esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
### Machine Learning in WSNs  
ML has emerged as a powerful tool in Wireless Sensor Networks (WSNs), enabling systems to learn from data and adapt to dynamic environments. ML methodologies can be leveraged across various stages of WSN operation, including data acquisition, analysis, and communication. ML can be used to improve Wireless Sensor Networks, extending their capabilities and applications, and ML can enable them to be more intelligent with data, make more informed decisions, and use resources efficiently. Consequently, ML approaches are being widely adopted in this field. The advantages of using ML in WSNs include improved adaptability to changing network conditions, the ability to learn from data for more intelligent decision-making, and enhanced performance in tasks such as clustering, anomaly detection, and data prediction. However, there are also notable challenges: WSN nodes typically have limited computational power and memory, which restricts the complexity of the ML models that can be deployed. The data collected by sensors may be noisy or incomplete, affecting the reliability of ML-based decisions.  
Furthermore, many ML algorithms assume centralised data processing, while WSNs often require distributed or in-network computation to save energy and bandwidth. Therefore, the aim of this work is to propose methods that are designed with these considerations in mind, aiming to balance the benefits of ML with the realities of WSN environments. Lastly, it may be difficult to deploy Wireless Sensor Networks in large environments [10].
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
###### Abstract  
The accuracy of land surface temperature (LST) observations is critical to many applications. Any observation of LST is subject to incomplete knowledge, so an accurate assessment of the uncertainty budget is critical. We present a comprehensive and consistent approach to determining an uncertainty budget for LST products. We apply this approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Aqua satellite. In order to generate the uncertainty model, a new implementation of the generalised split-window algorithm is applied, in which retrieval coefficients are categorised by viewing angle and water vapour. Validation of the LST against in situ data shows a mean absolute bias of 0.37 K for daytime and 0.73 K for nighttime. The average standard deviation per site is 1.53 K for daytime and 1.21 K for nighttime. Uncertainties from the implemented model are estimates in their own right and are also validated. We do this by comparing the standard deviation of the differences between the satellite and in situ LSTs, and the total uncertainties of the validation matchups. We show that the uncertainty model provides a good fit. Our approach offers a framework for quantifying uncertainties for LST that is equally applicable across different sensors and different retrieval approaches.  
Keywords:land surface temperature (LST); consistent uncertainty model; uncertainty validation +
Footnote †: journal: Remote Sensing
## 6 Discussion and Future Directions
### Challenges and Considerations  
#### 6.3.1 Communication Overhead Cost for FL  
For most applications, FL has shown promising success with regard to model accuracy. In Figure 8, [PERSON] et al. [149] compared its performance to a traditional ML approach. It shows that FL was more effective in addressing IDS problems. However, the communication overhead cost for FL is somewhat expensive [158]. This has an overall impact on the performance of the FL-enabled framework. To mitigate that, effective algorithms and frameworks that would lower the communication overhead could be developed.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
###### Abstract  
E-Learning is an emerging dominant phenomenon in education, making the development of robust models that can accurately represent the dynamic behavior of learners in MOOCs even more critical. In this article, we propose the Temporal Sparse Attention-Gated Recurrent Unit (TSA-GRU), a novel deep learning framework that combines TSA with a sequential encoder based on the GRU. This hybrid model effectively reconstructs student response times and learning trajectories with high fidelity by leveraging the temporal embeddings of instructional and feedback activities. By dynamically filtering noise from student interactions, TSA-GRU generates context-aware representations that seamlessly integrate both short-term fluctuations and long-term learning patterns. Empirical evaluation on the 2009-2010 ASSISTments dataset demonstrates that TSA-GRU achieved a test accuracy of 95.60% and a test loss of 0.0209, outperforming Modular Sparse Attention-Gated Recurrent Unit (MSA-GRU), Bayesian Knowledge Tracing (BKT), Performance Factors Analysis (PFA), and TSA in the same experimental design. TSA-GRU converged in five training epochs; thus, while TSA-GRU is demonstrated to have strong predictive performance for knowledge tracing tasks, these findings are specific to the conducted dataset and should not be implicitly regarded as conclusive for all data. More statistical validation through five-fold cross-validation, confidence intervals, and paired \(t\)-tests have confirmed the robustness, consistency, and statistically significant superiority of TSA-GRU over the baseline model MSA-GRU. TSA-GRU's scalability and capacity to incorporate a temporal dimension of knowledge can make it acceptably well-positioned to analyze complex learner behaviors and plan interventions for adaptive learning in computerized learning systems.  
MOOCs; TSA-GRU; Temporal Sparse Attention (TSA); Gated Recurrent Unit (GRU); self-attention; learner behavior analytics; knowledge tracing; student response time prediction; hybrid deep learning model +
Footnote †: journal:  
Article
## 5 SDN-based VANETs Security Challenges
### Security Threats
#### 5.1.3 Communication APIs  
Significant dangers are instability of APIs and absence of institutionalization. The southbound API is commonly presented to man-in-the-center, listening stealthily, or accessibility attacks. SDN-based VANETs need institutionalized and tweaked OpenFlow APIs and northbound APIs. NBI/SBI  
Figure 11: Threats taxonomy in SDN-based vehicular ad-hoc networks (VANETs) with respect to vehicular system, information, and infrastructure.  
APIs between controllers are additionally not institutionalized [63] in the upper information plane. Vindictive programmable switches can publicize counterfeit system topologies or upset the lower information plane. Dangers originate from vulnerabilities of the numerous, heterogeneous remote correspondence conventions, and from programming characterized radio, as new applications and highlights might be downloaded through remote connections with reconfiguration abilities, potentially causing honesty infringement in various programming layers [64]. Threats additionally incorporate gadget cloning (Sybil attacks) empowering unapproved access from another gadget (i.e., another vehicle). Heterogeneity of gadget and systems is a significant source of weakness and disappointments. Factors, for example, portability, dynamic topology, and low-dormancy infringement (e.g., for emergency administrations) may likewise complex the system security checking or lead to street mishaps such as traffic (re-steering, seizing, denial of service (DoS)), or issues with the controller. Dangers likewise incorporate depleting system assets, or performing side-channel attacks to remove delicate data [65].
# Study of 5G-NR-MIMO Links in the Presence of an Interferer
## 1 Introduction
The first is called analog Radio Frequency (RF) interference cancellation, and the second is digital interference cancellation [30]. We focus here on digital interference received at the combiner process. It is a complicated phenomenon that requires deep research. We must also remain focused on significant targets in 5G-NR, e.g., control model antennas, array beam patterns, and flexible air interfaces, to evaluate the algorithm design's impact on RF link performance. In addition, we must develop and research mechanisms that create a trade-off between dealing with jammers or strong signal interferences and increasing the channel's capacity with minimal energy. The remainder of this paper is organized as follows: in Section 2, we present the basic principles of diversity transmission using the Alamouti MIMO technique. In Section 3, we present different interferer techniques: a general interferer, a smart jammer, and a PBN jammer. In Section 4, we present the simulation results in Bit Error Rate (BER) vs. SIR. Section 5 summarizes and concludes the paper.
## 4 Results
### Antioxidant Activity  
In Figure 4A, our study results of sheep yogurt beverage indicate that the antioxidant activity considerably increased with rising honey concentration, from 9.52 \(\pm\) 0.14 at 1% to 30.89 \(\pm\) 0.01 at 5%. In Figure 4B, in cow yogurt beverage, the increase in honey concentration considerably boosts antioxidant activity, peaking at 5% concentration (39.34 \(\pm\) 0.02). The natural phenolic chemicals and flavonoids in honey contribute to this action. Honey markedly enhances the antioxidant potential of probiotic yogurt owing to its elevated levels of phenolic components and flavonoids. In Figure 4C, in blended yogurt beverage, the antioxidant activity increases with higher honey concentrations, reaching 42.36 \(\pm\) 0.01 at 5%. The boost is attributed to the phenolic chemicals and flavonoids present in honey. Antioxidant activity is enhanced by honey owing to its bioactive components. The elevated quantity of honey in probiotic sheep yogurt beverage enhances its antioxidant qualities, indicating that honey's antioxidant effects are mostly due to its abundant phenolics, flavonoids, and organic acids, which effectively scavenge free radicals. Our findings corroborate those of ref. [36], who also asserted that honey augments antioxidant activity in functional yogurt owing to its phenolic and flavonoid constituents. In a separate investigation, ref. [37] found that the polyphenols in honey enhanced oxidative stability in dairy products, consistent with the noted incremental improvement in antioxidant properties. Moreover, ref. [38] noted that honey supplementation enhanced the antioxidant capacity of yogurt.  
Figure 4: (**A–F**) represent the effect of honey concentrations on antioxidant activity and simulated gastrointestinal digestion (in vitro digestion TBC) of yogurt beverage made from sheep, cow, and blended milk, respectively. Different letters represent statistically significant differences between the treatments.
# Multipath Data Storage by Third-Order Nonlinear Optical Properties in Zinc Oxide Nanostructures
## 3 Results
### Multipath Load Balancing Assisted by Nonlinear Optical Properties
The irradiation of a single pulse in different regions of the sample was conducted in single-shot mode. Pulsed irradiation close to the ablation threshold in the ZnO nanostructures gives origin to a strong change in the optical nonlinearities of the ZnO material. However, microscopy or spectroscopy studies are not able to clearly identify this modification in the sample. The decryption stage was undertaken by the evaluation of the nonlinear optical absorption and optical Kerr effect in the nanostructures. XOR logic enables both encryption and decryption routes. Remarkably, by XOR encryption, the ciphering of the data can be guaranteed to be unbreakable without the clear identification of the two arguments involved in the logic gating. Comparative results in ZnO nanostructures clearly pointed out their third-order nonlinear optical effects to be good candidates for building up high-throughput nanoscale platforms with potential applications in signal processing [29]. Collective interactions in ZnO nanostructures seem to be attractive for designing nonlinear optical nanosystems based on sculptured thin solid films [30]. We believe that our strategy opens up a perspective for flexible developing of all-optical functions with simultaneous actions related to absorptive and refractive nonlinearities. In particular, laser irradiation processes in ZnO nanostructures can be considered for improving manufacturing methods related to the implementation of dynamic multilevel diffractive optical elements. It can be envisioned that the coupling between nanostructures and nonlinearities could lead to exciting venues that may play an essential role in all-optical devices.
# Shape Effect and Accuracy Analysis of Rock Tensile Strength Test
## 3 Comparison and Analysis of the Accuracy Between Direct Tension and Brazilian Splitting Tests  
### Test Scheme  
#### 3.1.1 Numerical Simulation Test Specim  
First, a standard direct tensile specimen of 50 mm (Diameter) \(\times\) 100 mm (Height) is established for comparison [24], as shown in Figure 5a. For the specimens used to measure rock tensile strength in this numerical simulation test, a model as shown in Figure 5 is established. Specifically, the specimen in Figure 5b is referred to as the curved-transition surface dumbbell specimen, the specimen in Figure 4c is referred to as the straight-transition surface dumbbell specimen, and the specimen in Figure 5d is a disc specimen. The centre effective test area of both dumbbell specimens has a height-to-diameter ratio of 2:1. The radius of the curved-transition surface in the curved-transition dumbbell specimen is approximately (1-2) \(\times\) d [2], where d is the diameter of the standard cylindrical specimen, as described by [PERSON] et al. In this numerical simulation test, the transition surface radius is taken as 50 mm, and the transition angle of the straight-transition dumbbell specimen is set at 63\({}^{\circ}\)[25]. The stress distribution in samples with a 63\({}^{\circ}\) angle can be practically accepted as uniaxial. Additionally, no holding or fixing issues were observed in tests on both soft and hard rock materials. This angle is evaluated as the most ideal geometric shape for obtaining accurate results for various rock materials and is more suitable for assessing direct tensile strength, as shown in Figure 5c. Compared to the standard cylindrical tensile specimen shown in Figure 5a, the transition surface of the dumbbell specimen effectively eliminates the stress concentration at the ends caused by loading during direct tensile testing. This results in a more manageable stress distribution and significantly reduces the occurrence of invalid failure in regions far from the specimen's centre [5; 26; 27]. Additionally, the dumbbell specimens are easy to clamp during laboratory tests, as shown by 1 and 2 in Figure 5b,c. The curved-transition dumbbell specimen can have the clamps directly mechanically held at the vertical edges of the \"dumbbell\" ends, or at the edges of the transition surface. Similarly, the straight-transition dumbbell specimen can be simply clamped at the vertical edges of both ends of the specimen, or directly at the transition surface [28; 29].  
Figure 5: Numerical simulation test specimen. (**a**) Direct tensile standard specimen; (**b**) Dumbbell-shaped specimen with curved-transition surface; (**c**) Dumbbell specimen with linear-transition surface; (**d**) Brazilian disc specimen.
## 4 Discussion  
It is known that the peak frequency of the sediment particles impacting with each other or the acoustic device relates to the sediment particle size. From the laboratory experiments, [PERSON] [55; 56; 57] proposed the following equation, which links the peak frequency of the particles' inter-collision and the sediment size distribution:  
\[f_{peak}=224/D^{0.9}, \tag{4}\]  
where \(D\) is the particle size in meters. The computed peak frequency of particle instrument collisions with a size of 0.011 m using this equation is around 13,000 Hz. The peak frequency of the gravel particles of 0.011 m impacting on the GPVS in our experiment is around 600-8300 Hz, which is much lower than that given by Equation (4). One possible explanation is that the peak frequency in this study is the frequency of particles impacting on the measurement device (i.e., the GPVS), whereas the peak frequency estimated from the above equation is the frequency of particle instrument collisions, which could be much higher than the frequency of particles impacting on the device. Furthermore, previous studies reveal that the signal frequency of the particle's movement has a fairly large range. For example, [PERSON] et al. [5] observed bedload sediment transport by placing a hydrophone in a tidal stream. They found that the analysis of the acoustic signals showed that the peak frequency was in the wide range of 4000 Hz to 20,000 Hz for moving particles whose sizes ranged from 4 mm to 170 mm. While the field observation carried out by [PERSON] et al. [37] in the Alpine Drau River with large gravel showed that the bedload acoustic signals had a frequency between 1000 Hz and 60,000 Hz. This illustrates that the peak frequency indicating the inter-collision of sediment particles (or impact on the measurement apparatus) has a very wide range.  
In fact, the frequency of the recorded acoustic signals from gravel movement is controlled by a number of factors, including gravel particle size and distribution, riverbed conditions, flow conditions, gravel movement patterns, and so on. As no theoretical expression for estimating bedload transport rates from hydrophone measurements has been developed, only the calibration approach has been used so far.
## 1 Introduction
Therefore, we first propose a technique for online realization of SFR model which is a useful model in power system frequency studies. Then, an optimal multi-stage UFLS technique is proposed for avoiding the consequences of severe disturbances. The suggested optimization problem is solved by adopting a powerful meta-heuristic solver, i.e., ICA. The proposed technique use the WAMS technologies for real-time power system monitoring. In this regard, the power deficit is computed instantly after a disturbance occurs in which the speed of proposed UFLS technique is highly improved which leads to better results in comparison with other techniques. Though the frequency threshold can be fixed, based on the optimization algorithm, the amount of load-shed does not necessarily have to be fixed and is based on the fault level. In the adaptive case, based on the rate of change of frequency the amount of load to be shed can be determined before the frequency falls to critical levels unlike in traditional UFLS schemes. Based on system constraints, a minimum amount of load is shed to restore the frequency to acceptable levels. The main contributions, in this paper, are  
* Wide-area measurement system-based online aggregate frequency response model realization is proposed. * Evolutionary computing-based optimal multi-stage UFLS technique is proposed for mitigating the under/over load-shedding situations. * Three objective functions for achieving optimal UFLS are suggested for modern power systems considering practical constraints. The rest of this paper is organized as follows. Section 2 explains the use of WAMS and how the model can be set up based on the obtained data. In Section 3, an \(n\)th order SFR model-based WAMS is presented. In Section 4, the proposed optimal multi-stage load-shedding is presented. An optimization algorithm is introduced in Section 5. The power system under investigation are described in Section 6. Section 7 presents simulation results of different scenarios, while Section 8 concludes.
# Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts
## 1 Introduction
Therefore, the traditional first-order mathematical model is not accurate enough to describe the actual EMA model. On the basis of [22], the deformation of the reduction gear under braking pressure is analyzed and combined with the ideal EMA mathematical model, and subsequently, the actual EMA mathematical model is rebuilt. The backstepping control method has shown its effectiveness in dealing with systems with multiple dynamics and mismatched uncertainties, but a major problem is that certain functions must be linear in the unknown parameters and tedious analysis is needed to determine the regression matrices. The RBF neural network has strong input and output mapping functions, which make it suitable to use a stable neural network controller to estimate certain nonlinear functions. With the help of RBF neural networks, the linear-in-the-parameters assumption of the nonlinear function and the determination of the regression matrices can be avoided. According to the characteristics of the actual EMA model, an adaptive backstepping control method is proposed and the RBF neural network is used to address the uncertain compound disturbance of the electric braking system. Compared with the routine control strategies, the servo performance of the braking pressure and the adaptive ability of the control system are improved significantly. The remainder of this paper is organized as follows: Section 1 introduces the control problems of the electric braking system in aircrafts. Section 2 presents the working principle of the electric braking system and structure of the EMA. Then, the deformation of the reduction gear is analyzed in detail, the actual EMA mathematical model is established, and the corresponding control target is put forward. Section 3 presents the backstepping adaptive controller and the design method of the RBF neural network, and the stability analysis of the proposed controller is carried out using Lyapunov functions. Section 4 discusses the experimental results and the hardware in loop experiments. The conclusions are summarized in Section 5. The main contributions of this paper are summarized as follows: first, the paper analyzed the ideal EMA mathematical model and the nonlinear factors, and then the actual EMA mathematical model is rebuilt by considering the deformation of the reduction gear. Second, the proposed adaptive nonlinear control method can overcome the nonlinear factors of the electric braking system and improve the response speed and control precision of the EMA. Third, the control effect of the newly designed EMA controller is fully validated through the HIL experiments.
## 2 Materials and Methods
### Bayesian Network Model
#### 2.4.3 Inference  
Predictions for the outcome were made using exact inference [28], where the posterior probability is calculated based on a set of events. These events consist of all possible values of the nodes connected to our outcome.  
Specifically, for cases (A), (B), and (C) in Figure 2, the joint probability distributions can be described by Equations (1)-(3), respectively:  
\[P_{A}=P(f1).P(outcome|f1) \tag{1}\]  
\[P_{B}=P(f1).P(f2).P(outcome|f1,f2) \tag{2}\]  
\[P_{C}=P(f1).P(f2).P(outcome|f1,f2).P(f3|outcome) \tag{3}\]  
where \(P(f1)\), \(P(f2)\), and \(P(f3)\) are the probability distributions of feature 1, 2, and 3. \(P(outcome\mid f1)\) represents the conditional probability distribution of the outcome given feature 1. \(P(outcome\mid f1,f2)\) denotes the conditional probability distribution of the outcome given feature 1 and 2, and \(P(f3\mid outcome)\) describes the conditional probability distribution of the feature 3 given the _outcome_.  
We define features 1 and 2 as \"parent\" node of the \"child\" node _outcome_, while feature 3 is the \"child\" of its \"parent\" node _outcome_. Therefore, we can generalise the joint probability distribution for all other Bayesian network structures as shown in Equation (4):  
\[P(X_{1},...,X_{n})=\prod_{i=1}^{n}P(X_{i}|P_{a}(X_{i})) \tag{4}\]  
where \(Pa(X_{i})\) denotes the parent of node \(X_{i}\). The conditional probability for a node without parents is simply its prior probability \(P(X_{i})\).  
To develop the Bayesian Network model, we utilised the bnlearn package in R [36].