esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis
### Baselines
By comparing our model with Deformable-DETR, we can assess our model's effectiveness in handling complex and dynamic student behaviors, while also evaluating its performance in terms of computational efficiency and real-time application potential in classroom settings. DETR with Improved deNoising anchor boxes (DINO) [47]: DINO is a significant enhancement of the original DETR (Detection Transformer) framework, focusing on improving the convergence speed and accuracy of object detection tasks. DINO introduces denoising techniques and enhanced anchor box mechanisms to address the slow convergence and small-object detection challenges commonly associated with DETR models. By refining the object query process and improving the training efficiency, DINO achieves faster and more accurate detection, particularly in scenarios involving complex and dynamic environments. In the context of student behavior recognition, DINO is included as a baseline model due to its robustness in handling diverse and nuanced student actions, offering a strong comparison point for our model's performance in both accuracy and detection efficiency. EfficientNet [48]: EfficientNet utilizes a compound scaling method that balances network depth, width, and resolution. It achieves state-of-the-art performance while keeping the number of parameters and computational cost relatively low. Its highly optimized architecture allows for real-time inference, making it a strong baseline for tasks requiring speed and accuracy. In our study, we included EfficientNet as a baseline model because its balance of performance and computational efficiency is comparable to the goals of our proposed student behavior detection system, allowing for a meaningful evaluation of detection capabilities in resource-constrained environments like classrooms. RTMDet [49]: RTMDet is a lightweight object detection model designed for high efficiency and speed, and it is optimized for real-time detection tasks. Its architecture incorporates several innovations, including efficient feature extraction modules and optimized anchor-free detection strategies, which reduce computational complexity while maintaining a strong detection accuracy. In our comparison, RTMDet serves as a baseline model because its real-time detection focus aligns closely with our model's objectives, allowing us to assess both the speed and detection accuracy of our approach in dynamic educational settings.
## 3 Results and Discussion
### Results of Linear and Nonlinear Regression Based Models
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Equation**} & \multirow{2}{*}{\(\Delta\)**Linear and Nonlinear Regression Based Equations**} & **CC** & **RMSE** & **MAE** & **SI** & **CC** & **RMSE** & **MAE** & **SI** \\ \cline{3-10} \multicolumn{2}{c}{**No.**} & & \multicolumn{3}{c}{**Training Data Set**} & \multicolumn{3}{c}{**Testing Data Set**} \\ \hline \multirow{2}{*}{(14)} & \(DE/E_{\text{max}}\) (\%) & \(-\)2.2166 \(Log10(N)+\) & \multirow{2}{*}{0.9492} & \multirow{2}{*}{6.9755} & \multirow{2}{*}{5.4797} & \multirow{2}{*}{0.1209} & \multirow{2}{*}{0.9495} & \multirow{2}{*}{6.9864} & \multirow{2}{*}{5.4785} & \multirow{2}{*}{0.1218} \\  & \(74.6\,Log10(Hdam/dc)-52.6495\,Log10(\alpha)+60.376\) & & & & & & & \\ \hline \multirow{2}{*}{(15)} & \(\frac{DE}{E_{\text{max}}}\) (\%) & \(=\)51.0958 & \(+\)0.1722 & \(+\)2.2017\(\left(\frac{Hdam}{dc}\right)-\) & \multirow{2}{*}{0.8572} & \multirow{2}{*}{11.4138} & \multirow{2}{*}{9.0656} & \multirow{2}{*}{0.1979} & \multirow{2}{*}{8524} & \multirow{2}{*}{11.6493} & \multirow{2}{*}{9.2226} & \multirow{2}{*}{0.2032} \\  & \(0.82750\,a\) & \(-\)0.8916 & \(-\)0.8916 & \(-\)0.8916 & \(-\)0.8916 & \(-\)0.8917 & \(-\)0.89174 & \(-\)0.1761 & \(-\)0.1761 & \(-\)0.1914 & \(-\)0.2312 & \(-\)0.79501 & \(-\)0.1784 \\ \hline \multirow{2}{*}{(17)} & \(\frac{DE}{E_{\text{max}}}\) (\%) & \(=\)\(-\)1399.148N\({}^{-0.658}\) & \(+\) & & & & & & \\  & \(2191.05\left(\frac{Hdam}{dc}\right)-210.779a\)\({}^{0.0862}-1927.49\) & & & & & & & & \\ \hline \multirow{2}{*}{(18)} & \(\frac{DE}{E_{\text{max}}}\) (\%) & \(=\)328.003N\({}^{-0.0054}\left(\frac{Hdam}{dc}\right)^{(0.0988}-\)0.0719\({}^{-0.0719}-262.94\) & & & & & & \\ \hline \multirow{2}{*}{(19)} & \(\frac{DE}{E_{\text{max}}}\) (\%) & \(=\)1.899N\({}^{-0.9916}-6.6805\)\(/\left(0.0899N^{-1.048}\right)\) & & & & & & & \\  & \(-\)0.0412\(\left(\frac{Hdam}{dc}\right)-0.9982\)\({}^{-0.9962}-0.00209a\)\({}^{0.385}\) & & & & & & & & \\ \hline \multirow{2}{*}{(20)} & \(\frac{DE}{E_{\text{min}}}\) (\%) & \(=\) & & & & & & & & \\  & \(-\)0.8925\(\left(\left[(0.02953N^{0.0088})-0.0876\left(\frac{Hdam}{dc}\right)^{-1.1634}\right) & & & & & & & & \\  & \(+\)0.03405\(a^{-0.1494}\right)\) & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Regression equations for the relative energy dissipation in spillway. Further, single-factor ANOVA showed that the _F_-value (0.005627) was less than _f_-critical (3.8454) and _p_-value (0.9402) was greater than 0.05, which suggested that the difference in predicted values by the Equation (14) based model and actual values is insignificant.
# High-Efficiency Adsorption of SARS-CoV-2 Spike 1 Protein by Plasma-Modified Porous Polymers
###### Abstract  
Under the background of the COVID-19 pandemic, this study reports an affordable and easily prepared porous material modified by nanosecond-pulsed discharge plasma, which can adsorb SARS-CoV-2 S1 protein efficiently. Both Western blotting and an enzyme-linked immunosorbent assay were used to detect the adsorption efficiency of SARS-CoV-2 S1 protein. The physical and chemical properties of the modified porous polymer were characterized by scanning electron microscopy, X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy. We found that the new type of porous polymer material presented an excellent performance on SARS-CoV-2 S1 protein adsorption, whose adsorption efficiency reached 99.99% in 1 min. Both the physical and chemical characterizations showed that the material has many fresh pores on the material surface and that the surface is implanted with polar functional groups (C\(-\)O, C\(=\)O, O\(-\)C\(=\)O and \(-\)NH), which gives the material a high chemisorption performance along with an enhanced physical adsorption performance. Notably, the material can be prepared at prices ranging in the tens of dollars per kilogram, which shows that it could have great applications for respiratory virus protection in global epidemic states.  
SARS-CoV-2 S1 protein; protein adsorption; nanosecond pulsed discharge; modified porous polymers +
Footnote †: journal: Applied Sciences
# Two-Factor Authentication Scheme for Mobile Money: A Review of Threat Models and Countermeasures
## 2 Related Work
### Threat Models
#### Attacks against Integrity  
This category is where an attacker accesses and modifies the user information in the MMS. Attacks against integrity are classified into three, namely, MITM attack, salami attack, and insider attacks.  
* MITM attack: This is an attack where the intruder intercepts communications between mobile money users and the MMS, between mobile money agents and MMS, between mobile money users and the bank, or between the MMS and the bank and becomes familiar with the messaging system, thereby transmitting fake data to either party. The attacker sits between mobile money users and the MMSP and makes them believe that they are communicating directly to each other, when in fact the adversary controls the entire conversation [11; 46]. According to [PERSON], [PERSON], and [PERSON] [44] and [PERSON] et al. [23], the attacker controls the traffic into the mobile money platform to manipulate the credentials of the user and to perform transactions on behalf of the victim. Likewise, the adversary can use a BTS with the same mobile network code as the subscriber's real network to perform a MITM attack since the network authenticates users [26; 43; 45]. Furthermore, the information carried within the communication channel is in plaintext, thus making USSD data vulnerable to attack and redirection [20; 21; 52].
* Salami attack: This is where an employee of a financial institution installs a malware like a Trojan horse on the server hosting the application to withdraw a small amount of money from the subscribers' accounts and deposit it into their account. The salami attack can be internal or external and difficult to notice since the malware modifies the data in financial systems and a small amount is deducted from the customers' wallets [8; 18; 59; 60; 61].
* Insider attacks: This is where an employee of the MMSP who has enough information about the organisation's security practices, MMS, and data attacks the mobile money application server or MMS. According to [PERSON] [62] and [PERSON] et al. [10], insiders and employees of the MMSP due to inside access facilitate most mobile money fraud. This has resulted in organisations losing huge amounts of money to the tune of billions of shillings because of employee fraud within the companies or institutions [10]. Additionally, [PERSON] and [PERSON] [49] and [PERSON] [62] noted that less scrupulous employees abuse their privileges by accessing customer mobile money information and stealing money from customers' wallets.
# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 8 Conclusions and Future Work  
In this paper, a review of well-known deep learning algorithms, such as backpropagation, autoencoders, variational autoencoders, restricted Boltzmann machines, deep belief networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, capsules, transformers, embeddings from language models, bidirectional encoder representations from transformers, and attention in natural language processing, was provided. The architecture of each algorithm was discussed. A comparison between the provided deep learning algorithms based on factors such as advantages, disadvantages, applications, and classification as supervised/unsupervised was presented. A comparison of deep learning algorithms based on their domains of application and their accuracies has been provided.  
Moreover, the applicability of deep learning algorithms in healthcare has been examined, including distinguishing between different diseases using image processing capabilities, such as COVID-19 and viral pneumonia. In addition, deep learning has been used to make decisions, such as whether to ease or enforce lockdown. An analysis of deep learning algorithms reported in the literature related to the health sector, especially COVID-19 prediction, classification, and detection strategies, has been presented. Details of how deep learning algorithms can be used to fight the COVID-19 pandemic, dementia, and cancer have also been provided. For future work, the plan is to extend the review to include more deep learning algorithms, covering deep-reinforcement machine learning algorithms, including model-based and model-free deep learning algorithms. We plan to conduct a comprehensive review of these models along with their pros, cons, and practical applications.  
In addition, challenges of deep learning were also presented, such as those of AutoML-Zero, neural architecture search, evolutionary deep learning, and others.  
Finally, future directions were provided to deal with the given challenges of deep learning.  
This paper presented the following for researchers who desire to start working in the deep learning area and healthcare: a review of many deep learning algorithms and the results of comparisons among these algorithms; applications of several deep learning algorithms in healthcare; and an introduction to the challenges of deep learning.  
This research received no external funding.  
Not applicable.  
The authors would like to thank the Arab Open University for supporting this research paper.  
The authors declare no conflict of interest.
## 3 Results
### Comprehensive Performance Evaluation
#### 3.2.4 Performance Among Language Pairs  
The impact of MuRXLS varies significantly across language pairs, revealing interesting patterns in cross-lingual transfer:  
* **Burmese\(\rightarrow\)English** shows the largest improvements across all models, with gains of up to +4.20 ROUGE-L points. This suggests that for languages with significant structural differences from English, contextually relevant examples provide crucial guidance for the summarization process.
* **Pashto\(\rightarrow\)English** consistently benefits from retrieval-based examples, with substantial improvements across all models and shot settings. This further supports the observation that linguistically distant language pairs benefit more from retrieval augmentation.
* **Thai\(\rightarrow\)English** and **Gujarati\(\rightarrow\)English** show moderate but consistent improvements, indicating that even for language pairs where baseline performance is already reasonable, retrieval-based selection still provides meaningful benefits.
## 2 Road Traffic Situation Assessment Model
### Traffic Situation Assessment Indicator Analysis
#### 2.2.1 Traffic Situational Awareness Indicator Analysis  
Traffic flow data are the basis of traffic dynamics prediction problems, which can be divided into two major categories: static data and dynamic data. Static traffic data mainly  
Figure 3: The road traffic situation cooperative monitoring model under emergency.  
contain data such as the name of the area to which the road section belongs, road network structure, road section direction, road section number and road section length, which can be used to describe the topology of the road network, while dynamic traffic data mainly contain the delay time ratio, free flow speed, average travel speed, traffic flow, etc., which are indicators that can be used to evaluate the road section traffic condition.  
(1) Traffic flow  
Traffic flow is the number of vehicles passing through the monitored road cross-section range per unit of time, which can be currently calculated from the monitoring data of the surveillance cameras at the intersection, as shown in Equation (4).  
\[Q=\frac{N_{\text{vehicle}}}{\tau} \tag{4}\]  
where \(Q\) is the unit traffic flow, the unit is vehicle per hour (pcu/h), pcu (Passenger Car Unit) represents the number of vehicular traffic; \(N_{\text{vehicle}}\) is the traffic flow, and the unit is vehicle (pcu); \(\tau\) is the time interval, the unit is hour (h), usually take 15 min (0.25 h) as the time interval, and take the monitoring value within this range as the calculation data. In the case of little interference from external factors, the traffic flow usually presents a periodicity. That is, the traffic flow trend of the same road section is similar every day, every week and every month.  
(2) Average travel speed  
Traffic speed is divided into instantaneous speed and average speed, where instantaneous speed is the instantaneous speed of a vehicle passing through the observed section of the roadway. Furthermore, the average travel speed is the ratio of the total distance and total time of all vehicles passing through the area. The calculation method is shown in Equation (5).  
\[v_{ab}=\frac{\sum_{c=1}^{x}L_{abc}}{\sum_{c=1}^{x}t_{abc}} \tag{5}\]  
where \(v_{ab}\) is the average travel speed of the \(b\)th section in a time of length \(a\), and the unit is kilometers per hour (km/h); \(L_{abc}\) is the distance traveled by the \(c\)th vehicle in the \(b\)th road section at time \(a\), and the unit is kilometers (km); \(t_{abc}\) is the time required for the \(c\)th vehicle to pass the \(b\)th road section in time \(a\), and the unit is hour (h); \(x\) is the number of observed vehicles.  
There are many other traffic data indicators, such as delay time ratio, congestion length, traffic density, etc. However, under the condition of existing equipment, traffic flow is relatively easier to collect, and in the actual scenario, these data are also more intuitive to show the road condition, so this paper selects traffic flow as the input data for road traffic status identification.
# Laser Applications in Metal Orthodontic Bracket Debonding: A Systematic Review
###### Abstract  
Objective: The aim of this systematic review was to evaluate the effectiveness and safety of various laser wavelengths for debonding orthodontic metal brackets compared to traditional plier-based methods. The primary outcomes assessed were enamel damage, pulp temperature changes, adhesive remnant index (ARI), and shear bond strength (SBS). Materials and Methods: In September 2024, an electronic search was performed across the PubMed, Web of Science (WoS), and Scopus databases, adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the PICO framework. The initial search yielded 453 records. After eliminating 256 duplicates, 197 unique records were left for screening, which ultimately led to the qualification of 8 articles that met the inclusion criteria for both qualitative and quantitative analyses. The risk of bias in the articles was assessed by two independent reviewers. Results: The included studies demonstrated that laser-assisted debonding generally resulted in less adhesive residue on the enamel surface compared to conventional methods, as evidenced by the reductions in ARI scores reported in two studies. Temperature increases during laser use varied depending on the laser type and power settings. The Nd:YAG (neodymium-yttrium, aluminum, garnet) laser was found to cause significant temperature rises, posing a potential risk to pulp tissue, while the Er:YAG (erbium--yttrium, aluminum, garnet) and ErCr:YSGG (erbium, chromium--yttrium, scandium, gallium, garnet) lasers produced only negligible increases in pulp temperature. SBS comparisons revealed no significant differences between the laser-assisted and traditional debonding methods. Additionally, diode lasers demonstrated the potential to minimize enamel damage, particularly when operated at lower power settings. Four publications were assessed as high quality (low risk of bias), and another four as moderate quality (average risk of bias). Conclusions: In conclusion, laser-assisted orthodontic metal bracket debonding, when conducted with appropriately calibrated parameters, is a safe method for preserving tooth tissue. However, its advantages appear to be minimal compared to conventional plier-based methods, highlighting the need for further research to justify its broader clinical application.  
**Keywords:** brackets; debonding; fixed appliances; laser; orthodontic appliance; removal
## 3 Results and Discussion
Each of these directions registers a separate change in corresponding acceleration, respectively. The mathematical expression of the total shock vector is: \(\overset{\rightarrow}{I}=\sqrt{x^{2}+y^{2}+z^{2}}\). The values of the total impact vector are given in Figure 2.7  
As can be seen from the results, the values of the total impact vector range from 0.5 to 4.5 g when the range of measurements is 32 measurements under the impact of the shipment model. The highest values of the total impact vector are at the beginning of the impact and reach about 4.5 g, and around the middle of the measurements, when the values vary between 2 and 4.5 g. Impact vector values can change because the parcel model flips and rotates in different directions when falling, even when it hits the floor or another obstacle, for example. The highest values of the impact vector are obtained where the shipment experiences the greatest impact. The magnitude of the impact vector depends on several factors, including the force, direction, and duration of the impact, as well as the mass and stiffness of the object. The force with which the impact affects the object and the direction of the force directly affects each component of acceleration (in the X, Y, and Z directions). The duration of the stroke also plays a role, as longer strokes can result  
Figure 2.8 Impact strength data in X, Y, and Z axis directions. in greater overall acceleration. In addition, the mass of the object and the stiffness of the material determine how it reacts to impact; heavier and stiffer objects generally experience smaller changes in acceleration than lighter and softer ones. All of these factors together influence the total magnitude of the shock vector, which is calculated as the sum of the individual components (X, Y, and Z). The impact detector, which registers impact data, the location of the shipment on the map, and provides data in real time, is extremely useful as it ensures the continuous monitoring of the shipment and allows for a quick response to possible violations. It provides detailed information on the strength and direction of the impact, so it is possible to deter-mine exactly when and where the shipment was hit and assess the potential damage. If the detector has integrated tracking of position coordinates, it allows for tracking the location of the shipment at any time, and real-time data transmission ensures that all necessary information is immediately available [1; 2; 3; 4]. This helps to solve problems promptly, improve logistics processes, and ensure greater protection of shipments and higher customer satisfaction.
## 3 Research Methodology
### Hybrid Feature Selection (HFS)
#### 3.3.3 Genetic Algorithm (GA)  
GA is an adaptive heuristic search algorithm, the goal of which is finding an optimal solution [41]. GA uses a fitness function to estimate the significance of results from the evaluation step. Two GA operators, mutation and crossover functions, randomly transfer chromosomes and affect the fitness value [55]. Crossover specifies two chromosomes that will generate a new offspring chromosome. However, mutation is the process used to change genes in chromosomes from their initial state [10; 56]. This study selected the four best chromosomes to be part of the next generation and used a single-point crossover and binary mutation.  
GA minimizes the fitness function value, which is shown as \(FF\) and calculated for each chromosome, using Equation (6). \(SVM\_RMSE\) is the root mean square error (RMSE) of an SVM model, \(w\) is a weight of the specified number of factors (\(n_{f}\)), \(s_{i}\) '1' if the factor \(i\) is selected or '0' if the factor \(i\) is not selected, and \(c_{i}\) is the cost of factor \(i\).  
\[FF=SVM\_RMSE\times(1+w\times(\sum_{i=1}^{n_{f}}c_{i}\times s_{i})) \tag{6}\]In the first step of HFS, ReliefF measures the weight of all CLP factors based on their correlations. Then, it selects all weights greater than or equal to a defined threshold as inputs of the wrapper method. In the second step, GA randomly generates the initial population, where each chromosome is an available feature subset for the problem. In the third step, using the selected factors from ReliefF as inputs, training of the SVM model begins and the RMSE of SVM is measured. Then, the \(FF\) calculation for each chromosome is completed. In the fourth step, if the result meets the termination criteria, the process stops; otherwise, the process goes back to the GA operation to find a better solution. Once the termination criteria are satisfied by the final generation, the iteration stops, and the final generation contains the factors that most influence CLP.