esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Two-Stage Time-Domain Equalization Method
### Up/Downsampling Method  
In this study, we adopted linear interpolation-based upsampling and downsampling methods to control the time resolution of the signal.  
In the first-stage equalization, the acquired transmitted and received signals are upsampled before being input into the network for training. The linear interpolation method is employed in the upsampling process. For an original signal \(x[n]\), upsampling by a factor of \(L\) involves two steps: (1) zero insertion, where \(L-1\) zeros are inserted between each pair of adjacent samples; and (2) linear estimation of the intermediate values. The interpolated signal is given by the following formula:  
\[x_{\text{up}}[k]=x[n]+\frac{k\text{ mod }L}{L}\cdot(x[n+1]-x[n]),\quad\text{ where }k=nL+i,\;i=0,1,\ldots,L-1. \tag{7}\]  
Here, \(x[n]\) and \(x[n+1]\) denote two adjacent original signal samples. The index \(k\) refers to the position in the upsampled sequence, while \(n\) is the corresponding original index such that \(k=nL+i\). The operator mod returns the remainder when \(k\) is divided by the upsampling factor \(L\), and thus, \(\frac{k\bmod L}{L}\) represents the relative position between \(x[n]\) and \(x[n+1]\). This term acts as a linear interpolation weight. The resulting \(x_{\text{up}}[k]\) is the interpolated value at the \(k\)-th position of the upsampled signal.  
In the second-stage equalization, the coarse equalized signal output by the first-stage equalization model and the transmission signal are downsampled and then the second-stage equalization training is performed. In order to avoid aliasing and retain the waveform trend, we use linear interpolation to reconstruct the continuous approximation of the upsampled output. The signal is then evaluated at a lower rate position to generate a downsampled sequence.  
\[x_{\text{down}}[m]=x(mD) \tag{8}\]  
In this equation, \(x(t)\) denotes the continuous signal reconstructed from the original discrete sequence via linear interpolation. The index \(m\) indicates the position in the downsampled sequence, and \(D\) is the downsampling factor. The value \(x_{\text{down}}[m]=x(mD)\) is obtained by evaluating the reconstructed signal at intervals of \(D\), thereby producing a lower-resolution version of the original signal while retaining its essential shape.
## 2 System Architecture and Device
### System Architecture  
The system flow chart for our novel electronic lock is shown in Figure 3. Firstly, the mobile phone coded the user's numerical ciphers into a set of Morse code and sent out the according flashlight signals generated by its light-emitting components (LED). Secondly, the decoder's photoresistor depicted the optical analog signals received on a voltage-time coordinate to translate it back to their original Morse code set through the built-in microcontroller. Thirdly, the system compared the result with the user's command. If the codes match, the electronic lock would be opened and simultaneous reports on the user's identity, system condition and time would be uploaded to the MCS cloud monitoring system by Wi-Fi so that distant users could keep control on their home security.  
After the initial trial, we revised the system by substituting the original fixed resistor with a MCP41010 digital potentiator and used an environmental photosensor and fuzzy theory to prevent interference from ambient light. The new designed system worked as follows:  
The environmental photosensor received ambient light intensity (Lux) as the input into the calculator to obtain the wiper for the digital potentiator control. In this circuit, both the photoresistor and the digital potentiator adjusted proportionally to lightening variables, resulting in a stable voltage output independent of ambient light intensity. As shown in our experimental results, the circuit dramatically enhanced the accuracy of the optical Morse-code decoder.  
Figure 2: System Architecture diagram of the fuzzy controller.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.5 Robot Control I  
In [73], the authors introduce a framework for RL and ROS to govern robots in simulated environments. The authors state that there are no existing frameworks that focus on robot safety and simulation of staircases for tracked robots. This framework extends their previous research [65], which relied on different configurations to evaluate the movement and state of the robot. The work in [73] used a structure API to merge Domain Randomization (DR) (for more information about DR, please refer to [91]) with the physics engine and the OpenAI Gym environment. This makes it easier to learn full control and use more advanced DR methods, such as gradual Gaussian settings.  
The authors in [73] used the SAC RL algorithm to teach robot control strategies in challenging environments such as stair cases. The authors uses an ROS to facilitate communication between simulation tools, low-level control systems, sensor components,  
Figure 10: ROS-based multirobot DRL framework. For more information, please refer to Table 4.  
and the OpenAI Gym environment. This approach integrates the SAC algorithm, which allows the robot to learn through trial and error, as shown in Figure 11. The experiment shows that gradual adjustment of discount rates improved the convergence of rewards and safety performance. Policies trained with gradual adjustments led to more progress towards the goal, enhanced stability, and lower pitch angular velocities, suggesting smoother and safer task execution.  
The above-mentioned work shows that the framework works well for creating reliable control strategies for tracked articulated robots operating in difficult environments. Testing the DR method in difficult outdoor conditions with different types of robots, such as those on wheels or legs, can verify how well it works in different settings. In addition, testing and integrating other RL algorithms could optimize control rules for complex tasks such as obstacle avoidance or collaborative operations.
# Exploring the Direct and Indirect Use of ICT Measurements in DODME (Dynamic OD Matrix Estimation) +
## 2 Direct Problem Formulation
\(\Delta_{i}\sim Be(1/2,\ \pm 1)\), which is a Bernoulli distribution with the probability \(1/2\) for each \(\pm 1\) outcome, is the usual perturbation used. The spacing coefficient \(c_{k}\) and the step size \(a_{k}\) are decreasing sequences of positive real values that satisfy some regularity conditions to ensure the convergence of the method, as detailed in [28]. A detailed discussion on how the best values can be estimated and the possibility of auto-adaptive calculation approaches can be found in [6; 9]. This paper implements a version following the recommendation of [28], which involves averaging a predefined number \(n_{g}\) of independent estimations of the gradient of Equation (5) to contribute to a more stable and quicker convergence of the SPSA (stochastic perturbation stochastic approximation) method. Therefore, the gradient estimation is finally  
\[\hat{g}(\mathbf{X}_{k})=\frac{1}{n_{g}}\sum_{j=1}^{n_{g}}\hat{\mathbf{g}}_{k}^{j}(\bm {X}_{k}) \tag{6}\]  
where \(\hat{g}_{k}^{j}(\mathbf{X}_{k})\) is calculated as in Equation (5). The objective function is formulated in this DODME, including travel times, as:  
\[Z(\mathbf{X})=w_{1}\sum_{ijr}\Big{(}x_{ijr}-x_{ijr}^{H}\Big{)}^{2}+w_{2}\sum_{lt} (y_{lt}-\hat{y}_{lt})^{2}+w_{3}\sum_{k}(tt_{k}-\hat{t}_{k})^{2} \tag{7}\]  
where the discrepancies between the estimated origin-to-destination (OD) matrix \(\mathbf{X}\) and the historical OD matrix \(\mathbf{X}^{H}\), the measured link flows \(\hat{\mathbf{Y}}\) and the corresponding estimated flows \(\mathbf{Y}\), and the measured \(\hat{\mathbf{t}}\) and estimated travel times \(\mathbf{tt}\) are quadratic functions with weights \(w_{1},\ \ w_{2}\), and \(w_{3}\) that must be adjusted in order to adapt the magnitudes of each measure and to give more or less importance to each measure depending on its reliability. The indices \(i\) and \(j\) identify an origin and a destination, respectively, that belong to the set \(N\) of all origin-destination pairs, the index \(r\) corresponds to the trip departure time interval; the index \(l\) identifies the link with a counting station in the subset \(\hat{L}\) of links with counting stations; \(t\) identifies the counting time interval; and the index \(k\) identifies the subpath belonging to the set \(K=K_{T1}\cup K_{T2}\cup\ldots\cup K_{Tn}\) of the subpaths for periods \(\{T_{1},T_{2}\ldots T_{n}\}\) in which the time horizon \(\mathcal{T}\) is split.
# Influence of Stray Current on Fastening System Components in Urban Railway Tracks
## 4 Results
### Visual Inspection and Corrosion Classification on Fastening System Components
#### 4.2.1 Rails  
In all samples, material loss occurred at the edges of the rail foot. Figure 12 shows the conditions of the rails after the completion of the test. The measured minimum dimensions of rail foot height are shown in the figures. At the measured locations, the reference height of the rail foot was 8 mm, indicating that corrosion caused thinning of the rail foot by up to 4 mm for the ZG 3/2 fastening system, 3 mm for the PPE and DEPP fastening systems, and 2 mm for the Zagreb 21-CTT fastening system.  
The dimensions of the rail base surface affected by corrosion and the depth of the corrosion damage were measured for each rail sample. For each corrosion damage point, the corrosion level was classified as follows:  
* Low corrosion level: thinning of the rail foot up to maximum 1 mm,
* Medium corrosion level: thinning of the rail foot from 1 to 3 mm, and
* High corrosion level: thinning of the rail foot of more than 3 mm.  
The percentage of each corrosion level was calculated with respect to the total corrosion surface area of each rail sample, and the results are listed in Table 4. Samples with a high level of corrosion were classified as critical.
## 4 Discussion
### Short-Term Improvement Promotes Soil Fertility Enhancement  
Organic matter is widely recognized as a crucial component of soil fertility. In a rice-seeding-raising experiment utilizing short-term improvement, the organic carbon (SOC) in the seedling-raising soil exhibited significant enhancement (Figure 3). In alkaline soils, gypsum, which serves as an exogenous source of calcium, exerts a strong bridging effect that facilitates the connection between organic carbon and minerals, thereby promoting soil clay flocculation [34]. This function enhances the stability and accumulation of SOC [35; 36]. Furthermore, decomposed substances from straw can act as a cementing agent [37], contributing to the formation of microstructures, such as calcium-bonded organic carbon [38]. Research has demonstrated a positive correlation between exchangeable calcium (ECa) and SOC [39]. Our findings indicated that after two years of improvement, the SOC of the JCWH-treated soil was the highest and did not show significant differences compared to that of the ZCK soil.  
The composition of soil organic matter profoundly influences soil properties and the multifunctionality of ecosystems. Humic acid (HA) and fulvic acid (FA) are the primary active components of organic matter. HA significantly impacts soil fertility and structural stability [40]. The aromatic compounds in HA, including carboxyl and hydroxyl groups, can form organic-inorganic complexes with polyvalent cations at the active sites of min eral colloids, thereby enhancing the formation of microaggregates [41]. In this study, all amendment treatments significantly increased the amounts of HAC and FAC in the soils (Figures 4 and 5). In accordance with the findings of [PERSON] et al. [42], the FAC/SOC ratio in 2022 decreased in the JCWH treatment, while the HAC/SOC ratio increased, indicating a transformation trend from FA to HA. From the perspective of humic acid accumulation, the JCWH treatment demonstrates distinct advantages.  
In this study, after two years of short-term improvement, the PQ values and \(\Delta\)lgK values of HA in all amendment treatments were found to be higher than those of the control group, whereas the \(\Delta\)lgK values of FA were lower than those of the control group (Table 2). The addition of straw organic materials promotes the renewal and activation of soil organic matter, thereby enhancing the degree of humification in the soil [43]. Notably, the PQ value of the JCWH treatment increased progressively over the years, while that of the JCWF treatment exhibited a year-on-year decline. This indicates that the application of sulfuric acid significantly enhances the quality of soil humus. The HA \(\Delta\)lgK value of the JCWH-treated soil was the highest, suggesting that the addition of sulfuric acid effectively reduces both the oxidation and aromatization degrees of HA, increases the aliphatic carbon content, and simplifies its structure [44].
## 4 Results
### Model Performance and Evaluation
#### 4.3.1 Model Performance Scores  
We tested four algorithms: Logistic Regression (LR), Decision Tree (DT), Light Boosting (LB), and Extreme Gradient Boosting (XGB). The evaluation of these algorithms was based on metrics such as accuracy, precision, f1-score, and kappa value. We then selected the best-performing model to conduct an interpretable analysis of influential variables using SHAP feature importance. The results presented in Table 9 demonstrate that non-linear models, such as tree-based models, outperformed the linear model (Logistic Regression) across all wearing surface types. In particular, Light boosting yielded the highest performance in modeling deck maintenance across different wearing surfaces, with an average accuracy of 83.6% and AUC of 86.3%. Therefore, our further analysis is based on the Light Booting model.  
However, the AUC and kappa values suggest that certain surface-wearing types, such as Monolithic Concrete, Gravel, and Low Slump Concrete, were modeled reasonably well, as indicated by higher Kappa values and AUC scores (above 50%). On the other hand, the modeling of Bituminous and Other surface types showed less-than-ideal performance, possibly due to low and imbalanced sample sizes in the testing data. These findings imply that variations in the performance of the models can be attributed to the specific characteristics of the wearing surface types.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **F1 Score** & **AUC** & **Kappa** \\ \hline \multicolumn{6}{c}{Monolithic Concrete} \\ \hline LR & 0.767 & 0.725 & 0.735 & 0.841 & 0.735 \\ DT & 0.884 & 0.870 & 0.865 & 0.881 & 0.747 \\ XGB & 0.911 & 0.881 & 0.897 & 0.963 & 0.819 \\ LB & 0.905 & 0.872 & 0.893 & 0.964 & 0.808 \\ \hline \multicolumn{6}{c}{Gravel} \\ \hline LR & 0.779 & 0.565 & 0.568 & 0.768 & 0.413 \\ DT & 0.859 & 0.712 & 0.724 & 0.818 & 0.630 \\ XGB & 0.881 & 0.747 & 0.778 & 0.939 & 0.694 \\ LB & 0.873 & 0.737 & 0.768 & 0.936 & 0.669 \\ \hline \multicolumn{6}{c}{Wood or Timber} \\ \hline LR & 0.673 & 0.572 & 0.562 & 0.696 & 0.302 \\ DT & 0.714 & 0.616 & 0.633 & 0.702 & 0.399 \\ XGB & 0.761 & 0.675 & 0.700 & 0.839 & 0.501 \\ LB & 0.760 & 0.681 & 0.689 & 0.832 & 0.492 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative analysis of model performance for Logistic Regression (LR), Decision Tree (DT), XGBoost (XGB), and LightBoost (LB), across different bridge wearing surface types: Monolithic Concrete, Gravel, Wood or Timber, Bituminous, Low Slump Concrete, and Other.
## 3 State-Constrained Sub-Optimal Tracking Controller
### Stability of the Proposed Controller  
By the matrix inversion lemma [25,26],  
\[\begin{array}{ll}\mathbf{I}-\mathbf{C_{\mathit{h}}}\mathbf{B_{d}}\mathbf{R_ {\tau}}^{-1}\mathbf{B_{d}}^{T}\mathbf{C_{\mathit{h}}}^{T}\mathbf{Q}_{\tau}&= \mathbf{I}-\mathbf{C_{\mathit{h}}}\mathbf{B_{d}}\Big{(}\mathbf{R}+\mathbf{B_{ d}}^{T}\mathbf{C_{\mathit{h}}}^{T}\mathbf{Q}_{\tau}\mathbf{C_{\mathit{h}}} \mathbf{B_{d}}\Big{)}^{-1}\mathbf{B_{d}}^{T}\mathbf{C_{\mathit{h}}}^{T}\mathbf{ Q}_{\tau}\\ &=\Big{(}\mathbf{I}+\mathbf{C_{\mathit{h}}}\mathbf{B_{d}}\mathbf{R}^{-1} \mathbf{B_{d}}^{T}\mathbf{C_{\mathit{h}}}^{T}\mathbf{Q}_{\tau}\Big{)}^{-1}, \end{array} \tag{39}\]  
\[\mathbf{I}-\mathbf{Q}_{\tau}\mathbf{C_{\mathit{h}}}\mathbf{B_{d}}\mathbf{R_{ \tau}}^{-1}\mathbf{B_{d}}^{T}\mathbf{C_{\mathit{h}}}^{T}\mathbf{C_{\mathit{h}}} ^{T}=\Big{(}\mathbf{I}+\mathbf{Q}_{\tau}\mathbf{C_{\mathit{h}}}\mathbf{B_{d}} \mathbf{R}^{-1}\mathbf{B_{d}}^{T}\mathbf{C_{\mathit{h}}}^{T}\Big{)}^{-1}. \tag{40}\]  
Therefore, the following equations hold:  
\[\mathbf{Q}_{2}=\left[\begin{array}{cc}\mathbf{C}_{y}\\ \mathbf{C}_{h}\\ \mathbf{C}_{h}\mathbf{A}_{d}\end{array}\right]^{T}\left[\begin{array}{cc} \mathbf{Q}&0&0\\ 0&\mathbf{Q}_{x}&0\\ 0&0&\mathbf{Q}_{\tau}\Big{(}\mathbf{I}+\mathbf{C_{\mathit{h}}}\mathbf{B_{d}} \mathbf{R}^{-1}\mathbf{B_{d}}^{T}\mathbf{C_{\mathit{h}}}^{T}\mathbf{Q}_{\tau} \Big{)}^{-1}\\ \end{array}\right]\left[\begin{array}{c}\mathbf{C}_{y}\\ \mathbf{C}_{h}\\ \mathbf{C}_{h}\mathbf{A}_{d}\end{array}\right], \tag{41}\]  
\[\mathbf{Q}_{\tau}\Big{(}\mathbf{I}+\mathbf{C_{\mathit{h}}}\mathbf{B_{d}} \mathbf{R}^{-1}\mathbf{B_{d}}^{T}\mathbf{C_{\mathit{h}}}^{T}\mathbf{Q}_{\tau} \Big{)}^{-1}=\Big{(}\mathbf{I}+\mathbf{Q}_{\tau}\mathbf{C_{\mathit{h}}} \mathbf{B_{d}}\mathbf{R}^{-1}\mathbf{B_{d}}^{T}\mathbf{C_{\mathit{h}}}^{T} \mathbf{)}^{-1}\mathbf{Q}_{\tau}. \tag{42}\]  
These equations show that \(\mathbf{Q}_{2}\) is positive semi-definite since \(\mathbf{Q}\) is positive definite and \(\mathbf{Q}_{\tau}\), \(\mathbf{Q}_{\tau}\) are positive semi-definite or null. Assume that \(\text{pair}\left(\mathbf{A}_{c},\left[\begin{array}{cc}\mathbf{C}_{y}\\ \mathbf{C}_{h}\\ \mathbf{C}_{h}\mathbf{A}_{d}\end{array}\right]\right)\) is observable since \(\mathbf{C}_{y}\) is different from \(\mathbf{C}_{h}\) in general and suitable \(\mathbf{A}_{d}\) may be selected by changing \(\tau\). Then, Equation (35) can be rewritten as  
\[\mathbf{P}_{s}\mathbf{A}_{c}+\mathbf{A}_{c}{}^{T}\mathbf{P}_{s}=-\Big{(} \mathbf{P}_{s}\mathbf{B}\mathbf{R_{\tau}}^{-1}\mathbf{B}^{T}\mathbf{P}_{s}+ \mathbf{Q}_{2}\Big{)}. \tag{43}\]Substituting (36)-(38) into (1) and applying \(\mathbf{r}=0\) and \(\mathbf{w}=0\) yields  
\[\dot{\mathbf{x}}=\mathbf{A}_{c}\mathbf{x}. \tag{44}\]  
Therefore, it is concluded that the closed-loop system is stable by the Lyapunov stability theorem [27; 28] if the observability condition is satisfied.
## 1 Introduction
### Antidiabetic and Anti-Obesity Properties  
Currently, the number of individuals who are overweight and obese has become a great concern to the World Health Organization (WHO) [78]. In 2016, more than 1.9 billion adults (18 years and older) were overweight, and of these, over 650 million were obese [79]. For this reason, the overall goal of the WHO strategy is to promote and protect health through healthy eating and physical activity [78]. Overweight and obesity pose a major risk for other health concerns like dyslipidemia, type 2 diabetes mellitus, coronary artery disease, and hypertension--which are in turn all considered risk factors for cardiovascular disease [80]. The singular metabolic dyslipidemia of obesity consists of increased triglycerides, associated with low levels of high-density lipoprotein (HDL) cholesterol and normal or slightly increased low-density lipoprotein (LDL) cholesterol [80]. Diabetes mellitus is also recognized as an increasingly serious threat to global public health, characterized by deficient plasma glucose regulation, that has been associated with sedentary lifestyles and unhealthy dietary habits [78]. In this instance, the risk of developing obesity and type 2 diabetes could be reduced by the changing of dietary patterns and particularly by the regular consumption of natural products such as phenolic-rich plants [3, 81, 82, 83].  
Phenolic compounds, especially flavonoids and phenolic acids, have been claimed to control the risks of metabolic syndrome, a cluster of hypertension, dyslipidemia, obesity and associated disorders (e.g., cardiovascular disease, neuropathy, nephropathy and retinopathy) [3, 81]. In particular, these compounds were shown to inhibit the activity of key digestive enzymes involved in the metabolism of carbohydrates and lipids, namely \(\alpha\)-glucosidase, \(\alpha\)-amylase and pancreatic lipase. \(\alpha\)-Amylase and \(\alpha\)-glucosidase are responsible for the digestion of dietary carbohydrates to glucose, which enters the blood stream. The inhibition of these two enzymes results in the slowing of liberation and absorption of glucose, consequently reducing levels of blood glucose and ultimately contributing to the suppress of hyperglycemia and managing of diabetes [3, 84, 85]. In turn, lipase inhibition decreases the digestion of dietary triglycerides in the small intestine [86]. Dietary plant polyphenols may modulate other relevant events: e.g., fighting against body weight (bw) gain, fat deposition and adipocyte dysfunction and weakening oxidative stress and related inflammatory processes, among others [3, 84, 85, 86].  
In the following section, a summary of antidiabetic and anti-obesity properties of _Salvia_ extracts is presented based on in vitro and in vivo studies, including clinical trials. Whenever possible, correlations to active phenolic compounds are also described.
# Analysis of BDS-3 Onboard Clocks Based on GFZ Precise Clock Products
## 2 Methods and Data Collection
### Data Collection  
Although many multi-GNSS experiment (MGEX) analysis centers provide BDS-2/3 precise clock offset products, such as the European Space Agency (ESA), the Center for Orbit Determination in Europe (CODE), Shanghai Astronomical Observatory (SHAO), GFZ, and Wuhan University (WHU), only WHU, GFZ, and ESA provide BDS-3 precise clock offset products. In this paper, the GFZ's precision clock products with an interval of 30 s were selected to analyze the performance and the characteristics of BDS-2/3 satellite clocks. The time span is from DOY 1 to 300, 2021. The detailed information of satellite clock and orbit types are listed in Table 1.
# Nitrite: From Application to Detection and Development
## 5 Electrochemical Sensors
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Electrodes** & **Detection Range** & **Detection Limit** & **References** \\ \hline PdAg/C/TP/GCE & 5.0–1000 \(\mu\)M & 1.24 \(\mu\)M & [95] \\ \hline Nafion/Hb/AuNRs-WS\({}_{2}\)/CILE & 1.0–22.0 mM & 0.33 mM & [96] \\ AgNPs@PANI/rGO/GCE & 1.0–28.2 \(\mu\)M & 56 nM & [97] \\ FeTMPV/g\({}_{2}\)Nb\({}_{3}\)O\({}_{10}\) & 0.02–1.35 mM & 1.2 \(\mu\)M & [98] \\ Bi\({}_{3}\)MoO\({}_{6}\)@PmA-Au & 0.3 \(\mu\)M-4.4 mM & 0.08 \(\mu\)M & [99] \\ CuO NPs/CC & 0.5–3000 \(\mu\)M & 0.043 \(\mu\)M & [101] \\ Ag-coated Nanofiber SERS Platform & \(10^{-1}\)–\(10^{-4}\) M & \(2.216\times 10^{-12}\)\(\mu\)M & [102] \\ AuNPs-PTH/SGN/GCE & 0.0002-0.1277 & 0.06 \(\mu\)M & [103] \\ AuNP/MnOX-VOx/ERGO/GCE & 0.30–1000 \(\mu\)M & 10.0 \(\mu\)M & [104] \\ CLS/GCE & 0.5–4000 \(\mu\)M & 0.09 \(\mu\)M & [105] \\ Bi/HCNFs-SPE & 0.1–800 \(\mu\)M & 19 nM & [106] \\ Au/Ni-60s NTs & 0.4–40,000 \(\mu\)M & 0.13 \(\mu\)M & [109] \\ Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) & 0.01–1.0 mM & 3.33 \(\mu\)M & [110] \\ PA-TaCoPc@ZnO & 1–10 \(\mu\)M & 21 nM & [112] \\ AGO & 0.5–85 \(\mu\)M & 250 nM & [116] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection range and detection limit of different electrodes for the determination of nitrite.