esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Latent Factors and Collaborative Filtering for Students' Performance Prediction
## 1 Introduction
Trying to come up with an approach to ascertain the exact number of factors is very complex, especially if one does so by studying in depth the learning process in the students' context. Therefore, in order to solve the question of how many latent factors there are, we propose a method to be applied together with the optimization of the pair (\(\beta\),\(\lambda\)). In this study, we propose to find the optimal number of latent factors, the learning rate, and the regularization factor by applying algorithmic methods, which are very useful to solve optimization problems where the computational resources are unable to perform simple direct searches. In our approach, the optimization algorithms are applied to calculate the minimum prediction error by determining the optimal values of \(K\), \(\beta\), and \(\lambda\). The remainder of this paper is structured as follows. After a succinct literature review in Section 2, the problem of predicting students' performance, formulated by means of matrix factorization and gradient descent, is described in Section 3.1. Here, different strategies to select the training and test datasets are presented, as well as a method to obtain consistent data when extracted from real datasets; in this case, the students' activity in the online campus. Next, Section 4 details our proposal for improving the prediction, considering two simultaneous methods: selecting the ideal number of latent factors and obtaining the best values for the learning rate, and the regularization factor for the collaborative filtering algorithms. Section 5 presents the experimental results considering several representative datasets that assess the accuracy of the prediction. In light of the discussion of these results, several conclusions, as well some suggestions for further research are drawn in Section 6.
## 3 Experimental Results
### Results  
#### 3.4.1 TVSum Dataset  
The best results across all learning architectures in Table 2-A do not use stepwise regression (denoted as SW in the tables), while the second-best results do use it for the dimensionality reduction of the feature space. HEVC-based temporal subsampling achieves the highest results in the TVSum dataset, regardless of using stepwise regression or not. The highest overall scores appear with the use of the LSTM network.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Reduction**} & \multirow{2}{*}{**Temporal**} & \multirow{2}{*}{**F-Score**} & **Time** & **Time** & \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Reduction**} & \multirow{2}{*}{**Temporal**} & \multirow{2}{*}{**F-Score**} & **Time** \\ \cline{3-3} \cline{5-10}  & & & **(K = 5)** & **(K = 1)** & & & & **(K = 5)** & **(K = 1)** \\ \hline
1D-CNN & Stepwise & HEVC-based & 0.728 & 20.36 & 4.07 & 1D-CNN & None & PCA-based & 0.61 & 12.38 & 2.48 \\
1D-CNN & None & HEVC-based & 0.737 & 48.86 & 9.77 & 1D-CNN & None & HEVC-based & 0.644 & 16.29 & 3.26 \\ RF & Stepwise & HEVC-based & 0.737 & 22.13 & 4.43 & LSTM & None & PCA-based & 0.646 & 49.51 & 9.9 \\ RF & None & HEVC-based & 0.74 & 49.74 & 9.95 & LSTM & None & HEVC-based & 0.676 & 65.14 & 13.03 \\ LSTM & Stepwise & HEVC-based & 0.725 & 81.43 & 16.29 & RF & None & PCA-based & 0.72 & 13.22 & 2.64 \\ LSTM & None & HEVC-based & **0.758** & 195.42 & 39.08 & RF & None & HEVC-based & **0.737** & 17.04 & 3.41 \\
**(A)** & & & & & & & **(B)** & & & \\ \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Reduction**} & \multirow{2}{*}{**Temporal**} & \multirow{2}{*}{**F-score**} & **Time** & **Time** & \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Reduction**} & \multirow{2}{*}{**Temporal**} & \multirow{2}{*}{**F-score**} & **Time** \\  & & & & & & & **(K = 5)** & **(K = 1)** \\
1D-CNN & Stepwise & Cosine-based & 0.827 & 6.17 & 1.23 & 1D-CNN & Stepwise & HEVC-based & 0.728 & 13.86 & 2.8 \\
1D-CNN & None & HEVC-based & 0.84 & 22.94 & 4.59 & 1D-CNN & None & HEVC-based & 0.744 & 32.36 & 6.7 \\ RF & Stepwise & Cosine-based & 0.852 & 6.86 & 1.37 & LSTM & Stepwise & HEVC-based & 0.753 & 55.43 & 11.1 \\ RF & None & HEVC-based & 0.864 & 24.7 & 4.94 & LSTM & None & HEVC-based & 0.77 & 133.03 & 26.6 \\ LSTM & Stepwise & Cosine-based & 0.866 & 25.52 & 5.1 & RF & Stepwise & HEVC-based & 0.799 & 14.67 & 2.9 \\ LSTM & None & HEVC-based & **0.879** & 91.75 & 18.35 & RF & None & HEVC-based & **0.808** & 35.21 & 7 \\
**(C)** & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proposed solutions: F-scores of the two best-performing models using the three proposed learning architectures, with and without the reduction of the feature space, and across the three proposed temporal subsampling methods on the TVSum (A), SumMe (B), OVP (C), and VSUMM (D) datasets. Bold scores indicate the highest and underlined scores indicate the second highest.  
Figure 7: General overview of learning architecture with averaged results over five folds of cross-validation.
## 3 Results and Discussions
### The Scenario of Sensor Location for Optimum Monitoring and Control
Vertical variability examines the variations in temperature, humidity, and light at different heights from the ground. Given the challenging conditions for plant growth in winter, this study emphasizes the significance of seasonal factors. It seeks to determine how the design and management of CSGs impact the maintenance of ideal conditions. Understanding spatial, temporal, and vertical variations enables growers to implement precise strategies for optimal plant growth and yield, even during unfavorable seasons. Sensor-based systems are employed to monitor and control the ambient conditions in CSGs effectively. These systems employ a network of strategically positioned sensors within the CSG environment. These sensors provide real-time information about parameters like temperature, humidity, light, CO\({}_{2}\) levels, and other relevant parameters. The collected data are then processed and analyzed using advanced algorithms and machine learning techniques. By considering the variability of ambient conditions, sensor-based monitoring and control systems can dynamically adjust environmental parameters to maintain optimal growing conditions for plants. Furthermore, by integrating machine learning algorithms, the system can learn from historical data and make predictions about future variations in ambient conditions. This predictive capability enables proactive adjustments to be made in advance, ensuring that the CSG maintains stable and optimal growing conditions. Figure 15: The bar graph depicts the measured temperature and humidity data from different sensor locations in the CSG with and without plants. The letters placed above the bar represent the significant differences (at \(p<0.05\)) determined through the Tukey HSD test. Overall, a consideration of the variability of ambient conditions within CSGs is crucial for the successful implementation of sensor-based monitoring and control systems. Increasing the number of sensors and the diversity of their positions would provide more robust insights, although the benefits of using additional sensors should be weighed against their costs. By accurately measuring and responding to these variations, CSGs can provide consistent and ideal growth environments for plants, leading to improved crop yield, quality, and resource efficiency.
###### Abstract
[33] developed a framework to account for the lifecycle carbon footprint of prefabricated buildings within the CE principles, whereas [PERSON] and [PERSON] [34] conducted a systematic literature review to identify how modularity can contribute to the CE. However, circular buildings remain rare despite the acknowledged environmental and economic benefits of the CE approach [16; 35; 36]. Achieving circularity in the building sector demands a holistic approach that considers the building's full lifecycle and emphasizes collaboration and engagement among stakeholders [37; 38]. [PERSON] et al. [39] emphasized that the full potential of a CE in the construction of buildings cannot be achieved without integrating its principles throughout all stages of a building's lifecycle. Furthermore, according to [PERSON] et al. [40], most studies focus on a single stakeholder perspective without examining the diverse viewpoints across the building value chain. This study builds on these insights to evaluate the significance and feasibility of CE strategies within the building sector. This includes identifying key CE strategies, analyzing their applicability across various building types and lifecycle stages, and assessing the roles of stakeholders in influencing CE adoption. By addressing these points, this study seeks to provide actionable insights and targeted recommendations to support the effective integration of CE principles in building construction projects. Policymakers can use these findings to develop targeted incentive schemes to encourage industry stakeholders to align their practices with CE principles. Moreover, the findings provide key stakeholders, including construction managers, project planners, designers, developers, and investors, with actionable insights to prioritize the most impactful strategies for CE adoption. Addressing the stated objectives, the following research questions were established:  
1. Which CE strategies are most feasible and impactful for the wider adoption of circular practices in the building sector? 2. How does the type of building influence the feasibility of adopting CE strategies? 3. What roles do different stages of a building's project lifecycle play in the effective implementation of CE strategies? 4. How do various stakeholders influence the adoption of CE strategies in the building sector?
# Development and Application of Digital Twin-BIM Technology for Bridge Management
## 4 Building Information Management
### Application of Bridge Digital Twin for Structure Maintenance  
Digital twin-BIM technology can be utilized for the maintenance of bridge structures. As illustrated in Figure 32, it is feasible to automatically extract crack information (maintenance data) from captured image data during inspections and incorporate it as attribute information into the 3D model of the structure. This enables the storage and management of maintenance information using BIM technology. Hence, digital twin-BIM technology plays a vital role in managing the complete life cycle of the bridge. Additionally, as mentioned in the Section 1, structural health monitoring has been planned based on the GNSS data observation as depicted in Figure 32. This figure shows the setup of the monitoring system and observation data in past research. By a continuous 3D displacement monitoring of bridges, it is expected that valuable data will be available for the maintenance of the bridge.  
Figure 32: Implementation of GNSS monitoring for bridges in past research. (**a**) Setup of GNSS monitoring system; (**b**) observation data from the GNSS monitoring system.
## 8 Conclusions  
The objective of this review is to provide a comprehensive and unified source of information for scientists, engineers, and researchers engaged in the investigation of recent advancements and challenges associated with the implementation and control of PV systems. These systems employ cutting-edge techniques from artificial intelligence and cybersecurity domains. The role of the remote control of PV systems and the use of AI and cybersecurity techniques for PV systems was subjected to a comprehensive evaluation. A review of remote-control, AI algorithm, and cybersecurity techniques has been conducted, with particular attention paid to their potential benefits. The remote control of PV systems necessitates the capacity to rapidly respond to alterations in the system's operational status, which can be achieved through the utilization of artificial intelligence algorithms. Artificial intelligence approaches can effectively supplant traditional physical modelling methods, as they necessitate fewer computational resources and are not dependent on sophisticated knowledge of internal system parameters. Such techniques have been successfully applied in numerous PV-related contexts. Conversely, the deployment of remote-control techniques and AI solutions may potentially increase the risk of cyber-attacks. The potential for cyber-attacks on PV systems is significant. These attacks may include false data injection, replay, denial of service, and brute force credential attacks. Such attacks could affect the PV system's operational mode by exploiting vulnerabilities. These include the use of insecure communication protocols, a lack or poor encryption techniques, a lack of access control, and a lack of parameter sanitization. The integration of innovative hybrid remote-control, AI, and cybersecurity techniques will foster greater confidence in the reliability of PV technology. This research is limited to PV systems which are designed to use monitoring and control methods combined with AI and cybersecurity approaches. Based on this analysis, future work will focus on implementing a stand-alone PV system that can be remotely controlled using AI and cybersecurity best practices. A new mobile application will be developed to cover the issues found, with a user-friendly interface which will offer important information about our PV system and its working mode by using AI technology. The cybersecurity measures will be implemented to ensure safe communication between the PV system and the mobile application.  
Conceptualization, A.-M.C. and D.T.C.; validation, A.-M.C., D.T.C., and P.A.C.; formal analysis, A.-M.C.; resources, A.-M.C., D.T.C. and P.A.C.; data curation, A.-M.C. and D.T.C.; writing--original draft preparation, A.-M.C.; writing--review and editing, A.-M.C., D.T.C., and P.A.C.; visualization, A.-M.C. and D.T.C.; supervision, D.T.C. and P.A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.
# Hydration, Setting and Crack-Resistance Properties of Activated HVFACs at Early Stages
###### Abstract  
In order to develop a simple and effective activation method of low calcium fly ash concrete (FAC) and to understand their basic properties, the present paper experimentally investigates the influences of the volume of fly ash (FA), the dosage of the activator (AC) and the water to binder ratio (_w/b_) on the hydration, setting times and the crack-resistance properties of activated FACs. Results verify that increasing FA can effectively reduce the hydration heat of the FA-cement system and increase the setting time of FACs, as well as indicate that FA can delay the occurrence of the initial crack and reduce the maximum width and total area of cracks in these concretes through a new quantitative test. The curing time of concrete affects the hydration heat of FA paste with/without AC. When the curing time of FAC increases, the decrease in amplitude of hydration heat reduces gradually. Additionally, it was found that the used ACs can effectively stimulate the activity of FA in Portland cement (PC) systems, which reduces their setting time slightly. However, high dosages of ACs also accelerate the development of early cracks in the FACs, as well. According to the study, the combined activator (10% CaO + 1.5% Na\({}_{2}\)SO\({}_{4}\)) was suggested as the most effective way to promote FA-PC concrete for obtaining acceptable properties at early stages. Besides, the existing calculation model for predicting the setting times of FACs was examined and modified to be more suitable to FACs activated by 10% CaO + 1.5% Na\({}_{2}\)SO\({}_{4}\). The proposed model was verified and can evaluate the experimental results well.  
high volume fly ash concrete; hydration heat; setting time; crack-resistance; curing time +
Footnote †: journal: Applied Sciences
# Amharic Speech Search Using Text Word Query Based on Automatic Sentence-like Segmentation
## 6 Results and Discussion
### Comparison of Segmented Speech Using Training Set  
The performance of the ASR system was compared by varying the training sets and keeping the test transcriptions the same for every iteration. This was conducted so to check how the ASR performs while changing the training set and its effect on the automatically segmented speeches. Figure 4 shows how the performance of ASR varies while changing the training set, keeping the LM and test speech the same, and using similar vocabulary. The total sentences used to create a LM were 10,602. All characters used in the test transcription were also available in the training set. From the result in Figure 4, the bar chart of A-I and Mseg shows a very slight difference in WER. As the WER of the Mseg decreases, the WER result of the A-I also decreases, and vice versa. However, the WER result of A-II was very large when compared to that of the Mseg in every experimental iteration. This implies that sentence-like automatic segmentation of A-I would yield a very close result to Mseg. On the other hand, A-II shows a very large difference with the Mseg in every experimental iteration.  
Figure 3: The effect of the LM on the ASR.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & **Test Set** & & & **OOV Rate** & \\ \hline
**No** & **LM** & **No.TSLM** & **Unique Vocabularies** & **Mseg** & **A-I** & **A-II** & **Mseg** & **A-I** & **A-II** \\ \hline
1 & OOV5000 LM & 5000 & 13,573 & 90 & 91 & 245 & 218 & 219 & 222 \\
2 & OOV10000 LM & 10,000 & 25,673 & 90 & 91 & 245 & 153 & 154 & 158 \\
3 & OOV10600 LM & 10,600 & 26,364 & 90 & 91 & 245 & 100 & 101 & 103 \\ \hline \hline \end{tabular}
\end{table}
Table 3: OOV rate and unique vocabularies of the LM Mseg. A-I, and A-II as the test text.
## 2 Materials and Methods
### Data analysis  
Marker, force plate and full wave rectified EMG data were all passed through lowpass, zero lag, Butterworth filters with cut-off frequencies set respectively to 6, 50 and 100 Hz based on previous work [14; 19]. Relative joint angles were calculated using Cardan rotation matrices prioritizing the sagittal plane. Maximum relative joint angles (MJRA) during the ALA swing phase, minimum foot clearance (MFC; the vertical distance between the heel and the obstacle or platform front edge) and mean COM velocity (COMv; as the mean velocity in sagittal plane of pelvis COM during the ALA stride) were also calculated. Newton-Euler inverse dynamics equations with a custom-made program were used to estimate lower limb kinetics and energetics respectively for net muscle moments of force (MMF) and muscle mechanical power (product of the MMF and relative joint angular velocities). These data were normalized to body mass, and muscle mechanical work of the trailing limb was calculated as the mathematical integral of targeted positive and negative power bursts corresponding to ankle plantar flexor generation (push-off; A2), knee extensor absorption at the end of stance (K3), the delayed knee extensor absorption around mid-swing (K3D), knee flexor generation at toe-off (K5) and hip flexor generation at both toe-off (H3) and mid-swing (H3D). Hip hiking work (HH) around toe-off was also calculated as the area under the positive power burst from the product of the vertical hip joint reaction force and vertical hip joint velocity with positive power corresponding to energy from the pelvis.  
The areas under muscle bursts for BF and ST EMG activity that aligned with the K5 power burst for obstacle avoidance [1] were calculated. As in a previous study [14], we took electromechanical delays into consideration by choosing these muscle bursts to begin at the local minimum prior to the K5 power burst and continue for the duration found for the corresponding K5 power burst of that trial. This allowed EMG data to provide physiological confirmation of knee flexor strategies across conditions. To compare across participants, percentage changes in these EMG bursts from the mean unperturbed trials for each perturbation time within each environment block were also calculated. As there was no appropriate hip flexor muscle activity recorded (RF has not been shown to be a good measure of a hip flexor activity [20]), no corresponding EMG areas were presented for hip flexion.  
For descriptive time series data, EMG, kinematic, kinetic and energetic (muscle mechanical power) data were normalized to 100% of stride, with toe-off fixed at 60% and ensemble averaged across trials for each condition.
## 4 Discussion
These advancements are expected to improve the efficiency of motion analysis and facilitate its application in telemedicine and rehabilitation. This study has several limitations. First, while the conical model provides a practical framework for estimating thumb motion in three dimensions, it has certain limitations. Specifically, the model assumes that the CMC, MP, and IP joints align along a straight line during motion, potentially oversimplifying the actual biomechanics of the thumb. Additionally, the conical model does not fully account for the complex trajectories that can occur during thumb movements involving non-planar or irregular paths. These simplifications could reduce the accuracy of motion estimation in certain scenarios. Future research should explore more advanced modeling approaches to address these limitations and improve the comprehensiveness of thumb motion analysis. Second, the relatively small sample size of 18 participants may have increased the risk of overfitting in the machine learning models. To mitigate this issue, we carefully split the data into training and testing sets to ensure robust evaluation. Nevertheless, future studies should validate the model using larger and more diverse datasets to confirm its generalizability and reliability. Another important limitation of this study is the lack of external validation. Furthermore, this study was performed on healthy volunteers in only a few positions, excluding individuals with arthritis or neurological disorders. As a result, this research should be considered preliminary, and further studies involving actual patients are highly encouraged. Further research is needed to investigate the relationship between these measures and functional aspects of the thumb.