Table 5.  
Compared with Tables 4 and 5, the total system cost in each scheduling scene increases by 10.1%, 7.33%, 5.08%, 8.73%, and 3.51%, respectively. It is obvious that the total system cost of the two-stage optimal scheduling model increases with the prediction error. Comparing the basic scene and synthetic scene, two-stage scheduling of wind, solar, thermal, and storage systems with demand response have a proportional increase in total system costs with the prediction, but the increase is minimal. Meanwhile, the unit adjustment cost is relatively lower. It is shown that the proposed model is better than other models in dealing with prediction errors. With economic dispatching as the objective function, the prediction error of wind power and photovoltaic power is kept 20% to study the effect of load prediction. The change curve of integrated scene system operation cost under different load prediction errors is shown in Figure 9. It can be seen from the figure that the total system cost increases with the load prediction error. The reason is that to ensure the safe and stable operation of the power  
Figure 8: The optimal scheduling plan in case 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cost/5** & **Case1** & **Case2** & **Case3** & **Case4** & **Case5** \\ \hline Adjustment & 87,120 & 65,024 & 45,317 & 78,352 & 23,824 \\ \hline Total & 421,551 & 396,069 & 332,716 & 401,899 & 337,597 \\ \hline \hline \end{tabular}
\end{table}
Table 5: System optimal scheduling with an uncertainty of 30%.  
system, the number of thermal power units is increased during the peak load period to meet the power demand. In the valley period, thermal power units may run in low load conditions, in which the operating cost is higher. However, compared with the traditional scheduling method (Case1), the two-stage optimal scheduling method (Case5) proposed in this paper is less affected by the load prediction error, which indicates that the ability of power system to deal with the prediction error can be improved by the addition of source-load interaction in the day-ahead and intraday stages.
## 2 Mathematical Model of the Induction Motor
### The Model of Induction Motor in the \(\alpha\beta\) Coordinate System  
For \(\beta\) (\(\omega_{x}=0\))--a fixed coordinate system with axes linked to stator-solving Equations (17) and (19) with respect to the derivatives of the flux linkages gives the following form:  
\[\begin{array}{c}\frac{d\psi_{1x}}{dt}=U_{m}cos\ cos\ (\tau)-R_{1}i_{1a}\ \frac{d\psi_{1\beta}}{dt}=U_{m}cos\ cos\ (\tau)-R_{1}i_{1b}\ \frac{d\psi_{2a}}{dt}=\\ -R_{2}i_{2a}+\omega\psi_{2\beta}\ \frac{d\psi_{2\beta}}{dt}=-R_{2}i_{2\beta}+ \omega\psi_{2\alpha}\,\end{array} \tag{20}\]  
The equation for the change in rotor speed is  
\[\frac{d\omega}{d\tau}=(T_{e}-T_{l})/T_{M}, \tag{21}\]  
The current values are obtained from flux linkages expressions:  
\[\begin{array}{c}i_{1\alpha}\ \ =\frac{(X_{2}\psi_{1a}-X_{4a}\psi_{2a})}{ \Delta}\ i_{1 ax}=\frac{(X_{2}\psi_{1\beta}-X_{4a}\psi_{2b})}{\Delta}\ i_{2 ax}\\ =\frac{(X_{1}\psi_{2a}-X_{4a}\psi_{1a})}{\Delta}\ i_{2\beta}=\frac{(X_{1}\psi_ {2b}-X_{4a}\psi_{1b})}{\Delta}\end{array} \tag{22}\]where \(\Delta=X_{1}X_{2}-X_{ad}X_{ad}\).  
\[T_{e}=X_{ad}\big{(}i_{1a}i_{2g}-i_{1g}i_{2a}\big{)} \tag{23}\]  
And the load torque is the following:  
\[T_{l}=SM\omega_{2}^{2}+SMC \tag{24}\]  
where:  
* \(\psi_{1g}\), \(\psi_{1a}\), \(\psi_{2\beta}\), \(\psi_{1a}\) are the stator and rotor flux linkage components in the \(\alpha\beta\) coordinate system;
* \(i_{1x}\), \(i_{1y}\), \(i_{2x}\), \(i_{2y}\) are the stator and rotor current components in the \(\alpha\beta\) coordinate system;
* \(\omega\) is the rotor rotation speed;
* \(U_{m}coscos\left(\tau\right),-U_{m}sinsin\left(\tau\right)\) are the stator applied voltage;
* \(R_{1},R_{2},X_{1},X_{2},X_{ad}\) are the induction motor parameters in per units;
* \(T_{e}\), \(T_{l}\) are the electromagnetic and load torques, respectively;
* \(SM,SMC\) are the motor's variable and constant torques coefficients.  
Using the developed IM model in the \(\alpha\), \(\beta\) coordinate system, there is an opportunity to model various dynamic operating regimes, such as direct starting, and direct starting considering current displacement in the slots.  
This IM model in the \(\alpha\), \(\beta\) coordinate system allows for the direct comparison of single-phase outcomes with experimental data without the need for additional transformations.
## 4 Numerical Study
### Controller Design  
The complete control system consists of three controllers: a PI controller for the torque control, a second PI controller for the pitch control, and a nonlinear PI controller for the power tracking control. Parameters are obtained by multi-objective optimisation, as used in [33; 34]. The obtained parameters are summarised in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Wind Profile** & **Description** \\ \hline Scenario 1 & Region III & Tracking power control on Region III. \\ Scenario 2 & Region II \& III & Tracking power control with Pref \textless{} Pmax. No \\ Scenario 3 & Region II \& III & facing power control with Pref \textgreater{} Pmax and \\  & & & integrator reset. No gain scheduling. \\ Scenario 4 & Region II \& III & Tracking power control with Pref \textgreater{} Pmax and \\  & & & power reference change. No gain scheduling. \\ Scenario 5 & Region II \& III & Tracking power control with Pref \textgreater{} Pmax, power \\  & & & reference change, and gain scheduling. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation scenarios for up/down-regulation control.  
Figure 10: Power and wind speed profiles for simulation purposes.
## 3 Results and Discussion  
Using a numerical finite difference time domain (FDTD) method [21; 22], we calculate the effective modal index of fundamental guided mode (\(n_{eff}\)) and study the effective mode area, non-linear coefficient, birefringence, confinement loss, and dispersion properties of our proposed PCFs. In the numerical analysis, we consider the case of mode confinement in the PCF core through total internal reflections (TIR). The calculated \(n_{eff}\) is strongly dependent on the design parameters, especially air hole arrangement in the cladding of the PCF. Note that the design parameters of PCFs in this paper are optimized for optical telecommunications windows near 1550 nm (1100 nm to 1900 nm).
dispersion in PCFs can be easily controlled by modifying the air hole dimensions, numbers and center-to-center pitch. The dispersion characteristics of a fiber is usually calculated in terms of the dispersion parameter \(D\), defined as [2; 7]  
\[D=-\frac{\lambda}{c}\frac{d^{2}n_{eff}}{d\lambda^{2}} \tag{4}\]  
where \(n_{eff}\) is the effective modal index of guided mode in the PCF core. The dispersion curve of C-PCF and E-PCF with different core dimensions and \(d=0.692\), \(\Lambda=0.867\), \(a=1\)\(\upmu\)m as a function of wavelength is illustrated in Figure 3.  
As seen in Figure 3, by changing the core dimension ZDW can be tuned to different excitation wavelengths near 1550 nm. In particular, by increasing \(d_{c}/\Lambda\) from 0.93 to 1.01 in C-PCF (Figure 3a), ZDW shifts from 1.61 \(\upmu\)m to 1.53 \(\upmu\)m. This tunability of ZDW exists in a wide range of wavelengths in our proposed composite PCF (ZDW can be tuned from 1100 nm to 1900 nm). The same trend exist in E-PCFs. For example, as seen in Figure 3b, by increasing \(b/a\) from 0.71 to 0.79, ZDW shifts from 1.57 \(\upmu\)m to 1.52 \(\upmu\)m. In Figure 4, we study the effect of air-hole dimensions on the dispersion characteristics of C-PCFs and E-PCFs.  
Figure 3: Dispersion of (**a**) circular-core fiber (C-PCF) and (**b**) elliptical-core fiber (E-PCF) with different core dimensions as a function excitation wavelength. The C-PCF parameters are: \(d=0.692\), \(\Lambda=0.867\), and E-PCF parameter is \(a=1\)\(\upmu\)m.  
ZDW is sensitive to hole-dimensions in the cladding of the PCF. As seen in Figure 4a, by increasing \(d/\Lambda\) from 0.676 to 0.692, the ZDW shifts from 1.61 \(\upmu\)m to 1.51 \(\upmu\)m. In E-PCFs this shift in ZDW is even more sensitive to hole dimensions. As seen in Figure 4b, increasing \(d/\Lambda\) from 0.576 to 0.754 can result in a shift in ZDW from 1.77 \(\upmu\)m to 1.45 \(\upmu\)m. We confer that ZDW in both E-PCFs and C-PCFs is totally controllable by modifying the hole and/or core dimensions. Additionally, for a E-PCF with \(d/\Lambda=0.692\), \(\Lambda=0.867\upmu\)m, \(b/a=0.75\) and \(a=1\)\(\upmu\)m, the ZDW is at 1550 nm, which is the wavelength of interest in optical communications.