esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracing Student Activity Patterns in E-Learning Environments: Insights into Academic Performance
## 3 Methodology and Results
### The Markov Chain Model, the Graphs, and the Eigenvectors  
#### 3.2.1 The Markov Chain Model  
As mentioned in the previous section, sequences were generated as each user progressed through various states, forming their sequence of events. Changes observed in the states across the sequence are termed transitions, with associated probabilities known as transition probabilities. The code employed iterates through each sequence in the dataset, capturing the transition from one state to another along with its probability. This process effectively constructs and utilizes a Markov Chain Model to represent state transitions based on data-derived probabilities. These transition probabilities were then used to populate a transition matrix. Multiple transition matrices were generated, representing the behavior of each individual participant (e.g., student with ID 144--Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**STATE** & **Assignment** & **Course** & **Folder** & **Forum** & **Glossary** & **Other** & **Quiz** \\ \hline Assignment & 0.41451 & 0.57513 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.01036 \\ Course & 0.23956 & 0.21099 & 0.04835 & 0.38022 & 0.02198 & 0000 & 0.09890 \\ Folder & 0.0000 & 0.88000 & 0.12000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 \\ Forum & 0.00771 & 0.43702 & 0.00000 & 0.55527 & 0.00000 & 0.00000 & 0.00000 \\ Glossary & 0.00000 & 0.76923 & 0.00000 & 0.00000 & 0.23077 & 0.00000 & 0.00000 \\ Other & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.00000 \\ Quiz & 0.00000 & 0.55952 & 0.00000 & 0.00000 & 0.00000 & 0.00000 & 0.44048 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The transition probability matrix for student with ID 144.  
In that matrix, each row signifies the starting state, while each column represents the ending state. The values within the cells indicate the probability of transitioning from the starting state to the ending state. Table 2 presents a row stochastic matrix, where the entries in each row signify probabilities, and the sum of probabilities in each row equals 1. These probabilities indicate the likelihood of transitioning from one state to another for a particular user. For instance, for a student with ID 144, the probability of transitioning from Course to Assignment is 0.23956 and from Glossary to Course is 0.76923. Many transition probabilities for this specific user are zero as there is no observed movement between those specific states for that students. Similar probability matrices are constructed for every student, thus facilitating our analysis thereafter.
## 2 Experimental Investigation
### Pull-Out Test Set-Up  
The test method for a direct pull-out test considers two widely accepted standards of testing on FRP-reinforced structures--namely, ACI 440.3R-04 [22], and CAN/CSA-S806-02 [27]. The test procedures outlined in both standards are very similar, with a minor variation in the maximum loading rate, where a 1.3 mm/min maximum is mentioned in the former, whereas a maximum of 1.27 mm/min is suggested in the latter. The pull-out test in this study was carried out using a 300 kN Instron universal testing machine in the Deakin University Structures Laboratory at Geelong Waurn Ponds Campus. The loading rate applied to the steel and basalt FRP bars was 1 mm/min, and was measured using the automated data acquisition system of the machine. The pull-out specimen cube embedded with basalt FRP was placed and tightened in a steel frame fitted in the testing machine, as seen in Figure 7.  
Figure 5: Pull-out specimens filled with SCGC.  
Figure 6: Pull-out specimens after 28 days of curing.  
The steel frame consisted of bottom and top steel plates with holes on all four sides and in the centre, allowing for the FRP bar to pass through. The bottom plate of the steel frame was connected to the jaws of the testing machine, and acted as the reaction end when the pull-out load was resisted by the specimen under testing, as seen in Figure 8.  
The applied load was recorded by the data acquisition system and connected to the computer. During the loading, the slip of the basalt FRP bar corresponding to the applied load was measured continuously and recorded in the Bluehill software mod  
Figure 8: Placing of the pull-out specimen inside the UTM.  
Figure 7: Universal testing machine with automated data acquisition system for pull-out testing.  
ule of the Instron universal testing machine. The tests were run until failure for each individual specimen.  
The pull-out specimens are labelled according to the bar diameter and embedment length; for example, in the specimen named 6-1-1, 6 corresponds to a pull-out specimen of 6 mm diameter basalt FRP, 1 corresponds to an embedment length of 5 times the bar diameter (\(\mathrm{d_{b}}\)), and 1 signifies the 1 st specimen of similar configuration. Similarly, for pull-out specimens with steel reinforcement bars, the label is S-10-1, where S corresponds to the steel bar, 10 is the bar diameter, and 1 signifies the 1 st specimen of similar configuration. In the case of steel specimens, only one embedment length of 50 mm--corresponding to 5 \(\mathrm{d_{b}}\)--was used, since the failure mode was already established as being of the pull-out type by existing literature. The pull-out test was carried out on 3 identical pull-out specimens of each configuration, totalling 21 specimens. The specimen details and their corresponding bar diameters and bond lengths are depicted in Table 3 below:
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## 5 Conclusions
The average convergence time of single-frequency PPP with external ionosphere correction is reduced. Compared with the quad-constellation b2 single-frequency UC approach, the improvement in the convergence time of BIM-, NTCM- and GIM-constrained solutions is 15.2%, 24.8% and 28.6%, respectively. The improvement in positioning accuracy of the ionosphere-constrained PPP is not obvious for the given higher weighting after convergence. The positioning accuracy of the GIM-constrained PPP is the best and NTCM-constrained PPP solutions perform better than the BIM-constrained solutions in the E and U components. For multi-GNSS dual-frequency PPP, the performance of IF and UC solutions are the same. The improvement in convergence time of dual-frequency PPP varies for different constellations. The existing GLONASS pseudo-range IFBs cause the improvement in the GLONASS-only solutions to be the lowest of all schemes. Compared with the quad-constellation b1/b2 UC approach, the improvement in convergence time of the BIM-, NTCM- and GIM-constrained solution is 5.2%, 6.2% and 8.5%, respectively. The positioning accuracy of the GIM-constrained PPP is the best in all positioning schemes. Also, the combination of multi-GNSS can improve the positioning performance for all three positioning schemes. Conceptualization of the Manuscript Idea: K.S. and S.J. ; Methodology and Software: K.S. and M.M.H. ; Writing-Original Draft Preparation: K.S. ; Writing-Review and Editing: S.J. and M.M.H. ; Supervision and Funding Acquisition: S.J. This research was funded by the National Natural Science Foundation of China-German Science Foundation (NSFC-DFG) Project, grant number 41761134092. The authors gratefully acknowledged the GFZ for providing products and IGS for the data from MGEX. Many thanks go to Dr. [PERSON] for his valuable suggestions. We also thank the anonymous reviewers for their detailed and constructive comments that significantly improved the manuscript. The authors declare no conflict of interest.
# Cross-Section Shape and Asymmetric Support Technology of Steeply Inclined Thick Coal Seam Roadway
###### Abstract  
The dip angle and thickness of coal seams are key geological determinants in mine system engineering. Roadways excavated in steeply inclined or thick coal seams typically exhibit significant deformation, with the combined geological configuration of steeply inclined thick seams thus presenting heightened support demands. Therefore, taking the 1502 level roadway in the Dayuan Coal Industry--situated in a steeply inclined thick coal seam--as an engineering case, mechanical models of roadways with different cross-sectional shapes are established, and the deformation and failure mechanisms of surrounding rock under different coal seam dip angles are analyzed. Based on this analysis, an asymmetric support technology scheme is proposed, followed by surrounding rock deformation monitoring and a support effectiveness evaluation. Key findings include the following: (1) in steeply inclined thick coal seam roadways with different cross-sectional shapes, the stress distribution and plastic zone development of surrounding rock follow a descending sequence, inclined roof trapezoidal section > rectangular section > arched section. Among these, the arched section is identified as the optimal roadway cross-sectional shape for this engineering context. (2) The stress-concentration area in the arch roadway aligns with the inclined direction of the coal seam, forming asymmetric stress concentration patterns. Specifically, as the coal seam dip angle increases, stress increases at the arch shoulder of the upper sidewall and the wall foundation of the lower sidewall. Concurrently, such stress concentration induces shear failure in the surrounding rock, which serves as the primary mechanism causing asymmetric deformation and failure in steeply inclined thick coal seam roadways. (3) In the 1502 level roadway, the asymmetric support technology with dip-oriented reinforcement was implemented. Compared to the original support scheme, roof deformation and sidewall convergence decreased by 46.17% and 46.8%, respectively. The revealed failure mechanisms of steeply inclined thick coal seam roadways and the proposed asymmetric support technology provide technical and engineering references for roadway support in similar mining conditions.  
high-dip thick-seam structure; roadway section shape; non-uniform support; stress concentration; arch roadway
# Using a Cable-Driven Parallel Robot with Applications in 3D Concrete Printing
## 4 Dual Simplex Algorithm for the Optimization of Cable Tension, Taking into Account Cable Sagging
### Cable Length Computation with Cable Sagging
The dual simplex algorithm is used to find the set of tension combinations such that the target function sum of the tension force is minimum, provided that the tension force must be within the upper and lower limit; the lower limit of the tension is selected based on the equilibrium conditions, ensuring the rigidity and stability of the moving platform. The upper limit of the tensioners is selected based on the motor capacity. The Trust-Region-Dogleg algorithm was used to calculate the cable length, taking into account cable sagging and the variability in weight of the moving platform of the concrete extruder during the printings. Figure 8 shows the length of 8 cables for circle interpolation, with an r = 0.8 m trajectory. The computational trajectory is continuous, showing that all the knot points are in the WFW. The cable lengths change from 2600 mm to 4300 mm. Figure 8: Length of the 8 cables for circle interpolation (r = 0.8 m trajectory). Figure 7: Circle trajectory of r = 800 (mm). Figure 9 shows the straight form and offset lengths for the sagging and tension of the 8 cables in the circle interpolation trajectory planning. The offset length of each cable is inversely proportional to the tension and length of the cable. Because the cable is inelastic, the offset length is always positive, the longer the cable and the larger the cable weight, the greater the cable deflection. For cable tension, when the tension is bigger, the cable has more tension, so the smaller the deflection of the cable. The results of this calculation show that this calculation method gives suitable results, which can be applied to calculate the control effect for the actuators-winch. The tension of all cables is maintained equal to or greater than the lower bound \(\tau_{\text{min}}\) (50 N) and less than the upper bound \(\tau_{\text{max}}\), set at 500 N. During this trajectory, the offset lengths range from 0 mm to a maximum of 2 mm. When the cable tension is greater than 300 N, the offset length reaches zero (mm). Figure 9: Offset length vs. tension of Cables 1–4 for circle interpolation (\(r=0.8\) m).
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 7 Discussion  
### Datasets  
The reviewed studies reveal a strong preference for the BoT-IoT and TON_IoT datasets due to their suitability for both binary and multiclass classification tasks. Both datasets offer rich, labeled data and are gathered from real IoT/IIoT environments and are therefore highly relevant for cybersecurity research. However, their significant class imbalances pose challenges that require additional preprocessing or algorithmic strategies to ensure balanced performance across classes. The BoT-IoT dataset, with its smaller number of attack classes, is particularly popular, while the more diverse TON_IoT dataset provides opportunities to evaluate model performance across a wider range of scenarios. Comparatively, datasets like CIC-IDS2017 are also used, but their specificity to certain attack types and features makes them less applicable in general for all IoT usage. The distribution of datasets among the reviewed papers is summarized in Figure 1.  
There is no significant difference in the popularity of datasets used for binary and multiclass classification tasks. BoT-IoT remains the most popular dataset, followed very closely by TON_IoT, because both datasets are eligible for these two purposes. Binary classification--labeling data flows as benign or attack--appears to be tackled more often (42 studies) than multiclass classification, which focuses on distinguishing between different attack types (27 studies). The distribution of datasets employed for binary and multiclassification comparisons is presented in Figure 3 and Figure 4, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AVT** & **ADT** & **Highest Accuracy ML Technique** \\  & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[ms]** & **[ms]** & **Highest Accuracy ML Technique** \\ \hline
[38] & 100 & 100 & - & 100 & 0.022 & - & GADAD-RF \\
[55] & 100 & 100 & 100 & 100 & 0.795 & - & RF \\
[61] & 99.9 & 99.9 & 99.9 & 99.9 & - & 170 & DT \\
[58] & 99 & 99 & 99 & 99 & - & - & LSTM \\
[53] & 98.5 & 98.2 & 95.9 & 97.4 & - & - & KNN \\
[75] & 96.32 & 93.12 & 84.55 & 88.63 & - & - & Voting (DT, MLP RProp, Logistic Regression) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Multiclass classification performance for TON_IoT (sorted by accuracy); AVT: Average Validation Time, ADT: Average Detection Time.  
Figure 3: Distribution of datasets used for comparison of binary classification.
###### Abstract  
Assessing myocardial strain remains challenging, particularly in the pediatric population, due to the smaller heart sizes, higher heart rates, and variability in strain parameters compared to adult populations. This study aimed to investigate the utility of myocardial strain measurements using cardiac magnetic resonance-feature tracking (CMR-FT) for early diagnosis of Duchenne muscular dystrophy (DMD) in pediatric patients. Twenty-eight DMD patients and 20 healthy controls were involved in this study. Global circumferential, longitudinal, and radial strain (GCS, GLS, and GRS) were measured for the left ventricle (LV) using CMR-FT. Segmental strain values only of the inferolateral and anterolateral LV segments in DMD patients without late gadolinium enhancement (LGE) and DMD patients with LGE were compared to the healthy controls. Strain measurements using CMR-FT in DMD patients were considerably lower than those of healthy controls, with all \(p\)-values lower than 0.001. DMD patients without LGE showed decreased inferolateral and anterolateral segmental values only relative to healthy controls. The same behavior was maintained for the LV geometry. Multivariable linear regression demonstrated that the end-systole (ES) wall thicknesses and thickening were associated with decreased GCS and GLS. CMR-FT is crucial in detecting cardiac abnormalities in patients with DMD. It represents an innovative imaging biomarker that can detect initial myocardial alterations in DMD cardiomyopathy without relying on gadolinium.  
cardiomyopathy; strain measurement; cardiac MRI; feature tracking +
Footnote †: journal: applied sciences
## 4 Experiment Results and Comparison
### Data Sets Used in Experiment  
Data sets gathered from different environments are distinct and this makes it essential to identify the appropriate data sets. These data sets are typically used to meet a variety of classification requirements. The analysis of various data sets allows us to gain a more comprehensive understanding of a traffic classification algorithm. This is due to the fact that researchers usually select different data sets in order to meet their requirements [2]. For example, if the goal of a classification process is to detect various types of network attack traffic, they must select data sets that include a variety of malicious traffic for analysis. The proposed approach is evaluated using two data sets. These data sets have been chosen since they were collected from a variety of domains and are commonly used to evaluate anomaly detection systems. Detailed descriptions of both data sets are explained in the following two sections.
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## 4 Design of Cooperative Tracking Guidance Laws
### Design of Vector Field for Fast Convergence Target Tracking
\right] \tag{14}\]  
The Lyapunov vector field and inverse Lyapunov vector field can be shown intuitively in Figure 7. As shown in Figure 7, the vectors in the ILVF point away from the standoff circle. When the start point for UAVs is too close for UAVs to avoid intersecting with the circle, the ILVF is enabled to guide UAVs to move away from the loiter circle. By searching for the optimal guidance parameter \(c\) when UAV is far away from the standoff circle and fly away from the circle when it is too close, the proposed guidance law can guide UAV to converge to the standoff circle as fast as possible under the premise that the trajectory of the UAV does not cross the circle. In addition, the computation burden is lower than the method in reference [12], so that we can update the optimal guidance parameter in real time. In this way, we can adaptively balance the converging speed and stable tracking in real time.
## 3 Methodology
### Inclusion and Exclusion Criteria  
In order to meet the objectives of this review, the following criteria were defined for source inclusion:  
* Directly related to the context of medicine and healthcare.
* Addressing clinical problems with AI algorithms, such as machine learning (ML), DL, computer vision or other kinds of either data- or knowledge-based methods.
* Including a clear focus on any kind of ethical or technical information about explainability, transparency or related issues.
* Published in English and with full text available.  
A bottom time limit for the publications included was not established, as one of the objectives of this review was to cover some studies from the first medical AI systems (1970s onwards) to take into account the lessons learned from that period that might be of interest for current and future AI-focused systems. No constraints about the kind of publication were defined either; articles, reviews, and studies of any kind were included as long as they met the inclusion criteria and were of interest for this review.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Explainable** & XAI, explanation, explainability, interpretable, interpretability, trust, trustworthy, ethical, causality, understandable \\ \hline
**Artificial Intelligence** & AI, machine intelligence, computer intelligence, machine learning, deep learning, neural network, convolutional, computer vision, bayesian, black box, classifier, expert system, prediction model, algorithm, big data, data mining, knowledge discovery, pattern recognition, natural language processing, supervised learning, reinforcement learning \\ \hline
**Medicine** & Medical, clinical, health, healthcare, doctor, physician, diagnosis, prognosis, drug, illness, disease, cancer, mri, ct, treatment, therapy, patient, radiology, surgery, dermatology, hematology, oncology, cardiology, neurology, psychiatry, immunuve, immuve, bacteria \\ \hline \hline \end{tabular}
\end{table}
Table 1: Keywords used for the literature search.  
Articles that did not meet this criteria were excluded, such as those that used other types of computer approaches that do not fall under the scope of AI, those that did not talk about clinical applications or those that only included matters of explainability as a side topic without going into much detail.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Topologies of WSNs  
Usually, an LS-WSN is composed of a large number of high-density nodes deployed in a detection area, with one or more base stations within or near the area. The base station communicates with the sensors via requests or commands, and the sensors communicate with each other via wireless communications. They participate in performing data-detecting and -sending tasks to the base station. In sensor networks, the energy consumed for communication is much higher than that required for detection or computing. For example, the transmission of a single bit of data is equivalent to 800 instructions [43]. A wireless sensor network can be organized according to different topologies, including star, mesh, and hybrid topologies.
# Assessment of Skin Deep Layer Biochemical Profile Using Spatially Offset Raman Spectroscopy
## Materials and Methods  
### Tissue Phantom Preparation  
Based on previous studies [20], we employed two-layer optical tissue phantoms to simulate skin tissue for Raman measurements. The phantoms consisted of a top and a bottom gelatin layer of different optical properties, representing epidermis and dermis respectively, and a 3-layer parafilm (PF) with a total thickness of 0.39 mm which was either laid at the top or embedded in the gelatin matrix. The gelatin-gel based layers were prepared according to previously published, adapted protocols [21,22] (Supplementary Material).  
Two types of skin tissue phantoms were fabricated: (i) Type A with a PF layer at the top of the gelatin matrix, representing superficial types of skin cancer and (ii) Type B with PF buried in a depth of 3 mm (representing lesions of deeper layer origin).  
Different concentrations of Intralipid were added to the top and bottom layers in order to match the lower limits of the reduced scattering coefficient for epidermis and dermis found in the literature [23, 24, 25, 26, 27]. During top layer fabrication, 5 mL of Intralipid were added for a reduced scattering coefficient of 20 cm\({}^{-1}\), whereas for the bottom layer, a volume of 10 mL Intralipid were added to achieve a value of 10 cm\({}^{-1}\) reduced scattering coefficient. Intralipid was optically characterized in previous work [12] and additional absorbing agents were not added during phantom fabrication. All phantoms were prepared and measured in three replicates.