esa-sceva/satcom-chunk-collection huggingface dataset:
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 3 Computation and Comparison
### Analysis of the Accuracy of Parallel Ultra-Rapid POD  
Aiming at the problem that the predicted orbit accuracy of GNSS ultra-rapid products becomes worse as the extrapolation time is longer, we apply the parallel approach to the multi-GNSS ultra-rapid POD. Based on the results of the improvement of efficiency with different number of stations in the previous section, 88 IGS/MGEX stations are selected for multi-GNSS ultra-rapid POD. The distribution of stations is shown in Figure 2.  
Figure 5: Speedup of satellite orbit determination with multi-process parallel computing.  
Figure 6: Serial and parallel computational time for satellite orbit determination with different number of stations.
## 3 Numerical Simulation of Direct Shear Test of Bonded and Naturally Rough Concrete-Rock Interfaces  
### Methodology of the Numerical Simulation  
The numerical simulation of the shear behavior of concrete-rock interfaces under low normal loading was performed in the finite element software Abaqus. The model used is a combination of two behaviors, the contact cohesive behavior, to simulate the concrete-rock bonds, and the frictional behavior, to simulate the friction at the interface. This model is hereby referred to as the cohesive frictional model and will be presented later.  
The simulation relies upon the separation of two scales of roughness, the micro-roughness (here submillimetric) and the macro-roughness (here millimetric). In the case of the shear behavior of bonded and naturally rough concrete-granite interfaces, the micro-roughness is approximatively the one obtained after the bush-hamming process (refer to Sections 2.2.1 and 2.2.2). This way, the bush-hammered interface can then be approximated to a flat surface with homogeneous micro-roughness such that concrete and granite form strong bonds. The behavior law of the model is used specifically to simulate the influence of the micro-roughness and the influence of the cohesive bonds between concrete and granite on the shear behavior. Consequently, this model is calibrated using the results of the tests with bush-hammered interfaces. The influence of the macro roughness is directly taken into account by the 3D explicit representation of roughness.  
The calibrated cohesive frictional model is then used to simulate the direct shear test of bonded and naturally rough concrete-granite interfaces. The results of these simulations are compared with their experimental counterparts presented in Section 2.2.3 to assess the performance of the model in terms of prediction of the peak shear strength.
# SATCOM Earth Station Arrays Anti-Jamming Based on MVDR Algorithm
## 4 Simulation Analysis  
Different from the MVDR algorithm that assumes the array elements are isotropic point sources, in this paper, the array elements are satellite communication earth station reflector antennas with gain following Equation (5), and therefore, the directional vector of the array receiving signal follows Equation (10).  
Assuming that the array in this paper is a uniform linear array composed of identical satellite communication earth station reflector antennas, the array deployment method is the same as that required in Section 2. Considering the actual jamming scenario of satellite communication downlink, the jamming direction may vary, so the power flux density (PFD) at the earth station antenna is used to represent the size of the signal and jamming. The power after passing through the antenna can be calculated using the following equation  
\[[P]=[F]+[G(\theta)]+10\lg\left(\frac{\lambda^{2}}{4\pi}\right) \tag{27}\]  
Here, \([F]\) represents power flux density. The power flux density of the signal is set according to the actual size, with a reflector antenna diameter of \(D=0.5\) m, antenna efficiency of \(\eta=0.7\), signal frequency of \(f=12.5\) GHz, and the expected signal wave direction of \(\theta_{0}=0^{\circ}\). The incident angle range is \(-90^{\circ}\leq\theta\leq 90^{\circ}\). Since the diagonal loading method used in this paper is an optimization problem under the worst performance, the size of the diagonal loading has little effect on the performance of the algorithm within a certain range [25]. Therefore, in this experiment, the diagonal loading amount uses noise power and Matlab 2016.
###### Abstract  
Non-destructive ultrasonic testing has attained popularity due to its robustness and cost-effectiveness in monitoring the structural health and performance evaluation of pavements, thus replacing traditional methods. This paper presents the application of an explicit finite element method for the modeling of ultrasonic wave propagation through asphalt concrete. Prior to modeling, non-destructive ultrasonic testing was conducted on four different types of asphalt concrete (AC-13, SMA-13, AC-20, and AM-20). Based on acoustic information (wave velocity) obtained in non-destructive testing (NDT) and density, the dynamic moduli of these asphalt concretes were evaluated and used in numerical modeling of ultrasonic wave propagation using the commercial software package ABAQUS. The ultrasonic wave results obtained by numerical modeling were compared with experimental results. This comparison showed a good fit between the finite element (FE) results and the experimental results and confirmed a good FE approach for ultrasonic wave propagation. In addition, the influence of varying dynamic moduli, density, varying location, and crack size/depth on ultrasonic wave propagation was analyzed.  
Keywords:non-destructive ultrasonic testing; explicit finite element method; asphalt concrete; dynamic modulus; acoustic information +
Footnote †: journal: Applied Sciences
## 1 Introduction
This paper introduces three different steps which have been duly scrutinized to achieve the desired optimization of parameters, i.e., makespan, energy consumption, machine utilization rate, and reliability of services. Initially, a supplier classification problem is explored under a manufacturing setup in which the suppliers are classified into task specific suppliers using text mining. Thereafter, a many-objective mathematical model is developed to achieve the above-mentioned competing objectives. We then compared the effectiveness of the above propounded approach by equating the outcomes with the proposed multi-objective Hybrid Moth Flame Optimization (HMFO) algorithm with another benchmark algorithm such as NSGA-II. The research contributions of this paper can be outlined as given below:  
* Proposing an integrated text-mining assisted process planning framework for distributed manufacturing systems;
* Employing a machine-learning-based text-mining method to identify the potential enterprises and sharing of resources effectively across the network and tested its feasibility with various other machine-learning algorithms;
* A multi-objective evolutionary algorithm-based Hybridized Moth Flame Optimization Algorithm (HMFO) is used to solve the considered problem in the scenario of distributed gear manufacturing industries;The results of the proposed HMFO method are validated with the Non-Dominated Sorting Genetic Algorithm (NSGA-II) to evaluate their usefulness by both experimental and practical instances. Finally, the superiority of the proposed HMFO is confirmed with the help of various performance indicators. In this paper, Section 2 deals with the literature, Section 3 describes the problem and the developed mathematical model developed. Section 4 explains the proposed framework and algorithm for the text-mining approach. In Section 5, experimentation with a case study of the gear manufacturing industry is presented, and the corresponding outcomes are explained in Section 6. The paper is concluded in Section 7 by providing scope for future work.
## 6 Conclusions  
Supplying energy to consumers in power grids plays a major role in increasing greenhouse gas emissions. Utilizing high-efficiency technologies along with managing energy networks can play an effective role in reducing pollution emissions and system operation costs. Thus, this paper studied the optimal operation of a hybrid MCMG in the presence of CAES and P2G converter with the aim of minimizing the operation costs and pollution in a multi-objective framework. Electricity and natural gas were considered as inputs for the proposed MCMG to supply local loads. The proposed CAES can work in different modes, including simple-cycle and charging/discharging modes for supplying loads in critical periods as a generator. The DRP, based on the shifting strategy, was extended to manage the load curve and improve eco-emissions benefits. The proposed multi-objective model was solved by the weighted sum and min-max fuzzy approach. Numerical results were discussed for two cases, and the effects of several smart grid technologies on the operation cost and emission pollution were analyzed. Results revealed that integrating P2G as a vital technology for capturing CO\({}_{2}\) and mixing with H\({}_{2}\) has a substantial role in greenhouse gas emission mitigation. Lastly, the integration of CAES, P2G, and DRP results in up to 14.2% and 9.6% reduction in operation cost and pollution, respectively.  
Figure 11: Optimal operation schedule of CAES in case 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CAES** & **CAES and P2G** & **Integrated CAES, P2G, and DRP** \\ \hline Cost (\$) & 8690.1 & 8129.37 & 7456.81 \\ \hline Emission (kg) & 11364.81 & 10,681.53 & 9656.11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effects of smart grid technologies on the operation cost and emission pollution in case 2.  
In summary, this model enables the energy systems to couple various energy carriers, which yields various benefits from operational and economic perspectives to coordination between the interconnected multi-carrier microgrid communities. Indeed, the coordination by the centralized optimization of the proposed network brings the system into an optimum situation, particularly when an incident occurs in the network. Moreover, the probabilistic method can increase the trustworthiness of the optimal solution by considering more uncertain parameters.  
K.H.: writing--original draft, visualization. S.S.: resources, data curation, investigation. S.E.: software, methodology. M.A.: conceptualization, formal analysis, validation, writing--review and editing. H.G.: project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available with authors upon request.  
The authors declare no conflict of interest.
## 3 Results
### Themes Covered Across the Included Articles
#### 3.3.3 Sustainable Energy  
Two of the included articles centred on energy, specifically the incorporation of green energy and the potential application of photovoltaic technology in bus-stop shelters. [PERSON] et al. [26] modeled energetic systems to assess the technical and financial viability of a \"warm\" bus shelter in Nur-Sultan, Kazakhstan. The study integrated passenger load surveys to estimate maximum daily ridership, determine the shelter's heating load and power demand, and examine three green technologies: ground source heat pumps, photovoltaic arrays, and wind turbines. The findings showed that a combination of 40% ground source heat pump and 60% electric heat was a feasible and attractive option.  
In a separate study, [PERSON] [1] explored the photovoltaic potential of bus-stop shelters in Lisbon, Portugal. The researchers utilized Geographic Information Systems-based solar radiation maps to pinpoint suitable locations, as well as proxies for tourist and commuter demand. Additionally, they conducted visual analyses via ortho-photo and Google Street View tools. The results revealed that 54% of the existing shelters in Lisbon hold potential for photovoltaic systems, which could contribute to local electricity production. These two studies underscore the increasing interest in integrating sustainable energy solutions into bus-stop shelter design.
## 3 System Model  
We considered a wireless network with one target node \(T\), whose location is to be estimated, and \(N\) boundary nodes or APs, whose locations are known. A spoofing device (or attacker) is assumed to be flying over the target area located at position \(X_{S}=[x_{s},y_{s},z_{s}]\). The AP and the target are deployed in a 2-D plane with locations \(X=[x,y]\), as shown in Figure 3. The communication channel between the target and AP is modeled using the power loss model [28; 29], where the power decreases with increasing distance. A node can communicate with the target node if both have the required signal-to-noise ratio (SNR), which is defined as the difference between the received power and the total noise received by the node (\(SNR=P_{r}-Noise\)) [30]. The SNR is a measure of how much stronger the signal is than the noise, where a higher SNR means that the signal is more likely to be received correctly [31; 32].  
In the presence of a spoofed UAV, the noise at the receiver increases, leading to a decrease in the SNR. This increases the bit error rate (BER) [33]. Nodes under attack can be classified into three categories based on their SNR: spoofed nodes that may communicate with the \(AP\) and have an \(SNR>\gamma\), edge nodes located at the edge of the spoofed area and have an \(SNR\approx\gamma\), and unhearing nodes with an \(SNR<\gamma\). The power received at a node is described by the following equation:  
\[P_{r}=\frac{P_{t}G_{t}G_{r}}{4\pi d^{2}}+\chi \tag{10}\]  
where \(P_{r}\) is the received power, \(P_{t}\) is the transmitted power, \(G_{t}\) is the transmit antenna gain, \(G_{r}\) is the receive antenna gain, \(d\) is the Euclidean distance between the transmitter and receiver \(d=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2}}\), and \(\chi\) represents Gaussian noise with a zero mean.  
To locate the target node, three anchor nodes, or APs, need to communicate with the target node to collect localization information such as the ToA. This information is then used by a localization algorithm at the central node. However, in the presence of a UAV, the transmit signal from the target node may be deceived by the spoofing signal from the UAV. This can cause a delay in the ToA and a decrease in the SNR, which may result in significant localization errors.  
Figure 2: Typical LSTM network [26].
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 2 Materials and Methods
### Classifier Algorithms  
Several classification methods have already been applied to texture mapping over agricultural fields [37; 38; 39]. With these methods, in addition to the selected feature vector, a set of data samples is associated with different class labels. This set is called the training dataset, and is used to estimate the parameters associated with each classifier. The point-based classification used with the SVM is adopted.  
SVMs were selected because a simple feature vector can be used as input for the discrimination task. In parallel, the random forest (RF) classifier was trained to compare the soil texture classifications obtained using these two techniques, which are presented in more details in the following sub-sections.
# A High-Sensitivity Magnetic Field Sensor Based on PDMS Flexible Resonator
## 3 Results and Discussion
### Radius of the PDMS Flexible Resonator  
Based on the model parameters mentioned above, by keeping the coupling position at 0.3 h unchanged, the change in the radius under different magnetic fields is obtained by changing the size of the PDMS flexible resonator radius, and the magnetic field sensitivity of the PDMS flexible resonator with different radius is fitted, as shown in Figure 7a.  
The magnetic field sensitivities for different PDMS flexible resonator radii are shown in Figure 7a, The PDMS flexible resonator radii are 1.5 mm, 1.2 mm, 1 mm, 0.9 mm, 0.75 mm and 0.65 mm,. The magnetic field sensitivity is obtained by linearly fitting the relationship between the magnetic field and the amount of radiation. The magnetic field sensitivity is minimized at a radius of 1.5 mm. By varying the radius of the PDMS flexible resonator, the magnetic field sensitivity of the sandwich structure is also varied. The sensitivity is calculated to be 3.41 nm/mT when the radius is 1.2 mm, 4.04 nm/mT when the radius is 1 mm and 4.11 nm/mT when the radius is 0.9 mm. Figure 7b depicts the trend of the magnetic field sensitivity with the change in the radius of the PDMS flexible resonator. With a decreasing radius, the magnetic field sensitivity gradually increases. When the radius of the PDMS flexible resonator is reduced to 0.65 mm, the magnetic field sensitivity of the sandwich structure is 5.14 nm/mT. The gradual increase between the radius of 1 mm and 0.9 mm shows that the different radii of the PDMS flexible resonator have a greater effect on the magnetic field sensitivity of the sandwich structure. The sensor is  
Figure 6: (**a**) Magnetic field sensitivity at different coupling positions. (**b**) Trend of magnetic field sensitivity with coupling position.  
Figure 7: (**a**) Magnetic field sensitivity for optimized PDMS flexible resonator radius. (**b**) Trend of magnetic field sensitivity with radius.  
made by an external magnetic field acting on the magnet squeezing the PDMS resonator, and the PDMS resonator undergoes a change in radius due to the force. As the radius of the PDMS resonator gets larger, the larger it gets, the harder it is for the resonator to change. Therefore, the sensitivity decreases as the resonator radius increases. It provides a dimensional reference for the subsequent PDMS flexible resonator preparation process.
## 4 Results and Performance Evaluation  
To validate the effectiveness of the DLFBR method, this algorithm was employed to detect impulses caused by a coal fuel powder attack by using the dataset described in Section 2. Four datasets were collected, each corresponding to a different pinhole that was opened. Each dataset included more than 100 one-second signals. The normal signal was recorded when all of the pinholes were closed to create a baseline for comparison of the leakage cases. Both the signals in the leakage case and the normal case showed a similar signal with the impulses in the time domain, as shown in Figure 5.  
The high amplitude impulses that appeared in both the leakage and normal signals do not relate to the intrinsic properties of the leakage phenomenon. Nevertheless, this kind of impulse, which was caused by the interaction between the coal powder flow and the steel tubes, protruded from and overlaps with the real leakage signal. Moreover, these interacting impulses appeared randomly, and the impulse width was variable; this made it more difficult to exactly detect their position and width in the time domain signal.  
Figure 5: Collected signals: (**a**) no leakage signal and (**b**) leakage signal.
# A Survey on MLLMs in Education: Application and Future Directions
## 4 Discussion and Future Directions
### Shift of Tides in Future Education  
One of the most significant changes is that future education will be full of lifelong learning opportunities. Education is evolving beyond the confines of traditional institutions and specific age groups. Large language models (LLMs) have the potential to enhance lifelong learning by creating adaptive learning paths tailored to individuals at any stage of their careers. These models can curate personalized educational experiences, recommend relevant resources, and even simulate mentorship. For example, platforms like Coursera utilize AI-driven algorithms to suggest courses aligned with user preferences and identified skill gaps [121]. Additionally, Duolingo's integration of GPT-4 for personalized language instruction demonstrates how LLMs can revolutionize continuous learning [46]. Recent research by [PERSON] et al. [122] explores how LLMs can adapt content delivery based on real-time learner feedback, further personalizing the educational experience.  
At the same time, artificial intelligence is redefining the roles and responsibilities of educators. Artificial intelligence will not replace educators but will augment their roles. LLMs can handle routine tasks such as grading, administrative work, and resource generation, allowing teachers to focus on fostering critical thinking and creativity. For instance, AI assistants like Gradescope by Turnitin have already streamlined grading workflows. Future LLM-driven systems could further assist by providing insights into student behavior, recommending tailored interventions, and offering real-time teaching assistance. Projects like Khan Academy's Khanmigo, developed using OpenAI's GPT-4, exemplify how AI can support educators by providing personalized tutoring and aiding in lesson planning [123]. With proper use of LLMs tools, they can also analyze student interactions to offer educators actionable insights, enhancing the effectiveness of teaching strategies [124].  
Looking ahead, it will be crucial to ensure that LLM-driven educational tools support diverse local and cultural contexts. One proposal is to encourage modular integration of LLMs within existing course management systems, allowing educators to select, adjust, and refine AI-driven resources without overhauling their entire digital infrastructure. Another avenue involves enhancing the explainability and transparency of LLM outputs, enabling teachers and learners to understand the reasoning behind suggestions and decisions. Additionally, collaborations between AI developers, educators, and policymakers could yield standards and best practices that preserve data privacy, maintain ethical rigor, and promote fair access. Such efforts may include open data frameworks that allow educational institutions to share vetted learning materials, or the development of certification programs that recognize teachers trained in effectively leveraging LLM tools.
## 3 Numerical Results
### Probability Distribution  
For \(10^{5}\) iterations of the Monte Carlo method, it is shown in Figure 2 that the probability density function of the SINR is close to the Gamma distribution.  
Figure 2 shows a comparison between the exact Gamma distribution and the simulated PDF of the SINR via the Monte Carlo method for \(m=2,\Omega=1\), \(K=16\) users, and \(M=2\) for different values of the number of reflectors \(N\).  
It is possible to note that the SINR follows a gamma distribution for a wide variety of parameters in such a way that the analytical calculations of the moments and the statistical parameters of the SINR are valid for the formulas proposed for the probability of bit error and the secrecy outage probability.