esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study and Modelling on the Structural Response of Fiber Reinforced Concrete Beams
## 2 Materials and Methods
### Fibers  
Short 12 mm long polypropylene fibers (PB Eurofiber MF 1217) have been added in small amount to reduce FRC shrinkage. Additionally, three main types of fibers were used in the research:  
1. Steel fibers: Krampe Harex hooked end steel fibers of 35 mm in length were chosen for the first FRC group. Figure 1a shows the fibers and Table 1 summarizes the properties of the fibers;
2. PP fibers: Strux PP 40 mm long fibers were used in the second FRC group to compare the structural properties of two different fiber groups. Figure 1b shows the fibers, their properties are given in Table 1;
3. PP fibers: Durus PP 45 mm long fibers were used in the third FRC group, employing the opportunity to compare the results of the samples with the same type of fiber, but of different fiber length, produced by a different company, and ultimately compare them with the samples made using different types of fiber. Figure 1c and Table 1 provide the illustration and the properties of this fiber, respectively.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Fibres**} & **Steel Fibres** & **PP Fibres** & **PP Fibres** & **PP Fibres** \\  & **DE 35/0.55-E446** & **Strux** & **Durus** & **PB Eurofiber MF 1217** \\  & **(Krampe Harex GmbH & **(GCP Applied** & **(ADFIL, Belgium)** & **(Baumbueter Extrusion** \\  & **& **& **GmbH, Germany)** \\ \hline Shape & Hooked ends & Straight & Straight & Straight \\ \hline Cross Section & Round & Rectangular & Non-regular & Round \\ \hline Length, mm & 35.00 & 40.00 & 45.00 & 12.00 \\ \hline Volume fraction, \% & 1 & 1 & 1 & 0.1 \\ \hline Diameter & 0.55 mm & 0.43 mm & 0.90 mm & 15.4 \(\upmu\)m \\ \hline Aspect ratio & 63 & 90 & 50 & — \\ \hline Density, kg/m\({}^{3}\) & 7900 & 2100 & 905 & 910 \\ \hline Tensile strength, N/mm\({}^{2}\) & 1700 & 620 & 465 & 282.1 \\ \hline Modulus of elasticity, N/mm\({}^{2}\) & \(2\times 10^{5}\) & 1389 & 3350 & 1000 \\ \hline Quantity of fibers (ref. 1 kg) & 15.319 & 187.000 & 38.600 & \(\sim\)500 \(\times\) 10\({}^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fiber’s specification, by supplier’s data.  
As regards the concrete mix design, water-to-cement ratio was W/C = 0.3 in each group. Other ingredient amounts are given in Table 2. Volumetric concentration of fibres in the mixes was 1.0% and 0.1% from the total volume of a concrete, according to the Table 3.
# Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs
## 2 Related Work  
### Prediction in MOOCs  
Over the past few years, researchers have made substantial progress in mining data from MOOCs, such as mining texts from discussion forums [7], mining video behaviors [8; 9], performance prediction [10; 13; 21; 23], plagiism detection [11; 12], and dropout detection [18; 19]. These data mined from MOOCs have been widely used for predicting student achievement.  
[PERSON] [10] summarized the data mining application for learning with the Moodle system. They used the attributes of the number of assignments done, the number of quizzes done, the number of quizzes passed, the number of messages sent to the teacher, the number of messages sent to the forum, etc., to group the students and predict student performance. [PERSON] et al. [13] proposed a method to increase students learning efficacy in the traditional classroom as well as in the case of MOOCs. They suggested that assessments (such as quizzes) in MOOCs can help to perform timely predictions for each student, allowing the instructor to make appropriate interventions. [PERSON] [21] examined the students learning behaviors using data extracted from MOOCs, such as the total time spent on the forum and the number of words posted on the forum, and identified four distinct patterns of the learning process, revealing student groups with different levels of achievement (including the social, individually focused, procrastinator, and non-procrastinator groups). These outcomes have apparent significance for increasing student performance in MOOCs. [PERSON] [23] reported that 65% of specific course items (e.g., videos started, quizzes finished, resources read) were related to the final exam grade. Students who passed the course spent more time on these items compared to those who failed. However, there was little difference in the sequence of activities among diverse students.  
These works mainly consider the overall behaviors of students in MOOCs, which cannot fully reflect students' learning processes, and thus affect prediction accuracy.
# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 3 Experiment and Result Analysis
### Apple Grading Experiments
#### 3.3.2 Comparative Analysis of Apple Grading Experiments  
The study on apple grading algorithms in this paper was based on three features: color, diameter, and shape. By setting up a series of comparative experiments with mainstream machine learning algorithms, multiple evaluation metrics were used to assess the performance of different algorithms in apple grading. To determine the best apple grading model, six apple grading models were established, and their performance was evaluated using 10-fold cross-validation to effectively reduce bias in the model evaluation process. The ensemble learning algorithm models included BC, RF, and GBDT, while the non-ensemble learning algorithms included KNN, SVM, and MLP. The specific experimental results of the different apple grading algorithms are shown in Table 7.  
Based on the cross-validation experimental results in Table 7, it can be observed that the overall performance of non-ensemble learning algorithms is weaker than that of ensemble learning algorithms. Among the ensemble learning algorithms, the GBDT apple grading algorithm demonstrated the best overall performance across various evaluation metrics. Specifically, compared to other ensemble learning algorithms, the GBDT algorithm achieved the best F1 Score values under three different averaging methods, which were 0.9513, 0.9476, and 0.9506, respectively. It also attained the highest values for Jaccard Score, P, and R, highlighting the GBDT algorithm's excellent classification ability, robust performance, and strong generalization capability, making it suitable as an apple grading model.  
To more effectively verify the superior performance of the GBDT apple grading algorithm, we visualized the prediction probabilities of different algorithms on the test set using confusion matrices. This visualization illustrates the models' prediction accuracy across various categories and allows for further analysis of the grading performance of different models on four apple grades, as detailed in Figure 14. Overall, it is apparent that among non-ensemble algorithms, the MLP algorithm achieves relatively better grading performance. In contrast, the KNN and SVM algorithms struggle to effectively distinguish between Grade A and Grade B apples, with the KNN algorithm having a prediction probability of only 0.550 for Grade A Fruit and a 0.25 probability of misclassifying it as Grade B Fruit. Among ensemble learning algorithms, RF, BC, and GBDT all achieve relatively accurate grading for four apple grades. The GBDT algorithm demonstrates the best grading performance, effectively capturing the distinguishing features among different apple grades, with prediction probabilities of 1.000, 0.994, 0.875, and 0.916 for the four apple grades, respectively, thereby achieving a near-perfect classification.
## 1 Introduction
This method is feasible for DSSS, but it introduces certain errors in the final estimation results when applied to M-SS communication, as the process of M-SS despreading requires not only inputting the signal into the Doppler precise estimator but also into the spreading code index library, resulting in a two-dimensional correlation peak matrix composed of the search frequency shift dimension and the spreading code index dimension. Previous studies typically assumed that the frequency shift position corresponding to the highest peak in the matrix should be selected as the Doppler estimation result, but this approach is not rigorous for M-SS communication, as the correlation between spreading codes has been disrupted under conditions of pronounced channel time variability, and at the searched frequency shift positions, it is possible for peaks at positions with larger errors to exceed those at positions with smaller errors. Although the corresponding index decoding position may be correct, the accumulation of errors can still lead to subsequent decoding inaccuracies. To address the aforementioned issues, we propose a method for updating matched filters based on correlation cost factors for M-SS communication. By calculating the correlation cost factors, the accuracy of Doppler estimation for each symbol can be assessed, which allows for guiding the direction of Doppler estimation for subsequent symbols in subsequent processing, thereby preventing excessive accumulation of estimation errors. Through simulations and sea trials, the reception method utilizing correlation cost factor-based updates to matched filters significantly outperforms traditional reception methods in various aspects. The remainder of this paper is organized as follows. Section 2 describes the newly proposed M-ary spread spectrum communication transmission module, the conventional reception method for M-SS, and the theoretical aspects of the reception method based on correlation cost factor updates to matched filters; Section 3 presents numerical simulation studies, including parameter settings and decoding performance tests under various mobility conditions; Section 4 analyzes data from sea trial experiments; Section 5 provides a summary of the entire paper.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility
### Predictive Coordination Between NTN and RIS  
In high-mobility scenarios, such as UAV or high-speed vehicular communication, the rapid variation in channel coefficients due to mobility-induced Doppler effects poses a significant challenge for the real-time configuration of the RIS. To address this, our framework includes a predictive coordination module that proactively tracks and forecasts channel behavior using both historical CSI and mobility information.
## 4 Conclusions  
ITO/AZO/n-Si heterostructure was proposed for enhanced photovoltaic applications. The ITO thin film on the top served as a transparent conducting window layer. The AZO inter-layer was deposited on an n-Si substrate to form a high-quality AZO/Si heterojunction via a dual-beam co-sputtering technique. It allowed junction engineering by introducing uniform Al doping in AZO with precise control of the doping concentrations. With moderate Al doping (i.e., Al: 6.34 w.t.%) in an AZO layer, the thin film residual stress was significantly released from \(6.74\times 10^{8}\) Pa to \(0.08\times 10^{8}\) Pa, which improved the AZO crystalline characteristics and the AZO/Si junction qualities. This boosted the \(qV_{bi}\) of AZO/n-Si heterojunction from 0.21 eV to 0.74 eV and provided enhanced V\({}_{\rm{oc}}\) photovoltaic performance from 0.14V to 0.42 V. The best device we obtained showed a conversion efficiency (Eff) of 5.03% with J\({}_{\rm{sc}}\) of 26.04 mA/cm\({}^{2}\) and V\({}_{\rm{oc}}\) of 0.42 V. To the best of our knowledge, this is the highest V\({}_{\rm{oc}}\) value reported for ZnO/Si heterojunction devices prepared by a sputtering method. The results demonstrated the validity of our proposed structure and junction engineering approach, which could provide new insights and opportunities for ZnO/Si heterojunction photovoltaics.  
Supervision, J.-H.Y. and J.K.; Writing--original draft, C.T.; Writing--review and editing, M.K., S.J.K., J.-H.Y., and J.K. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support of Incheon National University Research Grant in 2018.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Distinction of Passes  
Fifty-eight passes could be detected by applying the algorithm for the distinction of passes. It matched with the number of lifting and lowering points, which is an important indication for the algorithm's functionality. Figure 11a presents the D-GNSS position data of the lowering, lifting and in-work points with the corresponding RHP values, while Figure 11b highlights a zoomed area for a more detailed representation. Some single lowering and lifting points comparatively far away from the headlands can be observed in Figure 11a. This could indicate, e.g., corrections by the driver where he lifted the plow, moved back some distance and lowered it again to correct a driving error. Another explanation would be wedges at the field boundary where it is not necessary or rather possible to complete the whole pass.  
In Figure 11b, the main part running in the southwest-northeast direction can be distinguished from the shorter headland running perpendicular. Furthermore, a varying distance between all passes is obvious. This was well expected by using a low-cost D-GNSS receiver. The work in [21] considers the pass-to-pass average error, which is also defined as the relative error, as the most important error for dynamic positioning. This relative error varies with the same receiver due to the testing date and time as well as vehicle speed and it highly depends on the used receiver.  
As the RHP values were slightly below the in-work threshold of 60%, it was easier to visually distinguish the variations. It is interesting that the RHP had comparatively low values for passes at the edges of the main parts and headlands. This was the first indication for a specific driving scheme in these areas. These schemes can be varied and depend on the local conditions as well as the driver's preferences. In the case of a reversible moldboard plow, this could indicate, e.g., a reduced working width by only setting the last few shares in operation or even a deviation from the usual plow rotation scheme.
### Virtual Reality and BIM Models  
BIM has been found to facilitate communication and information transmission among different stakeholders in a project [37]. BIM methodology integration with virtual reality, augmented reality and mixed reality are successful combinations to improve poor spatial cognition [38], collaboration, information processing, ability to standardise processes and reduction in risk [39; 40; 41].  
BIM has been increasingly used in the infrastructure design and construction phase, because it allows high-precision, configurable, three-dimensional information models that are compatible with a variety of graphical environments, including stereoscopic (immersive) and holographic visualisation technologies, to be generated [39; 42; 43]. Other similar approaches are presented in [44; 45; 46]. In the study of [44], a design methodology for a virtual training system is presented. It is based on rules and typologies commonly used in a first-person point-of-view game and addresses how a BIM model is used to create a virtual environment using a game engine and creating a mission based on rules and scoring, similar to those used in video games. Similarly, the authors of [45] staged an application of computer games in design visualisation for education and a solution to address interoperability between games and building models to enhance architectural visualisation and education. The authors introduced the concept of a BIM-game prototype integrating building information modelling and gaming into architectural visualisation. Another example of a direct BIM-game system was given by [46], who integrated a BIM model and a game typology (rules) for architectural visualisation. Similarly, the work of [47] shows a process of creating a video game based on BIM modelling and interaction with a simulation of fire and smoke engines for fire evacuation training in structures, evaluating the effect of human behaviour during the evacuation process. Following a similar structure, the work of [39] presented a VR tool for BIM reviewers (VBR) for global engineering collaboration. Using an avatar, the students could immerse themselves in the BIM model and identify problems individually or collaboratively as a team. In general, these works propose different methodologies and programming flows to convert a BIM-type construction model into a model compatible with VRML (virtual reality modelling language) formats.
## 4 Robotic Arm Position Detection
### Results  
To qualitatively assess how CSI varied across key locations, we computed and plotted the PMFs of CSI amplitude distributions for eight distinct keys. As shown in Figure 6, each plot maps WiFi subcarriers (\(x\)-axis) against amplitude bins (\(y\)-axis), with colour intensity indicating the concentration of CSI values. While the PMFs shared a general shape, typically with three local maxima and two minima, the amplitude distributions differed noticeably between keys, particularly for spatially distant ones such as L and Z. This variation was attributed to unique WiFi signal propagation and distortion caused by the hand hovering over each key. In contrast, adjacent keys (e.g., M vs. N, E vs. D) produced more similar PMFs, suggesting reduced spatial resolution at finer scales. Figure 6e-h highlight this overlap, and Table 2 summarises key visual features and placement context. These findings indicated that CSI distributions were location-dependent and supported our motivation to apply the machine learning framework in Section 4.3, with classification results reported in Tables 3 and 4.  
The \(f_{1}\) scores for our classifier, which predicts the location of a robotic arm above each key on a keyboard, are presented in Tables 3 and 4. It is worth noting that the \(f_{1}\) score is the harmonic mean of precision and recall scores, with a higher score indicating a better-quality classifier. We are pleased to report an overall high accuracy, with an average \(f_{1}\) score of 0.99. However, it is worth highlighting that some letters, such as A, F, G and L, performed slightly worse with an \(f_{1}\) score of 0.98. These results demonstrate the impressive ability of WiFi sensing devices to detect minute changes in location within the channel. Overall, the outcome suggests that CSI can be used to predict the location of an arm in an ideal scenario with no trembling and highly repeatable positioning.  
In summary, this section establishes a performance upper bound for keystroke localisation using a highly controlled robotic arm. With an average \(f_{1}\) score of 0.99 across 26 keys, the results confirm that WiFi CSI can capture fine-grained spatial variations when subject motion is consistent and repeatable. This forms a benchmark for subsequent comparisons with more variable human movements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Key** & **PMF Shape Characteristics** & **Key Placement** & **Visual Distinction** \\ \hline L & 3 peaks, high variation & Bottom-right & Clear \\ \hline R & 2 strong peaks, symmetric & Top-right & Moderate \\ \hline B & Smooth distribution & Bottom-center & Moderate \\ \hline Z & Diffuse, less distinct peaks & Bottom-left & Distinct \\ \hline M & Clustered, mid-range & Bottom-right & Subtle \\ \hline N & Simiar to M & Right of M & Overlapping \\ \hline E & Sharp central peak & Top-left & Moderate \\ \hline D & Slight spread, left-shifted & Mid-left & Slightly distinct \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of PMF patterns for selected keys.
## 1 Introduction
The intrinsic factors that intervene for explaining these differences are the size and composition of the training set, the quality of the data used to build the model, the relevance of the descriptors, the performances of the modeling algorithm, and the quality of the model validation process [17; 19; 20; 21; 22; 23]. Different SAR and QSAR models have been proposed for discovering new potential repellents against mosquitoes (reviewed in [PERSON] [24]) but to our knowledge, no study has been carried out to estimate the impact of a molecular descriptor calculated with different QSPR models on the variability and reliability of the prediction results of the repellent activity of compounds. Recently, [PERSON] et al. [25] have proposed a SAR model for predicting the skin repellent activity of molecules against the yellow fever mosquito (_Aedes aegypti_). The model was computed from a large set of 2171 molecules and among its constitutive parameters, the 1-octanol/water partition coefficient (log \(P\)) was found to be a key physicochemical descriptor. In this context, by using the same dataset and methodology, the goal of our study was to statistically investigate the impact of log \(P\) calculated according to two different methods on the prediction of repellent activity.
# Cybersecurity of Microgrid: State-of-the-Art Review and Possible Directions of Future Research
## 3 The Cybersecurity Aspects of Microgrid
### Traditional Security Tools in Microgrid Systems  
The microgrid is connected to the Internet through the control centre, which is the main component of these systems. It connects and links all distribution substations. DNP3, Modbus, and other Internet-enabled communication protocols carry out control commands and transfer status data from the various microgrid devices to the control centre. These Internet-enabled connections are vulnerable to several cyber-threats that disrupt power supply to the microgrid. Therefore, early solutions involved the use of traditional security tools, such as firewalls and intrusion detection systems, to secure these protocols.  
To filter incoming network traffic, firewalls are installed in the router and the gateway to prevent unauthorised users from accessing the private network. Firewalls can inspect and discard suspicious packets by using such properties as their port numbers, IP address locations, and time delays. However, firewalls depend on a set of predefined rules that can turn into conflicts in many cases because hundreds of configurable rules are obtained in commercial-grade firewalls. However, this process can be complicated owing to the rare availability of information because the grid depends on a proprietary software platform. Moreover, perfect knowledge of cyber-assets is needed to develop accurate rules for firewalls [41].  
Numerous identification-based approaches have been developed to address the issue of anomalies in firewall policies [41; 42; 43]. A high-level security policy has also been proposed by the American National Standards Institute (ANSI)/International Society for Automation (ISA) for best practices in mitigating threats in the control system. Another drawback of firewalls is that spoofed messages can bypass protections that contain filtering rules. In addition, the vulnerabilities in software allow for cyberattacks to be performed by the attackers. Firewalls may also be unusable in WANs owing to the high latency of communication among devices.  
The cryptographic protection mechanism has become a critical issue in cybersecurity for building and developing data confidentiality and integrity. The power industry has developed various communication protocols and devices prior to implementing cybersecurity to protect data security. SCADA, the substation automation system (SAS), the phasor measurement unit (PMU), and DER, which use such protocols as Modbus and the DNP3 in a smart grid, have been applied, but cannot protect against cyberattacks [41]. High access to the network by many users in the WAN may increase security risks, especially when such protocols as DNP3 have been used. The authors of [42] proposed solutions for the MODBUS authentication framework. A secure frame format has also been proposed to overcome the drawbacks of DNP3 [43].