esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data
## 1 Introduction
The predictions are first compared with a subset of the actual sensor logs recorded from high-end monitoring stations as ground truth, then used to predict an Air Quality Index following European standards, assessing the accuracy in identifying the correct category. Despite the undeniable loss in terms of performance with solutions including the pollutants in the feature set, we show that accurate estimates can still be obtained for most pollutants and therefore a fairly accurate AQI can be predicted even when relying only on environmental information. In summary, the contributions of this paper are as follows:  
* We study the problem of air quality estimation in urban areas with a focus on environmental features, discussing the relations between meteorological measurements and vehicle transits with air pollutants;
* We test and propose a series of experiments conducted with different popular machine and deep learning models, highlighting advantages and drawbacks of each solution;
* We demonstrate the feasibility of this task, showing fairly robust estimations without introducing past measurements of pollutants in our experiments. The paper is organised as follows. Section 2 introduces: (i) the data sources used for the experiments, describing the process of acquisition, preprocessing and analysis; (ii) the frameworks and tools used to manipulate the data; and (iii) the evaluation measures and the experiment setup adopted. Section 3 reports the results obtained from the adopted solutions discussing their performance. Lastly, in Section 4 concludes with a summary of the study and delineates possible future works.
## 2 Measurement Principles and Methods
### Sub-Pixel Edge Detection and Fitting
#### 2.1.1. Gray-Level Co-Occurrence Matrix (GLCM) Method  
The GLCM sub-pixel edge detection method operates on the principle of invariant GLCM, comparing the actual edge with an ideal edge model to accurately localize the image edge. This method presupposes that the one-dimensional ideal edge model resembles a step function, \(u(x)\), with gray values \(g_{1}\) and \(g_{2}\) on either side of the edge and a step height of \(h\). The ideal edge configuration is therefore represented as follows:  
\[E(x)=(g_{1}-g_{2})\times u(x-h)+g_{1} \tag{5}\]  
where \(\omega_{1},\omega_{2}\) are the proportions of pixel points with gray values \(g_{1},g_{2}\), respectively, relative to the total number of pixel points in the entire edge region, adhering to the equation: \(\omega_{1}+\omega_{2}=1\). Assuming \(x_{j}\) represents the GLCM value at an edge point, we consider the first three moments of this value.  
\[\overline{m}=\sum\limits_{j=1}^{2}\omega_{j}g_{j}^{i}=\frac{1}{n}{\sum\limits _{j=1}^{n}{x_{j}^{i}}} \tag{6}\]  
Here, \(n\) is the total number of pixel points, with the actual edge density denoted as \(\omega=k/n\). This value of \(\omega\) can be deduced through calculation as follows:  
\[\begin{array}{c}\omega_{1}=0.5\bigg{[}1+\bar{s}\sqrt{1/\big{(}4+\bar{s}^{2} \big{)}}\bigg{]}g_{1}=\overline{m_{1}}-\overline{\sigma}\sqrt{\frac{\omega_{2 }}{\omega_{1}}},g_{2}=\overline{m_{2}}-\overline{m_{1}}\\ \bar{s}=\frac{1}{n}\sum\limits_{i=1}^{n}{\frac{(x_{i}-\overline{m_{1}})}{ \sigma^{3}}}=\frac{\overline{m_{1}}+2\overline{m_{1}}^{3}-3\overline{m_{1}} \overline{m_{2}}}{\sigma^{3}}}{\sigma^{3}}\end{array} \tag{7}\]  
Consequently, the edge position in the actual image is determined as follows:  
\[k=np_{1}-\frac{1}{2}=\frac{n}{2}\sqrt{\frac{s}{4+\bar{s}^{2}}}+(n-1)/2 \tag{8}\]  
Figure 3: Schematic calculation of slanted-edge method.
## 4 Experimental Analysis and the Results  
In this paper, the performance and efficiency of the CCA algorithm are tested using four real datasets from two different domains, which are Phisher URL and Healthcare, including one linear classification synthetic dataset, as is described in Table 2 The results will be evaluated and discussed, then compared with other common classification algorithms. Several papers have introduced Web Phishing Attacks techniques such as Feature Selection, preparing data, and other related issues. Therefore, in this study, phishing and healthcare datasets were retrieved from [30; 31; 32; 33].  
Figure 3: Flowchart of CCA algorithm.  
In Table 3, five different datasets have been examined, labeled as Experiment 1-5. CCA was applied to each of these datasets with four different iterations and four different cluster numbers (\(K\)-values). The results show that for dataset 1, which is a linear classification case, the accuracy was the highest and clustering was not necessary for CCA because the Core of each class is completely represented, meaning that it carries all the characteristics of this class. For the rest of the experiments 2-5, that were not linearly separable, the use of the CCA showed different better results for different clusters, all of them with better accuracy as compared to the case of a single cluster.  
In Table 4, the CCA examined using the confusion matrix and we calculated the F1-score, precision, and recall. We examined the four datasets 1-4 [30, 31, 32, 33], and the CCA has given different results for different datasets. We set the iterations of all experiments at 2.
## 3 Results

Table 11: Minimum inhibitory concentration (MIC, mg/mL) values of methanolic extracts from the callus, cell suspension cultures, and in vitro shoot culture of _Eryngium planning, Lyclnis flos-cuculi_, and _Kickxia elatine_ tested against _S. aureus_, _S. epidermidis_, _P. aeruginosa_, and _C. albicans_. Figure 3: Post-docking analysis visualized by BIOVIA Discovery Studio 2025 in the 3D conformation of _Acanthamoeba_ profilin 1B (PDB 1 ACF) with (**A**) acetoside, (**B**) chlorogenic acid, (**C**) _p_-comamaric acid, (**D**) ferulic acid, and (**E**) rosmarinic acid. The molecular docking results on the molecular interaction between selected phytochemicals sourced from _E. planum_, _K. elatine_, and _L. flos-cuculi_ to target proteins (dihydroproteate synthase, gyrase B, and sterol 14-demethylase) are presented in Table 12 and visualized in Figures 4-7, respectively. The in silico analysis results indicated that rosmarinic acid showed the strongest predicted binding to dihydroproteate synthase with \(\Delta\)G \(-\)7.13 and \(-\)6.50 kcal/mol and inhibition constants of 5.95 and 17.14 uM (Grampositive and -negative, respectively. Chlorogenic acid also displays a substantial affinity for this target, registering a \(\Delta\)G of \(-\)6.35 and \(-\)6.33 kcal/mol along with 21.97 and 22.92 uM inhibition constant scores for Gram-positive and Gram-negative, respectively (Table 1, Figures 4 and 6). Both compounds also showed potent interactions with gyrase B, with \(\Delta\)G values around \(-\)6 kcal/mol for both Gram-positive and Gram-negative. On the other hand  
Acteoside, chlorogenic acid, and rosmarinic acid exhibited the most negative \(\Delta\)G values with \(-7.94\), \(-7.89\), and \(-7.77\) kcal/mol and inhibition constant scores of 1.5, 1.66, and 2.0 \(\upmu\)M, respectively. The results suggested a strong binding affinity of these compounds towards the sterol 14-demethylase active site, thus promising antifungal agents from these species by inhibiting fungal cell wall biosynthesis and their growth. -6.63. Meanwhile, moderate binding affinity was presented by azelaic acid, ferulic acid, and \(p\)-coumaric acid with a \(\Delta\)G within \(-5\) kcal/mol.
## 1 Introduction
The method was shown to be more successful against five different methods with / without GP-based. [PERSON] et al. presented a uniform LBP (uniform LBP) approach in rotation-sensitive, simple and gray-scale variances [22]. [PERSON] et al. proposed the Wigner-based LBP identifier (WLBP, Wigner-Based Local Binary Patterns), which uses the Wigner distribution together with LBP [23]. In experiments using different classifiers, the method performed better than conventional LBP. [24] was presented a two-part Feature Based Local Binary Pattern (FB-LBP) approach. In the first part, traditional LBP is used, while in the other part the mean and variance of the magnitude vector is used. [PERSON] et al. proposed a novel local texture descriptor, generalizing the well-known LBP approach. Four LBP-like descriptors, two local intensity-based Central Intensity LBP (CI-LBP) and Neighbors Intensity LBP (NI-LBP), and two local difference-based descriptors Radial Difference LBP (RD-LBP) and Angular Difference LBP(AD-LBP), were presented to extract complementary texture information of local spatial patterns [1]. The descriptors had the same structure as the standard LBP and not require training and parameter adjustment. In the study conducted in three different texture, the descriptors showed better performance in gray-scale and rotation-sensitive pattern classification compared to traditional LBP. A plethora of neural networks have been presented image processing [25; 26; 27]. [PERSON] et al. introduced a two-stage approach to the use of pixel based neural networks trained by back propagation and GA [26]. The network is trained on samples, which cut out of from large pictures. In the first stage, weights of networks are adjusted GA and in the second stage they tested the method on three object detection problems. The results show that the back-propagation algorithm has stronger generalization ability than GA. In [27] is proposed two phase GP method compared neural network approach [25; 26] on three object detection problems. The experimental results show that GP has better performance neural network in terms of object detection accuracy. The paper is organized as follows: Background, which includes subsections of Local Binary Pattern and GP-descriptor are introduced in Section 2. Section 3 explains in detail general procedure of algorithm, ABCP, suggesting ABCP-descriptor and fitness function. Section 4 provides detailed information on the experimental design, including datasets and parameters. The performance analysis, which includes subsections overall results and discussion of the results from the extracted models are explained in Section 5. The paper is finalized with Conclusions in Section 6.
## Materials and Experimental Procedure
### Materials
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & **AlMg3** & **St37-2** & **AISI 304** & **Units** \\  & **(UNI EN 1706)** & **(UNI EN 10025)** & **(UNI 6900-6901)** & **Units** \\ \hline Density & 2.70 & 7.85 & 7.90 & (g/cm\({}^{3}\)) \\ Solidus temperature & 600 & 1420 & 1450 & (\({}^{\circ}\)C) \\ Liquidus temperature & 650 & 1460 & 1400 & (\({}^{\circ}\)C) \\ Latent heat of fusion & 400 & 250 & 290 & (J/g) \\ Specific heat & 900 & 470 & 480 & (J/kg K) \\ Thermal conductivity & 130–140 & 51 & 16 & (W/m K) \\ Tensile strength & 140–240 & 360–510 & 490–685 & (MPa) \\ Yield strength (at 0.2\%) & 80–130 & 235 & 185 & (MPa) \\ Elongation (at 50 mm) & 1 & 20–26 & 45 & (\%) \\ Young’s modulus & 68 & 206 & 193–200 & (GPa) \\ Brinell Hardness & 52–88 & 130 & 170–360 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main properties of the adopted materials. Compared to steel, stainless steel provides further benefits such as corrosion resistance and oxidation resistance, and it is used in a large variety of applications. Due to the high melting temperature and low viscosity of the oxides generated from the reactions of iron and other alloy elements, oxygen laser cutting of high-strength steel is a more difficult task [19]. Therefore, the use of inert gas is commonly preferred when cutting stainless steel. In particular, AISI 304 is a non-magnetic austenitic stainless steel. It is composed of chromium (~18%) and nickel (~10%) with iron and other elements. Fatigue strength is enhanced by the addition of N (nitriding), induction surface hardening or through cold forming. Thus, AISI 304 is characterized by good weldability and toughness. However, in laser cutting of stainless steel, the exothermic reaction of iron is complicated by the presence of these alloy elements. Therefore, when tight tolerances are required, the use of an inert gas such as nitrogen, argon or helium is suggested [20].
## 1 Introduction  
Cutaneous fibrous histiocytomas (CFHs) are common benign cutaneous lesions, also referred to as dermatofibromas (DFs). Typically occurring on distal extremities, they show a slight female predominance [1]. Clinically, they usually arise in the form of variably-sized (from 5 mm to 2 cm), brownish to purple in colour, painless nodules or polyps. CFHs have been considered fibrous inflammatory or reactive process since sometimes they are associated with previously reported local traumas such as insect bites; however, the clonal/neoplastic nature of the lesions is best supported since more often CFHs arise spontaneously without history of traumas [2]. Their dermoscopic pattern usually consists of a peripheral pigment network and a central white patch [3], although the unusual pattern may dermoscopically mimic malignant melanoma, vascular tumors, or trichoblastic carcinoma/basal cell carcinoma. Predominantly dermal-based, CFHs are circumscribed lesions but with irregular and unencapsulated borders, exhibiting fibroblatic and macrophage lineage differentiation, being composed of a nodular proliferation of fibroblasts and macrophages. Characteristically, CFHs exhibit alternating densely cellular and sclerotic/hypocellular areas. Additional histologic features include intersecting fascicles of spindle cells set in a variably collagenous/myxoid stroma. Foamy macrophages and multinucleated giant cells (Touton giant cells) are often found intermixed with spindle cells. Chronic inflammatory cells and hemosidierin deposits are also frequently seen. Many histologic variants have been described, including the aneurysmal, hemosidierotic, cellular, epithelelioid, atypical, lipidized, clear cell, combined, palisading, atrophic, keloidal, myxoid, lichenoid, balloon cell, and signet-ring cell. Among those variants, two of the rarest are the CFH with osteoclast-like giant cells [4] and the granular cell CFH. Recognizing even the rarest variants of CFHs is important to avoid misdiagnosis.  
We herein report two uncommon variants of CFHs: (i) an ossifying DF with osteoclast-like giant cells (OLGCs); (ii) a granular cell CFH. The diagnostic clues and differential diagnosis are emphasized.
## 1 Introduction
The novelty of using the AdaBoost algorithm is in detecting the signal's features by recovering the originally transmitted data from the noisy received signal and, therefore, helping to improve the BER. The innovation comes from using an algorithm that is well known in the field of image processing and feature recognition and applying it to wireless communication signals, where it can be a benefit from its boosting effect in improving the detected results. The rest of this paper is organized as follows. In Section 2, the AdaBoost algorithm is explained. Digital modulation classification is then presented in Section 3. Section 4 discusses the simulation results. Section 5 is devoted to the main conclusion.
## 1 Introduction
In order to completely eliminate the integral saturation of the system, the desaturated PI control structure is developed based on the idea of anti-saturation [33; 34; 35]. The method can completely remove the integral from the saturation state through the reverse accumulation of errors. However, it should be noted that if the reverse error accumulation is too large, it is easy to weaken the role of the integral link, so that the drive system has steady-state error and increases the response time of the system. In order to make PMSM sensorless system have better observation and drive performance, the original contributions of this paper are shown as follows:  
1. Compared with the traditional model parameter equivalence, this paper uses MATLAB/System Identification toolbox to realize the parameter identification of high-order system, and the identification accuracy can reach 91.22%. Meanwhile, root locus and Bode diagram are used to complete the stability analysis of the closed-loop system and the selection of VPDPI regulator parameters. 2. In terms of the ideal response curve as the design objective, this paper analyzes the causes of system saturation phenomenon. Moreover, the influence of saturation phenomenon on dynamic and steady performance of sensorless system is considered in this paper, especially the ability to suppress overshoot, and the VPDPI regulator is innovatively proposed. Meanwhile, the control rules of the proposed regulator are further given. 3. Simulations and experiments under various working conditions are presented in detail, and the effectiveness and reasonability of the proposed method are both amply verified. In Section 2, the overall structure and mathematical model of PMSM sensorless drive system are presented step by step. In Section 3, the requirements of ideal response curve are reviewed, and the design rules and threshold parameter selection rules of VPDPI regulator are given in detail. Section 4 provides the comprehensive test results of the proposed method on MATLAB/Simulink experimental platform. Meanwhile, the validity of the proposed method on hardware platform is further verified in Section 5. Finally, the brief conclusions are drawn in Section 6.
## 4 Conclusions  
In this paper, the surface discharge process in a C4F7N/CO2 gas mixture is obtained by COMSOL simulation. On this basis, the influence of the applied voltage and dielectric constant of the insulator on the discharge is studied, and the surface discharges in C4F7N/CO2 and SF6 are compared, which provides a theoretical basis for the optical detection of surface discharge in the environmentally friendly gas C4F7N/CO2. The following are the main conclusions:  
1. Surface discharge in C4F7N/CO2 can be divided into three stages: it develops along the upper surface of the insulator, transitions along the rounded corner, and develops along the right surface of the insulator. The streamer branch appears during the transition phase, develops for a short time, and then stops in the third phase.
2. When the streamer reaches the vicinity of the cathode, the electric field intensity increases from \(7.84\times 10^{7}\) V/m to \(1.07\times 10^{8}\) V/m, the photon flux increases from \(2.18\times 10^{9}\) m\({}^{-3}\)s\({}^{-1}\) to \(3.83\times 10^{9}\) m\({}^{-3}\)s\({}^{-1}\), and the change law of the photon flux is similar to that of the electric field intensity. The expression of the maximum photon flux by fitting is \(I=2.138\times 10^{10}e^{-1899\mu_{1}}\) (stage 1) and \(I=5.999\times 10^{8}e^{367.2d_{2}}+0.6474e^{5838d_{2}}\) (stage 3).
3. As the applied voltage increases, the peak of electric field intensity and the peak of photon flux in the streamer head increase, and the streamer growth speed increases.  
\begin{table}
\begin{tabular}{l l l l} \hline  & **Stage 1** & **Stage 2** & **Stage 3** \\ \hline C4F7N/CO2 & \(5.64\times 10^{7}\) V/m & \(5.07\times 10^{7}\) V/m & \(1.43\times 10^{8}\) V/m \\ SF6 & \(5.09\times 10^{7}\) V/m & \(4.30\times 10^{7}\) V/m & \(7.59\times 10^{7}\) V/m \\ C4F7N/CO2 & \(5.40\times 10^{21}\) m\({}^{-3}\) & \(3.36\times 10^{21}\) m\({}^{-3}\) & \(8.95\times 10^{21}\) m\({}^{-3}\) \\ SF6 & \(4.35\times 10^{21}\) m\({}^{-3}\) & \(2.04\times 10^{21}\) m\({}^{-3}\) & \(6.76\times 10^{21}\) m\({}^{-3}\) \\ \hline \end{tabular}
\end{table}
Table 2: The data in Figures 12 and 13.  
Figure 17: Variation in photon flux of the streamer head in SF6. (**a**) Stage 1; (**b**); Stage 2 (**c**); Stage 3.  
The peak electric field intensity of the streamer head likewise increases along with the insulator's increased dielectric constant, which further accelerates the streamer's development.
* As an alternative to SF6, C4F7N/CO2 has a similar surface discharge phenomenon and a slightly faster discharge rate than that of SF6. The electric field intensity, electron concentration, and photon flux generated by discharge in C4F7N/CO2 are higher than those in SF6, and the streamer channel is also more obvious than that in SF6.  
Conceptualization, X.Y.; methodology, X.Y.; software, X.Y.; formal analysis, X.Y.; data curation, X.Y.; supervision, X.Z., Z.L., Y.Q. and G.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NO. 62075045).  
Data sharing not applicable.  
The authors declare no conflict of interest.
# Research on Hard, Transparent and Hydrophobic Coating on PMMA Sheet
## 3 Results and Discussions
### Hardness and Adhesion of Coatings  
The hardness of PMMA substrate and coatings was tested according to ISO 15184:2020 Paints and Varnishes-Determination of film hardness by pencil test. The pencil is tilted at 45\({}^{\circ}\) to the coating by the hand-pushed pencil hardness tester, and the load applied on the coating surface by the tip of the pencil is 750 g. The cross-cut method was used to detect the adhesion of coatings according to the ISO 2409:2020 Paints and Varnishes-Cross-Cut test. During the test, six parallel cuts are introduced in the coating by the multi-blade cutting tool, and then another six cuts are introduced perpendicular to the first cuts. The spacing of cuts was set as 1 mm. before qualifying the adhesion levels, the loose particles of coatings were removed by brushing and using pressure-sensitive adhesive tape. The detected results of coating hardness and adhesion are listed in Table 2.  
As shown, the hardness of the coating was higher than the that of PMMA substrate, suggesting better wear resistance of the coating samples. In addition, the added amounts of nano SiO\({}_{2}\) particles were too low to have effects on the coating hardness, which showed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Samples** & **Hardness** & **Adhesion Levels** \\ \hline PMMA & \(B\) & - \\ S0 & \(4H\) & 0 \\ S05 & \(4H\) & 0 \\ S1 & \(4H\) & 0 \\ S2 & \(4H\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardness and adhesion levels of PMMA and coatings.  
Figure 5: Water contact angles of samples with different nano SiO\({}_{2}\) particles.  
the same hardness of 4H. However, the adhesion of the S2 coating was worse than the other coatings, showing an adhesion level of 1. The continuity and integrity of the organic coating would be destroyed by adding inorganic SiO\({}_{2}\) particles, generating more interfaces, which might develop into crack sources, thus reducing the adhesion of the coating.