esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Second, an object detector based on the Convolutional Neural Networks (CNN) was developed which is able to perform character segmentation and character recognition simultaneously. These tasks are carried out separately in the traditional ALPR systems. For LPCR methods using conventional optical character recognition approaches, the segmentation of each character in the LP image must precede the character recognition. Therefore, successful character segmentation greatly affects the LPCR performance. However, the CNN-based object detector of an LPCR is performed with a segmentation-free end-to-end manner that is not affected by character segmentation problems. Third, an extensive test of algorithms was performed with 22,117 real LP images under the various conditions. The real LP images were not used in any of the training phases of LP-GAN-based LPCR module. As an LPCR module trained with the generated dataset using LP-GAN achieved comparable \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Number of Images** & **Country** & **Year** \\ \hline Caltech Cars [5] & 126 & USA & 1999 \\ EnglishLP [7] & 509 & Europe & 2003 \\ UCSD-Stills [8] & 291 & USA & 2005 \\ ChineseLP [9] & 411 & China & 2012 \\ AOLP [10] & 2049 & Taiwan & 2013 \\ OpenALPR-EU [11] & 108 & Europe & 2016 \\ SSIG-SegPlate [12] & 2000 & Brazil & 2016 \\ UFPR-ALPR [6] & 4500 & Brazil & 2018 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Public license plate image datasets. accuracy to that trained with a real LP image dataset, it was successfully shown the high feasibility of data generation using GAN for LP images. The rest of this paper is organized as follows. Section 2 reviews the GAN models studied so far, especially image-to-image translation methods and recent studies related to LP recognition technology, including traditional LP recognition methods. The three GAN-based image-to-image translation methods used in this study and the generation of Korean LP images using LP-GAN are discribed in Section 3. The CNN-based object detector that can perform character segmentation and character recognition simultaneously in a segmentation-free end-to-end manner is discussed in Section 4. Section 5 describes the dataset configured for the experiments, give details on the procedure of generating Korean LP images using the three LP-GANs and using the generated LP images as the training data for the LPCR module, and finally report the performance of the LPCR on the real LP images. Conclusions on this study are given in Section 6.
## 1 Introduction
Further factors affecting the kinetic of a photochemical reaction are the intensity and wavelength of the incident radiation, the optical path of the radiation, the nature of the irradiated compound, the concentration and the solution in which it is present [34]. Diclofenac can be excited by wavelengths up to 320 nm. The terrestrial solar UV radiation starts at 295 nm [35]. Excitation of diclofenac with UVA light (>300 nm) leads to different reaction pathways like ring closure, decarboxylation [36], hydrolysis [37] or dehydration [36; 37], thus a multitude of different phototransformation products (like carbazoles [36], quinones [38; 39], dimers [39; 40]) are formed. The transformation of diclofenac by UV light is highly inhibited by the formation of products. These products are able to absorb the incident radiation in the same wavelength range as diclofenac and with higher extinction coefficients [39]. The excitation of diclofenac is delayed as the phototransformation products absorb the incident radiation. The more diclofenac gets converted into inhibiting products the less radiation is available for the conversion of diclofenac [37; 41]. This transformation rate reduction of diclofenac over time by the above described reasons was described in the literature as an \"inner\" filter effect [40]. The removal of phototransformation products during UVA light irradiation of diclofenac would increase the reaction rate, as transformation would not be hindered by these products anymore. The \"inner\" filter effect is reduced. In this work, an adsorbing material (hydrophobic polyvinylidene difluoride, PVDF) is introduced during UVA irradiation of diclofenac. Up to date PVDF was only applied as a support for photocatalysts [42; 43], or molecularly imprinted polymer nanoparticles [44] which remove diclofenac from water. Here, PVDF adsorbs phototransformation products but not diclofenac itself [44]. The inhibiting products are drawn from the solution to reduce the \"inner\" filter effect and increase UVA transformation of diclofenac. So far, it is the first time, that UVA irradiation and adsorption of phototransformation products are combined to show an increased removal of a pollutant from water. This work demonstrates a new concept, which opens up new possibilities for pollutant elimination from water. Experiments were performed under an UVA lamp with and without PVDF. The diclofenac transformation was monitored with high performance liquid chromatography (HPLC), UV-Vis spectroscopy and by analysing the decrease of total organic carbon (TOC). The toxicity towards _Vibrio fischeri_ was examined according to DIN EN ISO 11348-1: 2009-05. Density functional theory (DFT) was used to simulate the phototransformation products known in literature [36; 39] as well as further products identified via gas chromatography-mass spectrometry (GC-MS). The absorption spectra, reaction enthalpies (\(\Delta\)H) and Gibbs free energy of reactions (\(\Delta\)G) were calculated. Reusability and recovery tests of PVDF were performed under the best-established conditions.
## 4 Simulation Results and Discussion
### Comparison between Constant and Tuning Parameters of VSG
#### 4.3.2 Load Transient  
In this case, the load transient is applied to the system supplementary with the high fluctuation PV power. The power demand waveform is shown in Figure 20. To create the load transient event, 45  
Figure 19: Frequency response: Constant Vs Adaptive parameters.  
Figure 18: Parameter adapting based on BSA.  
percent of total power demand is connected and disconnected at time \(t=5\) s and \(t=12\) s, respectively. Three control schemes such as modified droop control, constant VSG parameter and adapted VSG parameter are compared. The frequency response of the system demonstrates in Figure 21. By observing at the time \(t=5\) s and \(t=12\) s, the system operates in unacceptable frequency range when the system is equipped with the modified droop control (\(cyan-graph\)). In case, the constant VSG parameter is applied, the system has a relative large deviation. But, it is still in acceptable frequency range \(\pm 0.5\) Hz (\(blue-graph\)). While the adaptive parameter (\(red-graph\)) is adopted, the frequency deviation overshoot is efficiently suppressed which is in range \(\pm 0.2\) Hz. This witness that the online tuning method could reduce the frequency deviation more effectively compared to other two control schemes.
## 9 Evaluation
### Case Study  
There are two kinds of input sensitive information for CSI-based attacks, and we separately choose unlock patterns and keystrokes as the representative finger motions for consecutive motions and discrete motions to do the experiments. The results are as shown in Figure 11. [PERSON] et al. [50] found that there exist typical strategies for frequently-used unlock patterns, such as the top left corner is usually used as a starting point, and straight lines are more popular in the patterns. According to this, 15 unlock passwords are randomly chosen as the tested unlock passwords according to the habits of people's daily use, and the 15 tested unlock passwords are shown in Figure 11c. Besides, Numpad 0-Numpad 9 on the right of the keyboard are chosen as the tested keystrokes, as shown in Figure 11b.  
The results of the recovery accuracy of the two case studies are shown in Figure 12. From Figure 12a,c, we know that when there is no channel interference, the recovery accuracy is relatively high: the average recovery accuracy of the 15 unlock password patterns is 82.33%, and the average recovery accuracy of the 10 keypads is 92%. The results of unlock patterns and keyboard recovery indicate that the wireless signal will reveal the user's privacy, which should be a warning for users.  
From Figure 12b,d, we know that when the channel interference exists, the recovery accuracy is relatively low, and the average recovery accuracy of the 15 unlock password patterns is 21.67%, and the detailed recovery results are shown in Figure 13; the average recovery accuracy of the 10 keypads is 42%, and the detailed recovery results of keypads for 20 times are shown in Figure 14. Compared with the recovery accuracy without channel interference, the recovery accuracy with channel interference significantly decreases. The results show that channel interference can defeat the CSI-based attacker effectively.  
Figure 11: Two case studies. (**a**) Unlock patterns; (**b**) keystrokes; (**c**) 15 tested patterns for Android applications.  
Figure 12: Users’ behavior recognition when there is no channel interference and exists channel interference. (**a**) Pattern recognition without interference; (**b**) pattern recognition with channel interference; (**c**) keyboard recognition without interference; (**d**) keyboard recognition with channel interference.
## 2 Theoretical Considerations  
For two fundamental lasers with input powers \(P_{1}\) and \(P_{2}\) incident on a nonlinear crystal of length \(L\), the theoretical sum frequency output power can be estimated using the relation  
\[P_{SFG}=\frac{32\pi^{2}d_{eff}^{2}P_{1}P_{2}L}{n_{SFG}\varepsilon_{0}\lambda_ {SFG}^{2}c(n_{1}\lambda_{1}+n_{2}\lambda_{2})} \tag{1}\]  
and the corresponding conversion efficiency can be given as  
\[\eta=\frac{P_{SFG}}{P_{1}P_{2}} \tag{2}\]  
where \(c\) is the speed of light in vacuum, \(\varepsilon_{0}\) is the permittivity in vacuum, and \(d_{eff}=2d_{33}/\pi\) (\(d_{33}=27.2\) pm/V) is the effective nonlinear coefficient of MgO:PPLN [16]. \(n_{SFG}\), \(n_{1}\), and \(n_{2}\) are refractive indices at \(\lambda_{SFG}\), \(\lambda_{1}\), and \(\lambda_{2}\), respectively. For the cascaded module, the expected conversion efficiency for two crystals can be shown as  
\[\eta_{C1+C2}=(\sqrt{\eta_{C1}}+\sqrt{\eta_{C2}})^{2} \tag{3}\]where \(\eta_{C1}\) and \(\eta_{C2}\) are the nonlinear efficiencies measured individually for Crystals 1 and 2, respectively, which follows from the superposition principle as detailed in [17].
## 4 Discussion
### Relationship Between Root Water Uptake and Vegetation Transpiration  
In water-limited regions, the balance between water supply and demand determines plant survival under water stress conditions. During wet seasons, transpiration is mainly sustained by current precipitation, whereas in dry seasons, it predominantly relies on precipitation from prior months or seasons [79; 80; 81]. The relationship between water uptake and transpiration reflects how plants regulate water use in response to drought stress. Our findings revealed a significant positive correlation between transpiration and water uptake at both monthly and annual scales, aligning with previous studies [82; 83]. Over the long-term simulation period, the dynamic experiment demonstrated a stable balance between transpiration and root water uptake, suggesting that water availability did not limit vegetation growth from 1980 to 2020 (Figure 11a).  
During the growing season, approximately 92% (78.5-101.1%) of the transpiration was derived from root water uptake within the upper 2 m of soil (Figure 11b). This finding is consistent with a previous study that observed that transpiration in apple trees and \(R\). _pseudoacacia_ mainly came from shallow soil (0-2 m), contributing about 68% and 81% of the total transpiration [35]. Observations from apple and peach orchards further indicated that approximately 95% and 97% of the transpired water originated from newly available water (i.e., recent precipitation), with a smaller portion coming from pre-existing soil moisture (water age more than planting years) [62]. Deep water uptake (>2 m) contributed 0.3-20.5% to the transpiration in the growing season, particularly higher in dry years (Figure 11b). This aligns with previous research indicating that deep-rooted plants extract more water from deeper soil layers to sustain transpiration during drought periods [84; 85]. Additionally, the ratio of transpiration to total evapotranspiration was 53% in the dynamic experiment and 61% in theodynamic experiment. Prior studies have reported transpiration-to-evapotranspiration ratios for broad-leaved forests ranging from 43% to 79% [72; 86; 87], further supporting the validity of our findings.  
The regulatory role of plant-water interactions on long-term water balance is likely to be broadly applicable, especially in areas characterized by deep-rooted vegetation, thick loess deposits, and semi-humid climatic conditions. However, the Loess Plateau exhibits considerable spatial heterogeneity in terms of climate, soil properties, and vegetation structure, all of which can influence plant water use strategies and hydrological responses. Hence, we recommend that future studies conduct long-term monitoring and simulations across diverse regions to further validate our findings and improve the regional applicability of the model.  
Figure 11: **(a)** Transpiration and root water uptake in the growing season and (b) the ratio of water uptake in different soil layers to transpiration from 1980 to 2020. The star means outliers for box plot.
## 3 System Model Design
### Using the Fuzzy Inference System to Compute the Transmission Preference
#### 3.3.2 The Fuzzy Inference Component  
In the [PERSON] fuzzy system, each input variable (mobile similarity or social similarity) corresponds to three different fuzzy sets which are calculated by the three different membership functions, meaning the mobile and social similarities correspond to six membership degrees and possess nine different combinations of the fuzzy sets according to the fuzzy logic. Additionally, the nine fuzzy rules are concisely presented in Table 1. Based on the \"If-Then\" rules in Table 1, the node, in an opportunistic social network, evaluates the levels of data transmission preference by analyzing the comprehensive consideration of \(MS(u,v)\) and \(SS(\gamma_{u},\gamma_{v})\), and the message carrier executes the suitable forwarding operation for messages.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Rule No.**} & \multicolumn{2}{c}{**If (And)**} & \multicolumn{2}{c}{**Then (Transmission Preference)**} \\ \cline{2-4}  & \(MS(u,v)\) & \(SS(\gamma_{u},\gamma_{v})\) & \\ \hline
1 & High & High & Definitely strong (level 1) \\
2 & High & Medium & Very strong (level 2) \\
3 & High & Low & Stronger (level 3) \\
4 & Medium & High & Strong (level 4) \\
5 & Medium & Medium & Middle (level 5) \\
6 & Medium & Low & Weak (level 6) \\
7 & Low & High & Weaker (level 7) \\
8 & Low & Medium & Very weak (level 8) \\
9 & Low & Low & Definitely weak (level 9) \\ \hline \hline \end{tabular}
\end{table}
Table 1: If-Then rules for our proposed fuzzy inference system.  
Figure 2: Three different membership functions of the normal distribution.  
As shown in Table 1, the impact of the mobility similarity of nodes on data transmission is obviously deeper than that of the social similarity between them, because the mobility similarity mainly reflects the degree of overlap between the moving trajectory for a pair of nodes. Moreover, a pair of nodes may obtain more meeting opportunities to transmit messages to each other if there is a relatively high node similarity between them. Otherwise, social similarity is usually used to divide nodes with a similar attribute characteristic into the same community, and the probability of transmitting messages between the nodes belonging to the same community is also relatively high.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 5 Stream Classification Algorithms and Architectures
### Algorithms
#### 5.2.4 Rule-Based  
An intuitive classification approach are rule-based systems [37; 120; 122]. The idea is that combinations of different features indicate a class label (see Figure 7d). For instance, specific intervals of numerical attributes are associated with a respective class. Thus, traditional rule-based methods cannot be used in stream scenarios with evolving feature spaces or other changes in the data. Exemplary algorithms extending the idea of rule-based systems to the streaming scenario are, e.g., STAGGER, FLORA, and AQ-PM [121]. STAGGER consists of two stages to handle shifts in the distribution: in the first step, the weights of the features, which lead to the applied rules, are adjusted. Second, new features are added to existing rules [53]. FLORA (as well as the extension FLORA3) is based on the idea of temporal windowing [144]. A collection of temporal contexts can be disabled or enabled over time, e.g., to handle seasonal drifts. The AQ-PM approach keeps only the training data close to the rules' decision boundaries [145]. Thus, AQ-PM can learn new concepts by forgetting older examples. However, the major drawbacks of rule-based systems are their inflexibility and low learning speed [121].
## 3 Results and Discussion
### Fruit Characteristics and Spectral Interpretation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Class** & **N** & **Mass (g)** & **Volume (cm\({}^{3}\))** & **Width (mm)** & **Length (mm)** & **MC (\%)** & **SSC (\%)** & **TA (\%)** \\ \hline A & 146 & 2114.16 \(\pm\) 8.04 \({}^{\circ}\) & 4237.03 \(\pm\) 19.83 \({}^{\circ}\) & 193.82 \(\pm\) 39.3 \({}^{\circ}\) & 216.91 \(\pm\) 1.93 \({}^{\circ}\) & 87.9 \(\pm\) 1.7 \({}^{\circ}\) & 12.4 \(\pm\) 0.8 \({}^{\circ}\) & 0.86 \(\pm\) 0.09 \({}^{\circ}\) \\ B & 166 & 1953.23 \(\pm\) 6.32 \({}^{\circ}\) & 3956.70 \(\pm\) 17.46 \({}^{\circ}\) & 190.86 \(\pm\) 8.09 \({}^{\circ}\) & 2129.3 \(\pm\) 8.66 \({}^{\circ}\) & 86.9 \(\pm\) 1.4 \({}^{\circ}\) & 10.9 \(\pm\) 0.6 \({}^{\circ}\) & 0.82 \(\pm\) 0.13 \({}^{\circ}\) \\ C & 76 & 1900.83 \(\pm\) 17.45 \({}^{\circ}\) & 3358.68 \(\pm\) 18.38 \({}^{\circ}\) & 177.72 \(\pm\) 8.17 \({}^{\circ}\) & 202.90 \(\pm\) 0.46 \({}^{\circ}\) & 852.5 \({}^{\circ}\) & 10.3 \(\pm\) 0.5 \({}^{\circ}\) & 0.64 \(\pm\) 0.09 \({}^{\circ}\) \\ D & 120 & 1696.63 \(\pm\) 17.0 \({}^{\circ}\) & 3322.64 \(\pm\) 12.97 \({}^{\circ}\) & 177.51 \(\pm\) 3.68 \({}^{\circ}\) & 197.29 \(\pm\) 5.88 \({}^{\circ}\) & 84.2 \(\pm\) 3.2 \({}^{\circ}\) & 10.1 \(\pm\) 0.8 \({}^{\circ}\) & 0.63 \(\pm\) 0.07 \({}^{\circ}\) \\ E & 92 & 1546.62 \(\pm\) 14.69 \({}^{\circ}\) & 4268.45 \(\pm\) 15.79 \({}^{\circ}\) & 162.03 \(\pm\) 8.87 \({}^{\circ}\) & 172.58 \(\pm\) 6.76 \({}^{\circ}\) & 83.3 \(\pm\) 6.5 \({}^{\circ}\) & 10.0 \(\pm\) 0.6 \({}^{\circ}\) & 0.54 \(\pm\) 0.05 \({}^{\circ}\) \\ \hline \end{tabular} Note: Values with different superscripts in the same column are significantly different (\(p<0.05\)); N is the number of samples; Class A to Class E represent 0%, 1–25%, 26–50%, 51–75%, and greater than 75% granulation, respectively.  
\end{table} 
Table 1: Fruit characteristics, moisture content (MC), soluble solids content (SSC), and titralte acidity (TA) of honey pomelo for each granulation class. Figure 6: External characteristics of honey pomelo: (**A**) Samples of Class A; (**B**) Samples of Class B; (**C**) Samples of Class C; (**D**) Samples of Class D; (**E**) Samples of Class E. Granulation levels of honey pomelo: (**a**) Samples of Class A; (**b**) Samples of Class B; (**c**) Samples of Class C; (**d**) Samples of Class D; (**e**) Samples of Class E.  
fruit granulation is related to changes in the cell wall structure of juice vesicles that may increase the content of cell wall components, including lignin, hemicellulose, and pectin [5; 8; 53].
# Asian Affective and Emotional State (A2 ES) Dataset of ECG and PPG for Affective Computing Research
## 4 Data Preprocessing and Features Extraction  
### Ecg  
Augsburg Biosignal Toolbox (AuBT) is a MATLAB-based emotion recognition toolbox developed by a team of researchers at the University of Augsburg, Germany [55]. The functionality of the toolbox includes a comprehensive graphical user interface (GUI) with ECG preprocessing, feature extraction, feature combination, feature selection, and classification. Additionally, the toolbox also has the capability to process EMG, SC, and RSP. This toolbox was adopted for the A2 ES ECG data preprocessing and feature extraction. The toolbox has also been adopted in prior research, such as in [14].  
Prior to the extraction of the HR and HRV features from the ECG signals, lowpass filtering and normalization were applied during the preprocessing. Next, each P, Q, R, S, and T peak was detected. There were nine feature types, with a total of 81 mixed variations that could be extracted using the AuBT. The list of statistical features is shown in Table 3.  
Figure 12: Data collection lab setup.  
The mean, median, standard deviation (Stdev), max, min, and range (max-min) of an interval refer to the amplitude characteristics of the time series. For the heart rate variability (HRV), the RR interval of the time series was taken for measurement. Both the heart rate (HR) and HRV features could be used to detect emotions and stress. The HRV feature pNN50 is the number of adjacent R-to-R intervals, also known as the normal-to-normal, and the percentage was greater than 50 ms. The mean of the frequency spectrum (specRange) of the HRV was calculated based on the calculated range. The triangular index (TriInd) represents the sum of all normal-to-normal intervals divided by the height of the histogram of all RR intervals restrained on a distinct scale with bins of 7.8125 ms. In short, 66 HR statistical features from the ECG intervals and selected amplitudes with 15 time domain HRV features were extracted and combined, leading to a total of 81 features extracted by the AuBT.
## 3 Proposed Method
### Notation  
We describe the basic graph notation used in the proposed method. For the input graph \(G=(\mathcal{V},\ \mathcal{E})\), \(\mathcal{V}\) indicates a set of nodes, and \(\mathcal{E}\) represents a set of edges. In the graph, \(n=|\mathcal{V}|\) is the number of nodes, and \(E=|\mathcal{E}|\) is the number of edges. The feature vector of node \(\mathrm{i}\in\mathcal{V}\) is \(h_{i}\). The positional vector is denoted by \(p_{i}\), and the edge feature connected to neighbor node \(j\in\mathcal{N}_{i}\) is represented by \(e_{ij}\in\mathcal{E}\), where \(\mathcal{N}_{i}\) is a neighbor node of node \(\mathrm{i}\). The depth of the layer is \(l\), where the input layer is \(l=0\), and the message-passing function for the information of each feature is indicated by \(\mathcal{F}_{h},\ \mathcal{F}_{p},\ \mathrm{and}\ \mathcal{F}_{e}\).
## 5 Results and Discussion
### Effects of the Parameters on \(\overline{Nu}\)
#### 5.2.3 The Effect of \(H/d\) on \(\overline{Nu}\)  
Figure 11 provides the response surface plots of different H/d for the average Nusselt number within the operating parameter range (\(\phi=0.9\)-\(1.2\), \(Re=600\)-\(1200\)). It can be found that \(\overline{Nu}\) slightly decreases when \(H/d\) increases because more cold ambient air is involved in the post-flame and wall jet regions of the PMIFJ. This causes the temperature to drop, thus reducing the global heat transfer performance [59]. In addition, it can be inferred from the response surface plots that \(Re\) affects the global heat transfer performance more obviously than \(\phi\).