esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions
A two-stage optimization process takes care of tuning the hyper-parameters of the classification model and performing an additional feature selection to ensure the selection of optimal granules of information. In order to address the behavior of the granulation strategies, we performed a two-fold comparison in terms of classification performance and number of granules of information. To this end, 10 different open-access datasets of labeled graphs pertaining to different application domains have been considered. Computational results show that, regardless of whether cliques or paths are employed, running a class-aware granulation is beneficial in terms of classification accuracy. However, it has the potential drawback of yielding a higher number of granules of information (i.e., higher dimensional embedding space). Although in this paper we used the BSAS free-clustering algorithm as the core clustering algorithm for the Granulator module, in principle, any clustering algorithm suitable for dealing with structured data and suitable for being equipped with an ad hoc dissimilarity measure can be employed instead. In the GRALG framework, the Granulator process is repeated many times (i.e., at least as many times as the are individuals in the first optimization stage), and therefore, the computational complexity of the clustering algorithm plays an important role in order to ensure reasonable training times (i.e., the BSAS clustering algorithm is known to scale linearly with respect to the number of patterns). Future research endeavours may investigate the trade-off in terms of training times against granulation quality with different clustering algorithms. Conceptualization, A.M. and A.R. ; methodology, A.M. and L.B. ; software, A.M. and L.B. ; validation, A.M. and L.B. ; formal analysis, A.M. and L.B. ; investigation, A.M.; resources, A.R. ; data curation, A.M. and L.B. ; writing--original draft preparation, A.M. and L.B. ; writing--review and editing, A.M. and A.R. ; supervision, A.M. and A.R. ; project administration, A.M. and A.R. All authors have read and agreed to the published version of the manuscript. This work received no external funding. Not applicable. The six datasets taken from the IAM Repository can be downloaded from [[https://fki.tic.heia-fr.ch/databases/iam-graph-database](https://fki.tic.heia-fr.ch/databases/iam-graph-database)]([https://fki.tic.heia-fr.ch/databases/iam-graph-database](https://fki.tic.heia-fr.ch/databases/iam-graph-database)) (access date 26 April 2022). The four datasets taken from the TUDataset Repository can be downloaded from [[https://chrsmrs.github.io/datasets](https://chrsmrs.github.io/datasets)]([https://chrsmrs.github.io/datasets](https://chrsmrs.github.io/datasets)) (access date 26 April 2022). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} BSAS & Basic Sequential Algorithmic Scheme \\ GRALG & GRanular computing Approach for labeled Graphs \\ GED & Graph Edit Distance \\ \(K\)-NN & K-Nearest Neighbors \\ nBMF & node Best Match First \\ \end{tabular}
## 4 Results and Discussion
Further reduction of the discrepancy in the low-energy region may be achieved by considering the population transfer processes in neighboring charged states of Xenon ions, such as radiative recombination, resonance excitation and inner-shell impact ionization, as seen in the Refs. [53; 88; 89] for population correction in Neon-like iron. The high-sensitivity of 3F to the radiative cascade suggests that the interionic population transfer process described above can readily improve the population of the upper level of 3F by enhancing the 4p-3s or the other cascade channel. Therefore, it can be inferred that the CRM containing only a single Xe44+ underestimates the emission intensity of 3F, due to the omission of the unknown cascade channel across charged states. Our results suggest the existence of this unknown process, which is likely to be associated with low-charged ions such as Xe43+, Xe42+ or lower. This is because the experimental theoretical discrepancy is apparent only at low-energy region, where the concentration of low-charged Xenon ions is higher. In tokamak plasma, Ne-like Xe reaches high concentration with an electron-temperature 4-10 keV [7; 9]. According to our CRM simulation, in the ideal Maxwellian plasma where the EIE rates are calculated by convoluting \(\sigma v\) (rate coefficient) with Maxwellian energy distribution function, the line-intensity ratio of 3F/3D remains constant at 0.526 \(\pm\) 0.002 within this wide temperature range. This result theoretically indicates that the 3F/3D line-intensity ratio is insensitive to electron temperature, but it is inconsistent with the X-ray spectra for Ne-like Xe in EAST [9] and C-Mod tokamak [8]. The experimental 3F/3D ratios are clearly higher than above-mentioned theoretical value, especially at low electron temperature [8]. This trend implies that the unknown process described in the previous paragraph, which is also more significant at low-energy region, may have caused the discrepancy between the predicted ratios and the experimental results in tokamak.
## 1 Introduction
The simplest way to monitor this color development is by using an absorption spectroscopy reader, which is a cost efficient, reliable and semi-portable (it fits into a backpack) detector. This principle can be applied for the rapid monitoring of CMs and OPs in food  
Figure 1: (**a**) In vivo, the neurotransmitter acetylcholine is hydrolyzed to choline and acetic acid by AChE; (**b**) in vitro hydrolysis of various substrates (acetylthiocholine iodide (AThL, [PERSON]’s assay substrate), indoxyl acetate (IDA) and \(\alpha\)-naphthyl acetate (\(\alpha\)-NAc)) by AChE producing different colored products. extracts providing high throughput, when using a 96-microwell plate, minimum reagents consumption and LODs at the ppb level, depending on the matrix [20; 21]. In this study, the development and validation of an AChE assay for CM and OP pesticides is presented. During assay optimization, various parameters were investigated including a critical comparison of commonly applied AChE substrates for the first time. Following our recently published paper, which addressed the issues related to the validation and benchmarking of biosensors in food safety [8], comprehensive validation datasets for carbofuran, carbofuran-3-hydroxy and dichloros in strawberry and lettuce extracts are presented. The optimized AChE assay was benchmarked towards an accredited LC-MS/MS method, following the regulatory requirement (Decision 2002/657/EC) of confirming bioassays results with an instrumental method.
## 5 Results and Discussion
### Sample Size Calculation Using the Neyman Allocation Method
#### 5.2.2 Sample Size Calculation for CHF3 Tier 3 DRE Using the Neyman Allocation Method  
In this study, the total number of scrubbers required for the Tier 3 development of target gases was obtained to confirm the information related to the population. The number of CHF3 scrubbers, considering scrubber types, is presented in the following Table 15.  
For CHF3, the standard deviation is also required to understand the stratum characteristics. Therefore, the mean and standard deviation of Tier 3 DRE for CHF3 gas by scrubber type were calculated and presented in the table.  
The results of the DRE calculations by scrubber type for CHF3 show that the plasma type had the highest value at 0.996, followed by the catalytic type at 0.986, and the burn type had the lowest value at 0.967, along with the highest standard deviation (Table 16).  
In the case of CHF3, the determination of the sample size using Neyman allocation considered the same error level (1%) and confidence level (95% confidence interval) as the previous method of estimating the sample size using CV. The sample size was calculated according to Neyman allocation considering the characteristics (standard deviation) of the strata, and the sample size per stratum is shown in Table 17 considering the proportion of the population in each stratum. The sample size determination revealed that a total of six samples were required to develop a DRE for CHF3, with one burn type, four plasma types, and one catalyst type per layer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Burn** & **Plasma** & **Catalyst** \\ \hline Mean & 0.967 & 0.996 & 0.986 \\ SD & 0.052 & 0.007 & 0.012 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Tier 3 DRE mean and standard deviation by scrubber type in CHF3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Population** & **Estimated Number of Samples** \\ \hline Burn & 420 & 2 \\ Plasma & 2546 & 18 \\ Catalyst & 2 & 1 \\ Total & 2968 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Number of samples per scrubber type for CF4 DRE development considering the Neumann distribution method.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Scrubber** \\ \hline Burn & 369 \\ Plasma & 2546 \\ Catalyst & 2 \\ Total & 2917 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Number of scrubbers per scrubber type for DRE development in CHF3.
# Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection
## 2 Methodology
### Violent Pose Estimation Model  
The pose estimation model detects the action or movement of the body. MediaPipe is implemented here to achieve violent action as shown in Figure 1. The MediaPipe open-source framework, developed by Google, is used in this context. This provides a platform for creating real-time pipelines of multimedia processes [38]. A total of 33 key points are being covered by the MediaPipe of human bodies in a video or image [39]. These key points are the different locations on the body that analyze the position and help in movement categorization. The three types of action are trained here using this model and each action is trained in hundreds of parameters to achieve the accurate target pose. These actions are trained for violent objects. The input of images accepts the data types in the following form: images, video frame (which should be decoded) form, and real-time video feed. The output of the task provides image coordinates and 3-dimensional coordinates of the world [40].
# SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images
## 2 Data  
The Helioseismic and Magnetic Imager [33; 34] onboard the Solar Dynamics Observatory [35] provides high-resolution, full-disk images of the solar white-light continuum intensity in the Fe II absorption line at 6173 A. The spatial resolution of these images is 1\({}^{\prime\prime}\), with a sampling of 0.5\({}^{\prime\prime}\)/pixel. Some corrections such as exposure time, dark current, flat field, and cosmic-ray hits were applied to the level-1 data. About 15,000 continuum intensity images from 2010 May to 2017 December, with a four-hour cadence, were downloaded.  
The first step of this work is to build a dataset to train and test the deep learning model for multiscale sunspot detection and segmentation. The dataset was built using about 800 images, which correspond to two types: full-disk solar images and local region images. A total of 600 full-disk solar images in 2014 were selected, and about 200 local region existing sunspots with different scales or different characteristics were cropped from the full-disk images in other years. All images were resized to 860 \(\times\) 860 pixels\({}^{2}\), including multiscale sunspots. About 27,000 sunspots were labeled by Labelme [36], an open-source image polygon annotation software ([[https://github.com/wkentaro/labelme](https://github.com/wkentaro/labelme)]([https://github.com/wkentaro/labelme](https://github.com/wkentaro/labelme)), 1 July 2023), which is used to generate annotation data by annotating the polygon of the samples. The annotation data was converted into a mature labeling format: Microsoft Common Objects in Context [37]. Finally, the dataset was divided into a training set and a validation set with a ratio of 9:1.  
The test set was built from a total of 100 different high-resolution, full-disk solar images of 4096 \(\times\) 4096 pixels2 spanning the years 2010 to 2017, which is not an intersection with the training set and the validation set.
# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 3 Results
### Comparative Analysis of Results with Global Fire Emissions Database (GFED)  
The present estimates of carbon emissions during 2011 and 2013 were similar to the GFED data for forested areas (Table 7). However, in all years except 2011, there was a lack of correlation because of strong local discrepancies. The highest coincidence in the estimated amount of carbon was in 2013 in Plain and Hills of the Yucatan Peninsula (ecoregion 15.2), and the lowest was in 2012 in Southern Sierra Madre (13.5). In the latter, the cells with the highest emissions identified by GFED were towards the south of the cells with the highest emissions identified by this work. The MCD12Q1 land cover product, which is an input of GFED, identifies this zone as woody savannas, whereas the INEGI cartography of Mexico identifies it as oak forests, located on the leeward side of Southern Sierra Madre, where the climate is drier than that of the windward side. In the GFED, the savanna is not considered to be a forest. Hence, differences in the cartography of the Southern Sierra Madre are the main source of discrepancy in the results. In contrast, the correlation was higher when there were more emissions in the Plain and Hills of the Yucatan Peninsula (15.2) ecoregion. Another element to consider is the difference between the versions of the burned area product; the present work used the latest version (Collection 6), whereas GFED uses a previous version.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ecoregion** & **State** & **Id** & **Name** & **Total Emissions (Tg)** & **Cell Area (Km\({}^{2}\))** \\ \hline \multirow{3}{*}{13.5} & Guerrero & A & Western Guerrero State & 12.43 & 4300 \\  & Guerrero & B & Central Guerrero State & 4.23 & 2000 \\  & Oxacca & C & Sierra Juarez & 1.00 & 700 \\ \hline \multirow{3}{*}{15.2} & Quintana & \multirow{3}{*}{D} & \multirow{3}{*}{Site between Sian Ka’an and Calakmul} & 3.47 & 1600 \\  & Roq & & & & \\ \cline{1-1}  & Quintana & & & & \\ \cline{1-1}  & Roq & & & & \\ \cline{1-1}  & Campeche & F & Site between Calakmul and Laguna de Terminos & 1.59 & 900 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Areas of interest by forest fire emissions.  
Figure 8: Areas of interest in terms of forest fire emissions in three ecoregions of Mexico.
## 5 Conclusions  
This paper proposes a re-ID system that combines analytical feature extraction and similarity ranking schemes with human parsing. It shows that parsing masks provide sufficient information to overcome the known limitations of analytical re-ID methods. Two types of features, namely Lab color and LBP texture features, are utilized. In addition, several original pre-processing techniques are proposed. These features are combined with a class similarity evaluation scheme that is used to obtain a similarity ranking with results comparable with conventional rank-\(r\) evaluation metrics. The obtained results show a rank-1 accuracy that is comparable with supervised DL models and exceed that of unsupervised ones that were trained on the Market1501 and CUHK03 datasets. The reasons for reduced mAP accuracy are discussed, and potential solutions in adding new feature channels is proposed. It is shown that by having no trainable parameters, the proposed model has significant generalization potential, as illustrated by 93.5% and 63.9% rank-1 \"transfer\" accuracies achieved by a completely re-ID dataset-agnostic model. Our future work will include studying of additional features in order to improve accuracy, along with parser size minimization in order to realize the proposed system as an edge computing application.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is available on request from corresponding author.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**Market**} & \multicolumn{2}{c}{**DukeMTMC**} & \multicolumn{2}{c}{**CUHK**} & \multicolumn{2}{c}{**Market\(\rightarrow\)X**} & \multicolumn{2}{c}{**X\(\rightarrow\)Market**} \\
**Metric** & **R1** & **mAP** & **R1** & **mAP** & **R1** & **mAP** & **R1** & **mAP** & **R1** & **mAP** \\ \hline APNet-C [19] & **96.2** & **90.5** & 90.4 & 81.5 & **87.4** & **85.3** & 37.7 & **22.8** & 50.9 & 23.7 \\ Ours (cq) & 93.5 & 25.3 & - & - & 63.9 & 22.1 & **63.9** & 22.1 & **93.5** & **25.3** \\ \hline \hline \end{tabular}  
X is the DukeMTMC dataset for APNet-C and the CUHK03 dataset in our experiments. Best results are in bold.  
\end{table}
Table 6: Comparison between single-domain and cross-domain accuracies of supervised DL CNN models with the proposed approach.  
**Acknowledgments:** The author would like to thank [PERSON] and [PERSON] for their fruitful discussions, as well as [PERSON] for his assistance in problem formulation, choice of methodology, and supervision.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 6 Performance Study of the UWOC Link
### Impact of Data Rate and Transmit Power  
Consider the BER performance as a function of link distance \(Z\) for different \(R_{b}\). This latter is fixed by setting the OFDM symbol duration \(T_{s}\), while keeping \(N\) unchanged. From Figure 7, to have a sufficiently large LBI for all schemes and the considered bit rates, \(P_{\text{Tx,e}}\) is set to \(50\,\mathrm{mW}\). BER plots are presented in Figure 8, where we notice the best performance for ACO with the largest attainable link span, e.g., around \(89\,\mathrm{m}\) for \(R_{b}=20\,\mathrm{Mbps}\) and a BER of \(10^{-3}\) (the corresponding LBI and \(Z_{\text{max}}\) are summarized in Table 3). Thus, ACO is the most power-efficient scheme. This confirms the results previously presented in Figure 6 showing that ACO needs a lower \(P_{\text{Tx,e}}\) to get a target BER, compared to DCO and LACO. We further notice that, for all schemes, LBI shrinks when increasing the data rate, which is due to the decrease in \(T_{s}\), i.e., a shorter duration for collecting photons, see Equation (4).
# A Photoplethysmogram Dataset for Emotional Analysis
## 3 Results and Discussion
### Emotional Classification Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & \multicolumn{5}{c}{**Accuracies (\%) for Each Iteration**} & **Mean \(\pm\) Std.** \\ \cline{2-5}
**Block** & **1** & **2** & **3** & **4** & **5** & **Mean \(\pm\) Std.** \\ \hline B1 & 66.38 & 58.26 & 75.65 & 63.48 & 61.74 & 65.1 \(\pm\) 5.89 \\ B2 & 81.9 & 81.74 & 64.35 & 70.43 & 77.39 & 75.16 \(\pm\) 6.83 \\ B3 & 51.72 & 55.65 & 65.22 & 70.43 & 59.13 & 60.43 \(\pm\) 6.68 \\ B4 & 62.93 & 60.87 & 65.22 & 70.43 & 47.83 & 61.46 \(\pm\) 7.52 \\ B5 & 60.34 & 62.61 & 87.83 & 72.17 & 77.39 & 72.07 \(\pm\) 10.04 \\ B6 & 69.83 & 61.74 & 73.04 & 72.17 & 73.91 & 70.14 \(\pm\) 4.41 \\ B7 & 75 & 62.61 & 61.74 & 60 & 88.7 & 69.61 \(\pm\) 10.92 \\ B8 & 62.07 & 74.78 & 75.65 & 53.04 & 52.17 & 63.54 \(\pm\) 10.15 \\ B9 & 63.79 & 64.35 & 82.61 & 54.78 & 68.7 & 66.85 \(\pm\) 9.09 \\ B10 & 62.93 & 71.3 & 48.7 & 68.7 & 66.96 & 63.72 \(\pm\) 7.99 \\ B11 & 64.66 & 73.04 & 59.13 & 65.22 & 64.35 & 65.28 \(\pm\) 4.46 \\ B12 & 62.07 & 81.74 & 71.3 & 73.04 & 49.57 & 67.54 \(\pm\) 10.94 \\ B13 & 68.97 & 84.35 & 63.48 & 60.87 & 73.04 & 70.14 \(\pm\) 8.27 \\ B14 & 71.55 & 60 & 69.57 & 73.04 & 70.43 & 68.92 \(\pm\) 4.61 \\ B15 & 65.52 & 64.35 & 75.65 & 74.78 & 62.61 & 68.58 \(\pm\) 5.5 \\ B16 & 52.59 & 69.57 & 51.3 & 73.04 & 66.96 & 62.69 \(\pm\) 8.99 \\ B17 & 64.66 & 42.61 & 66.09 & 73.91 & 86.09 & 66.67 \(\pm\) 14.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracies of different network structure of the proposed dataset for arousal classification. The training and test were repeated five times. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Database** & **Signal Source** & **Method** & **Accuracy (\%)** \\ \hline
[32] & 2021 & DEAP & PPG & CNNs with statistical features & 71.50 \\
[29] & 2020 & DEAP & PPG & CNNs with statistical features & 76.2 \\
[22] & 2021 & DEAP & PPG & Logistic regression & 64.84 \\
[14] & 2019 & CLAS & PPG + GSR * & SVM * & 71.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Arousal classification accuracies in previous studies. The recognition accuracy depends on the network structure as it is known widely. Although this study conducted experiments with different network structures, different network structures may increase the accuracy in further studies. Testing accuracies by using different datasets is necessary to verify the presented network structure and may show inspirations for further studies, but it is not demonstrated because the goal of the current study was to present and evaluate a dataset. Another issue for the recognition accuracy is that we trained two different models for arousal and valence, because the network learned better with selected features rather than all the features. This could be partly because the size of training data is small, which can be overcome by training together with additional datasets or by employing a few-shots learning mechanism [38].