esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual Data and Pattern Analysis for Smart Education:
## 1 Introduction  
The development of information and communication technology in recent years has significantly transformed numerous sectors, including education. The rapid proliferation of artificial intelligence (AI) and big data has been a major driver in the evolution of online education. Online education not only saves time for both students and teachers, but also encourages students to think and process information independently. Teachers guide students to enhance their academic skills, soft skills, and personality development, preparing them for a brighter future [1].  
E-learning environments boost students' knowledge and academic performance through collaborative tasks and computer-assisted activities. Computer-Supported Collaborative Learning (CSCL) is a pedagogical strategy that facilitates learning through online or computer-mediated social interaction. This approach allows participants to share and produce information using technological tools, potentially improving learning outcomes. E-learning systems are popular for their scalability, ability to offer uninterrupted learning 24/7, and cost-effectiveness [2; 3]. AI is poised to further enhance these systems by providing personalised content. An AI-based adaptive and personalised e-learning system offers specific and targeted content to each learner, unlike traditional systems where all students at the same grade level receive identical content. Personalisation in e-learning, which adapts content delivery to a learner's strengths and weaknesses, can make learning more effective.  
However, most current AI-based personalised e-learning strategies lack integration and do not provide a comprehensive framework. Creating such tailored e-learning platforms can address the limitations of individual models [4; 5; 6]. Personalisation means evaluating and instructing each student differently, and AI systems can achieve this by identifying suitable topics and levels for each learner. For example, if a learner struggles with a topic, instructors may revisit it using a different teaching approach, while students with a better understanding can progress to more complex material [7; 8; 9].  
Moreover, while adaptive learning models like VARK cater to students' learning preferences [10; 11], they often overlook non-academic factors in performance prediction [12; 13; 14; 15; 16]. Furthermore, security is another major concern, as current e-learning platforms are vulnerable to data breaches [17; 18; 19; 20; 21]. The proposed model seeks to address these issues by integrating AI with Federated Learning, creating a secure, real-time, personalised learning ecosystem that takes both academic and non-academic factors into account.
## 2 Materials and Methods
### Species Assignment and Phylogenetic Clustering  
At least one strain of yeasts and bacteria isolated from each sample was subjected to sequencing analysis for the species assignment and the successive phylogenetic clustering. To this aim, targeted regions were amplified using specific primer sets. In particular, the D1/D2 region of 26 rRNA large subunit from the yeast gDNA was amplified using primers NL1 (5\({}^{\prime}\)-GCATATCAATAAAGCGGAGGAAAAG-3\({}^{\prime}\)) and NL4 (5\({}^{\prime}\)-GGTCCGTTTCAAAGACG-3\({}^{\prime}\)), as reported by [PERSON] et al. [30].  
Yeast Ab1 files obtained from the sequencing were processed using CodonCode aligner to trim the sequences based on phred score (phred 20). The sequence ends were trimmed according to primer length. High-quality sequences were aligned against YeastIP database [31]. Reference sequences were downloaded from YeastIP database, based on matches, e-value, and identity scores.  
Regarding bacteria, amplification of 16S rRNA gene was performed using primers 27f (5\({}^{\prime}\)-CTGGGATCCTTACTCGAGAGTTTGATCCTGGCTC-3\({}^{\prime}\)) and 1490r (5\({}^{\prime}\)-GGTTCCCCTAAAGCTTACCTTGTACGACTTC-3\({}^{\prime}\)), as reported by [PERSON] et al. [32].  
PCR amplicons were purified and cleaned using ExoSAP PCR cleanup reagent (Thermo Fisher Scientific) and sequenced by Eurofins MWG Biotech Company (Ebersberg, Germany). The strains sequenced were deposited in the UMCC Database ([https://umcc.bio-aware.com/](https://umcc.bio-aware.com/), accessed on 20 April 2021).  
Bacteria Ab1 files were processed as described for the yeast dataset. High-quality sequences were aligned against 16S rRNA sequence database from NCBI using blast algorithm. The top hit with the highest percentage of identities (considering a minimum threshold of 97%) was downloaded and used as reference.  
The sequences of selected strains and references were aligned all-vs.-all using Clustal W v2.1 [33]. For constructing the phylogenetic trees, the multiple sequences alignment was imported into MegaX v10.2 and trimmed to match the sequence length. The phylogeny was inferred computing a neighbor-joining phylogenetic tree based on 1000 bootstraps for both yeasts and LAB dataset. The Tamura-Nei DNA evolutionary model was used, applying a discrete Gamma distribution to model evolutionary rate differences among sites [34].  
Newick trees of both bacteria and yeasts were visualized using Interactive Tree of Life (ITOL) v4 [35] and rooted at outgroup reference strains. The sequences of D1/D2 region and 16S rRNA gene were submitted to GenBank/EMBL/DDBJ under the accession numbers from MZI70795 to MZI70810 and from MZI70701 to MZI70718, respectively.
## 2 Materials and Methods
### Reference and Training Datasets  
After pre-processing the SAR and OPTICAL dataset, they were stacked into a spectral-temporal cube, incorporating the DEM band, and then cropped to cover AOI. The resulting reference dataset for classification consists in a dense cube of 145 bands. Out of these,  
Figure 4: Sentinel-1 pre-processing and SAR dataset in GEE. (*) The images can fully or partially cover the AOI.  
120 bands represent the median of 10 Sentinel-2 bands for each of the 12 months, 24 bands represent the average VV and VH bands of Sentinel-1 for 12 months, and one additional band represents the DEM layer. The summary of the data used for the reference dataset can be found in (Table 1).  
The training points were exported from the QGIS environment to Google Earth Engine (GEE), where a sampling on the reference dataset was performed at 10 m pixel resolution. Consequently, the training dataset comprises 145 features and 14,611 observations, each containing a specific label and the corresponding spectral-temporal signature (Figure 5). In this dataset, a stratified random sampling technique was employed to extract 20% of the observations for each land cover class. These extracted samples were intentionally not used during the model training process. Instead, they were reserved exclusively for the subsequent testing phase to ensure an unbiased evaluation of model performance [74].  
To assess the contribution of individual bands in the classification process, vegetation indices were excluded from the analysis. While these indices have the potential to reduce dataset dimensionality while preserving crucial information, they were intentionally omitted to identify a specific group of spectral bands during a particular phase of the year, facilitating the differentiation between hazelnut groves and other land cover types.
## 4 Discussion  
### Findings and Their Implications  
Previous publications indicate that 3D-printed technologies, including exoskeletons, should be treated as a technologically separate group of medical devices. The development of exoskeleton technology is a consequence of advancements in 3D printing technology and materials, and it is essential to maintain the current pace of development in personalized medicine. Results interpreted from the perspectives of previous studies and the working hypotheses show that exoskeletons are a good rehabilitation tool for the rapid recovery of motor function, e.g., in stroke patients, with the potential to also reduce the number and severity of complications, thus lowering mortality. At the same time, current exoskeletons offer insufficient human-machine coupling and interfere with the user's movement, damaging joints and muscles in the process [31]. It is crucial that the human-exoskeleton system be properly aligned through 3D printing with an increase in degrees of freedom and an optimized control system. It is currently unknown how important the exoskeleton's high strength support (current products offer 58% strength support) and full joint range of motion are (a sufficient joint range of motion was achieved for the activities of daily living). It should be noted that, in the case of the upper limbs, five-finger exoskeletons are rare, and there are currently no solutions that meet all the requirements. These findings and their implications also show that the process of designing, manufacturing, and fitting the exoskeleton should be as short as possible [32].
## 3 Preliminaries
### Short-Time Fourier Transform (STFT)  
Short-time Fourier transform (STFT) [36] is a technique for time-frequency analysis that decomposes a signal into its frequency components over brief time intervals. STFT isbased on dividing the signal into overlapping short-time segments and applying Fourier transform to each segment. The resulting time-frequency representation provides information regarding the signal's frequency components and the temporal variation in those frequency components in spectrograms:  
\[STFT\{x(t)\}=X(\tau,\omega)=\int_{-\infty}^{\infty}x(t)w(t-\tau)e^{-j\omega t}\Delta t \tag{7}\]  
where \(\omega(\tau)\) is the window, \(x(t)\) is the signal, and \(x(t)w(t-\tau)\) represents the phase and magnitude of the signal over time and frequency.  
STFT can be applied to velocity components of the extracted key points from video frames along the temporal axis for motion detection. The resulting time-frequency representation can disclose the motion properties. Moving objects, for instance, may generate distinct spectral signatures that can be detected using STFT.
## 4 Simulation
### Experimental Results
Figure 10: Box plots for the F1-score performances. The top and bottom of the box indicate the third and first quartiles, respectively, and the horizontal bar inside the box indicate the median value. The ends of the vertical bar indicate the maximum and minimum values. Figure 13 shows the elapsed times of the algorithms for processing a 30-second-long audio clip. The algorithms were executed using MATLAB in a laptop with 3.5-GHz dual-core CPU and 16-GB memory. The RESUREv1 and RESUREv2 algorithms show nearly the same elapsed times. The NMF algorithm with the proposed RESUREv2 algorithms uses the least time, 85.12 s, making it worthwhile to combine the conventional NMF and the proposed RESURE algorithms in terms of computation time. Furthermore, the RESURE algorithms use much less computation time than the NMF methods, making them easy to combine with various NMF methods. The measured elapsed time of the RESURE algorithm does not include the onset detection process but there is no significant difference if we consider it because many lightweight onset detection techniques already exist. To compare the complexity of the proposed algorithm with the rank-estimation parts of the conventional methods, we measured the elapsed times for the rank-selection parts of each algorithm, (i.e., the parts for calculating the lower bounds of marginal likelihoods in the VAR-NMF and GaP-NMF algorithms, and the part for calculating relevance in the ARD-NMF algorithm) under a changing input-signal length from 10 s to 40 s. Figure 14 shows the comparison results. Similar to the case of  
Figure 12: Performance comparisons of the proposed method with the conventional SURE-based method [16] by **(a)** the absolute values of estimation errors for the number of notes and **(b)** the F1-score performances. The top and bottom of the box indicate the third and first quartiles, respectively, and the horizontal bar inside the box indicate the median value. The ends of the vertical bar indicate the maximum and minimum values. Figure 13: Comparison chart for elapsed times of algorithms. The elapsed times were measured for processing a 30-second-long audio clip using MATLAB on a laptop with a 3.5-GHz dual-core CPU and 16-GB memory. Figure 13, the elapsed times of RESUREv1 and RESUREv2 are fairly identical; therefore, we display the elapsed time of RESUREv2 only. The graph shows that the proposed method requires the least time to estimate the rank independent of the input-signal length.
###### Abstract  
Digital energy systems rely on actual data about power consumption and generation, which are not always available and, in certain situations, can be replaced with synthetic forms. This study presents a methodology for generating synthetic time-series data of electrical power consumers. It is based on the Markov chains theory, and unlike previous studies, the data are divided into hourly and hour-change monthly records, which leads to the generation of 48 transition matrices for each month. This study aimed to ensure statistical and probabilistic similarity between the original and synthetic data, which was assessed using the Frobenius distance, the coefficient of determination, variance, and standard deviation. The methodology was applied to three load profiles obtained from different types of consumers--domestic, agricultural, and industrial. In all three cases, the statistical and probabilistic characteristics of the generated data were very similar to those of the original datasets; however, the visual comparison showed that it is recommended to increase the number of states to lower the data scattering. Based on the results, recommendations are proposed on choosing the number of states for the transition matrices to optimize the statistical and probabilistic similarity. The described methodology can be used by experts involved in the design of systems with renewable energy sources and by scientists dealing with long-term studies.  
Markov chains; load profile; synthetic data; probabilistic characteristics
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 2 Network Model and Problem Description
### Problem Description
: _Backup link interference degree, which is the number of links in the set \(\varepsilon^{k}(u,v)\)._\(\varphi^{k}(u,v)\) denotes the idle link set that interferes with the link (\(u\),\(v\)) allocated the channel \(k\), which is given by  
\[\varphi^{k}(u,v)=\left\{(u_{1},v_{1})\Big{|}(u_{1},v_{1})\in l^{k}(u,v)\ \&\ x(u_{1},v_{1})\
otin\alpha(s_{i},d_{i})\cup\beta(s_{i},d_{i})\right\}\ \gamma_{i}\in\Delta \tag{5}\]  
**Definition 7**. : _Idle link interference degree, which is the number of links in the set \(\varphi^{k}(u,v)\)._  
The set of link loads is denoted as \(\Psi=\left\{l^{k}_{(u,v)}\Big{|}u,v\in V,k\in K\right\}\), and the load of the link (\(u\),\(v\)) on channel \(k\) is denoted as \(l^{k}_{(u,v)}\) Mbps. The available bandwidth of the link (\(u\),\(v\)) on channel \(k\) is denoted as \(\mathrm{B}^{k}_{\psi}(u,v)\), which is given by  
\[\mathrm{B}^{k}_{\psi}(u,v)=B^{k}(1-\sum_{(a,b)\in l^{k}(u,v)}\frac{l^{k}_{(a, b)}}{B^{k}}) \tag{6}\]  
where \(\frac{l^{k}_{(a,b)}}{B^{k}}\) is the proportion of the bandwidth of the link (\(a\),\(b\)) used to transmit load \(l^{k}_{(a,b)}\).
## 7 Conclusions and Future Work
### Future Work  
The promising results of our framework open several avenues for future research and development:  
* Extension to Other Attack Types: The methodology could be adapted to generate and defend against other forms of web application attacks, such as Cross-Site Scripting (XSS) or Remote Code Execution (RCE) exploits, enhancing the overall security landscape.
* Real-Time Adaptation: Integrating real-time monitoring and adaptive learning capabilities could enable the WAF to respond dynamically to emerging threats without manual intervention.
* Ethical Frameworks and Policy Development: Collaborating with stakeholders to develop ethical guidelines and policies governing the use of generative AI in cybersecurity will be crucial for ensuring these tools are used responsibly.
* Enhanced Validation Mechanisms: Improving the validation process for generated payloads to include a wider range of database systems and configurations could increase the robustness of the framework.
* Machine-learning-based SQLi detection: Generative AI could be used to generate datasets to train machine-learning-based SQLi detection models, so as to improve the robustness of the models.
## 2 Materials and Methods
### Data Sources  
Remote sensing data: EOS/MODIS 500 m resolution net primary productivity product (MOD17A3H) from 2000 to 2019. The data used the BIOME-BGC model and the solar energy utilization efficiency model to calculate the annual net primary productivity (NPP) of the regional terrestrial vegetation ecosystem. The data were downloaded from the MODIS website ([[https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)]([https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)), accessed on 6 August 2020).  
Land cover data: MODIS land cover product MCD12Q1 data, land cover classification scheme 1 was used: IGBP global vegetation classification scheme. The data were used to analyze the carbon sink capacity of different vegetation types, including cultivated land, wetland, grassland, shrub, woodland, construction land, and unused land (7 categories, covering 17 subcategories). The data were downloaded from the MODIS website ([[https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)]([https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)), accessed on 6 August 2020) to analyze the difference in carbon sink capacity under the coverage of different vegetation types.  
Meteorological data: the monthly mean temperature and monthly precipitation data of 63 stations in the Yangtze River Delta from 2000 to 2019 were downloaded from the website of China Meteorological Data ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 6 August 2020). The data for 2020 were not downloaded due to no authorization resulting from the website revision.
## 2 System Model
The received signal after decoding in sub-time slot \(t1\) can be expressed as  
\[\tilde{\mathbf{y}}_{t1}^{[1,1]}=\mathbf{U}^{[1,1]H}\sqrt{g_{1}^{[1,1]}}\mathbf{H}_{1}^{[1,1]}\mathbf{V}^{[1,1]}\mathbf{s}_{t1}^{[1,1]}\!\!+\! \mathbf{U}^{[1,1]H}\sqrt{g_{1}^{[1,1]}}\mathbf{H}_{1}^{[1,1]}\mathbf{V}^{[2,1] }\mathbf{s}_{t1}^{[2,1]}+\mathbf{U}^{[1,1]H}\mathbf{n}_{t1}^{[1,1]}, \tag{5}\]  
and in sub-time slot \(t2\) as  
\[ \begin{split}\tilde{\mathbf{y}}_{t2}^{[2,1]}&= \sqrt{g_{R1}^{[2,1]}}\mathbf{U}^{[2,1]H}\mathbf{H}_{R1}^{[2,1]}\mathbf{G}_{1} \rho_{(R1)}\mathbf{y}_{t1}^{R1}+\sqrt{g_{R2}^{[2,1]}}\mathbf{U}^{[2,1]H} \mathbf{H}_{R2}^{[2,1]}\mathbf{G}_{2}\rho_{(R2)}\mathbf{y}_{t1}^{R2}+\mathbf{ U}^{[2,1]H}\mathbf{n}_{t2}^{[2,1]}\\ &=\sqrt{g_{R1}^{[2,1]}g_{R1}^{R1}}\rho_{(R1)}\mathbf{U}^{[2,1]H} \mathbf{H}_{R1}^{[2,1]}\mathbf{G}_{1}\mathbf{H}_{1}^{R1}\mathbf{V}^{[2,1]} \mathbf{s}_{t1}^{[2,1]}+\sqrt{g_{R1}^{[2,1]}g_{R1}^{R1}}\rho_{(R1)}\mathbf{U}^{ [2,1]H}\mathbf{H}_{R1}^{[2,1]}\mathbf{G}_{1}\mathbf{H}_{1}^{R1}\mathbf{V}^{[1, 1]}\mathbf{s}_{t1}^{[1,1]}\\ &+\sqrt{g_{R2}^{[2,1]}g_{R2}^{R2}}\rho_{(R2)}\mathbf{U}^{[2,1]H} \mathbf{H}_{R2}^{[2,1]}\mathbf{G}_{2}\mathbf{H}_{R2}^{[2,2]}\mathbf{s}_{t1}^{ [2,2]}+\sqrt{g_{R2}^{[2,1]H}g_{R2}^{R2}}\rho_{(R2)}\mathbf{U}^{[2,1]H}\mathbf{ H}_{R1}^{[2,1]}\mathbf{G}_{2}\mathbf{H}_{R2}^{R2}\mathbf{V}^{[1,2]}\mathbf{s}_{t1}^{[ 1,2]}\\ &+\sqrt{g_{R1}^{[2,1]}}\mathbf{U}^{[2,1]H}\mathbf{H}_{R1}^{[2,1]} \mathbf{G}_{1}\rho_{(R1)}\mathbf{n}_{t1}^{R1}+\sqrt{g_{R2}^{[2,1]}}\mathbf{U}^{ [2,1]H}\mathbf{H}_{R2}^{[2,1]}\mathbf{G}_{2}\rho_{(R2)}\mathbf{n}_{t1}^{R2}+ \mathbf{U}^{[2,1]H}\mathbf{n}_{t2}^{[2,1]},\end{split}  \tag{6}\]  
\[ \begin{split}\tilde{\mathbf{y}}_{t2}^{[1,1]}&= \sqrt{g_{1}^{[1,1]}}\mathbf{U}^{[1,1]H}\mathbf{H}_{1}^{[1,1]}\mathbf{V}^{[1,1] }\mathbf{s}_{t2}^{[1,1]}+\sqrt{g_{R1}^{[1,1]}}\mathbf{U}^{[1,1]H}\mathbf{H}_{ R1}^{[1,1]}\mathbf{G}_{1}\mathbf{y}_{t1}^{R1}+\mathbf{U}^{[1,1]H}\mathbf{n}_{t2}^{[1,1]} \\ &=\sqrt{g_{1}^{[1,1]}}\mathbf{U}^{[1,1]H}\mathbf{H}_{1}^{[1,1]} \mathbf{s}_{t2}^{[1,1]}+\sqrt{g_{R1}^{[1,1]}g_{R1}^{R1}}\rho_{(R1)}\mathbf{U}^{ [1,1]H}\mathbf{H}_{R1}^{[1,1]}\mathbf{G}_{1}\mathbf{H}_{1}^{R1}\mathbf{V}^{[2, 1]}\mathbf{s}_{t1}^{[2,1]}\\ &+\sqrt{g_{R1}^{[1,1]}g_{R1}^{R1}}\rho_{(R1)}\mathbf{U}^{[1,1]H} \mathbf{H}_{R1}^{[1,1]}\mathbf{G}_{1}\mathbf{H}_{1}^{R1}\mathbf{V}^{[1,1]} \mathbf{s}_{t1}^{[1,1]}+\sqrt{g_{R1}^{[1,1]}}\mathbf{U}^{[1,1]H}\mathbf{H}_{ R1}^{[1,1]}\mathbf{G}_{1}\mathbf{n}_{t1}^{R1}+\mathbf{U}^{[1,1]H}\mathbf{n}_{t2}^{[1,1]}, \end{split}  \tag{7}\]  
where \(\mathbf{U}^{[i,1]}\in\mathbb{C}^{N\times N_{i}}\), \(\mathbf{U}^{[i,1]H}\mathbf{U}^{[i,1]}=\mathbf{I}_{N_{i}}\) is the receive beamforming matrix and \(\mathbf{\tilde{n}}_{ij}^{[i,1]}=\mathbf{U}^{[i,1]}\mathbf{n}_{ij}^{[i,1]}\) is the effective noise vector of the \(i\)-th user in Cell 1. Details on the design of the proposed TPIA scheme are introduced in the next section.
# Improved Cutting Force Modelling in Micro-Milling Aluminum Alloy LF 21 Considering Tool Wear
## 1 Introduction
[13] built a simulation of the micro-milling process based on Abaqus/CAE, which considered the arc radius of the cutting edge and the material properties of the workpiece, output stress distribution of the workpiece and plugged it into the analytic force model to achieve the prediction of the micro-milling force. It can be seen that the material elastic recovery, cutter runout, tool wear, relative vibration between cutter and workpiece and friction in the metal cutting dead zone all affect the micro-milling force. So far, there is not a micro-milling force model that considers all the above factors, and the prediction accuracy of the micro-milling model still needs to be improved. Conducting the thorough research of the aluminum alloy LF 21 micro-milling process dynamic characteristics, combined with simulation of the cutting process, processing experiments and semianalytical modeling technology, established the micro-milling force prediction model. Relying on the early stage of the micro-milling system dynamics theory, the vibration displacement solution was realized. Based on the simulation of the micro-milling process, the prediction of tool wear was realized. Combining experimental and semi-analytical modeling technology, an improved micro-milling force model was built considering the material elastic recovery, cutter runout, tool wear, relative vibration between cutter and workpiece and friction in metal cutting dead zone. The research provides a reference for the prediction of cutting force and the exploration of the cutting mechanism of micro-milling LF 21.