esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer Vision-Based Approach for Reading Analog Multimeter
## 2 Framework and System Setup
### Two Methods for Locating ROIs by Template Matching  
According to the Equations (1), (3) and (5), it is obvious that the identification of \(\theta_{s}\), \(\theta_{l}\), \(\theta_{r}\), and \(\theta_{p}\) are based on a horizontally aligned A-meter and can be achieved with the template matching scheme. Therefore, several fundamental templates were built in prior and used in our approach. To perform the template matching, the edge-based geometric matching and pyramidal gradient matching techniques were adopted in our system. Edge-based geometric matching uses the curves obtained from the edge information of a template image as the basis for the features that are used for matching. Pyramidal gradient matching is able to reduce the size of the image and the template to improve the accuracy and the execution time of pattern matching as well as to increase the performance of matching the blur or low contrast image. The edge information was used in both of the two algorithms. Since the algorithms for edge-based geometric matching (**EGM**) and pyramidal gradient matching (**PGM**) have been widely used and embedded in the NI (National Instruments) vision software for machine vision applications [21] and for LabVIEW [19], we do not review their principles here, but we applied them into our approach with the following expressions. Let **I** be an input image and **T** denote the template, which will be introduced later, for matching process. Both **I** and **T** are grayscale images. The return result of each matching includes three elements, i.e., _flag_, _ROI_, and _rotation_, and can be formulated as  
\[\textbf{Edge-based geometric matching: }[\textit{flag, ROI, Rotation}]^{EGM}=\textbf{EGM}(\textbf{I}, \textbf{T}) \tag{6}\]  
and  
\[\textbf{Pyramidal gradient matching: }[\textit{flag, ROI, Rotation}]^{PGM}=\textbf{PGM}(\textbf{I}, \textbf{T}), \tag{7}\]  
where _flag_ denotes the matching successes (1) or not (0). _ROI_ represents the four coordinates of the located region if _flag_ is 1. In addition, since the functions of **EGM** and **PGM** can deal with the rotation cases, the parameter _rotation_ reflects the rotation angle of the located _ROI_ if the _flag_ is 1.
###### Abstract  
Molecularly imprinted polymers (MIPs) for benzylpiperazine (BZP, **1**), an illicit designer drug, were developed by using both self-assembly and semi-covalent approaches. From an array of potential functional monomers (FMs) and using a combination of pre-synthetic interaction studies (by molecular modelling and NMR analysis) and binding assays, the highest performing self-assembly **1**-MIPs were confirmed to result from methacrylic acid (**7**) as FM, ethylene glycol dimethacrylate (EGDMA) or trimethylolpropane trimethacrylate (TRIM) as crosslinkers and chloroform as the porogen and rebinding solvent at template (T): FM ratios of 1:1 and 1:2, giving imprinting factors (IF) 3 to 7. The semi-covalent **1**-MIPs were designed using benzylpiperazine (4-vinylphenyl)] carbamate (**16**) as the template-monomer adduct in combination with either EDGMA or TRIM. Our comparative analysis showed the semi-covalent polymers to have a stronger affinity for **1** (significantly lower K\({}_{\text{d}}\) values and higher IFs) and faster uptake than the self-assembly systems. Both approaches have comparable cross-reactivity: marginal to low against cocaine (**17**) and morphine (**18**) and high against ephedrine (**19**) and phenylpiperazine (**20**). They also have comparable selectivity: highly selective towards **1** against **17**, moderate against **18** and non-selective against **19**. EDGMA-based self-assembly MIPS displayed a greater imprinting effect (higher IFs and NIP-to-MIP K\({}_{\text{d}}\) ratios) than TRIM-based MIPs, while the TRIM-based semi-covalent MIP outperformed its EGDMA-based equivalent. By virtue of its modest selectivity against the test illicit drugs, **1**-MIPs could potentially be used as a dummy MIP for the broad-based capture and enrichment of illicit drug blends for subsequent laboratory analysis.  
Keywords:molecular imprinting; molecularly imprinted polymers; MIP; self-assembly MIP; semi-covalent MIP; benzylpiperazine; benzylpiperazine MIP +
Footnote †: journal: Molecular Sciences
# Optical Design of a Miniaturized Airborne Push-Broom Spectrometer
## 4 Objective Lens Design  
After analysis and calculation, the optical parameters of the objective lens are determined as shown in Table 4.  
The front objective lens is a wide-angle, broad-spectrum optical system. A telecentric optical system should be used to achieve pupil matching with an Offner spectrometer [20]. The optional objective lenses include double Gauss objective, inverted telephoto objective, and fisheye lenses. The double Gauss objective lens is a large-aperture, wide _FOV_ optical system, but it is difficult to achieve image telecentricity with it. Fisheye lenses can achieve very large _FOVs_, but the distortion is  
Figure 4: The SNR of the spectrometer in the spectral range of 0.4\(\sim\)1.0 \(\upmu\)m.  
Figure 3: The quantum efficiency of the CMOS.  
Figure 2: The diffraction efficiency of the grating.  
difficult to eliminate. The inverted telephoto objective lens has a certain ability to eliminate distortion. By complicating the front group of the inverted telephoto objective lens, the \(FOV\) can be enlarged, and the image telecentric can be formed by using a field lens in front of the focus. Finally, an inverted telephoto is adopted with good corrections for aberrations, especially chromatic aberrations. The final design result of the front objective lens is shown in Figure 5, which consists of 10 spherical lenses. The total length and back focal length are 85 and 11.7 mm, respectively. Figure 6 shows the modulation transfer function (MTF) of the optimized front objective lens at 77 LP/mm, and the MTFs of all bands are greater than 0.32.  
The field curvature and distortion of the system are shown in Figure 7, and the maximum relative distortion is less than 1.2%. The relative illumination is shown in Figure 8, and we can see that the illumination of the edge \(FOV\) is about 70% of the center \(FOV\).
## 4 Discussion
### Application in Resource Exploitation and Geological Storage  
In the pursuit of sustainable energy development, massive storage techniques have emerged as a critical research focus. One promising approach is the re-utilization of depleted oil and gas fields and the exploitation of porous strata for gas or liquid storage. To optimize storage efficiency and minimize costs, it is important to identify porous media with large pore sizes (>100 \(\mu\)m) and good connectivity, as illustrated in Figure 11 [78; 79; 80]. Such a technique can also be applied to the geological storage of carbon dioxide (CO\({}_{2}\)) [4; 81]. Saline formations (brine) and depleted oil and gas fields are two types of reservoirs with porous and permeable rocks that have been identified as having the largest capacity for CO\({}_{2}\) storage [1; 82; 83]. CO\({}_{2}\) is stored at depths normally greater than 800 m in a dense form to increase storage efficiency and security [2]. However, the porosity and permeability of the reservoirs play a critical role in injection difficulty and capacity, as does the fracturing behaviour of the gas or supercritical CO\({}_{2}\) at high pressure [82].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Particle Size/nm** & **Printing Technique** & **Permeability/mD** & **Corresponding Rock** \\ \hline
10 & TPP & 0.000733 & Fresh granite \\
100 & EHD jet printing and & & Fresh limestone and \\  & ECAM & 0.073297 & dolomite \\
1000 & \(\mu\)SLA, PuSL, DIW, & 7.329714 & Fresh sandstone \\  & and Micro-SLS & & \\  & CLIP, Micro-CAL, AJ & & \\
10,000 & deposition, and & 732.9714 & Oil reservoir rocks \\  & Micro-SLM & & \\
100,000 & FDM & 73,297.14 & Highly fractured rocks \\
1,000,000 & Most & 7,329,714 & Highly fractured rocks \\ \hline \hline \end{tabular}
\end{table}
Table 5: Permeability of medium with different particle sizes in 3D printing techniques and the corresponding rock type with a similar permeability in nature [74; 75; 76; 77].
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 3 DFT-Based Channel Estimation Algorithm
### Channel Estimation Algorithm Based on Segmented Threshold DFT
Therefore, the value of \(\sum\limits_{n=0,n\in\Theta}^{N-1}\left|\bar{w}[n]\right|^{2}\) can be approximated as the middle value of its upper and lower bounds, i.e.,  
\[\sum\limits_{n=0,n\in\Theta}^{N-1}\left|\bar{w}[n]\right|^{2}\approx\left( \frac{K}{N}+\frac{1}{2}\left(1-\frac{K}{N}\right)\ln\left(1-\frac{K}{N}\right) \right)\frac{N_{\text{P}}}{N}\sigma_{s\text{-}wf}^{2}. \tag{25}\]  
Finally, the MSE of the kth subcarrier of the ST-DFT channel estimation algorithm can be approximated as:  
\[\widehat{\text{MSE}}_{\text{ST-DFT}}[k]\approx\left\{ \begin{array}{cc}\sum \limits_{n=0,n\in\Theta}^{N-1}\left|\hat{h}_{\text{V}}[n]\right|^{2}+\frac{N_{ \text{P}}}{N}\sigma_{s\text{-}wf}^{2}-2\frac{KNp}{N^{2}}\sigma_{s\text{-}wf}^ {2},&T=P_{\text{max}},\\ \sum\limits_{n=0,n\in\Theta}^{N-1}\left|\hat{h}_{\text{V}}[n]\right|^{2}+\frac {N_{\text{P}}}{N}\sigma_{s\text{-}wf}^{2}-2\left(\frac{K}{N}+\frac{1}{2}\left( 1-\frac{K}{N}\right)\ln\left(1-\frac{K}{N}\right)\right)\frac{N_{\text{P}}}{ N}\sigma_{s\text{-}wf}^{2},&\text{otherwise}.\end{array} \right. \tag{26}\]
# Data Exchange in Cluster Structure for Longevity of IoT
## 4 Performance Evaluation
### Dead Node Rates  
In order to evaluate the efficiency of network operation, the dead node rates are measured according to the course of time. Figure 8 shows the average values of dead node rates when the number of nodes is 50, 70, and 100 and the number of queries exchanged in a round is 100, 150, and 200, respectively. In the existing data transfer method, as the operating time progresses, the number of packet interchanges to handle a query increases excessively. Hence, the energy consumption of nodes also increases significantly. Consequently, the dead node rates increase abruptly. From the figure, we can observe that the nodes over 90% are dead in 16 rounds in non-CPDT. However, the CPDT method requires 150 rounds to obtain the death rate of 90% because only the nodes relevant to the property information participate in the data exchange. Thus, the CPDT method can improve the reliability of IoT networks.
## 2 Materials and Methods
### Mathematical Modelling of In Vitro Drug Release  
To study the release kinetics, the extracted drug release data from all 17 publications were fitted to four conventional drug release kinetic models described by Equations (1)-(4).  
\[F=k_{0}\cdot t \tag{1}\]  
Equation (1) describes the zero-order model, where \(F\) is the fraction of the drug released at time \(t\) and \(k_{0}\) is the zero-order release rate constant. The amount of drug released plotted against time gives \(k_{0}\) as the gradient [55; 56; 57; 58].  
\[\ln(1-F)=-k_{1}\cdot t \tag{2}\]  
Equation (2) describes the first-order model, where \(k_{1}\) is the first-order rate constant. By plotting the cumulative % drug remaining against time, \(k_{1}\) can be determined by the gradient [56; 57; 58; 59].  
\[F=k_{H}\cdot\sqrt{t} \tag{3}\]Equation (3) describes the Higuchi model, where \(k_{H}\) is the Higuchi release rate constant, which is determined as the gradient when the amount of drug released is plotted against the square root of time [56; 57; 58; 59].  
\[\ln[-\ln(1-F)]=\beta\cdot\ln(t_{d})+\beta\cdot\ln(t) \tag{4}\]  
Equation (4) describes the Weibull model, where \(t_{d}\) is the lag time prior to drug release and \(\beta\) characterises the shape of the release curve. This model does not give fundamental release kinetic values and is used as a descriptive model to understand the release profile [55; 56; 57; 60].  
Parameters  
The goodness-of-fit of the models to the data was assessed using two statistical measures, the coefficient of determination, \(R^{2}\), (Equation (5)), and by calculating the standard deviation of the residuals represented by the standard error of estimate \(Sy.x\) (Equation (6)).  
\[R^{2}=1-\frac{SS_{res}}{SS_{tot}}=1-\frac{\sum_{i}{(y_{i}-\hat{y}_{i})^{2}}}{ \sum_{i}{(y_{i}-\overline{y})^{2}}} \tag{5}\]  
In this equation, \(SS_{res}\) is the residual sum of the squared errors, and \(SS_{tot}\) is the total sum of squared errors. The residual term \(y_{i}-\hat{y}_{i}\) represents the difference between the observed value, \(y_{i}\) and the predicted value, \(\hat{y}_{i}\). Further, \(\overline{y}\) is the mean of the observed data [50].  
\[Sy.x=\sqrt{\frac{\sum_{i}{(\hat{y}_{i}-y_{i})^{2}}}{n-k}} \tag{6}\]  
In this equation, \(n\) is the number of observations, and \(k\) is the number of parameters that are included in the regression [50; 61].  
The \(p\) value was also recorded to highlight the strength of the results with a small value, suggesting strong evidence behind the results [62]. All statistical analyses and model fitting were performed in GraphPad Prism version 9.1.2 (225) for Mac OS Catalina [63]. Methodological improvements and future considerations can be found in Supplementary Information.
## 3 New Compact Models  
An MILP model for the PCMCA-WT that is polynomial in size was recently proposed in Chou et al. (2018). The model is based on a multicommodity flow formulation (Kang and Kasten, 2018) that extends the flow conservation constraints in order to satisfy the precedence relationships between vertex pairs. However, the model suffers from computational limitations caused by the large number of variables and constraints, both in the order of \(O(n^{3})\). In this section, we derive two new models for the PCMCA-WT that are also polynomial in size. Compared to the previous models, the new ones use less variables and constraints for the description of the precedence relationships among pairs of vertices. This solves the memory issues that were encountered when using the multicommodity flow model originally introduced in (Kasten, 2018), making compact models competitive against other solutions.  
Figure 1: Instance of the PCMCA-WT problem and relative solution. The instance is depicted in the top of the figure, with its arc costs. The precedence relationship \((1,3)\in R\) is depicted through a dashed arrow. The bottom part of the figure depicts the optimal arborescence of cost \(8\).
# Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios +
###### Abstract  
Local Positioning Systems (LPS) have become an active field of research in the last few years. Their application in harsh environments for high-demanded accuracy applications is allowing the development of technological activities such as autonomous navigation, indoor localization, or low-level flights in restricted environments. LPS consists of ad-hoc deployments of sensors which meets the design requirements of each activity. Among LPS, those based on temporal measurements are attracting higher interest due to their trade-off among accuracy, robustness, availability, and costs. The Time Difference of Arrival (TDOA) is extended in the literature for LPS applications and consequently we perform, in this paper, an analysis of the optimal sensor deployment of this architecture for achieving practical results. This is known as the Node Location Problem (NLP) and has been categorized as NP-Hard. Therefore, heuristic solutions such as Genetic Algorithms (GA) or Memetic Algorithms (MA) have been applied in the literature for the NLP. In this paper, we introduce an adaptation of the so-called MA-Solis Wets-Chains (MA-SW-Chains) for its application in the large-scale discrete discontinuous optimization of the NLP in urban scenarios. Our proposed algorithm MA-Variable Neighborhood Descent-Chains (MA-VND-Chains) outperforms the GA and the MA of previous proposals for the NLP, improving the accuracy achieved by 17% and by 10% respectively for the TDOA architecture in the urban scenario introduced.  
Cramer-Rao Bound; local positioning system; memetic algorithm chains; node location problem; time difference of arrival; variable neighborhood descent +
Footnote †: This manuscript is an extended version of the conference paper. [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Node Distribution Optimization in Positioning Sensor Networks through Memetic Algorithms in Urban Scenarios. In Proceedings of the 7 th Electronic Conference on Sensors and Applications, 15-30 November 2020.
# Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis
## 4 Experimental Results and Analysis
### Ablation Experiment  
To further clarify the robustness and scalability of our proposed model, two larger data sets, such as SST-2 and IMDBR, are used in this section to conduct ablation experiments on the AdaECELM model. The comparison models are SVM, an ensemble learning model (ELM) introduced with an ensemble learning scheme, and an ensemble learning model (ECELM) introduced with a CS-SVM optimizer. The models are compared with AdaECELM, and the statistical data are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{6}{c}{**SST-2**} & \multicolumn{6}{c}{**IMDBR**} \\ \cline{2-10}  & **Acc** & **Pre** & **Rec** & **F1** & **Acc** & **Pre** & **Rec** & **F1** \\ \hline SVM & 0.6220 & 0.6367 & 0.6214 & 0.6290 & 0.7731 & 0.7655 & 0.8114 & 0.7878 \\ ELM & 0.6420 & 0.6605 & 0.6698 & 0.6651 & 0.7760 & 0.7737 & 0.7694 & 0.7715 \\ ECELM & 0.6570 & 0.6498 & 0.7168 & 0.6817 & 0.7938 & 0.7931 & 0.7950 & 0.7940 \\
**AdaECELM** & **0.6880** & **0.6949** & **0.9379** & **0.7983** & **0.8180** & **0.8591** & **0.7979** & **0.8274** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of ablation results.  
Figure 6: Sensitivity analysis of imdbs data set feature optimization.  
As shown in Table 3, the AdaECELM model still has a strong effect on emotion classification in larger data sets and higher dimensional feature spaces. In addition to the fact that the recall of the IMDBR data set is slightly lower than SVM and ranks second, the two data sets are superior to SVM, ELM, and ECELM classification methods that reduce different modules in other indicators. At the same time, it is clear from the comparison model that (1) the effect of soft voting with ensemble learning is better than SVM in most cases; (2) after CS algorithm is introduced into the sub-classifier SVM in ELM to optimize the combined hyperparameters, the results of the ECELM scheme are better than ELM, and rank second among the comparison algorithms; and (3) the AdaECELM method based on the CS adaptive hyperparameter search and grid search strategy of text feature representation shows strong emotion classification performance in comparison.  
To sum up, we compared the accuracy results, analyzed the three indexes, including the F1-score, and carried out sensitivity analysis on the adaptive parameter adjustment extracted from the model's special detection. With extensive experimental results and data analysis, AdaECELM has an excellent performance in short-text sentiment analysis and can be used as a baseline method superior to most ML sentiment classification models. By contrast, in the test with a machine learning model, parameter sensitivity analysis of feature extraction and ablation experiment on two large-scale data sets, AdaECELM shows a very exciting effect when facing the emotion classification task of most classical data sets and has good model robustness and scalability.
# Evolution of Permeability and Sensitivity Analysis of Gas-Bearing Coal under Cyclic Dynamic Loading
## 1 Introduction
[26] examined the impact of scale effects on coal rock permeability and noted that sensitivity to effective stress varied at different scales. [PERSON] et al. [27; 28] not only explored the influence of effective stress on coal rock permeability but also analyzed the effects of strain on permeability. Regarding the influence of the Klinkenberg effect on gas permeability, [PERSON] et al. [29] investigated the sensitivity of the Klinkenberg effect to confining pressure and pore pressure and identified the inflection point of this effect, while [PERSON] et al. [30] conducted experimental studies on the aforementioned phenomenon, providing a quantitative analysis of the impact of the Klinkenberg effect on permeability reduction, and [PERSON] et al. [31] analyzed the discrepancies of the Klinkenberg effect between briquette coal and raw coal. The aforementioned studies primarily focused on the gas permeability characteristics of coal under static loading conditions. However, there is a paucity of research on the gas flow characteristics of coal under the combined effects of static load, dynamic load, and gas adsorption. The mechanisms of gas flow in coal under these combined influences remain to be further explored. This paper, utilizing self-developed instrumentation, investigates the impact of static load, dynamic load, and gas adsorption on the permeability of coal. The findings provide a theoretical foundation for understanding coal rock dynamic disaster mechanisms and gas extraction processes.
## 4 Molecular Imprinted Polymer in Solid Phase Extraction (MISPE)
### Offline Modes
Their MIPs and NIPs' molecular selectivity was determined through the evaluation of the sorbents via offline MISPE protocols. The MISPE was synthesized with a mixture of solvents with different porogens and types of template-monomer interactions during polymerization. Kirenol is a polar molecule that does not have any hydrophobic functional groups. According to the findings, MIPs made in tetrahydrofuran showed stronger recognition capabilities than those prepared in \(N\),\(N\)-dimethylform amide or acetonitrile:methanol (1:1). Different quantities of kirenol in solution were used to identify the optimal solvents (water, acetonitrile, and tetrahydrofuran), and were loaded in the MISPE column in the range of 0.5-2 mL. The eluate was collected, and an HPLC analysis was subsequently performed. The best solvent was found to be acetonitrile, which had an optimal volume of 1.5 mL. Furthermore, 5 mL of acetonitrile was chosen as the optimal volume for the washing step. The best elution solution was determined to be 5 mL of methanol/acetic acid (90:10, \(v/v\)). According to the findings, the spatial orientation of the functional groups in specific binding sites was a significant factor in non-covalent MIP molecular recognition. MISPE's binding affinity for kirenol was highly selective, and it could potentially rebind other structurally related diterpenoids. However, binding affinity for diterpenoids was lower than kirenol.