esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 4 Results
### Crowd Representation  
In this section, we present the effectiveness of the proposed visual descriptors, as depicted in Figure 1, for crowd anomaly detection. The utilisation of the Delaunay triangulation method to capture the spatial proximity and enhance the neighbourhood representation is illustrated in Figure 2.  
In Figure 2, we illustrate the concept of spatial proximity within a crowd using Delaunay triangulation, a technique that enhances the understanding of the crowd's neighbourhood dynamics. The figure serves to visually explain how the crowd's behaviour is interpreted through feature points and visual descriptors, which are calculated and then represented using Delaunay triangles.  
Figure 2a,b display Delaunay triangles overlaid onto a crowd scene where no abnormal activities are taking place. The crowd's behaviour is analyzed through sparse feature tracking, allowing us to define significant points that represent the scene. Delaunay triangulation helps to delineate the spatial relationships between these points, forming triangles that reflect the crowd's structure and arrangement.  
In Figure 2c, we observe the impact of abnormal behaviour within the crowd. As an anomalous event occurs, the crowd's typical cohesion breaks down, causing the individuals to scatter and move in various directions. This change in behaviour is effectively captured and illustrated through Delaunay triangulation. The method of sparse feature tracking allows us to follow these scattered movements and depict them as changes in the arrangement of the Delaunay triangles.  
A noteworthy feature that aids in discerning abnormal behaviour is the length of the edges within the Delaunay triangles. In a normal crowd scenario, the edges maintain a consistent length, reflecting the crowd's relatively uniform distribution. However, when an abnormal event disrupts the crowd, such as scattering due to a sudden disturbance, the edges' lengths can alter significantly and rapidly. This edge length variation  
Figure 2: Spatial proximity using Delaunay triangulation to better represent the neighbourhood. (**a,b**) represent Delaunay triangles on crowd scenes with no abnormal activities. (**c**) represents Delaunay triangles on crowd scene with abnormal activity. Green lines: Delaunay triangle edges,structuring crowd area. Red dots: indicate anomaly hotspots in crowd dynamics.  
is clearly depicted in the Delaunay triangles illustrated in Figure 2c. The uniformity of the edge lengths is disrupted, with certain edges becoming notably longer than others. This disparity in the edge lengths signifies the occurrence of abnormal behaviour within the crowd. The magnitude and rapidity of these changes are visually evident in the triangulation representation, providing a valuable tool to understand the dynamics of the crowd behaviour.
## 4 Results and Discussion
### DSR Results
The HSM blending process itself leads to an increase in \(\,\mid\)G*\(\,\mid\) by 15% at 58 \({}^{\circ}\)C. Furthermore, the addition of 5, 10, and 20% of Kraft lignin results in a significant increase in \(\,\mid\)G*\(\,\mid\) by 31, 47, and 81%, respectively, at 58 \({}^{\circ}\)C. For MM samples at 58 \({}^{\circ}\)C, the blending process increases the \(\,\mid\)G*\(\,\mid\) by 51%. Meanwhile, by increasing the Kraft lignin percentage, \(\,\mid\)G*\(\,\mid\) increases by 97, 105, and 97% for the 5L-MM, 10L-MM, and 20L-MM samples, respectively. Lastly, in terms of blending conditions, samples prepared with a MM exhibited higher \(\,\mid\)G*\(\,\mid\) and lower \(\delta\) values compared to those prepared with HSM (Figure 8). These findings indicate that the MM samples have greater stiffness and elasticity than the HSM samples. It is important to note that the HSM samples exhibit a distinctly different behavior compared to the MM samples. These findings highlight the significant impact of blending conditions on the rheological behavior of modified bitumen with Kraft lignin. Further, it can be concluded that the MM samples undergo more extensive oxidation and aging. These observations align with the results obtained from the BRV tests.
# Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures
## 3 Results and Comparisons
### Effect of the Number of Regions  
Initially, the influence of the number of regions on the NMI values that were obtained for the proposed method is investigated. Figure 5 illustrates the variation of NMI with respect to the different number of regions (\(N_{P_{R}}\)) of the segmentation map. The values of NMI represent the average of 10 runs for each setting. In Figure 5, it can be observed that the images have different values of \(N_{P_{R}}\) at which the NMI is the highest. Salinas-A has the highest value of NMI at the number of regions in the segmentation map equal to the number of clusters (\(N_{P_{R}}=k=6\)). A downward trend is observed for PaviaC, Salinas-A, and Indian Pines images as the number of regions increases. For PaviaU, it can be observed in Figure 5a that the quality of clustering increases as the number of segments increases across the segmentation map, whereas for Jasper Ridge and Urban images, at \(k=4\), the NMI values tend to converge to a certain value. Once the optimal value of \(N_{P_{R}}\) is set for each image, the NMI values are obtained for a varying number of clusters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Set** & **Number of Clusters (\(k\))** & **Varying \(k\) (Steps of 2)** \\ \hline Salinas & 16 & 16 to 26 \\ PaviaU & 9 & 9 to 19 \\ Indian Pines & 16 & 16 to 26 \\ Salinas-A & 6 & 6 to 16 \\ Samson & 3 & 3 to 13 \\ Urban & 4 & 4 to 14 \\ Jasper Ridge & 4 & 4 to 14 \\ PaviaC & 9 & 9 to 19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of clusters for each Hhyperspectral image (HSI) data set and experiment parameter setting.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 4 Intelligent Edge Computing
### Slices Classification Techniques
In this context, the method uses a linear combination of Manhattan distance and Tchebychev distance metrics, where the former represents the maximum group utility, and the latter represents the minimum individual weight of the \"opponent\". After obtaining the decision matrix and the weight vector, the method employs a set of mathematical operations, resulting in the ranking of the alternatives [56, 58]. The COPRAS method seeks to assess the superiority of an alternative by implementing an evaluation ranking that considers the performance of the alternatives in relation to different criteria and their weights. The method can be used to maximize or minimize criteria, and it is used in several areas of knowledge [57]. The choice of these methods for validating the proposed framework is due to their popularity in the literature for different types of problems, applications, and areas of knowledge, in addition to their proven efficiency. The details of the methods and their mathematical operations are beyond the scope of this work but can be obtained from the previously mentioned references.
## 4 Control Strategies
### Synchronization with the Electrical Network  
In many practical situations, the distribution network exhibits distorted and unbalanced voltages. In these cases, synchronization with the network strongly influences the performance of the entire control scheme of the power converter connected to the network. Determining the correct value of the fundamental-frequency positive-sequence (FFPS) component of the microgrid bus voltage vector is essential for reasonable control of the inverter. This information is obtained through a phase-locked loop (PLL) algorithm.  
Several PLL schemes for three-phase systems have been proposed in recent years, the synchronous reference frame PLL (SRF-PLL) being the most popular [23,24]. However, due to its poor performance when the voltages contain harmonics and/or unbalance, preferable alternatives have been presented [25]. In this paper, the PLL scheme that uses the generalized delayed signal cancelation (GDSC) method as a pre-filter to eliminate the effects of unbalances and harmonic components eventually present in the microgrid AC bus voltage was used [26,27]. The choice was based on the fast and accurate determination of the FFPS component of a three-phase signal, even under severe unbalanced and distorted conditions.
## 2 Methods
### Data Collection and Analysis  
In the experiment, BAL and BTE were similar situations and were not affected by fatigue or tension. They are both control groups, and the others are treatment groups. BFA and BFT were similar and were the basic or initial fatigue states before the ATCOs performed any night shift tasks. AFA represented the fatigue state during work, ATE represented the tension state, and AFT represented the fatigue and tension state. Data on the 11 static parameters (SBP, DBP, HR, STT, NDFO, RTS, RTL, ROA, TDSA, LSPWB, and ART) were measured in the eight situations. Four dynamic parameters (DHR_AVG, DHR_SD, RTOS_AVG, and RTOS_SD) were collected during the experiments of the four states. According to the measurement time of the data, \"situation\" and \"state\" were used to explain the static parameters and the dynamic parameters, respectively. The data of the 27 participants were collected successfully, and the data of the other three participants were excluded due to incomplete experiments.  
According to the questionnaire results, subjective fatigue levels were investigated in the eight situations, whereas tension levels were based on the subjective feelings during the experiments of the four states. The results show that the fatigue levels were obviously higher in the BFA (AVG = 4.37, SD = 0.75), AFA (AVG = 4.74, SD = 0.81), BFT (AVG = 4.35, SD = 0.81), and AFT (AVG = 4.28, SD = 0.87) situations than in the BAL (AVG = 1.91, SD = 0.32), AAL (AVG = 2.06, SD = 0.41), BTE (AVG = 1.94, SD = 0.35), and ATE (AVG = 2.17, SD = 0.43) situations. The tension levels were higher in the TE (AVG = 4.38, SD = 0.42) and FT (AVG = 4.25, SD = 0.46) states than in the AL (AVG = 2.13, SD = 0.34) and FA (AVG = 2.26, SD = 0.39) states. The results indicate that the expected states were achieved and the experimental data can be used to analyze the effects of fatigue and tension.  
In this study, we firstly used analysis of variance (ANOVA) with repeated measures to determine the differences in the parameters for the eight situations and four states and the post hoc pairwise tests were performed to assess the effect details. Then, the decreasing orders of influence of fatigue and tension on the indicators were determined according to the results of a paired \(t\)-test between typical situations and states.
# A Review of Treatment Techniques for Short-Chain Perfluoroalkyl Substances
## 3 Treatment Techniques for Short-Chain PFASs
### Advanced Oxidation/Reduction Techniques
#### 3.2.2 Photocatalytic Degradation
In addition to the above n-type photocatalysts of TiO\({}_{2}\), p-type photocatalysts involving In\({}_{2}\)O\({}_{3}\) and Ga\({}_{2}\)O\({}_{3}\) have attracted widespread attention because they could enhance the degradation capacity of PFASs. However, p-type photocatalysts showed a strong dependence on the material shape and microstructure [15]. For example, the In\({}_{2}\)O\({}_{3}\) porous microsphere had the highest photocatalytic activity on the degradation of PFOA, which was 74.7 times faster than TiO\({}_{2}\). Similarly, short-chain intermediate PFHpA could be completely degraded within 3 h by the In\({}_{2}\)O\({}_{3}\) nanoplates, whereas In\({}_{2}\)O\({}_{3}\) nanocubes were much less effective. However, in the degradation process, the short-chain intermediates were still generated from PFOA degradation, and the concentration of intermediates was positively related to their carbon chain length [50; 53]. New photocatalysts could decompose PFAS, but the information about the application for short-chain PFASs was still limited. According to the degradation data of long-chain PFASs, the degradation conclusions could be deduced as the following: photocatalytic degradation of PFASs was a gradual splitting decomposition of CF\({}_{2}\). The reaction of breaking the chain generated short-chain intermediates. The short-chain products showed strong resistance to photocatalytic degradation [15]. During the photodegradation process, the catalyst dosage and pH were imperative influencing factors. In the low concentration range between 20-100 mmol/L of persulfate, the photochemical reactivity could be improved with the concentration increase. Whereas further increasing persulfate concentrations could result in saturation of the reaction rate, Because SO\({}_{4}\)\({}^{\bullet-}\) could react with persulfate or itself, this side reaction would reduce the degradation efficiency [15]. Moreover, a high concentration of hydrated hydrogen ions (H\({}_{3}\)O\({}^{+}\)) would quench e\({}_{\text{aq}}\)\({}^{-}\) under acidic conditions, thus contributing to the reactivity decrease due to the quantum yield of e\({}_{\text{aq}}\)\({}^{-}\) declining sharply. However, under alkaline conditions, the reactivity would be enhanced due to the quantum yield of e\({}_{\text{aq}}\)\({}^{-}\) increasing by the reaction of H\(\cdot\) and OH\({}^{-}\)[54].
## 6 Simulation Experiment
### Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Error Item**} & \multicolumn{2}{c}{**True Value**} & \multicolumn{2}{c}{**Linear EKF**} & \multicolumn{2}{c}{**Nonlinear EKF**} \\ \cline{3-7} \multicolumn{2}{c}{} & & **Results** & **Relative Error** & **Results** & **Relative Error** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Scale factor of \\ FOGs (ppm) \\ \end{tabular} } & Kgx & 50 & 47.6136 & \(-\)4.77\% & 45.4885 & \(-\)9.02\% \\ \cline{2-7}  & Kgyy & 50 & 45.0199 & \(-\)9.96\% & 45.4232 & \(-\)9.15\% \\ \cline{2-7}  & Kgzz & 50 & \(\backslash\) & \(\backslash\) & 45.4936 & \(-\)9.01\% \\ \hline \multirow{3}{*}{\begin{tabular}{c} Misalignment \\ error of FOGs \\ (\(\mathrm{\SIUnitSymbolMicro rad}\)) \\ \end{tabular} } & Kgyx & \(-\)50 & \(\backslash\) & \(\backslash\) & \(-\)49.6124 & \(-\)0.78\% \\ \cline{2-7}  & Kgzx & 50 & \(\backslash\) & \(\backslash\) & 50.2019 & 0.40\% \\ \cline{2-7}  & Kgzy & \(-\)50 & \(\backslash\) & \(\backslash\) & \(-\)50.9442 & 1.89\% \\ \hline \multirow{3}{*}{\begin{tabular}{c} Scale factor of \\ ACCs (ppm) \\ \end{tabular} } & Kaxx & 100 & 69.7489 & 30.25\% & 115.5479 & 15.55\% \\ \cline{2-7}  & [PERSON] & 100 & 96.5638 & 3.44\% & 115.1622 & 15.16\% \\ \cline{2-7}  & [PERSON] & 100 & \(\backslash\) & \(\backslash\) & 114.9464 & 14.95\% \\ \hline \multirow{3}{*}{\begin{tabular}{c} Misalignment \\ error of ACCs \\ (\(\mathrm{\SIUnitSymbolMicro rad}\)) \\ \end{tabular} } & [PERSON] & \(-\)100 & \(\backslash\) & \(\backslash\) & \(-\)99.2220 & \(-\)0.78\% \\ \cline{2-7}  & [PERSON] & 100 & \(\backslash\) & \(\backslash\) & 100.4671 & 0.47\% \\ \cline{2-7}  & [PERSON] & 100 & \(\backslash\) & \(\backslash\) & 99.9669 & \(-\)0.03\% \\ \cline{2-7}  & [PERSON] & \(-\)100 & \(\backslash\) & \(\backslash\) & \(-\)100.4243 & 0.42\% \\ \cline{2-7}  & [PERSON] & \(-\)100 & \(\backslash\) & \(\backslash\) & \(-\)99.8454 & \(-\)0.15\% \\ \cline{2-7}  & [PERSON] & 100 & 95.4444 & 4.56\% & 100.5597 & 0.56\% \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Nonlinear error \\ of FOGs \\ (\(10^{-6}\) s/rad) \\ \end{tabular} } & dgx & 10 & \(\backslash\) & \(\backslash\) & 9.9872 & \(-\)0.13\% \\ \cline{2-7}  & dgy & 10 & \(\backslash\) & \(\backslash\) & 10.0211 & 0.21\% \\ \cline{2-7}  & dgz & 10 & \(\backslash\) & \(\backslash\) & 10.3901 & 3.90\% \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Nonlinear error \\ of ACCs \\ (\(10^{-6}\) s/m\({}^{2}\)) \\ \end{tabular} } & dx & 10 & \(\backslash\) & \(\backslash\) & 10.05221 & 0.52\% \\ \cline{2-7}  & day & 10 & \(\backslash\) & \(\backslash\) & 10.03140 & 0.31\% \\ \cline{2-7}  & daz & 10 & \(\backslash\) & \(\backslash\) & 10.0589 & 0.59\% \\ \hline \hline \end{tabular} Note: \(\backslash\)“means no results.  
\end{table} 
Table 3: \(Cont.\)  
Figure 10: The residual error after each iteration of the scale factor error ACCs.
# Detectron2 for Lesion Detection in Diabetic Retinopathy
## 2 Related Work
### Deep Learning (DL) Methods for DR Grading  
In this category of studies, deep neural networks are used to grade DR into different stages, including non-DR, mild NPDR, moderate NPDR, severe NPDR, and PDR.  
A study by [PERSON] et al. [6] described the development of a deep learning system called DeepDR for the early detection and grading of diabetic retinopathy. The system was trained on a large dataset from a single ethnic cohort of patients with diabetes. The system was trained using a large dataset from a single ethnic cohort of patients with diabetes. The performance of DeepDR was high in detecting microaneurysms, cotton-woool spots, hard exudates, and hemorrhages, with area under the curve (AUC) scores ranging from 0.901 to 0.967. The grading of diabetic retinopathy was also successful with AUC scores ranging from 0.943 to 0.972. However, further validation in multiethnic and multicenter cohorts is needed to confirm the robustness of lesion detection and grading.  
Another study by [PERSON] [5] aimed to evaluate the diagnostic performance of a deep learning system (DLS) for detecting referable diabetic retinopathy using retinal images. The DLS achieved a high accuracy with an area under the curve of 0.936, a sensitivity of 90.5%, and a specificity of 91.6%. However, it should be noted that the focus of this study was only on detecting referable diabetic retinopathy, which may not encompass all cases of diabetic retinopathy. Additionally, the study has limitations, including varying reference standards and a \"opaque\" effect, which may impact its acceptance by physicians.  
In the study [10], the authors present a machine learning-based approach to automate the classification of diabetic retinopathy (DR). Convolutional neural networks (CNNs), such as VGG-16 and VGG-19 [11], were employed to analyze fundus images and categorize the DR severity into five levels ranging from 0 (no DR) to 4 (proliferative DR). The system was evaluated using various performance metrics including accuracy, sensitivity, specificity, and AUC, and the results showed that the system achieved a high accuracy of 82%, sensitivity of 80%, specificity of 82%, and AUC of 0.904.  
These studies demonstrate the effectiveness of deep learning algorithms in grading DR, which can be useful in the diagnosis and prompt treatment of the disease.
# Study of a Miniaturizable System for Optical Sensing Application to Human Cells
## Materials and Methods
### MTT Assay on HDF and LoVo  
We tested Ru(bpy)\({}_{3}\)\({}^{2+}\) effect on the cells vitality by MTT assay. HDF cells were exposed to fluorophore concentrations ranging from 0.14 \(\upmu\)M to 14 \(\upmu\)M for 24 h and incubated with 0.05 mg/mL of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide salt. After 2.30 h of incubation at 37 \({}^{\circ}\)C, formazan salts, produced by succinate dehydrogenase activity in live cells, were solubilized with 0.1 N isopropanol/HCl and quantified spectrophotometrically by a Cary 50, Varian. The absorbance values were converted to optical density (\(\Delta\) OD: \(\lambda_{570}\)-\(\lambda_{650}\)). All samples were measured in triplicate. LoVo cells were exposed to 0.14 \(\upmu\)M of Ru(bpy)\({}_{3}\)\({}^{2+}\) for 24 h and MTT assays. The same procedure described above was performed 1, 2 or 5 days after exposure.
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 3 Noise Modeling and Building the Training Sets
### Building the Training Sets
#### 3.2.3 Noisy Speech Synthesis  
Assuming that additive noise can create noisy speech, we create a noisy speech database by adding noise to clean speech at real Signal to Noise Ratio (SNR) levels. In this  
Figure 4: Training set construction process.  
way, we overcome the data scarcity problem mentioned above by synthesizing pure-noisy speech pairs comprising pure and realistic synthesized noisy speech.
# A Fundamental Study on Circularly Polarized Double-Loop Antennas with Quasi-Two Sources
## 7 Conclusions  
We have studied two concentric double-loop antennas with a spacing \(S\) between the driven and parasitic ones. The antennas with outer and inner parasitic loops showed 3 dB axial ratio bandwidths of 23% and 40% for \(S=0.02\lambda_{0}\) and \(0.02\lambda_{0}<S<0.05\lambda_{0}\), respectively. Subsequently, we modified the antenna's straight feedline into a crank one for impedance matching. It was found numerically and experimentally that the antenna had a 3 dB gain drop bandwidth of 31%, where the axial ratio and VSWR are less than 3 dB and 2, respectively. The antenna is made of wires with a fixed radius. The effects of the wire radius on the antenna characteristics have yet to be investigated.  
Conceptualization, K.H.; software, K.H.; validation, M.H.; investigation, M.H.; resources, K.H.; data curation, M.H. and S.M.; writing--original draft preparation, K.H.; writing--review and editing, K.H.; visualization, K.H.; supervision, K.H. and H.N.; project administration, K.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: Numerical and experimental data used to support the findings of this study are included in this article. The authors declare no conflicts of interest.
## 5 Models Based on Canonical Piece Wise Linearization
### Decomposed Vector Rotation (DVR) Model  
It is a derived from the canonical piecewise-linear (CPWL) function and is a modified form of it. The model's nonlinear function is constructed from piecewise vector decomposition and is entirely different to the one used in previously discussed Volterra series. The model is more flexible and can perform better even with a comparatively small coefficient number than the conventional models. DVR is expressed as:  
\[\begin{array}{l}y(n)=\sum\limits_{i=0}^{Q_{\text{DVR}}}a_{i}|x(n-i)|+\sum \limits_{k=1}^{K_{\text{DVR}}}\sum\limits_{i=0}^{Q_{\text{DVR}}}a_{ki,1}\;||x( n-i)-\beta_{k}|e^{i\theta(n-1)}\\ +\sum\limits_{k=1}^{K_{\text{DVR}}}\sum\limits_{i=0}^{Q_{\text{DVR}}}a_{ki,21 }\;||x(n-i)-\beta_{k}|e^{i\theta(n-1)}.|x(n)|+\sum\limits_{k=1}^{K_{\text{DVR}} }\sum\limits_{i=0}^{Q_{\text{DVR}}}a_{ki,22}\;||x(n-i)-\beta_{k}|.x(n)\\ +\sum\limits_{k=1}^{K_{\text{DVR}}}\sum\limits_{i=0}^{Q_{\text{DVR}}}a_{ki,23 }\;||\;x(n-i)|-\beta_{k}\;|.x(n-i)+\sum\limits_{k=1}^{K_{\text{DVR}}}\sum \limits_{i=0}^{Q_{\text{DVR}}}a_{ki,24}\;||\;x(n-i)|-\beta_{k}\;|.x(n-i)\\ \end{array} \tag{13}\]  
where \(x(n)\) is I/P and \(y(n)\) is O/P.  
\(K_{\text{DVR}}\) is elements in the partition and \(Q_{\text{DVR}}\) is the memory depth. \(\beta_{k}\) represents the breakpoint.