esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 3 Methodology
### Research Framework  
This paper's main contribution is its innovative approach to evaluating student adaptability predictions from ML models, a critical component in educational settings. The proposed method, outlined in Figure 1, is a comprehensive, multi-stage process that begins with meticulous data collection, ensuring a robust foundation for subsequent analysis. The preprocessing stage addresses the typical challenges of educational data, such as encoding categorical variables and normalizing features, to prepare a clean, analysis-ready dataset.  
The method's thorough training of a wide range of machine learning models, from simpler ones like Multinomial Logistic Regression to more complex ensembles, is one of its distinguishing features. This inclusive approach not only compares predictive power across various algorithms but also enhances the process of interpretation analysis by providing diverse perspectives on the same dataset. The central tenet of the methodology is the application of multiple XAI techniques, such as SHAP, LIME, Anchors, Counterfactuals, and ALE. This diverse mix allows for a detailed exploration of how different features influence the adaptability predictions, providing global and local interpretability. Each XAI technique offers unique insights: SHAP quantifies feature contributions, LIME provides locally faithful explanations, Anchors generate high-precision rules, Counterfactuals suggest minimal changes for outcome alteration, and ALE analyzes feature effects, averting confounding interactions.  
The subsequent comparative analysis of these XAI techniques is a methodological innovation that critiques their coherence, identifies consistencies, and pinpoints anomalies in their explanations. The final synthesis of findings distills a holistic understanding of the ML models, offering actionable insights for educational stakeholders. This methodology empowers educators and policymakers with a deeper comprehension of the pre  
Figure 1: Block diagram for implementing the proposed framework.  
dictive models, facilitating informed decisions to foster student adaptability in diverse learning environments.
###### Abstract
These security issues, include, but are not limited to, malicious code attacks, inability to receive security patches, hacking smart meters, eavesdropping, sniffing attacks and Distributed Denial of Service (DDoS) attacks [4]. DDoS attacks are a common threat to the network, although the attacker typically does not aim to steal any data. Basically, DDoS attacks aim at consuming system resources until the target is not available to offer its services. DDoS attacks could be divided into three categories: application layer attack, protocol attack and volumetric attack. For volumetric attack, an attacker can deplete the available resources of the victim or bandwidth towards the target. Not only the data plane in the SDN, but also the controller and southbound interface, could suffer from this kind of attack as well; this is because a client host can trigger inquiry from the data plane to control plane. Although there have been a lot of discussions about DDoS attacks in the SDN and IoT networks, the large number of IoT gadgets is still a good chance to launch attacks, as well as the communication link between controllers and switches in SDN [5; 6]. Additionally, more validations in the real network are required. Moreover, the programmability and centralised control in the SDN give users more options to probe into this threat. In this paper, volumetric attack is implemented. Research Contributions  
IoT could benefit from the combination with SDN, particularly in the reliability owing to the global vision of SDN controller. SDN-based IoT networks allow dynamic access control in home networks as well as support authentication and authorisation [7]. However, numerous types of devices and their tremendous numbers in the IoT make them an ideal target for DDoS attackers. In this paper we conduct the following. * We extend our previous work [8; 9] from DoS to DDoS attacks using IoT traffic models and complex scenarios. Moreover, we focus on adapting and improving our previous algorithm in order to detect and block DDoS attacks in the SDN-based IoT networks hinged on several IoT traffic scenarios. * We also deeply analyse the state-of-the-art DDoS attacks in the SDN and SDN-based IoT networks. * We replicate IoT realistic traffic models and DDoS attacks by emulations and physical SDN-based IoT hardware and software equipment. From the real implementation, we have authentic results to evaluate the impact and outcome of DDoS attacks in the IoT networks. * Our results show that the improved version of our SECOD algorithm [8; 9] is able to efficiently detect and block DDoS attacks in SDN-based IoT networks. * We also report observations on the effect of DDoS attacks with periodical and event trigger IoT traffic in the SDN architecture. The rest of this article is organised as follows. Section 2 reviews related work. Section 3 describes existing IoT traffic model applications. The SDN-based IoT architecture is presented in Section 4. The implementation of DDoS attacks and defence techniques is covered in Section 5. Results and discussion are given in Section 6. Finally, Section 7 concludes this article and gives directions for future work.
## 3 Results
### Influence of the Size of Stator Hub Clearance on the Evolution of Flow Topologies
#### 3.3.3 Results at the NS Condition  
The oil-flow visualization results at the NS condition are shown in Figure 9. As mentioned above, the hub corner stall occurs at zero clearance. When a small stator hub clearance (0.25\(\uptau\)) is introduced, the tip corner separation almost does not change; as to the hub corner, the flow structure does change significantly either, i.e., the corner stall still exists, but with a smaller pitchwise scale (also can be seen from Figure 7). It should be noted that at this condition the introduction of a small stator hub clearance is beneficial to improve the flow, which is contrary to the situation at DE and PP condition. Actually, the absolute different influence of the hub leakage flow is associated with the flow structure in the hub corner, i.e., when the hub corner stall occurred at NS condition, the secondary flow had large velocity component that points to the upstream, which led to the massive low energy flow in the end wall boundary being transported into the recirculation region around focus point F; when a small clearance (0.25\(\uptau\)) was introduced, even though the leakage flow had low streamwise velocity component, it can still reduce the transport effect of the secondary flow, hence, the pitchwise scale of the hub corner stall decreased. When the stator hub clearance increased to 0.50\(\uptau\), the hub leakage flow was strong enough to push the secondary flow away from the suction/hub corner and significantly prevent the secondary flow transporting the end wall boundary flow into the recirculation region around focus point F, as a result, the recirculation region was much decreased and the hub corner stall disappeared, but as the leakage flow was not strong enough to push away all the secondary flow, hub corner separation still existed at the rear part, just like the flow structure of 0.25\(\uptau\) at DE condition and 0.5\(\uptau\) at PP condition. When the stator hub clearance increased to 1.00\(\uptau\), the flow topologies were similar to that at 0.50\(\uptau\), but the spanwise scale of SL1 decreases and the spanwise scale of SL2 increased and SL4 was located further away from the blade suction side.  
Combined with the five-hole probe results in Figure 7, it could be concluded that at zero clearance and 0.25\(\uptau\), the hub corner is predominated by both the corner stall and hub leakage flow, and when stator hub clearance increases to 0.50\(\uptau\) and 1.00\(\uptau\), instead, the hub corner is predominated by both the corner separation and the hub leakage flow.
## 5 Conclusions  
This paper presents a novel MPPT algorithm referred to as Td-MPPT based on the classical P&O algorithm. This algorithm calculates the derivative of power during transient states of an undamped controller to reduce the number of oscillations around the MPP under steady-state irradiance. In this manner, it determines whether a perturbation was performed in the correct direction. Since the algorithm proposed is based on a conventional MPPT, the complexity and hardware implementation are relatively simple compared to other algorithms. Because of this, the implementation ease and cost of the proposed algorithm are reduced. Although the tracking speed of these algorithms is traditionally low, simulations showed that the Td-MPPT algorithm efficiently tracked MPP during a test based on the EN50530 standard multiplied by a factor of \(10^{2}\). An overall efficiency improvement over the algorithms benchmarked was observed for steady-state irradiance and rapid irradiation changes. Since the algorithm is based on a hill-climbing method, it is possible to combine it with partial shading techniques to address this issue. Furthermore, the presented method could be easily extended or mixed with variable perturbation times and perturbation sizes techniques already being applied to the classical P&O to improve its performance further. It was observed that an approximation of the natural frequency could be used to tune the proposed algorithm. However, this could not be optimal under every scenario. Because of this, the algorithm can be further improved by adding a self-tuning stage in the future. This modification could also facilitate the implementation of Td-MPPT for different kinds of topologies and PV-panel types. To this end, a common improvement to the classical P&O is to combine it with more advanced methods to track GMPP. The same modifications could be easily applied to the proposed method to make it suitable to operate under partial shading conditions for future works.  
[PERSON]C. participated in the literature review, methodology, formal analysis, laboratory test, and writing; [PERSON], conceptualization and supervision; [PERSON] and [PERSON] participated in review and editing; [PERSON] participated in data processing. All authors have read and agreed to the published version of the manuscript. The first author would like to thank Consejo Nacional de Ciencia Tecnologia (Av. Insurgentes Sur 1582, C.P. 03940, Mexico. Phone Number: +52-(55)-5322-7700) (CONACYT) for doctoral grant 713240. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
###### Abstract  
Compared with traditional immunoassay methods, microfluidic immunoassay restricts the immune response in confined microchannels, significantly reducing sample consumption and improving reaction efficiency, making it worthy of widespread application. This paper proposes an exciting multi-frequency electrothermal flow (MET) technique by applying combined standing-wave and traveling-wave voltage signals with different oscillation frequencies to a three-period quada-phase discrete electrode array, achieving rapid immunoreaction on functionalized electrode surfaces within straight microchannels, by virtue of horizontal pumping streamlines and transverse stirring vortices induced by nonlinear electrothermal convection. Under the approximation of a small temperature rise, a linear model describing the phenomenon of MET is derived. Although the time-averaged electrothermal volume force is a simple superposition of the electrostatic body force components at the two frequencies, the electro-thermal-flow field undergoes strong mutual coupling through the dual-component time-averaged Joule heat source term, further enhancing the intensity of Maxwell-Wagner smeared structural polarization and leading to mutual influence between the standing-wave electrothermal (SWET) and traveling-wave electrothermal (TWET) effects. Through thorough numerical simulation, the optimal working frequencies for SWET and TWET are determined, and the resulting synthetic MET flow field is directly utilized for microfluidic immunoassay. MET significantly promotes the binding kinetics on functionalized electrode surface by simultaneous global electrokinetic transport along channel length direction and local chaotic stirring of antigen samples near the reaction site, compared to the situation without flow activation. The MET investigated herein satisfies the requirements for early, rapid, and precise immunoassay of test samples on-site, showing great application prospects in remote areas with limited resources.  
**Keywords:** electrothermal convection; multiple frequency actuation; simultaneous sample transport and stirring; microfluidic immunoassay; smeared structural polarization
## 5 Conclusions  
This investigation used the validated CFD program to evaluate the impacts of seat inclination and misalignment of seat rows on the airflow and pollutant transport in a single-aisle aircraft cabin. The conclusions are as follows:  
The inclination of only three adjacent seats in a whole cabin did not change the flow and the pollutant transport in the cross-section very much. However, the seat inclination resulted in an increase in the pollutant concentration in the respiratory zone of the rear three rows while the pollutant concentration in the respiratory zone of the front three rows decreased, if the pollutant was released from a passenger in an inclined seat.  
The inclination of all of the seats in a half cabin while the other half cabin without seat inclination and the misalignment of seat rows resulted in a highly asymmetric seat layout. In such situations, the supplied air jets from both sides could easily penetrate the air curtain in the aisle and promote cross-contamination.  
There was no meaningful difference in the flows and pollutant concentration distribution when maintaining the symmetric seat layout with either all of the upright seats or all of the inclined seats. A symmetric seat layout aids in circumscribing the released pollutants in a small region and reducing the contamination both in the cross and the longitudinal sections.  
Based on the above findings, it is highly recommended to maintain all of the seats in an upright manner or be inclined completely on flights. Any random inclination of seats or misalignment of seat rows that results in a highly asymmetric seat layout should be avoided during the pandemic.  
Conceptualization, T.Z.; methodology, T.Z. and S.L.; software, M.F.; validation, M.F.; investigation, M.F.; writing--original draft preparation, M.F.; writing--review and editing, T.Z. and S.L.; visualization, M.F.; project administration, T.Z. All authors have read and agreed to the published version of the manuscript.  
This study was partially supported by the National Natural Science Foundation of China (NSFC) through Grant No. 52108084.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 2 Numerical Modeling
### Model Discretization  
A pile-supported highway embankment with a slope of 1 V:1.5 H is assumed to be constructed on a 30 m thick subsoil (see Figure 1) [36]. The embankment height (\(H\)), pile spacing (\(s\)), and pile length (\(L\)) are considered variables in this paper and have been annotated in the figure. The upper surface of the piles is flush with the subsoil surface, and the diameter (\(a\)) of the piles is 0.5 m, arranged in a square arrangement. Taking \(H\) = 4 m, \(s\) = 2.5 m, and \(L\) = 16 m, respectively, as the standard conditions, the material parameters of embankment fill and piles are shown in Table 1.  
Figure 2a shows the finite element mesh, comprising embankment fill, piles, and subsoil, modeled from the red-boxed region shown in Figure 1. In the finite element model, the piles are assumed to be impermeable and are represented using eight-node linear brick (C3D8) elements. The subsoil and embankment fill are considered permeable, with an initial permeability coefficient assigned using eight-node brick with trilinear displacement and trilinear pore pressure (C3D8P) elements. The groundwater level is situated at the surface of the subsoil. The plastic deformation of the pile is not taken into account, and a linear elastic constitutive model is used for its simulation. The subsoil is considered to undergo consolidation, and the Modified Cam-Clay (MCC) model is adopted. The embankment fill is modeled using the Mohr-Coulomb (MC) model.
## 3 Preliminaries
### Biometrics and Fuzzy Extractor  
Biometric authentication leverages unique physiological (e.g., fingerprint [28], iris) or behavioral (e.g., voice, signature) characteristics of an individual for identity verification [5; 9]. A significant challenge with biometric data is its inherent variability; readings of the same biometric trait are rarely identical due to sensor noise, environmental conditions, or physiological changes. Key performance metrics for biometric systems include the false acceptance rate (FAR) and false rejection rate (FRR). A **fuzzy extractor**, introduced by [PERSON] et al. [12], is a cryptographic tool designed to address this issue by reliably extracting a stable, uniformly random cryptographic key from noisy biometric inputs. Many IoD and related security protocols acknowledge its utility [5; 9; 10; 13]. It generally consists of two main algorithms.  
* **Generation (Gen)**: During the enrollment phase, this probabilistic algorithm takes an initial high-quality biometric sample \(\beta\) as input. It outputs a secret cryptographic key \(K_{\beta}\) (which should be close to uniformly random if \(\beta\) has sufficient min-entropy) and public helper data HD. Schematically: \(Gen(\beta)\rightarrow(K_{\beta},HD)\). The helper data HD is stored publicly and is used during the key reproduction phase. It is crucial that HD does not reveal significant information about either \(\beta\) or \(K_{\beta}\).
* **Reproduction (Rec)**: During an authentication attempt, this deterministic algorithm takes a fresh (potentially noisy) biometric sample \(\beta^{\prime}\) from the user and the stored public helper data HD as input. It attempts to reproduce the original cryptographic key \(K_{\beta}\). Schematically, \(Rec(\beta^{\prime},HD)\to K_{\beta}\). The reproduction is successful if the Hamming distance (or another suitable metric) between the enrollment template \(\beta\) and the current sample \(\beta^{\prime}\) is within a predefined error tolerance threshold \(\tau\), i.e., \(d(\beta,\beta^{\prime})\leq\tau\). The security of a fuzzy extractor relies on the assumptions that (a) the biometric data \(\beta\) has sufficient entropy, and (b) the helper data HD does not leak information about \(K_{\beta}\) beyond what is inherently necessary to correct minor variations in \(\beta^{\prime}\).
# Weibull-Open-World (WOW) Multi-Type Novelty Detection in CartPole3D
## 3 Method
### EVT-Based Probability of Per-Instance Novelty and Overall Novelty Detection
#### 3.2.2 KL-Divergence over Per-Episode Novelty-Detection  
The per-dimension approach produces a vector with per-time step probabilities of novelty. If the simulation and predictions were perfect, thresholding such per-time step data may be sufficient to detect novelty. However, we found probability from a single trial was too noisy to use directly. This is because the 200-time steps per episode and 100 s of episodes per trial, combined with the noise inherent in the simulation with a imperfect set-state function, have too high a probability of false detection. To improve robustness, we consider the distributions of the novelty probabilities over time and inspired by [8] we use KL-divergence to compare the normal distribution to the distribution from the test data, see Figure 3. To improve sensitivity for small differences, we also use the accumulation of novelty probability over time.  
The Kullback-Leibler divergence is a fundamental measure of the difference between distributions. It measures the relative entropy  
\[\text{KL }(P\|Q)=\int_{-\infty}^{+\infty}p(x)\ \log(\frac{p(x)}{q(x)})\ dx \tag{1}\]  
where \(p(x)\) is the probability density function of the testing vector, and \(q(x)\) is the probability density function of normal world vectors. In our case both the training and test vectorsare distributions of per-time novelty probabilities, and the goal is to detect changes in that distribution. Intuitively the training vector would be all small value and the test all large values, but KL allows us to formalize \"how\" different is the distributions.  
Making the classic Gaussian assumption for the pair of distributions, i.e., letting \(p(x)\sim\mathcal{N}(\mu_{t},\,\sigma_{t}^{2})\) and \(q(x)\sim\mathcal{N}(\mu_{n},\,\sigma_{n}^{2})\), be the distribution parameters of the test and normal worlds respectively, the KL divergence measure is:  
\[\text{KL}\,\left(P\|Q\right)=\log(\frac{\sigma_{n}}{\sigma_{t}})+\frac{\sigma_ {t}^{2}+(\mu_{t}-\mu_{n})^{2}}{2\sigma_{n}^{2}}-\frac{1}{2} \tag{2}\]
## 3 Results and Discussion
### Color Reproduction
After the experiments, we noticed bending and deformation of the stainless steel, which could potentially alter the laser's focal length and cause color cast problems. To investigate the causes of these color cast issues, we conducted an analysis experiment on the causes of color differences. In the analysis experiment on color difference causes, 2 mm thick 304 stainless steel was employed as the experimental material. With experimental variables such as laser parameters and focal length remaining constant, a color reproduction experiment was conducted on a tiger image on a 2 mm thick 304 stainless steel surface. The experimental results are presented in Figure 7. It is observable that the degree of bending in 2 mm thick 304 stainless steel was significantly reduced. The color casts on the black stripes, hind legs, face, and other areas of the tiger image on the surface of 1 mm thick 304 stainless steel did not persist on the surface of 2 mm thick 304 stainless steel. In terms of overall color reproduction accuracy, the tiger image on the surface of 2 mm thick 304 stainless steel also demonstrated improvement. Further color difference analysis was conducted  
Figure 6: The results of color reproduction of a tiger image and temple image. (**a,b**) present a color gamut mapping image and the corresponding laser-induced metal surface coloring image, respectively; (**c,d**) showcase another color gamut mapping image and the corresponding laser-induced metal surface coloring image, respectively. on the experimental images, measuring the color of the sample using the LAB mode of the X-Rite Ci64 and calculating the color difference \(\Delta E\) between the color-reproduced sample and the mapped image base color using Equation (11), The results are presented in Table 3. The calculation results indicate that the color reproduction accuracy of 2 mm thick 304 stainless steel is improved compared to that of 1 mm thick 304 stainless steel. The color difference of color separation 1, color separation 2, and color separation 3 decreased by 1.08, 1.39, and 1.31, respectively, and there was no significant color deviation area. The experimental results of color difference analysis demonstrate that image color reproduction on the surface of 304 stainless steel is reliable, and increasing the thickness of stainless steel can effectively mitigate color casts and enhance the accuracy of color reproduction. This study provides insights for image reproduction in the field of laser coloring, which is expected to be applied in industrial manufacturing processes.