esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Final Locations of the Co-Located GNSS/SAR Infrastructure  
The corner reflectors and GNSS antenna collocation was determined due to the MCDA, described in the previous sections. The inter-visibility between the GNSS antenna and the two corner reflectors was the last consideration to examine; thus, an LOS analysis was performed in the GIS environment, as illustrated in Figure 19. The LOS can define the visibility of sight lines of the instruments over obstacles composed of the DSM.  
The LOS analysis showed that the three instruments had obstructed sightlines, ergo, no inter-visibility. Hence, the suitability analysis for the selection of the most suitable locations was ended, and the sites were finalized.  
The subsequent phase of installing the collocated configurations, along with evaluating and validating the sites in terms of signal quality, are described below.
## 3 Results and Discussion
### Overall Acceptability and Emotions Induced in Consumers by the Edible Mushrooms
No significant  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Edible Mushroom Samples** & **OA** & \multicolumn{6}{c}{**Emotions Induced by the Samples (from 0 to 1)**} \\ \cline{3-10}  & & **Neutral** & **Happy** & **Sad** & **Angry** & **Surprised** & **Scared** & **Disgusted** & **Contempt** & **Valence** \\ \hline \multicolumn{10}{c}{**Thermal treated non-fermented**} \\ \hline BE-thermal & \(7.6\pm 1.2\,^{\ast}\) & 0.8068 \(\pm\) & 0.0106 \(\pm\) & 0.0675 \(\pm\) & 0.0224 \(\pm\) & 0.0086 \(\pm\) & 0.0037 \(\pm\) & 0.0156 \(\pm\) & 0.0014 \(\pm\) & 0.0852 \(\pm\) \\  & & 0.0096 \({}^{\ast}\) & 0.0015 \({}^{\ast}\) & 0.0028 \({}^{\ast}\) & 0.0018 \({}^{\ast}\) & 0.0017 \({}^{\ast}\) & 0.0006 \({}^{\ast}\) & 0.0004 \({}^{\ast}\) & 0.00075 \({}^{\ast}\) \\ Ca-thermal & \(8.9\pm 2.3\,^{\ast}\) & 0.8010 \(\pm\) & 0.0177 \(\pm\) & 0.0135 \(\pm\) & 0.0022 \(\pm\) & 0.0059 \(\pm\) & 0.0014 \(\pm\) & 0.0034 \(\pm\) & 0.0011 \(\pm\) & 0.2069 \(\pm\) \\  & & 0.0004 \({}^{\ast}\) & 0.0011 \({}^{\ast}\) & 0.0025 \({}^{\ast}\) & 0.0006 \({}^{\ast}\) & 0.0005 \({}^{\ast}\) & 0.0005 \({}^{\ast}\) & 0.0003 \({}^{\ast}\) & 0.0034 \({}^{\ast}\) \\ Ro-thermal & \(7.6\pm 1.4\,^{\ast}\) & 0.8196 \(\pm\) & 0.0015 \(\pm\) & 0.0433 \(\pm\) & 0.0263 \(\pm\) & 0.0024 \(\pm\) & 0.0209 \(\pm\) & 0.0071 \(\pm\) & 0.0006 \(\pm\) & 0.0847 \(\pm\) \\  & & 0.0115 \({}^{\ast}\) & 0.0006 \({}^{\ast}\) & 0.0021 \({}^{\ast}\) & 0.0023 \({}^{\ast}\) & 0.0003 \({}^{\ast}\) & 0.0004 \({}^{\ast}\) & 0.0018 \({}^{\ast}\) & 0.0008 \({}^{\ast}\) & 0.0002 \({}^{\ast}\) & 0.0004 \({}^{\ast}\) \\ \hline \multicolumn{10}{c}{**Ultrascalon non-fermented**} \\ \hline BE-ultrasound & \(7.8\pm 2.1\,^{\ast}\) & 0.8700 \(\pm\) & 0.0005 \(\pm\) & 0.0231 \(\pm\) & 0.0217\(\pm\) & 0.0130 \(\pm\) & 0.0087 \(\pm\) & 0.0029 \(\pm\) & 0.0009 \(\pm\) & 0.0440 \(\pm\) \\  & & 0.0086 \({}^{\ast}\) & 0.0004 \({}^{\ast}\) & 0.0019 \({}^{\ast}\) & 0.0021 \({}^{\ast}\) & 0.0015 \({}^{\ast}\) & 0.0009 \({}^{\ast}\) & 0.0003 \({}^{\ast}\) & 0.0004 \({}^{\ast}\) & 0.0029 \({}^{\ast}\) \\ Ca-ultrasound & \(8.3\pm 1.6\,^{\ast}\) & 0.7926 \(\pm\) & 0.0544 \(\pm\) & 0.0485 \(\pm\) & 0.0564 \(\pm\) & 0.0110 \(\pm\) & 0.0049 \(\pm\) & 0.0097 \(\pm\) & 0.0027 \(\pm\) & 0.0782 \(\pm\) \\  & & 0.0108 \({}^{\ast}\) & 0.0031 \({}^{\ast}\) & 0.0024 \({}^{\ast}\) & 0.0027 \({}^{\ast}\) & 0.0009 \(\pm\) & 0.0006 \({}^{\ast}\) & 0.0011 \({}^{\ast}\) & 0.0004 \({}^{\ast}\) & 0.0004 \({}^{\ast}\) & 0.0063 \({}^{\ast}\) \\ Ro-ultrasound & \(7.8\pm 1.3\,^{\ast}\) & 0.8822 \(\pm\) & 0.0019 \(\pm\) & 0.0162 \(\pm\) & 0.0144 \(\pm\) & 0.0057 \(\pm\) & 0.0059 \(\pm\) & 0.0072 \(\pm\) & 0.0005 \(\pm\) & 0.0356 \(\pm\) \\  & & 0.0147 \({}^{\ast}\) & 0.0005 \({}^{\ast}\) & 0.0018 \({}^{\ast}\) & \(\pm\) & 0.0015 \({}^{\ast}\) & 0.0018 \({}^{\ast}\) & 0.0007 \({}^{\ast}\) & 0.0013 \({}^{\ast}\) & 0.0002 \({}^{\ast}\) & 0.0022 \({}^{\ast}\) \\ \hline \multicolumn{10}{c}{**Thermal treated and fermented with _L.
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
###### Abstract  
A significant advantage of off-grid direction-of-arrival (DOA) estimation algorithms using difference coarrays is their ability to resolve more sources than the number of physical sensors. Current coarray-based off-grid DOA estimation algorithms experience a significant decline in estimation accuracy with limited snapshots. Moreover, most existing DOA estimation techniques exhibit a high computational complexity, limiting their practical implementation in real-time systems. To address these limitations, in this work, we propose a novel coarray-based off-grid DOA estimation algorithm that achieves a computationally efficient performance while maintaining a high estimation accuracy under snapshot-constrained conditions. The proposed algorithm first performs DOA estimation through coarray-augmented spatial smoothing multiple signal classification (SS-MUSIC), followed by noise suppression via multiplication with a constructed selection matrix. The off-grid angular deviations are sequentially refined based on the iterative correction mechanism. The disadvantage of a large number of snapshots requirement is overcome thanks to the combination of noise elimination and sequential angle refinement. Theoretical performance bounds are established through Cramer-Rao bound (CRB) analysis, while comprehensive simulations validate the estimation accuracy of the proposed algorithm and the robustness in off-grid scenarios.  
Keywords:difference coarrays; limited snapshots; DOA estimation; noise elimination; least squares +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Study Design  
The study comprised 4 visits to the laboratory, separated by a minimum of 72 h, during which cycloergometer exercise sessions were carried out. All sessions were conducted between 8:00 and 12:00. During these experiments, participants maintained their current physical activity and were asked to avoid heavy exercise, alcoholic beverages, energy drinks, and caffeine for 24 h before each laboratory session. Body mass (kg) and height (m) were measured using a WPT 200 medical scale (RADWAG, Radom, Poland) during the first visit. Resting systolic and diastolic blood pressure was measured with an aneroid sphygmomanometer (Riester, Jungingen, Germany). The incremental exercise test (IET) was performed according to the protocol of [PERSON] et al. (2013) to determine cardiovascular and respiratory capacity. During the test and during the 5 min post-test recovery, respiratory parameters were recorded. The participants wore a mask connected to a Quark respiratory gas analyzer (Cosmed, Rome, Italy), which was calibrated before the test. Respiratory parameters, including oxygen uptake (VO\({}_{2}\)), pulmonary ventilation (VE), tidal volume (VT), and breathing frequency (Rf) were measured in each recorded breath (breath-by-breath), and then averaged at 30 s intervals. Maximal oxygen uptake (VO\({}_{2}\)max), maximal pulmonary ventilation (VEmax), maximal tidal volume (VTmax), and maximal breathing frequency (Rfmax) were obtained from the recorded data. The second visit involved familiarization with the cycling sprint interval exercise (SIE) protocol. During the third and fourth visits, single cross-over SIE sessions were conducted, during which, in randomized order, SIE was performed with a load of 7.5% of the participant's body mass (SIE\({}_{7.5\%}\)) and with a load of 10% of the participant's body mass (SIE\({}_{10\%}\)).
# Ecosystem Metabolism in Small Ponds: The Effects of Floating-Leaved Macrophytes
## 3 Results
### Primary Production  
#### 3.2.1 Macrophyte Abundance  
Figures 3 and 4 show the distribution of floating-leaved and submerged macrophytes in the four ponds. Warner had a large percentage of its surface covered by floating-leaved macrophytes by the end of July, while Stickleback had much less of its surface covered compared to the other ponds (Table 3). Stickleback had the largest coverage of submerged macrophytes (Table 3). There was a significant difference among ponds in the actual biomass of macrophytes (Figure 5, ANOVA--F\({}_{3,257743}\) = 29,461, \(p<0.0001\)) with Stickleback having the lowest levels of macrophyte biomass both in terms of average macrophyte biomass per square meter and total biomass for the pond, while Warner had the highest macrophyte biomass per square meter and total biomass for the whole pond (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Pond**} \\ \cline{2-5}  & **Stickleback** & **Warner** & **Railtracks** & **Elephant** \\ \hline Submerged macrophytes (\% medium & & & & \\ or dense coverage) & **20.7** & 6.1 & 3.3 & 8.4 \\ \hline Floating-leaved macrophytes (\% & & & & \\ medium or dense coverage) & _0.9_ & **61.2** & 43.7 & 12.4 \\ \hline Total macrophytes biomass (kg) & _376.5_ & **4129.2** & 1166.5 & 1487.5 \\ \hline Average macrophytes biomass (g/m2) 1 ** & _24.9 (36.4)_ 2  & _229.3 (3186.9)_ 3  & 151.6 (117.3)_ 4  \\ \hline Phytoplankton biomass (\(\mu\)g chl \(a\)/L) 2  & 5.34 (1.17)_ 1  & _2.6 (1.2)_ 1  & 6.65 (1.3)_ 1  \\ \hline Mean GPP (mgO\({}_{2}\)/L/day) 1  & 3.2 (2.3)_ 1  & _2.2 (2.2)_ 2  & 4.3 (4.0)_ 3  & **5.5 (4.0)_ 4  \\ \hline Mean R (mgO\({}_{2}\)/L/day) 1  & \(-\)3.6 (2.7)_ 1  & \(-\)3.7 (3.4)_ 1  & \(-\)4.4 (4.9)_ 2  & **-6.8 (6.1)_ 1  \\ \hline Mean NEP (mgO\({}_{2}\)/L/day) 1  & \(-\)0.4 (1.8)_ 1  & \(-\)1.5 (2.4)_ 2  & **-0.1 (2.6)_ 1  & \(-\)1.3 (3.3)_ 2  \\ \hline \hline \end{tabular}
\end{table}
Table 3: Amounts of various primary producers (macrophyytes and phytoplankton) and ecosystem metabolism measures (GPP—Gross Primary Production, R—Respiration and NEP—Net Ecosystem Production) from four ponds in east-central Minnesota. Within parameters, values with the same letter are not significantly different from one another ([PERSON]’s post hoc test). Cells labeled in bold show the pond with the maximum value of a parameter, while those in italics show the pond with the minimum value.  
Figure 4: Maps of the extent of submerged macrophytes in four ponds in east-central Minnesota, USA.
## 3 Results
### Pressure
#### 3.3.1 Free vs. Guided Conditions  
For habitual tasks, both for simple and complex shape (Figure 5A,C) we observed that writing pressure significantly increases in the guided condition compared to the free one for both groups (\(p<0.01\)) and that PD patients exert lower pressure than H subjects both in the free (\(p<0.01\)) and the guided (\(p<0.05\)) conditions.  
Figure 5: Pressure (mean \(\pm\) SEM) across PD patients (red lines) and H subjects (blue lines) for all conditions: (**A**) Habitual simple l-loop, (**B**) Novel simple l-loop, (**C**) Habitual multiple l-loops, (**D**) Novel multiple l-loops, (**E**) Habitual multiple l-loops, (**F**) Novel multiple l-loops. Grey and black lines indicate the results of \(t\)-tests that gave significant differences. Stars indicate the results one-tailed \(t\)-tests corrected with FDR (\({}^{*}p<0.05\); \({}^{**}p<0.01\); \({}^{***}p<0.001\)).  
For novel tasks, we found that in the guided condition compared to the free condition, pressure significantly increased for H subjects (\(p<0.05\)) and slightly decreased for patients with PD. Indeed, we found that for simple shape in the free condition (Figure 5B) pressure exerted in the PD patients and the H subjects was not significantly different (\(p>0.69\)), whereas in the guided condition PD patients exert lower pressure than H subjects (\(p<0.01\)). As regarding novel complex tasks (Figure 5D) pressure does not change significantly in guided condition compared to free one for both groups (\(p>0.62\) for both groups).
## 1 Introduction
We jointly optimize transmission power, delay and rate with QoS constraints like: interference, delay, BER, Signal-to-Noise-Ratio (SNR) and Signal-to-Interference-Noise-Ratio (SINR). We vary network size and packet size while changing transmission power, delay and rate parameters to sustain QoS constraints. Packet size is varied for; (i) energy efficiency for improved network lifetime due to reduced consumption, (ii) PU interference reduction, (iii) transmission efficiency due to reduced collision between PUs and SUs, (iv) reliability in detection of events. Information carried by a signal during a transmission depends on packet size. Variation of these parameters with network size is also studied.
# Data Chunks Placement Optimization for Hybrid Storage Systems
## 2 Summary of Results
### Background Research for Distributed and Cloud Storage Systems  
Throughout the past few decades, erasure coding schemes, like Reed-Solomon codes, were an attractive alternative to replication in distributed storage systems, due to optimal balancing between storage cost and data durability. It was used as the main error-coding method in various papers (e.g., References [2; 3]) for reducing repair bandwidth costs. This method, however, has a higher rate of the coding, which is not suitable for many applications and storage systems, like \"Network Attached Storage\" (NAS). Moreover, erasure coding consumes a significant amount of recourses during the replication of the data over multiple storages [4].  
The idea of implementing Network Coding to the distributed storage systems was first proposed by [PERSON], [PERSON], and [PERSON] [5]. It has a number of advantages over standard erasure coding methods, like recoding already encoded data without destroying the properties of the code.  
The concept of performing erasure and Network Coding to the cloud storage systems was the main research objective for multiple papers, like [6] Reference [7]. Researchers were mostly concerned with the optimal way of storing the data, i.e., with distributing over the multiple cloud vendors [8]. When the data is stored on cloud storages, several \"Quality of Service Factors\" factors, like availability, durability, and vendor lock-in, are required to be taken into consideration. While striping data with traditional erasure codes performs well for short-term data storages uploading, for long-term storage, it is not always affordable, especially for the cases when there is a redundant storage/storage in the system. A repair operation of the redundant cloud storage node requires retrieving the data over the network from existing surviving cloud nodes. Because cloud vendors have high outgoing (repair) traffic prices, moving an enormous amount of data across clouds is not affordable (see Section 2.2). This triggered researchers to look into cost-effective alternatives of the erasure coding methods for maintaining data redundancy and fault tolerance over cloud storages. Further research on this topic mostly focused on minimizing repair traffic by using Network Coding and applications of it, e.g., \"NCCloud\" [9], which was built on top of a network-coding-based storage scheme called minimum-storage regenerating (MSR) codes.
# Utility-Centric Service Provisioning in Multi-Access Edge Computing
## 3 System Model and Problem Formulation
### Service Latency Estimation
#### 3.3.2 Processing Latency  
In our model, the processing units of MEC nodes are assumed to be allocated based on weighted proportional allocation [13; 43]. Each user receives a fraction of computation resources at the target MEC node \(n\) based on its demand. Let \(f_{un}\) be the computation resource allocated to user \(u\) by node \(n\). It is calculated as  
\[f_{un}=\frac{w_{s_{u}}}{\sum\limits_{u^{\prime}\in\mathcal{U}}y_{u^{\prime}n} w_{s^{\prime}}}F_{n} \tag{2}\]  
Then the processing time of user \(u\) at MEC node \(n\), denoted as \(\tau_{u,n}^{proc}\), is given by  
\[\tau_{u,n}^{proc}=\frac{w_{s_{u}}}{f_{un}} \tag{3}\]  
In case the request of user \(u\) is not scheduled to any MEC node in the collaboration space, it will be sent to the core cloud \(l\). Here, since the core cloud is generally equipped with abundant computation resources, the processing time at cloud can be ignored [14; 22]. However, serving the user at the core cloud, which is assumed to be located far away, causes much higher propagation delay than that of serving it by a MEC (i.e., \(D_{n_{u},l}>>D_{n_{u},n},\forall n_{u},n\in\mathcal{N}\)).
# Tracking Mobile Sinks via Analysis of Movement Angle Changes in WSNs
## 4 Performance Evaluation  
### Simulations with Different Trajectory Patterns  
To examine how the trajectory patterns of mobile sinks affect the performances of DAMST, we designed four kinds of trajectory patterns (Wave, Straight Line, Circle and Random), shown in Figure 9, which are different from each other due to specified changes of the movement deviation direction and the movement deviation angle of the mobile sinks.  
In the Wave pattern (a), the mobile sink changes its movement deviation direction from clockwise to anti-clockwise every 20 min with a 60\({}^{\circ}\) movement deviation angle. In the Straight Line pattern (b), the mobile sink just moves along a straight line. In the Circle pattern (c), the mobile sink makes a clockwise deviation every 10 min with 60\({}^{\circ}\) movement deviation angle. In the Random pattern (d), the mobile sink randomly changes its movement deviation direction every 20 min with a 60\({}^{\circ}\) movement  
Figure 8: Route deviation angles.  
deviation angle. In the simulation of each pattern, the mobile sink designates a node near its initial point as a RA.  
The length of the DAMST point list denotes the number of nodes in it. Simulation results on the length of the DAMST point list in the four patterns are shown in Figure 10. It can be seen that, only in the Straight Line pattern, the length of the DAMST point list increases continuously as the mobile sink moves, for the reason that all footprints on the trajectory of the mobile sink become DAMST points during the moving time. In the Random pattern, the number of nodes in the DAMST point list changes randomly due to its random trajectory. In the Wave pattern, for the mobile sink changes its movement deviation direction periodically, the length of the DAMST point list also changes accordingly. In the Circle pattern, the length of the DAMST point list increases at first then decreases to zero as the mobile sink moving back to its original place after a period of time.
# Investigation of High-Sensitivity pH Sensor Based on Au-Gated AlGaN/GaN Heterostructure
## 3 Results and Discussion
### Transfer Characteristics and Potential Sensitivity
The experimental results indicate that the S\({}_{\text{V}}\) of the sensor could reach 58.59 mV/pH with a linearity of 97.92% in the pH range from 4.0 to 9.18. This S\({}_{\text{V}}\) is very close to the Nernstian limit of 59.16 mV/pH at 25 \({}^{\circ}\)C.  
Table 1 shows the comparative analysis of this work with the previously reported AlGaN/GaN pH sensors. The significant difference between this work and some previous reports is that Au is used as the material of the sensing membrane. It is evident from Table 1 that the fabricated Au-gated AlGaN/GaN pH sensor of this work exhibits higher potential sensitivity.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 4 Proposed Method
### Anomaly Detection Metrics
#### 4.4.2 Anomaly Score Calculation  
Finally, the anomaly score is computed by combining the effects of both first- and second-order derivatives:  
\[\mathbf{v_{s}}=\max_{i}\left(\sum_{j=0}^{n-1}|d[i][j]|\times\frac{\text{total\_max }}{\max[i]}\right) \tag{14}\]  
where \(d[i][j]\) is the derivatives for node \(i\) in step \(j\), _total\(\_max\)_ is the product of the maximum derivative values across all nodes, and \(\_max[i]\) is the maximum observed derivative for node \(i\).  
To further illustrate the computation of Equation (14), we consider a small dynamic graph with **three nodes**, where each **row** in the \(d-\)matrix represents a node, and each column represents the derivatives of a node across three timestamps, as shown below:  
\[d=\begin{bmatrix}0.2&0.5&0.3\\ 0.8&0.6&0.7\\ 0.1&0.4&0.2\end{bmatrix}\]  
From the matrix, \(d\), the maximum derivative per node (_row\({}_{1}\)_, _row\({}_{2}\)_, _row\({}_{3}\)_) is \(\max=\{0.5,0.8,0.4\}\), and the total maximum is **0.8**.  
For **Node 1**, the anomaly score computed as  
\[v_{s}(1)=\sum_{j=0}^{2}|d[1][j]|\times\frac{\text{total\_max}}{\max[1]}\]  
\[=(0.2+0.5+0.3)\times\frac{0.8}{0.5}=\textbf{1.6}\]  
Similarly, for **Node 2**,  
\[v_{s}(2)=(0.8+0.6+0.7)\times\frac{0.8}{0.8}=\textbf{2.1}\]  
and for **Node 3**,  
\[v_{s}(3)=(0.1+0.4+0.2)\times\frac{0.8}{0.4}=\textbf{1.4}\]  
Finally, the anomaly score is determined as follows:  
\[\mathbf{v_{s}}=\max(1.6,2.1,1.4)=\textbf{2.1}\]  
This example illustrates how the anomaly score prioritizes nodes with significant derivative changes, emphasizing abnormal patterns in the dynamic graph. The final score, \(\mathbf{v_{s}}\), reflects the anomalous behavior of the node relative to its past and to other nodes.
## 3 Results and Discussion
### Factors Influencing J\({}_{\text{SC}}\) and Voc  
For the short-circuit photocurrent (\(J_{\text{SC}}\)) parameter, it is defined as [36]:  
\[J_{\text{SC}}=\int_{\lambda}LHE(\lambda)\phi_{\text{{\it inject}}}\eta_{\text{{ \it collect}}}d\lambda \tag{4}\]  
where \(LHE(\lambda)\) is the light harvesting efficiency at a given wavelength, and \(\phi_{\text{{\it inject}}}\) is the electron injection efficiency. \(\eta_{\text{{\it collect}}}\) denotes the charge collection efficiency relative to photoanode properties, and thus it can be viewed as a constant. \(LHE(\lambda)\) is expressed as:  
\[LHE=1-10^{-f} \tag{5}\]  
where \(f\) represents the oscillator strength of dye molecules corresponding to wavelength \(\lambda_{\text{max}}\). From Table S2, the value of oscillator is in this order of XY1 > XY1 and D35 > D35, resulting in the LEH trend of XY1 > XY1 and D35 > D35. Furthermore, driving force can be estimated from the process of electron injection, and calculated value of driving force is in this order of D35 > XY1 and D35 > XY1, which means that electron injection takes place easily for the desensitization compared with XY1.  
The improved LEH and electron transfer should be the reason for improving the J\({}_{\text{sc}}\) in the desensitization system. Furthermore, \(V_{\text{OC}}\) can be closed to the conduction band edge of the semiconductor substrate (\(E_{CB}\)), and \(\Delta E_{CB}\) is the shift of \(E_{CB}\) under adsorption [15; 17; 37]  
\[\Delta E_{CB}=-\frac{q\mu_{normal}\gamma}{\varepsilon_{0}\varepsilon} \tag{6}\]  
In this expression, dipole moment of \(\mu_{normal}\) is perpendicular to the surface of a semiconductor, and \(\gamma\) denotes the surface concentration of dyes. \(\varepsilon_{0}\) and \(\varepsilon_{0}\) represent the vacuum permittivity and the dielectric permittivity, respectively. It is obvious that \(\mu_{normal}\) is the key factor in determining \(V_{\text{OC}}\), and the calculated results is in this order of XY/D35 (27.3679D) > XY1 (\(-\)17.1053D) > D35 (13.9281D), meaning that the larger \(\mu_{normal}\) results in the larger \(V_{\text{OC}}\) in desensitization system.