esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 5 Comparative Analysis of Different Algorithm Experiments  
### Experimental Dataset Analysis  
In the field of computer vision, due to the diversity and complexity of real abnormal behaviors in the research of abnormal crowd behavior analysis, there is a lack of high-quality datasets. To overcome this shortage and improve the performance of algorithms, researchers have constructed a series of datasets with different characteristics, focusing on parameters such as duration, size, resolution, etc., and covering various monitoring environments and scenarios, providing an important reference basis for the research of crowd anomaly recognition. Commonly used datasets mainly include UCSD, UMN, Shanghai  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Design Ideas** & **Advantages** & **Limitations** & **References** \\ \hline CNN & Through local receptive fields and pooling operations, capture the spatial hierarchical features of the original data & Good at extracting local features and combining them into more complex patterns & Lack of time series processing capabilities & [42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56] \\ AutoEncoder & An unsupervised learning method that captures the main features of the data by encoding and decoding the original data & Reduce the data dimension and extract key features & Easily lead to overfitting of training data & [57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70] \\ GNN & It is composed of a generator and a discriminator, and the two play a game. close to real data & Reconstruct and generate new samples & Prone to model collapse and non-convergence & [71; 72; 73; 74; 75; 76] \\ LSTM & Introduces forget gates, input gates, and output gates to solve the problems of gradient vanishing and gradient explosion & Good at processing sequence data of any length & The structure is complex, and training and reasoning take a long time & [77; 78; 79] \\ Self-Attention & Automatically assigns different attention weights according to different parts of the input sequence & Capture global interdependencies & Easy to overfit on small datasets & [80; 81; 82; 83; 84; 85; 86; 87] \\ \hline \hline \end{tabular}
\end{table}
Table 4: The classification and characteristics of deep learning behavior anomaly recognition methods.  
Tech, CUHK-Avenue, and other related datasets. Figure 20 shows some abnormal behavior samples in these four abnormal behavior datasets.
## 4 Computational Experiments
### Experimental Results  
After setting the experimental parameters, CPLEX is used to solve the proposed two-stage stochastic programming model. The derived results indicate that the total amount of construction waste generated by 11 worksites every year is 7,827,582 tons on average, and the overall demand for recycled materials every year is 7,117,667 tons on average in the 20 scenarios. With the adjusted maximum total budget, \(C^{max}\), of 886 million CNY, the optimal results about the constructed area of the recycling center at the ten candidate locations, \(\alpha_{r}\), are shown in Table 7. For variables concerning the weight of construction waste transported from construction sites to recycling centers and landfills, apparently, the results vary across scenarios. Here, we solely present one scenario for demonstration purposes, as depicted in Figure 2. The locations of construction sites, recycling centers, and landfills are annotated using the same notation as shown in Figure 1. Transportation routes for construction waste from construction sites to recycling centers are indicated with black arrow lines. Meanwhile, red arrow lines represent the routes for transporting recycled materials from construction sites to landfills. Concurrently, the routes for moving recycling materials between recycling centers and construction sites are denoted by green arrow lines. The term \"recycled material\" indicates a supply-demand ratio: 412,288/995,628 tons. Here, 412,288 tons represent the actual amount of recycled materials received by the construction site, whereas 995,628 tons denote the total demand for recycled materials.  
According to Figure 2 and Table 7, it is evident that the recycling centers HDZ5, HDZ6, HDZ7, BZ6, ZZ23, and ZZ27 are scheduled for complete construction. ZZ25 is designated for partial construction, while the construction of ZZ22 and ZZ26 is not recommended. In this scenario, the cumulative volume of construction waste recycled totals 2,398,089 tons. To demonstrate the superiority of our model, this study has conducted comparative experiments to further evaluate the impact of incorporating the parameter of uncertainty in the decision-making process.
In addition to all these factors related to 3D printing, it is essential to consider various environmental conditions, such as temperature and humidity, which can affect the stability of the models, potentially causing problems in layer adhesion or material curing [11, 23, 24, 25, 26]. One of the main differences between the 3D-printed and the gypsum-poured stones is the design of the analogs. In conventional models, the implant analog is screwed to the impression transfer, and the cast is poured over it. Therefore, the analog is retained in the casting material during the procedure. In 3D-printed models, the implant analog is placed after the model has been manufactured and post-processed. Hence, multiple factors can affect the accuracy of the analog position, such as the analog design, the positioning method, the number of analogs, or the printing orientation. Thus, discrepancies in the analog positions compared to the real position would lead to the final misfit of the prosthesis. The knowledge about the influence of printing angulation on the accuracy of 3D implant models is scarce. The literature shows studies that analyze the variation of impression angulation in tooth-supported crowns, complete denture bases, printed orthodontic aligners, or surgical splints. However, few studies have analyzed the analog position in 3D models to fabricate implant-supported prostheses. Due to this, the present study aimed to investigate the accuracy (trueness and precision) of the analog position in printed models while varying the printing orientation (0, 45, and 90 degrees). The null hypothesis was that there would be no significant differences in the 3D position of implant analogs by employing several print orientation parameters for 3D-printed manufacturing.
# Advancing Brain Tumor Segmentation with Spectral-Spatial Graph Neural Networks
## 2 Methods and Material
### Data Preprocessing
#### 2.3.1 Normalization and Standardization  
In this research, normalization and standardization were integral preprocessing steps in the analysis of brain MRI data. Our methodology involved normalization to adjust the intensity values of MRI images to a common range, specifically to the \([0,1]\) scale. This step was critical for ensuring comparability of scans from diverse sources or under different conditions. Percentile scaling, with the 99 th percentile as the upper limit, was employed in our normalization process [21].  
Furthermore, standardization played a vital role in our preprocessing regimen. This step aimed to transform the data to have a zero mean and a unit standard deviation, essential for the machine learning applications in our study. It ensured that each feature (or pixel  
Figure 3: Tumor regions: 1. necrotic core 2. edema 3. enhancing tumor.  
intensity) received equal consideration in the analysis, thereby preventing the dominance of features with larger scales. The standardization process involved subtracting the mean value of the dataset from each data point and then dividing by the standard deviation.  
The application of both normalization and standardization played a key role in preparing the MRI data specifically for the task of segmentation. By employing these preprocessing steps, we aimed to optimize the data for segmentation, anticipating that such conditioning would enhance the accuracy and precision of the segmentation process. This approach was carefully chosen to ensure that the MRI data were adequately prepared for the intricate demands of segmentation analysis in our research.
## 2 Principle of DETL
### Tuning Principle
#### 2.2.2 Displacement-Based
[42] developed a dielectric elastomer-driven Fresnel lens with the structure shown in Figure 4c. The device structure comprises a binary phase Fresnel zone plate, a ring-shaped deformable electrode, and a DE membrane. By regulating the radius of the zone plate by DEA, the Fresnel lens can achieve a zoom function with a magnification ratio of up to 109.1% and a response speed of up to 23 ms.  
An Alvarez lens usually combines two curved lenses with different optical properties, whose curvature and thickness can be flexibly adjusted to form specific optical properties. The lateral displacement of the lens elements can change optical properties such as focal length and field of view [43]. The following cubic polynomial describes the free surface thickness distribution of a single Alvarez lens element:  
\[t=A\bigg{(}xy^{2}+\frac{x^{3}}{3}\bigg{)}+Dx+E \tag{6}\]  
where \(A\) is the depth of the control free surface modulation, \(D\) is the tilt coefficient, \(E\) is the voxel thickness added to the cubic surface equation at the centre, \(t\) is the phase distribution of the Alvarez lens, and \(x\), \(y\) are the transverse and longitudinal coordinates. When the DEA drives the two phase plates to produce a relative transverse displacement \(\delta\), the focal length \(f\) of the system is  
\[f=\frac{1}{4\delta A(n-1)} \tag{7}\]  
where \(\delta\) represents the lateral displacement, and \(n\) denotes the refractive index of the lens material [44]. [PERSON] et al. [45] used this principle to develop a dielectric elastomer-driven Alvarez lens based on DE, as shown in Figure 4d. The lens consists of multiple Alvarez lens elements and a DE film. Alvarez lenses based on dielectric elastomer actuation provide an innovative solution for high-performance DETL. By designing the phase plate, not only can a wide range of continuous zoom be realised, but also the field of view can be changed. However, this also challenges the preparation process and focal length control. The precision processing of the phase plate is related to the imaging quality of the lens, and the control accuracy is associated with the precise lens tuning.
## 2 Problem Formulation
### Objective Functions
#### 2.1.1 Reduction in Real Power Losses  
The total power losses are high due to the structure of distribution systems. Therefore, a reduction in total power loss is one of the main objectives. Total power loss (\(f_{1}\)) formula [30]:  
\[f_{1}(x)=min\sum_{k=1}^{N_{b}}R_{k}\cdot|I_{k}|^{2} \tag{1}\]  
\[Q_{Gi}^{total}\leq Q_{L}^{total} \tag{10}\]  
where \(P_{Gi}^{min}\) and \(P_{Gi}^{max}\) are the minimum and maximum active power limits of each DG, respectively. \(Q_{Gi}^{min}\) and \(Q_{Gi}^{max}\) are the minimum and maximum reactive power limits of each CB, respectively. \(P_{Gi}^{total}\) and \(Q_{Gi}^{total}\) are the total active power generated by all DGs and total reactive power generated by all CBs, respectively. \(P_{L}^{total}\) and \(Q_{L}^{total}\) are total active and reactive load, respectively.
## 2 Simultaneous Localization and Mapping
### FastSLAM 2.0
#### 2.1.3 Proposal Adjustment  
After the data association step, if one or more observations are matched, the latest robot pose estimation is updated based on the matched observations. The latest pose estimate is modeled as a normal distribution \(\mathcal{N}(y^{m},\Sigma^{m})\) and is initialized based on the predicted pose \(\mu_{0}^{m}=s_{t}^{m}\) and a covariance matrix that depends the motion noise \(Q_{t}\) and the Jacobian of the motion model with respect to the control variables \(H_{u}\).  
Subsequently, for each matched observation, the adjustment of the corresponding landmark pose estimate is carried out based on its covariance matrix \(C^{n}\), the measurement noise \(R_{n}\), the Jacobians of the observation model with respect to the particle pose \(H_{a}\) and the landmark pose \(H_{p}\), and the difference between the observation and the expected observation \((z_{n,t}-\hat{z}_{n,t})\) (innovation).  
The expected observation \(\hat{z}_{n,t}\) is obtained through the observation model in Equation (1).  
\[\begin{split} dx&=x_{t}^{m}-x^{m}\\ dy&=y_{t}^{m}-y^{m}\\ \hat{\rho}&=\sqrt{dx^{2}+dy^{2}}\\ \hat{\beta}&=\arctan\left(\frac{dy}{dx}\right) \end{split} \tag{1}\]  
A graphical representation of the observation model is available in Figure 2.  
The Jacobian matrix \(H_{a}\) is defined as follows:  
\[H_{a}=\begin{bmatrix}\frac{\partial\rho}{\partial x_{t}}&\frac{\partial \rho}{\partial y_{t}}&\frac{\partial\rho}{\partial y_{t}}\\ \frac{\partial\rho}{\partial x_{t}}&-\frac{\partial\rho}{\partial y_{t}}&\frac{ \partial\rho}{\partial y_{t}}\end{bmatrix}=\begin{bmatrix}\frac{dx}{\sqrt{r}}& -\frac{dy}{\sqrt{\rho^{2}}}&0\\ \frac{dy}{\sqrt{r}}&-\frac{dx}{\sqrt{r}}&-1\end{bmatrix} \tag{2}\]  
The procedure for the proposal adjustment is summarized in Algorithm 2.  
The complexity of the proposal adjustment step depends on the number of particles and the number of matched observations.
# Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion
## 5 Conclusions  
In this paper, a noise-level based detection method is proposed to identify FR forgery. To solve the problem of noise where the existing detection methods are inadequate, the proposed method directly uses the varying noise level in time to identify whether the video is forged by FR. The MAD based on wavelet transform is first used to estimate the standard deviation of each video frame. Next, FFT is employed to calculate the amplitude spectrum of the standard-deviation curve, and the PMR of the amplitude spectrum is calculated. Finally, according to the PMR of the suspected video, a hard threshold decision is performed to automatically identify whether the video is forged by FR. Experimental results show that the PMR of the original video is much smaller than that of the forged video, and when the threshold _Thr_ is set to be 5, compared with residual detection and similarity detection, the proposed noise-level detection presents a better detection performance on the NS and PS composed of test videos in different formats.  
In the proposed method, the threshold is set according to the experimental data, which cannot guarantee stable detection performance when changing the testing video database. Therefore, an adaptive threshold setting needs to be studied in the future.  
[PERSON] contributed to the concept of the research, performed the data analysis and wrote the manuscript; [PERSON] helped perform the data analysis with constructive discussions and helped perform the experiment. [PERSON] conducted the experiment and collected the data. [PERSON] revised the manuscript, and provided the necessary support for the research.  
This work was supported in part by the National Natural Science Foundation of China, under Grants nos. 61501393, and in part by Nanhu Scholars Program for Young Scholars of XYNU.  
The authors declare that there is no conflict of interests regarding the publication of this paper.
## 4 Conclusions  
In conclusion, we demonstrate a very simple method to achieve ultra-low efficiency droop for c-plane polar InGaN blue LEDs with a wide LQW. The LEDs with a 5.2-nm thick LQW show a very small efficiency droop with an EQE slightly reducing from a peak of 38.8% to 36.4% at 100 A/cm\({}^{2}\). The applications of wide LQW in the commercial polar LEDs are attractive due to the ultra-low efficiency droop, the low cost of the c-plane substrate and their easy adoption in commercialization compared to current approaches like growing small-sized and costly semipolar bulk GaN or using complicated InGaAlN/GaN EBLs designs. We expect the further improvement of the peak EQE to enhance the overall EQE by optimizing the growth condition of LQW and reducing the SRH nonradiative recombination.  
[PERSON] bring the concept and both [PERSON] and [PERSON] carried out the experiments, results analysis and useful discussion. [PERSON] wrote the manuscript.  
This research was funded by The National Natural Science Foundation of China (11847166) and The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (18 KJB510047).  
The authors declare no conflict of interest.  
Figure 4: (**a**) The relationship between the current and (**b**) internal quantum efficiency (IQE) versus carrier density for the three LEDs with various LQW thicknesses.
# Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering
## Appendix F Proof Theorem 5  
Let us first establish the following.  
\[\frac{\|\mathbf{H}\mathbf{x}-\hat{\mathbf{H}}\mathbf{x}\|_{2}}{\|\mathbf{H}\mathbf{x}\|_{2}}\leq\frac{ \|\mathbf{H}-\tilde{\mathbf{H}}\|_{2}\|\mathbf{x}\|_{2}}{\|\mathbf{x}\|_{2}}=\|\mathbf{H}-\tilde{ \mathbf{H}}\|_{2}.\] (A21)  
Therefore, to show the result of the theorem we only need to bound the spectral norm of the difference between \(\mathbf{H}\) and \(\tilde{\mathbf{H}}\). This can be done by the following chains of inequalities.  
\[\begin{array}{rcl}\|\mathbf{H}-\tilde{\mathbf{H}}\|_{2}&=&\|\sum\limits_{k=0}^{K} \alpha_{k}\mathsf{S}^{k}-\sum\limits_{k=0}^{K}\alpha_{k}\mathsf{S}^{k}\|_{2}\\ &=&\|\sum\limits_{k=0}^{K}\alpha_{k}(\mathsf{S}^{k}-\mathsf{\tilde{S}}^{k})\|_ {2}\\ &\leq&\alpha_{\max}\sum\limits_{k=0}^{K}\|\mathsf{S}^{k}-\mathsf{\tilde{S}}^{k} \|_{2}\\ &\leq&\alpha_{\max}\sum\limits_{k=0}^{K}k\epsilon\|\tilde{\mathsf{S}}\|_{2}^{k- 1}+\mathcal{O}(K\epsilon^{2}).\end{array}\] (A22)  
Here, we have considered \(\epsilon=\|\mathsf{S}-\tilde{\mathsf{S}}\|_{2}\) as stated in the theorem. Now, recalling that \(\|\tilde{\mathsf{S}}\|_{2}<1\) and \(\epsilon\leq 1\) by definition, we can further bound (A22) as  
\[\begin{array}{rcl}\|\mathbf{H}-\tilde{\mathbf{H}}\|_{2}&\leq&\alpha_{\max}\sum \limits_{k=0}^{K}k\epsilon\|\tilde{\mathsf{S}}\|_{2}^{k-1}+\mathcal{O}(K \epsilon^{2})\\ &\leq&\alpha_{\max}\sum\limits_{k=0}^{K}k\epsilon\|\tilde{\mathsf{S}}\|_{2}^{k- 1}\\ &=&\frac{\alpha_{\max}}{(1-\|\tilde{\mathsf{S}}\|)^{2}}\epsilon,\end{array}\] (A23)  
where the identity  
\[\sum\limits_{k=0}^{\infty}kx^{k-1}=\frac{1}{(1-x)^{2}}\ \ \text{for}\ \ |x|<1,\]  
has been used in the third inequality and the second-order error term has been dropped.  
Finally, as it is assumed that the spectral norm is strictly upper bounded by one, i.e., \(\|\mathbf{S}\|\in\mathcal{O}(1)\), we obtain the desired result.  
\[\|\mathbf{H}-\mathbf{\hat{H}}\|_{2}\in\mathcal{O}(\epsilon). \tag{100}\]
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 2 Background and Related Work
Understanding how commercial and open-source tools--such as PhishTank [34], OpenPhish [35], Proofpoint, and Microsoft Defender for Office 365--operate helps establish a benchmark for evaluating the effectiveness of phishing detection systems. These tools utilize a variety of phishing detection methods, ranging from blacklist-based detection to ML and AI-driven threat hunting. However, despite their widespread adoption, most of these tools exhibit inherent limitations, particularly in terms of adaptability, scalability, and accuracy. For instance, blacklist-based tools like PhishTank rely on static indicators of compromise (IOCs), making them ineffective against zero-day phishing attacks and new obfuscation techniques. Similarly, tools that utilize traditional ML models, such as OpenPhish, often require frequent retraining to maintain accuracy and lack the capability to dynamically learn from new data in real-time, a key advantage of RL-based models. Additionally, some commercial tools, such as Proofpoint, are heavily optimized for specific attack vectors like email phishing, limiting their effectiveness in detecting more sophisticated, multi-vector phishing campaigns. To provide a clearer comparison, Table 2 below summarizes these tools, outlining their core detection methods, key limitations, and how they contrast with our proposed RL-based framework. This analysis is not only essential for identifying the shortcomings of existing solutions but also for demonstrating the practical value of our approach. The proposed RL-based phishing detection framework addresses many of these challenges by incorporating a continuous learning process that adapts in real-time, significantly reducing false positives and improving detection accuracy. Unlike static models, our RL approach can evolve autonomously with emerging phishing tactics, ensuring that the system remains resilient against ever-changing threats. This adaptability, coupled with the ability to minimize false positives through reinforcement mechanisms, sets our model apart from existing tools and technologies. By addressing these critical gaps, our research contributes to the development of more robust, scalable, and adaptive phishing detection systems that can meet the evolving demands of modern cybersecurity environments.
###### Abstract  
Platelet concentrates (PCs) have been used for over 20 years in dentistry, as an adjunct to oral surgery procedures, to improve hard and soft tissue healing and control postoperative symptoms. Among various PCs, Leukocyte and Platelet-Rich Fibrin (L-PRF) has become very popular due to its excellent cost-effectiveness ratio, and to the simple preparation protocol, but comparative clinical studies with other PCs are lacking. The aim of this split-mouth cohort study was to evaluate the effect of Concentrated Growth Factors (CGF), a recently introduced PC, as compared to L-PRF for enhancing post-extraction socket healing. Methods: Patients in need of bilateral tooth extractions were included. Each side was treated with either CGF or L-PRF. Pain, socket closure and healing index were the main outcomes. Results: Forty-five patients (24 women), aged \(60.52\pm 11.75\) years (range 37-87 years) were treated. No significant difference in outcomes was found, except for Pain at day 1 (\(p<0.001\)) and socket closure in the vestibulo-palatal/lingual dimension at day 7 post-extraction (\(p=0.04\)), both in favor of CGF. Conclusions: based on the present results, CGF proved to be as effective and safe as L-PRF, representing a valid alternative option for improving alveolar socket healing and reducing postoperative discomfort.  
tooth extraction; oral surgery; autologous platelet concentrates; platelets; growth factors; PRF; CGF +
Footnote †: journal: Applied Sci. 2020, 10, 8256; doi:10.3390/app10228256