esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Ionic Conductivity and Dielectric Relaxation Investigations
#### 3.2.1 Broadband Ionic Conductivity Measurements
The above observations allude to concepts upheld by the Dyre-Schroder macroscopic model of ac conductivity in disordered solids (in the case of NASICON, defined by the intrusion of zirconia contaminants) [71; 76; 77]. The model assumes length scales on the order of the correlation length of charge transfer groups, and considers the distribution of disordered percolation pathways that are accessible for charge flow. Thus, long-range macroscopic conduction ensues once a charge percolation threshold is reached. The morphology of a percolation cluster affects the connectivity between nearest neighboring contacts, thus defining the tortuosity of the porous media. Over high frequencies, sub-percolation clusters become ac conductive, as rapid electric field fluctuations limit charge displacement to within the period of field reversal. At lower frequencies, charge transport can extend to longer distances, thus approximating dc flow, giving rise to dc plateaus accordingly. This can account for the diminishing dc plateaus over increasing frequencies and the emergence of the diagonal ridge feature of high ac conductivity observed in the phase-space mappings. The relatively similar activation energies are also accounted for by the fact that ac currents at extreme disorder (in this case induced by excess zirconia contaminants) are carried entirely by the percolating cluster above the threshold; thus, contributions from isolated low activation energy clusters are negligible [77].
# Covert Communications in Active-RIS-Aided NOMA Systems: Element Grouping or Not?
## 1 Introduction
Also, the findings in this paper are valuable and meaningful for a practical design of the ARIS-aided covert communications. The remaining sections of the paper are organized as follows: Section 2 presents the system model, including the ARIS-aided transmission scheme with element grouping (denoted as the w-EG scheme in the following) and the ARIS-aided transmission scheme without element grouping (denoted as the w/o-EG scheme in the following). The problem formulation and the proposed solutions are presented in Section 3. Simulation results are illustrated in Section 4. Finally, Section 5 concludes the work. _Notations:_ Throughout the paper, scalars are represented by italic letters, and vectors and matrices are represented by boldfaced lowercase and uppercase letters, respectively. \(\Pr\{\cdot\}\) denotes probability, and \(\|\cdot\|\) denotes the Euclidean norm of a complex vector. Superscript \((\cdot)^{T}\), \((\cdot)^{*}\), and \((\cdot)^{H}\) denote vector/matrix transpose, conjugate, and Hermitian transpose, respectively. \(\mathbb{C}^{M\times N}\) denotes the spaces of \(M\times N\) complex-valued matrices. \(\mathbf{A}\succeq 0\) indicates that \(\mathbf{A}\) is a positive semi-definite matrix. The symbols \(\mathrm{rank}(\mathbf{A})\) and \(\mathrm{tr}(\mathbf{A})\) denote the rank and trace of matrix \(\mathbf{A}\), respectively. \(\mathbf{0}_{N}\) and \(\mathbf{1}_{N}\) denote the \(N\)-dimensional all-zero and all-ones vectors, respectively. \(\mathbf{I}_{N}\) denote the \(N\times N\) identity matrix. \(\mathrm{diag}(\mathbf{a})\) denotes the \(N\times N\) diagonal matrix with diagonal elements \(a_{1},\ldots,a_{N}\). The notation \(\mathcal{CN}(\
u,\sigma^{2})\) denotes a circular symmetric complex Gaussian (CSCG) random distribution with mean \(\
u\) and variance \(\sigma^{2}\).
## 5 Conclusions  
In this study, we evaluated the use of images captured by UAVs and photogrammetry techniques using the SfM algorithm for the identification and delineation of treetops located in different spatial densities using deep learning. The architecture of the model adopted, the Mask R-CNN, corresponds to the state-of-the-art in the instance segmentation process and the images used considered exclusively the RBG sensors of digital cameras. Additionally, a methodology for the elaboration of the ground truth was adopted, which increased the viability of the analysis performed. An unsupervised algorithm was developed that contributes to the automation of instance segmentation in remote sensing images.  
Therefore, the main contributions of the proposed approach in this study are: (1) the effort reduction for the elaboration of the ground truth and of the training samples; (2) proposal of a model to identify and delimit dense tree canopies in images of different characteristics with high accurate results (Detection: Overall accuracy and F1-score 97.01% and 98.48%, respectively, and design: IoU \(>\) 0.5 average of 0.848 and average F1-score of 91.6%); (3) development of a methodology to encapsulate all procedures adopted, reducing the need for manual operation.  
The analysis performed did not cover all the possibilities involving the improvement of the instance segmentation process using SfM-based data. Future works will be carried out to explore other agriculture and forestry crops, where the crowns also present high spatial density, using data with different imaging characteristics.  
Conceptualization, F.L., F.M.B. and H.K.; methodology, F.L.; software, F.L.; validation, F.L.; formal analysis, F.L.; investigation, F.L.; resources, F.M.B. and H.K.; data curation, F.L. and F.M.B.; writing--original draft preparation, F.L.; writing--review and editing, F.M.B. and H.K.; visualization, F.L.; supervision, F.M.B. and H.K.; project administration, H.K. All authors have read and agreed to the published version of the manuscript.  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES)--Finance Code 001.  
Not applicable.  
The authors declare no conflict of interest.
## 6 Simulations Results  
According to the development of DOA estimation described in the introduction of this paper, some algorithms are summarized to compare the performance. The previous algorithm will not be elaborated in this paper. A total of nine algorithms are compared and analyzed in this section. Algorithm 1 is scalar MUSIC; Algorithm 2 is TF-MUSIC; Algorithm 3 is polarized MUSIC; Algorithm 4 is PTF-MUSIC; Algorithm 5 is the estimation of polarization DOA with eliminating strong interference. Algorithm 6 is TF-MUSIC based on cross terms. Algorithm 7 is a polarization ESPRIT based on time-frequency cross terms. Algorithm 8 is a polarization MUSIC based on time-frequency cross terms. Algorithm 9 is a dual-algorithm polarization DOA estimation based on time-frequency cross terms, which is the proposed algorithm in this paper. For convenience, these algorithms are referred to as A1\(\sim\)A9.
## 5 Classification and Regression of the Runway Overrun Risk
### Regression Analysis of the Runway Overrun Risk
#### 5.1.3 Regression Results  
In this paper, 60% of the flights are used as the training dataset, and 40% are used as the test dataset. The results are shown in Table 3. Considering the randomness of the algorithms, each algorithm is run 40 times, and the mean and standard deviation of the evaluation metrics are provided in Table 2. The best results are highlighted in bold.  
From Table 2, it can be observed that the neural network model has better prediction accuracy than the other algorithms, with an average MAPE of approximately 11.29%. The worst performing algorithms in terms of prediction accuracy are DT and SVR, with average MAPEs of 0.1908 and 0.2853, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Name** & **Physical Meaning** & **Correlation Coefficient** \\ \hline
1 & LEAVE\_MANNER & Leave from the middle or end of runway & \(-0.71\) \\
2 & ABK\_RATE & Automatic deceleration rate & \(-0.65\) \\
3 & MBK\_POS & Manual brake position & \(0.48\) \\
4 & IS\_EXIST\_HMBK & Is the emergency brake present? & \(-0.38\) \\
5 & HMBK\_RE\_DIS & Remaining runway distance when emergency braking & \(-0.44\) \\
6 & TD\_POS & Touchown position & \(0.33\) \\
7 & 50 FT\_KE & Kinetic energy at 50 feet & \(0.34\) \\
8 & GS\_LEAVE\_10 & Maximum ground speed in the last 10 s & \(-0.36\) \\  & & before reaching 20 knots & \\
9 & FLARE\_TIME & Time from 50 feet to touchdown & \(0.35\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features related to runway overrun.  
Furthermore, the random forest algorithm is used to provide a feature importance ranking, as shown in Figure 10. The automatic braking rate (ABK_RATE) feature has the highest weight (close to 0.32), followed by the leaving manner (LEAVE_MANNER) and touchdown position (TD_POS) features, with approximate weights of 0.3 and 0.2, respectively. The importance of the other features is below 0.1, which is consistent with previous correlation analysis results.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 2 Materials and Methods  
### GAN Explanation and Definition  
The structure of a GAN model is based on the idea of two neural networks--a generator that creates the data and a discriminator that has to decide if the data are real or fake. The role of the generator is to generate such data that the discriminator is fooled. Since the first introduction of the GAN methodology, various implementations and variations have been developed. A notable characteristic of the GAN architecture is that the generator and the discriminator are trained separately, and more importantly, alternately.  
Figure 1 shows the simplified structure of a GAN model, in which the interaction between the generator and the discriminator is visible. The generator generates data with random samples from the latent space, which is forwarded to the discriminator that classifies it as real (1) or fake (0). Since the generator's objective is to produce synthetic electrical load profiles that are virtually indistinguishable from real consumption patterns, and the discriminator's task is to differentiate between authentic and generated daily profiles, the loss function becomes a central mechanism in guiding the training process.  
For time-series data like electrical load profiles, where subtle variations in hourly consumption, peak timing, and temporal coherence matter, the loss function must be designed to encourage both networks to improve in a coordinated manner. It should drive the discriminator to become increasingly sensitive to fine-grained temporal differences between real and synthetic profiles, such as ramp-up rates, load spikes, or evening baseloads, which often characterize residential or industrial behavior. At the same time, it should guide the generator to produce profiles that not only match the statistical distribution of the real data (e.g., daily peak loads and standard deviations) but also preserve realistic load dynamics over time. Through this adversarial feedback loop, the loss function ensures that after each epoch the discriminator becomes better at flagging unrealistic patterns, whose loss function is denoted as d_loss, while the generator, denoted as g_loss, learns to correct and refine its output, gradually converging toward high-fidelity, temporally consistent load profiles that reflect real-world energy usage.  
For the training of the generator, a latent space, \(z\in\mathbb{R}^{d_{z}}\), is defined, and for the training of the discriminator, half of the real samples and half of the generated samples are taken.  
Figure 1: Simplified structure of a GAN model.
# A Novel System of Mixed RF/FSO UAV Communication Based on MRR and RIS by Adopting Hybrid Modulation
## 3 Performance Analysis
### Average Bit Error Rate
#### 3.2.1 End-to-End Uplink  
The average BER of FSO uplink using hybrid \(L\)-PPM-BPSK-SIM is derived as:  
\[\begin{split} P_{2}&=\int_{0}^{\infty}\dfrac{1}{2 }erfc\Big{(}\dfrac{\sqrt{\gamma L\text{log}_{2}L}}{4\sqrt{2}}\Big{)}f_{\gamma _{2}}(\gamma)\,d\gamma\\ &=\dfrac{\xi^{2}2^{(\alpha_{1}+\alpha_{2}+\beta_{1}+\beta_{2}-6) }}{\pi^{3/2}\Gamma(\alpha_{1})\Gamma(\alpha_{2})\Gamma(\beta_{1})\Gamma(\beta_ {2})}G\dfrac{10,2}{4,11}\Bigg{(}\dfrac{\alpha_{1}^{2}\alpha_{2}^{2}\beta_{1}^{ 2}\beta_{2}^{2}}{8A_{0}^{2}L\log_{2}L\bar{\gamma}_{2}}\bigg{|}\kappa_{1}\Bigg{)},\end{split} \tag{18}\]  
where \(\kappa_{1}=1,\dfrac{1}{2},\dfrac{\xi^{2}+1}{2},\dfrac{\xi^{2}+2}{2};\)\(\kappa_{2}=\dfrac{\xi^{2}}{2},\dfrac{\xi^{2}+1}{2},\dfrac{\alpha_{1}}{2},\dfrac{ \alpha_{1}+1}{2},\dfrac{\alpha_{2}}{2},\dfrac{\alpha_{2}+1}{2},\dfrac{\beta_{ 1}}{2},\dfrac{\beta_{1}+1}{2},\dfrac{\beta_{2}}{2},\dfrac{\beta_{2}+1}{2},0\). Substituting (17) and (18) into (16) and using the extended generalized bivariate Meijer's G function (EGBMGF) [46], the average BER of the end-to-end uplink is obtained as:  
\[\begin{split} P_{u}=&\dfrac{1}{2\sqrt{\pi}(N-1)!}G \begin{array}{c}2,1\\ 2,2\end{array}\Bigg{(}\dfrac{L\text{log}_{2}LB\bar{\gamma}_{1}}{32}\Bigg{|} \begin{array}{c}1-N,1\\ 0,1/2\end{array}\Bigg{)}+\dfrac{\xi^{2}2^{(\alpha_{1}+\alpha_{2}+\beta_{1}+ \beta_{2}-6)}}{\pi^{3/2}\Gamma(\alpha_{1})\Gamma(\alpha_{2})\Gamma(\beta_{1}) \Gamma(\beta_{2})}\\ &\times\Bigg{\{}G\begin{array}{c}10,2\\ 4,11\end{array}\Bigg{(}\dfrac{\alpha_{1}^{2}\alpha_{2}^{2}\beta_{1}^{2}\beta_{2 }^{2}}{8A_{0}^{2}L\log_{2}L\bar{\gamma}_{2}}\bigg{|}\begin{array}{c}\kappa_{1} \\ \kappa_{2}\end{array}\Bigg{)}-\dfrac{1}{(N-1)!\sqrt{\pi}}\\ &\times G\begin{array}{c}0,0:2,1:10,2\\ 0,0:2,2:4,11\end{array}\Bigg{(}\Bigg{|}\begin{array}{c}1-N,1\\ 0,1/2\end{array}\Bigg{|}\begin{array}{c}\kappa_{1}\\ \kappa_{2}\end{array}\Bigg{|}\dfrac{L\text{log}_{2}LB\bar{\gamma}_{1}}{32}, \dfrac{\alpha_{1}^{2}\alpha_{2}^{2}\beta_{1}^{2}\beta_{2}^{2}}{8A_{0}^{2}L \log_{2}L\bar{\gamma}_{2}}\Bigg{)}\Bigg{\}}.\end{split} \tag{19}\]
# Coverage and Rate Analysis for Location-Aware Cross-Tier Cooperation in Two-Tier HetNets
## Appendix C  
Firstly, we derive the coverage probability of a randomly chosen non-CoMP user, that is, \(u\in U_{l},l\in\{M,in;\ M,out;\ F\}\). Using the definition of the coverage probability, then:  
\[\mathcal{P}_{l}\left(\tau\right)=\int_{\Omega_{l}}\mathbb{P}\left(\frac{P_{K \left(l\right)}\left|h_{x}\right|^{2}x^{-\alpha_{K\left(l\right)}}}{\sum_{i=1}^ {2}l_{i}+\sigma_{z}^{2}}>\tau\right)f_{X_{K\left(l\right)}}\left(x\right)dx\]  
where \(\Omega_{s}\) is the range of the distance between the typical user and their serving BS, that is, \(\Omega_{M,in}=\left(0,D\right]\), \(\Omega_{M,out}=\left(D,\infty\right)\) and \(\Omega_{F}=\left(0,\infty\right)\). \[\mathbb{P}\left(\frac{P_{K\left(l\right)}\left|h_{x}\right|^{2}x^ {-\alpha_{K\left(l\right)}}}{\sum_{i=1}^{2}l_{i}+\sigma_{z}^{2}}>\tau\right) =\mathbb{P}\left(\left|h_{x}\right|^{2}>\frac{\tau x^{\alpha_{K \left(l\right)}}}{P_{K\left(l\right)}}\left\{\sum_{i=1}^{2}l_{i}+\sigma_{z}^{2 }\right\}\right)\] \[\overset{(a)}{=}\mathbb{E}_{I_{l}}\left[\exp\left(-\frac{\tau x ^{\alpha_{K\left(l\right)}}}{P_{K\left(l\right)}}\left\{\sum_{i=1}^{2}l_{i}+ \sigma_{z}^{2}\right\}\right)\right]\] \[\overset{(b)}{=}\exp\left(-\frac{\tau\sigma_{z}^{2}x^{\alpha_{K \left(l\right)}}}{P_{K\left(l\right)}}\right)\prod_{i=1}^{2}L_{I_{l}}\left( \frac{\tau x^{\alpha_{K\left(l\right)}}}{P_{K\left(l\right)}}\right)\]  
for which: (a) it follows that \(\left|h_{x}\right|^{2}\sim Exp\left(1\right)\), and (b) the above follows from the independence of \(I_{l}\), and \(L_{I_{l}}\left(s\right)\) is the Laplace transform of interference. Considering the definition of the Laplace transform, we have:  
\[ \begin{split} L_{I_{l}}\left(s\right)=\mathbb{E}_{I_{l}}\left[ \exp\left(-sI_{l}\right)\right]&=\mathbb{E}_{\Phi_{l_{i}}\left[ \left|h_{y}\right|^{2}\right\}}\left[\exp\left(-sP_{l}\sum_{y_{i}\in\Phi_{l_{ i}}\backslash\{x_{k};k\in\mathcal{K}\left(l\right)\}}\left|h_{y}\right|^{2} \left\|y_{i}\right|^{-\alpha_{i}}\right)\right]\\ &\overset{(a)}{=}\mathbb{E}_{\Phi_{l}}\left[\prod_{y_{i}\in\Phi_ {l_{i}}\backslash\{x_{k};k\in\mathcal{K}\left(l\right)\}}\mathbb{E}_{\left\{ \left|h_{y}\right|^{2}\right\}}\left[\exp\left(-sP_{l}\left|h_{y}\right|^{2} \left\|y_{i}\right|^{-\alpha_{i}}\right)\right]\right]\\ &\overset{(b)}{=}\mathbb{E}_{\Phi_{l}}\left[\prod_{i\in\Phi_{l} \backslash\{x_{k};k\in\mathcal{K}\left(l\right)\}}\frac{1}{1+sP_{l}\left\|y_{i }\right\|^{-\alpha_{i}}}\right]\\ &\overset{(c)}{=}\exp\left(-2\pi\lambda_{i}\int_{d_{\mathcal{K }\left(l\right)}\left(x\right)}^{\infty}\frac{v}{1+\left(sP_{l}\right)^{-1}v^{ \alpha_{i}}}dv\right)\end{split}  \tag{40}\]  
for which: (a) the above is obtained from the independence of \(\left|h_{y}\right|^{2}\), (b) it follows that \(\left|h_{y}\right|^{2}\sim Exp\left(1\right)\), and (c) the above follows from the probability generating functional (PGFL) of \(\Phi_{i}\) and by replacing \(v=\left\|y_{i}\right\|\).
# Design Trends and Comparative Analysis of Lightweight Block Ciphers for IoTs
## 5 Comparative Analysis
The pie chart in Figure 44 illustrates the structural classification of 58 lightweight block ciphers (LWBCs). The majority, 32.8%, follow the substitution  
Figure 43: Trade-off triangle in lightweight cipher design. permutation network (SPN) structure, which is widely favored for its strong diffusion and simplicity in hardware and software implementations. Feistel network (FN) ciphers make up 24.1%, offering advantages in invertibility and symmetric design. generalized Feistel networks (GFNs) account for 15.5%, balancing flexibility and performance. Hybrid designs, which combine elements from multiple structures, constitute 19%, reflecting innovation in cipher construction. The smallest group, ARX-based ciphers, makes up only 8.6% and is typically used for high-speed, software-oriented encryption. This distribution highlights the dominance of SPN and FN models in lightweight cryptographic design. A comprehensive comparison of 58 lightweight block ciphers proposed between 2018 and 2025 is provided in Table 4, detailing their structural classification, design parameters, implementation cost, and identified limitations to aid in performance and security evaluation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Cipher** & **Block Size** & **Key Size** & **Structure** & **Rounds** & **GE** & **Limitation** \\ \hline
1 & NVLC & 64 & 80 & SPN & 20 & 1134 & High power use for ultra-low-power IoTs. \\ \hline
2 & RARE & 64 & 128 & SPN & 13 & 1500 & Slow for real-time use; optimized for images. \\ \hline
3 & SAT\_Jo & 64 & 80 & SPN & 31 & 1600 & High rounds lead to overhead. \\ \hline
4 & CRAFT & 64 & 128 & SPN & 31 & 1584 & Slow decryption, tweak complexity. \\ \hline
5 & DoT & 64 & 80 & SPN & 31 & 993 & Slow due to high round count. \\ \hline
6 & HERMES & 64 & 128 & SPN & 30 & 1900 & Unverified software efficiency. \\ \hline
7 & Loong & 64 & 128 & SPN & 32 & 1766 & Higher latency due to dual SubCells. \\ \hline
8 & ACT & 64 & 80 & SPN & 31 & 1481 & Needs more cryptanalysis. \\ \hline
9 & ILEA & 64 & 128 & SPN & 20 & 1600 & 25\% slowdown due to BBM. \\ \hline
10 & PriPresent & 64/80 & 80/128 & SPN & 31 & 1350 & SHA-256 adds overhead. \\ \hline
11 &
\begin{tabular}{c} 3D\_ \\ RECTANGLE \\ \end{tabular} & 64 & 80 & SPN & 28 & 1400 & Slower due to 3D rotation. \\ \hline
12 & ULM & 64 & 80 & SPN & 15 & 1200 & Small S-box may lower non-linearity. \\ \hline
13 & LAO-3D & 64 & 128 & SPN & 20 & 1600 & 3D increases latency. \\ \hline
14 & FUTRE & 64 & 128 & SPN & 32 & 1300 & Limited cryptanalysis. \\ \hline
15 & IVLBC & 64 & 128 & SPN & 76 & 1450 & Low S-box non-linearity. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of 58 LWBC. Figure 44: Distribution of 58 ciphers by structure type.
# Optimization of Airflow Organization in Bidirectional Air Supply Data Centers in China
## 3 Results
### Flow Field Distribution  
Figure 8 presents the velocity distribution of cross-sections at the front (x = 2.7 m), middle (x = 5.7 m), and rear (x = 8.7 m) of the baseline model. Under the original air supply configuration, the collision between bidirectional airflow from the air conditioning system results in inconsistent perforated tile airflow directions, leading to non-uniform air distribution and chaotic airflow organization, accompanied by severe mixing of cold and hot air. In cold aisles AB and CD, the vertical upward momentum of the airflow from perforated tiles is insufficient at the front, middle, and rear sections of the rack rows, causing the recirculation of server exhaust heat into the cold aisles and impairing cooling effectiveness. In the bidirectional supply zone, cold aisles CD and EF experience excessive airflow at the front section of the rack rows, where cold air escapes from the top of the aisles without fully cooling the servers. At the middle section, the supplied airflow is influenced by the hot exhaust passing through the racks, resulting in cooling capacity loss and temperature rise. At the rear section, insufficient air supply is observed, and due to the higher power consumption of rear servers, the increased exhaust airflow leads to significant hot air accumulation in the upper part of the room under buoyancy effects, further exacerbating hot air recirculation. Additionally, a portion of cold air bypasses the racks through unoccupied spaces and enters the hot aisles, contributing to cooling capacity wastage.
# Protoporphyrin IX Based All-Solid-State Ion-Selective Electrodes for Choline Determination In Vitro
## 3 Results and Discussion
### Life Span  
The long-term stability of Ch sensors was examined by the potentiometric response in 35 days. The experiments were carried out every five days in freshly prepared ChCl solutions, with a concentration range of 10\({}^{-7}\)-10\({}^{-2}\) M. After each experiment, the sensor was rinsed with deionized water and stored in a dark closet. The sensitivities of \(54.40\pm 0.79\) mV/decade in Figure 6 illustrates that Ch sensor could perform stably for at least one month.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Interference** & **Constant Concentration (M)** & **Selective Coefficient (Log)** \\ \hline K\({}^{+}\) & 0.1 & \(-2.93\) \\ Na\({}^{+}\) & 0.1 & \(-4.54\) \\ Ca\({}^{2+}\) & 0.1 & \(-4.50\) \\ GC & 0.1 & \(-4.00\) \\ DA & 0.01 & \(-2.00\) \\ AA & 0.01 & \(-3.50\) \\ HA & 0.01 & \(-1.93\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selective coefficients to K\({}^{+}\), Na\({}^{+}\), Ca\({}^{2+}\), GC, DA, AA, HA.  
Figure 4: (**A**) Potentiometric response (\(10^{-5}\)–\(10^{-2}\) M of ChCl solutions) within a pH range of 7.0–8.0, in the background of acetate buffers; and (**B**) the effect of temperature on the sensitivities of the ChCl ISE within the range of 34–42 \({}^{\circ}\)C.  
Figure 5: Repeatability of five Ch sensors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Number of Ch ISE**} & \multicolumn{3}{c}{**C(Ch) (\(\mu\)M)**} \\ \cline{2-5}  & **3** & **10** & **100** \\ \hline \multirow{3}{*}{No. 1} & Mean (\(\mu\)M) & 2.92 & 10.15 & 108.11 \\  & S. D. (\(\mu\)M) & 0.16 & 0.29 & 1.99 \\  & R. S. D (\%) & 7.57 & 1.48 & 8.11 \\ \hline \multirow{3}{*}{No. 2} & Mean (\(\mu\)M) & 2.9 & 9.93 & 105.75 \\  & S. D. (\(\mu\)M) & 0.02 & 0.1 & 2.51 \\  & R. S. D (\%) & 8.1 & 0.71 & 5.75 \\ \hline \multirow{3}{*}{No. 3} & Mean (\(\mu\)M) & 2.97 & 9.98 & 107.46 \\  & S. D. (\(\mu\)M) & 0.06 & 0.19 & 0.59 \\  & R. S. D (\%) & 5.86 & 0.24 & 7.46 \\ \hline \multirow{3}{*}{No. 4} & Mean (\(\mu\)M) & 2.86 & 9.78 & 109.59 \\  & S. D. (\(\mu\)M) & 0.25 & 0.52 & 4.29 \\ \cline{1-1}  & R. S. D (\%) & 9.35 & 2.16 & 9.58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reproducibility of four Ch sensors.  
Figure 6: Sensitivities of the Ch sensor during 35 days.