esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Key Findings  
This paper presents an advanced GRU-based model for predicting student academic performance, with the key findings summarized as follows:  
* A new deep learning model utilizing a GRU to classify academically underperforming students was introduced.  
Figure 11: Correlation between the BA/BSc 5 th Semester Examination (IN_Sem5) and the internal evaluation grades obtained from the BA/BSc 6 th Semester Examination (IN_Sem6).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Precision** & **Recall** & **F-Score** & **Accuracy** \\ \hline RNN model & 0.96 & 0.99 & 0.98 & 95.34 \\ \hline ARD V.2 & 0.926 & 0.932 & 0.939 & 93.18 \\ \hline AdaBoost & 0.934 & 0.946 & 0.939 & 94.57 \\ \hline The proposed model & 0.986 & 0.963 & 0.974 & 99.70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between diverse classification approaches.  
Specific neural network features including dense layers, max-pooling layers, and ADAM optimization were incorporated.
* Training was conducted on a dataset of 15,165 student records from various academic institutions.
* A remarkable accuracy of 99.70% was achieved, surpassing other educational recommendation systems.
* Benchmarked against RNN models, AdaBoost, and Artificial Immune Recognition System v2, the proposed model showcased a superior performance.
* The model's potential in educational settings was emphasized for the identification of students needing additional academic support early.
* The efficiency and computational advantages of the GRU model in handling large datasets were highlighted.
* The practical application of deep learning was demonstrated in enhancing educational outcomes through data-driven insights.
* The strategic use of this model by educational institutions for timely intervention and support was advocated for.
* Avenues for future research in predictive analytics within education to further improve student success rates were opened.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 6 Performance Evaluation
### Emulation-Based Analysis for IoT Networks
#### 6.4.1 Emulation Configuration  
Similar to the system model illustrated in Figure 1, this emulation assumes a situation where \(n\) intermediate nodes exist between the source and destination nodes. Each node comprises a Raspberry Pi 4 Model B, which is a low-cost single-board computer. All encryption algorithms were implemented using C language with the GCC compiler, and the calculation time was obtained from the actual runtime on the Raspberry Pi. To evaluate the computational performance, it is necessary to obtain the following computational times: (i) ID-based signature (proposed method and ARAN); (ii) IBSAS (ISDSR+), and (iii) SHA-256 hash chain generation (SAODV). (Note that as the original ARAN utilizes public key-based authentication, which requires a public key infrastructure (PKI), we used the same ID-based signature for the proposed protocol and ARAN here in order to align the evaluation conditions.) All methods were implemented using PBC Library 0.5.14 [31], and the ID-based signature followed the method proposed in [20]. Furthermore, it is assumed that packets are transmitted based on ZigBee-based physical layer specification [32]. The communication component is derived from the theoretical performance of ZigBee-based communication. The main specifications of this emulation are summarized in Table 2.  
Based on the above conditions, we evaluated the routing overhead during one routing process by repeatedly generating, verifying, and hashing packets as many times as required by the routing protocols in an environment with \(n\) relay nodes on a single Raspberry Pi. After this routing emulation had been repeated 1000 times, we calculated the average performance. Note that IDs are assumed to be distributed to nodes in advance. Thus, this emulation result assumes no access points or centralized server (e.g., public key infrastructure (PKI)). In addition, this emulation does not consider any other time, as these times are sufficiently short compared with the communication and cryptographic parts.
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 3 Biocompatible 3D Printing Materials  
In the field of biofabrication, there is clear distinction between direct printing of a cell-seeded material, termed a \"bioink\", and the printing of a cell-free scaffold from a \"biomaterial ink\", which can subsequently be seeded with cells or directly implanted [38; 39]. Ink selection is driven by both the final function of the part and the printing technique to be used. Biomaterial inks are generally used to produce a rigid scaffold for the permanent or slow-degrading stabilization of a structure, while bioinks produce a softer scaffold that can be more rapidly replaced by the deposition of a new extracellular matrix (ECM) by the embedded cell population [32; 39].
Therefore, developing improved methods to assess key quality indicators in fresh tea leaves is of great importance. Hyperspectral technology, which collects detailed spectral information across many narrow and contiguous bands, has proven useful in studying complex compounds. However, current methods often rely on first- or second-order derivatives to highlight spectral features. While these methods work well for strong signals, they may not capture the subtle variations associated with compounds like TPs and AAs. Fractional-order differentiation (FOD) provides a more flexible approach by allowing the adjustment of the differentiation order. This flexibility can enhance weak spectral features while suppressing noise, offering potential advantages over traditional integer-order methods [11; 12]. Despite its promise, FOD has not been fully explored for its application in fresh tea leaves. In this study, mature and fully expanded tea leaves with an inner-circle diameter greater than 3 cm were selected as the research objects. While tender shoots and buds are traditionally considered key indicators of premium tea quality, mature leaves represent a substantial portion of harvestable biomass in many practical scenarios--especially in mechanized harvesting or late-season plucking. Moreover, mature leaves still contain significant levels of quality-related compounds such as amino acids and polyphenols and are widely used in mid- to low-grade tea production, functional tea processing, and quality monitoring at the canopy or field scale. Evaluating the spectral characteristics of such leaves thus has both agronomic relevance and practical implications for scalable, non-destructive quality assessment. We propose a method that combines fractional-order differential spectroscopy with Competitive Adaptive Reweighted Sampling (CARS) and Partial Least Squares Regression (PLSR) to estimate AA and TP content in fresh tea leaves. By shifting the focus from processed tea products to fresh leaves, our approach aims to develop a rapid, non-destructive tool for tea quality monitoring that could support improved agricultural management.
# Failure-Robot Path Complementation for Robot Swarm Mission Planning
## 3 Design and Principle of Path Programming
### Workflow of Multi-Vehicle Path Programming
#### 3.3.2 Establishing the Distance Array (Using A* to Avoid the No-Travel Zone)  
All data on the distance between nodes has been stored as a distance array, and distance information can be accessed from the array without spending time on redundant calculations.  
As shown in Figure 10, there is a no-travel zone on the map. Before programming the route of the system, the A* search was applied to determine whether there were any linear paths between any two points touching the zone (such as blue points 1 and 2). If there were, the A* search was used to determine alternative paths using turning points to bypass the no-travel zone. Once the turning point was used, the distance information of the new curved-route was provided to update the distance array. If there were not, the original algorithm continued running until all distances between targets were known.  
Figure 10: Schematic diagram of avoiding the no-travel zone.  
Figure 9: Schematic diagram of the turning points.
## 3 Architecture  
### Overview  
As stated in Section 1, the main focus of this work is to provide drone pilots with intuitive gesture control, drone tracking in AR, and a video feed displayed in context with the real environment. This section presents the system architecture developed (depicted in Figure 1) to support these functionalities.  
The two end-points of the system are the pilot and the drone. The pilot interacts with the system via an autonomous mixed reality headset that provides hand-tracking capabilities along with visualization in AR. A HoloLens application was developed to support gesture acquisition, give feedback to the pilot, display the received video feed and the drone's tracked position in AR, and handle connectivity and common settings. The drone connects with the system via a direct wireless link with its remote controller, which is connected to a smartphone running a custom Android app to support the needed functionalities. These include transmitting telemetry data from the drone to the AR device and control commands from the AR device to the drone. The communication between the AR device and the drone is routed through a message broker, while the video data are streamed via a direct socket connection between the smartphone and the AR device.  
There are three primary data flows pertinent to the desired functionalities:  
* Hand gestures are captured, interpreted, translated to drone commands, and forwarded to the drone.
* Telemetry data are used to estimate the drone's current position, which is then sent to the AR device.
* Live video is captured by the drone's camera, transmitted to the remote controller, and then streamed to the AR device directly.  
The remainder of this section further describes the individual system components and their functionalities, while the next section (Section 4) provides technical details regarding the main components of the solution.
## Influence of Pandemic on Burnout, Emotional Regulation, and Social Cognitive Scores  
The COVID-19 pandemic could influence the results of the study in varying proportions. Since there is no available literature to compare burnout levels in dental students and interns during the pandemic, we chose to review findings among dental practitioners. In a recent survey among dental defense unit professionals based in the UK, more than half of the participants (68%) reported an increase in their stress and anxiety during this pandemic [37]. On the contrary, lower psychological distress was reported in the UK's general dental practitioners during the national lockdown period compared to other reports [24]. Another study among Israeli dentists showed increased psychological distress among those with preexisting diseases, apprehension of getting infected with COVID-19 from their patients, and a higher workload, whereas those in a committed relationship and those that had higher self-efficacy scores exhibited lower burnout and distress [38].  
To the best of our knowledge, this study is the first of its kind that addresses the issue of burnout and its relation to emotion dysregulation and social cognition among interns and undergraduate students at a dental school in Saudi Arabia. This study is even more significant as it reflects the burnout among these groups during the COVID-19 pandemic. This paper is also one among few studies to evaluate burnout's multidimensional aspect by validating its emotional dysfunction and cognition domain associations. However, selection bias could be possible when assessing this study's generalizability, as data were collected from one geographical area. Moreover, burnout is a fluctuating and chronic phenomenon, reliability of cross-sectional study's reliability should be validated. Furthermore, the variables are measured by the subjective method without behavioral observation, which can under- or overestimate study results.  
We would like to make the following recommendations:  
1. It is important to engage in prevention of burnout and emotional dysregulation among female interns and postgraduate students by means of reduced work hours, arranging rooms for rest, and arranging for psychological counselling whenever the need arises.
2. Comprehensive training of supervisors, faculty members, and administration is necessary for a deeper understanding of students' problems and attitudes.
3. Initiating more positive stress-free learning and clinical environments is important to emphasize the stimulatory aspects of work.
4. Changing modalities of evaluation to reduce the importance of ranking method is recommended.
5. Nonacademic domains, i.e., cultural and sports, should be included as part of the dental curriculum to alleviate the interpersonal stress domain.  
6 Periodic continuing dental education programs/workshops to train and retrain young minds toward poised emotional and cognitive health should be targeted as a primary preventive measure against this multifaceted condition.
7 Introduction of interventions to improve emotional intelligence is recommended to enable emotional coping resources and social skills.
## 5 ML Intersection with 6G Enabling Technologies
### ML and UAV Integration for Future 6G Networks  
The use of UAVs to aid wireless communications has become widespread over the years [151; 152]. Its deployment will tremendously impact future 6G networks due to its mobility, low maintenance, and effectiveness in high-congestion environments and emergency scenarios [15; 151]. Researchers are now exploring several integrated approaches for its improved performance and optimization to enable future 6G networks [15; 133]. ML plays a crucial role in the deployment, control, and improved usage of UAV technology [152; 153]. Key highlighted roles of ML integration in UAV applications include trajectory and flight planning, routing, energy optimization, and obstacle avoidance. In the incoming 6G network, ML and UAV integration is expected to enhance UAV performance to meet the network requirements, and research is now geared towards that direction. ML-based LSTM approach was integrated into a UAV-assisted communication system for offload optimization [154]. The authors developed an LSTM-based trajectory prediction scheme to handle the high-speed mobility of UAV devices. The results showed that the LSTM-based approach yielded improvement in the allocation of task success and computational rate. [PERSON] et al. [155] explored the performance of different optimization approaches with various numbers of UAVs for the JTDPA optimization problem. The study investigated an integrated ML approach to achieve a balanced tradeoff between long-term reward and satisfactory QoS requirements of all UEs. The MADDPG approach showed better ASC and ANU values than that of the other two learning methods (MADDPG-FP and MAAC) with less computational complexity while the MADDPG-FP achieved the lowest computational time with an increasing number of UAV devices. Travel path trajectory is one area where ML has found dominance in its intervention for optimization purposes [156; 157; 158; 159]. ML has shown effectiveness in improving the performance of UAVs in terms of their travel path length, flying time, velocity, and energy consumption [160]. UAVs' ability to provide dynamic coverage in emergencies and even poor topography areas makes them critical in future 6G networks. ML has proved capable of adequately assisting UAVs in optimizing various parameters, such as their trajectory, localization, offloading, and power allocation, yielding significant improvements in better QoS, QoE, lowered cost,
## 5 Results and Discussion  
### Results of Outlier Handling and Sample Size Determination Using Coefficient of Variation (CV)  
This study used the CV to determine the target sample size for the Tier 3 DRE data in the semiconductor and display industry. The underlying assumptions were as follows. To determine the sample size using the CV, the significance and confidence levels need to be determined. For the significance level, a conservative 1% level was chosen for this study, rather than the typical 10% applied in general surveys. As for the confidence level, it was based on the most commonly used 95% confidence level. For outliers, we used IQR to find the extreme values and then calculated the sample size with no handling of outliers (Case 1), with outliers removed (Case 2), and with outliers adjusted to extreme values (Case 3).
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 4 Results
### Predicted vs. Actual Option Prices  
The scatter plot above compares the predicted prices of the deep neural network with the actual prices generated using the Black-Scholes formula.  
From Figure 3, to further evaluate the model's predictive accuracy, a scatter plot was generated to compare the predicted option prices with those derived from the Black-Scholes formula. The alignment of points along the diagonal line suggests a strong correlation between the model's predictions and the theoretical prices. The close adherence of the points to this line indicates that the DNN accurately replicates the Black-Scholes pricing function across various parameter configurations.  
A detailed analysis of the dispersion pattern reveals minimal deviations, particularly for lower option prices. While slight discrepancies emerge for higher option values, particularly those exceeding 0.8, these deviations remain within acceptable error margins. The overall alignment between predicted and actual prices confirms that the model successfully captures the fundamental pricing dynamics.  
Figure 2: Evolution of training and validation loss during the training process of the deep neural network.  
These results underscore the viability of deep neural networks as an alternative to traditional option pricing models, offering a computationally efficient solution that maintains high levels of accuracy. Compared to computationally intensive approaches such as physics-informed neural networks (PINNs), the proposed DNN achieves similar precision while requiring significantly less processing time. The ability to generalize across a wide range of input parameters further reinforces its applicability in financial markets.  
However, it is essential to acknowledge that the model's reliance on synthetic datasets presents certain limitations. Since synthetic data lack the stochastic volatility and market anomalies inherent in real-world financial systems, the immediate applicability of the model remains constrained. Future research should focus on incorporating historical market data to evaluate the model's robustness in dynamic environments, ensuring that it remains reliable under realistic financial conditions.
# Joint Power Charging and Routing in Wireless
## 4 Numerical Results
### Convergence Behavior  
In the GA, the convergence behavior is affected by many control parameters, such as the initial population, the mutation probability and the crossover mechanism. To the best of our knowledge, the conditions for GAs to converge have been proved only for binary encoding with Markov chain models [42]. However, for the GA algorithm with integer encoding, the convergence is still an open problem [43]. In this paper, rather than using an analytical approach, extensive simulations are employed to investigate the convergence issue. In our simulations, we set the maximum number of generations as 500. In fast, the number of generations depends on the number of individuals. For instance, more generations are needed for a greater number of sensors.  
Figure 5 plots the convergence behavior of the charging efficiency with the number of generations, and we can observe that the algorithm converges after approximately 300 generations for various numbers of sensors. It takes 50 s to converge for \(N=70\) sensors. This is sufficient for many applications. If we use a more powerful computer, it is expected that it can converge much faster. For our optimization problem, the charging efficiency with a random generated routing tree at the initialization is 72.9%, while the final obtained value after optimization with the GA is 99.5%, which showcases that the GA achieves a nearly 50% greater charging efficiency compared with that of the random routing tree. This also indicates that it is important to design a joint optimization including both routing and charging. Additionally, it is revealed that the converge speed can be substantially increased with a reduced number of sensors.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline The initial energy of sensor \(n\), \(E_{i}^{n}\) & 10,800 J \\ The minimum energy for working, \(E_{min}\) & 540 J \\ The mobile speed, \(V\) & 5 m/s \\ The full charging ratio, \(u_{Full}\) & 5 W \\ The minimum charging ratio, \(U_{min}\) & 1 J \\ The charging radius, \(R_{v}\) & 2.7 m \\ The sensing ratio of node, \(n\)\(\lambda_{n}\) & 1\(\sim\)10 kbps \\ The number of sensors, \(N\) & 20\(\sim\)80 \\ Distance-independent constant term, \(\beta_{1}\) & \(50\times 10^{-9}\) J/b \\ Coefficient of distance-dependent constant term, \(\beta_{2}\) & \(0.0013\times 10^{-12}\) J/(bm\({}^{4}\)) \\ Pass-loss index, \(\alpha\) & 4 \\ Energy consumption for receiving per data rate, \(C_{i}^{r}\) & \(50\times 10^{-9}\) J/b \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
## 2 Problem Definition and Proposed Models
### Multiple Linear Regression  
Multiple linear regression analysis is one of the most powerful tools used to model the association between problem variables (i.e., input and output). Modeling the association between these variables can predict future trends [17]. Furthermore, it is used to model the complex relation between dependent variable and multiple independent variables. Assuming \(y\) is the dependent variable and \(x_{1},x_{2},x_{3}\). \(\ldots\ldots\ldots,x_{j}\) are independent variables, then the MLR regression model can be expressed as follows:  
\[y=a_{0}+a_{1}x_{1}+a_{2}x_{2}+a_{3}x_{3}\ldots\ldots\ldots a_{k}x_{k}+e, \tag{1}\]  
where \(a_{0}\) is a constant, and \(a_{1},a_{2},\ldots\ldots,a_{k}\) represent regression coefficients.  
The main aim of MLR is to minimize the sum of squares of error (\(\sum e\)) by using the Least Square (LS) method [17]. It has been observed that with a large number of dataset observations, this technique gives more robust solutions, but if we have a small number of observations in the dataset, then we obtain poor results.