esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Flipped Classroom Integrated with the POEWSQ Learning Model on Programming Learning
## 5 Results
### Interview Analysis  
To gain a deeper understanding of learners' perceptions of the flipped classroom integrated with the POEWSQ learning model, 10 participants from the experimental group (5 males and 5 females) were invited to participate in one individual interview after the four-week experimental program. The interviews were conducted in a university conference room and lasted approximately 10 to 15 min each.  
The interviews were conducted face-to-face using semi-structured questions as a supplementary tool, focusing on the following areas: changes in the learning model, strengths and weaknesses of the learning model, suggestions for improvement of the learning model, perceptions of the learning system, and whether they would be interested in continuing to use a similar learning model in the future. Face-to-face interviews not only supplemented the analysis of questionnaire data but also provided a more precise understanding of learners' learning effectiveness and other experiences in the experiment. The interview results will serve as a valuable reference for future improvements to this study.  
According to the interview content analysis, the results can be summarized into two core categories: learning models and learning system functions. Learning models include flipped learning and the POEWSQ learning model. The former focuses on improving learners' freedom and interactivity, while the latter focuses on learning motivation and effectiveness. The system functioning examines the impact of the system functioning on  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Group** & **N** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **t** \\ \hline Necessity & Experimental & 24 & 4.26 & 0.53 & 2.114 * \\  & Control & 24 & 3.90 & 0.66 & \\ \hline Liking & Experimental & 24 & 3.56 & 0.70 & 1.119 \\  & Control & 24 & 3.36 & 0.52 & \\ \hline \hline \end{tabular}  
* \(p\) < 0.05, ** \(p\) < 0.01.  
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Learning Effect** & & \\ \cline{2-4}  & **Pearson** & **Significance** & & \\  & **Correlation** & **(Two-Tailed)** & **N** \\ \hline Learning Effectiveness & \(1\) & & & \\ Learning Motivation & 0.405 & 0.004 ** & 48 \\ \hline \hline \end{tabular}  
* \(p\) < 0.01.  
\end{table}
Table 7: Pearson correlation of learning motivation and learning effect.  
the learners' perceptions. Based on the interview analysis, the following conclusions can be summarized:  
1. The freedom and interactivity of flipped learning improve the learning experience of learners.
2. The POEWSQ learning model improves learners' learning motivation and effectiveness.
3. The practicality of system features affects learners' willingness to use them again.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 3 Adaptive Sub-Network Organization
### Dealing with Low Power and Lossy Links  
In low-power networks, it is common for the wireless channels to experience temporary degradation of the quality. Even in sub-networks where link quality is very high, it is common to experience periods where the LQI of received beacons are below the corresponding thresholds. Experimental evidence has proven that message transmissions, even from nearby devices, may fail at times due to interference from nearby electrical devices, even ones that do not have wireless communication capabilities (e.g., a microwave oven).  
To avoid removing the sub-network devices that have acquired the status of a consistent neighbor and are therefore characterized as being stable, which however for some the wireless channel has transient noise, the algorithm defines a timeout period during which, even if a beacon is not received, the communication channel is still considered valid.  
Previous experiments [7; 33; 36] have shown that up to two beacons may be lost (especially on dense deployment) so a good practice is to set the timeout close to a time period equivalent to three consecutive beacons. However, this may turn out to be non-trivial as beacon broadcasting periods are not predefined and change during the execution of the protocol as part of the adaptation process. Note that higher timeout periods cause the network to respond slowly to changes, while lower timeouts can create unwanted inconsistencies by reacting to misinterpreted changes of the physical topology.  
Taking into consideration the relaxed beaconing of the sub-network mechanism, defining the timeout period is not as straightforward. Timeouts should not only be bounded by \(I_{max}\) as devices can choose not to transmit a message during every interval. Therefore, to avoid falsely removing devices from a sub-network, the timeout should not also be less than \(\frac{5}{2}\ I_{max}\). This value is computed as the more relaxed interval between two successfully received consecutive beacons while two communication failures can happen: \(Timeout=2T_{lost}^{max}+T_{succesful}^{max}=2I_{max}+\frac{I_{max}}{2}=\frac{5I_{ max}}{2}\).  
Of course, the timeout selection depends completely on the special network characteristics but the above threshold provided a reliable behaviour during all our experiments.
## 2 Materials and Methods
### Ray Tracer  
For GNSS-RO virtual height calculations, the EDPs were input to Another Ionospheric Ray Tracer (AIRTracer; a model developed by [PERSON] at the Air Force Research Laboratory) to calculate ordinary-mode ray paths. The AIRTracer model is based on the Jones-Stephenson [39] formulation using the Booker quartic with no collisions, but re-implemented for computational efficiency in a modern code base. Since the EDPs are only specified at one location and the numerical ray tracer requires three dimensions, we assumed a constant EDP over the area surrounding each ionosonde site. In other words, we have ignored ionospheric tilts that may be present during the ionogram and GNSS-RO measurements. The ray tracer group paths were then used to calculate virtual heights for the E-region ionosphere so they could be directly compared against measured virtual heights from ionosondes.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements
### Acquisition of a Pilot Channel with Assistance
#### 3.3.4 Use of Non-Coherent Accumulation with Semi-Parallel Implementation  
In the same way, is exactly the same as when there is no assistance, and the processing time is the same as the semi-parallel coherent only implementation--therefore the processing time is given in Equation (21).
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 2 Materials and Methods
### Language Model-Based Transfer Learning  
BERT [19] is an extensively pre-trained, Transformer-based language model, capable of state-of-the-art performance in NLP tasks. The increased ability to understand the conceptual framework that characterizes it is due to the self-attention mechanism, through which the token-level importance is assigned based on the associations between the individual words (tokens) of the sentence. Additionally, due to the training as a masked language model, it is able to perceive the information of the text in a bidirectional manner, allowing the LM to produce vector representations that reflect the syntactic, semantic and grammatical relationships between words.  
BERT's architecture is composed of a number of \(N_{enc}\) consecutive encoder units with \(d_{BERT}\) hidden vector dimension, where \(N_{enc}=12\), \(d_{BERT}=768\) for the base version and \(N_{enc}=24\), \(d_{BERT}=1024\) for the large version. Furthermore, multiple BERT-based variations pre-trained on specific domains have been developed which achieve better performance in domain-specific tasks compared to BERT. As an example, BioBERT [20] and SciBERT [21] are two popular variations of BERT, pre-trained on large text corpora from the biomedical and scientific field, respectively.
# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 2 Background and Related Work
### Traffic Incident Detection  
Traffic incident detection is one of the key research topics in smart transportation systems [6; 7; 10; 11]. The traffic incidents can be classified into five categories: accidents (e.g., collisions), road maintenance, hazardous weather, special events (e.g., Marathon), and obstacle vehicles. Existing methods are mainly based on deep learning technologies [5]. [PERSON] et al. [6] proposed a traffic incident strategy based on the generative adversarial networks (GANs). The spatial and temporal features are extracted as variables from real traffic data. For feature engineering, the random forest was used to filter the features that are not important for detecting traffic incidents. To overcome the imbalance problem that the outlier samples are not enough, they utilized GANs to generate the data with the traffic incidents patterns. Support vector machine was used as the traffic incident detection method. This study utilized the datasets that have been labeled as outliers and inliers. However, the GANs model cannot generate the outlier patterns that were not in the datasets. [PERSON] et al. [7] constructed the transportation networks by using the temporal traffic data. The hybrid model combined with the long short-term memory (LSTM) and extreme value theory (EVT) was proposed to predict the traffic incident. LSTM is an improvement of the recurrent neural network. It drops the data that is not useful for predicting the traffic incident using the backward propagation algorithm. The EVT is applied to the loss function of the hybrid model. Via experiments, the proposed hybrid model achieves a better F1 score than the baselines. Since there is a possibility of overshooting in deep learning training, [PERSON] et al. [10] used fuzzy logic to solve this problem. This method considers extracting the spatial and temporal information in the traffic data for incident detection. The results indicated that the proposed model could reduce the training time and the training loss. [PERSON] et al. [11] collected the information from social media to detect the car accident from the traffic flow. The main idea is to collect the tweet contents about the traffic information. Then, they detect the accident by analyzing these tweet contents. However, they did not exploit the traffic flow directly for accident detection. Only using the information from social media to detect the accident lacks credibility. Unlike the existing works, we aim to develop a dynamic graph embedding model considering the correlation between the multiple traffic time series to detect incidents at intersections in the VEC environment.
## 5 Experimental Results and Discussion
### Manufacturing Design
#### 5.1.2 ANOVA: Experimental Results
After running all simulations and performing a thorough analysis on the variation of outputs under the various scenarios, it was decided by the company to:  
* adopt conveyors with a speed of 0.8 m/s instead of 1.5 m/s, with the aim of minimizing the energy consumption and reducing the number of investments on more advanced and expensive technologies, given the irrelevance of the factor on all the considered output measurements;
* adopt a reorder level of 60 wood panels;
* have 15 expert operators, with an efficiency of 90%. These choices were made by giving the highest weight to productivity with the aim of maximizing it as much as possible.
## 4 Experimental Results
### Ablation Experiments  
To verify the rationality and validity of the dual-branch network structure and the cross-attention fusion module design, we conducted four ablation experiments, as shown in Table 2. These four experimental groups are as follows: ResNet50 single-branch network, Swin-Transformer single-branch network, dual-branch network, and the dual-branch hybrid fusion network with the embedded cross-attention fusion module. These experiments were conducted in sequence for comparison.  
The experimental results, shown in Table 2, indicate that the proposed model shows significant improvements across all metrics. First, compared to the Transformer single-branch structure, the dual-branch structure improves the \(m1 ou\) by 0.57%. Compared to the CNN single-branch structure, the dual-branch structure improves the \(m1 ou\) by 1.64%. While ResNet50 or Swin Transformer networks have some ability to handle certain features in weld seam images, their performance still has limitations in complex environments, especially when dealing with blurred weld seam edges and strong background noise. In contrast, the dual-branch network, by combining the advantages of CNNs and transformers, significantly improves precision and recall. It can more seamlessly merge detailed details with global semantic information. This shows that the dual-branch structure offers higher accuracy in detecting weld seam positions and edges, and it better extracts semantic information.  
Additionally, after enhancement with the CaFF module, the dual-branch model improves the \(m1 ou\) by another 1.89%. This improvement shows that the CaFF module strengthens the interaction between detailed and comprehensive features, successfully increasing the model's accuracy in capturing weld seam boundaries and shapes. The experimental results confirm the key role of the CaFF module in enhancing the model's effectiveness.
# Linear Precoding Designs for MIMO VLC Using Multi-Color LEDs under Multiple Lighting Constraints
## 2 System Model
Before the equalization of detector, the offset vector \(\mathbf{H}\mathbf{d}\), which carries no information, can be subtracted from \(\mathbf{y}\). Then, the received signal after subtraction can be written as:  
\[\mathbf{\hat{y}}=\mathbf{H}\mathbf{F}\mathbf{x}+\mathbf{z}. \tag{5}\]  
In this paper, we adopt the linear minimum mean-squared-error (MMSE) equalizer \(\mathbf{W}\) to detect the received signal. Transmitted symbols estimated by the MMSE equalizer can be represented as follows:  
\[\mathbf{\hat{x}}=\mathbf{W}\left(\mathbf{H}\mathbf{F}\mathbf{x}+\mathbf{z} \right). \tag{6}\]
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 3 Results
### Economic Calculation  
A cost-benefit analysis of hydrogen production was performed. The hydrogen production simulation using SMR + CCS resulted in 212,400 kg per day from 28,847,770 Mscf of flare gas. The efficiency of CO\({}_{2}\) capture reached 90%. The total capital investment was calculated using the TPC and TCR metrics. The costs are detailed in Table 10.  
The annual revenue generated from the sale of hydrogen, priced at a market cost of USD 3 per kg, was USD 232,578,000, while the net Annual Benefit was USD 139,236,351.02. The payback period was estimated to be 4.55 years, which is a positive sign for potential investors to recoup their invested capital. Table 11 summarizes the effects of carbon credits on the key metrics.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Cost** & **Value (USD)** \\ \hline Total Plant Cost & 486,354,300.35 \\ Total Capital Investment & 634,308,226.202 \\ Fixed Cost & 20,307,150.334 \\ Variable Cost & 73,034,498.65 \\ Operating Cost & 93,341,648.98 \\ Plant Life & 25 years \\ \hline \hline \end{tabular}
\end{table}
Table 10: Economic parameter cost.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Without Carbon Credits** & **With Carbon Credits** \\ \hline Net Annual Cash Flow & USD 139,236,351.02 & USD 154,236,351.02 \\ Payback Period & 4.55 years & 4.11 years \\ Cost–Benefit Ratio & 1.96 & 2.15 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Effects of carbon credits on the key metrics.  
Figure 7: Efficiency score and potential reduction in gas of the DMUs.
## 3 Results and Discussion
### Importance of Torrefied Biopellets  
Figure 14 illustrates the key benefits and applications of torrefied biopellets, highlighting their importance in the context of sustainable energy solutions based on our study.  
Torrefied biopellets exhibit a significantly higher energy density compared to non-torrefied pellets. This is due to the increased fixed carbon content and reduced volatile matter, leading to enhanced calorific value and more efficient energy production. The torrefaction process reduces moisture and volatile compounds, resulting in biopellets that ignite more easily and burn more uniformly. This leads to lower ignition times and pro  
Figure 14: Importance and applications of torrefied biopellets.  
Figure 13: SEM images of biopellets before and after torrefaction.  
longed combustion duration, making them a more efficient and reliable fuel source. The higher fixed carbon content and lower volatile matter result in cleaner combustion with reduced smoke and particulate emissions. This makes torrefied biopellets an environmentally friendly alternative to traditional fossil fuels. Despite the removal of volatile substances, torrefied biopellets maintain excellent durability, with minimal fine particle generation. This reduces the risk of dust explosions and health hazards, ensuring safe handling and storage. The higher energy density and improved combustion efficiency of torrefied biopellets translate to cost savings in transportation and storage. Their use can also reduce dependency on fossil fuels, contributing to energy security and economic resilience. Torrefied biopellets can be used in power plants as a direct substitute for coal, reducing carbon emissions and enhancing the sustainability of energy production. Industries requiring high-temperature processes can benefit from the consistent and high energy output of torrefied biopellets.  
In conclusion, the torrefaction of biopellets from unused forest biomass and wood chip residues offers a promising pathway to produce high-quality, energy-dense renewable fuel. This technology not only enhances the fuel properties of biopellets but also contributes to environmental sustainability and economic viability.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 3 Proposed Method
### RSCS6D_Pruned: A Lightweight Version
#### 3.5.1 Network Slimming Pruning  
NS (Network Slimming) pruning is a classic filter-level pruning method. It reduces model complexity and computation by cutting channels in layers with high channel numbers. In CNNs, convolutional layers are often structured as \"convolution + BN + activation function\". NS pruning uses the scale factors of BN layers to measure channels for pruning.  
Set \(z_{in}\) and \(z_{out}\) as the input and output of the BN layer, and \(B\) as the current batch. The calculation formula for BN layer is as follows:  
\[\hat{z}=\frac{z_{in}-\mu_{B}}{\sqrt{\sigma_{B}^{2}+\varepsilon}}, \tag{18}\]  
where \(\mu_{B}\) and \(\sigma_{B}\) are the mean and variance of activations, and \(\varepsilon\) is a small positive number to prevent division by zero.  
The formula for calculating \(z_{out}\) is given by Equation (19):  
\[z_{out}=\gamma\hat{z}+\beta, \tag{19}\]  
Here, \(\gamma\) and \(\beta\) are trainable affine transformation parameters. Specifically, \(\gamma\) acts as a scale factor and \(\beta\) as a shift factor. This allows the network to adaptively adjust the distribution of the normalized features. The \(\gamma\) parameter controls the scaling of the normalized activation values, while \(\beta\) provides the shifting capability. This design enables the network to flexibly adjust feature distributions. It retains the benefits of normalization while avoiding excessive constraints on feature distribution, thereby enhancing the network's flexibility and adaptability.  
NS pruning introduces a scale factor \(\gamma\) for each convolutional layer using BN. It applies \(L_{1}\) regularization to \(\gamma\), encouraging sparsity in the convolutional kernels during training. The training loss function is Equation (20):  
\[L=\sum_{(x,y)}l(f(x,W),y)+\lambda\sum_{\gamma\in\Gamma}g(\gamma), \tag{20}\]  
This loss function has two parts: the training loss and the regularization term. \((x,y)\) are input data and target labels, and \(W\) represents trainable weights. The first term is the standard CNN training loss, while the second term \(\lambda\)\(\sum\)\(g(\gamma)\) is an \(L_{1}\) regularization term on \(\gamma\). \(\lambda\) is a penalty coefficient controlling the sparsity level. In experiments, \(g(\gamma)=|\gamma|\) because \(L_{1}\) regularization is effective for parameter sparsity and widely used in model compression.
## 3 Optimization Design  
In this design process, a universal two-step design algorithm is proposed, in which PRBM optimization and reliability-based multi-objective NSGA-II algorithm optimization are combined, as shown in Figure 7. All of these combinations form an RBDO for the CM in this section. The two implementation steps of the algorithm are specified as follows:  
1. PRBM optimization The problem and the DVs are defined and formulated by establishing the mathematical relation between DVs and the objective function. This is then solved using the \"fmincon\" function in MATLAB.
2. Reliability analysis integrated multi-objective NSGA-II optimization for the CM As a rule of thumb, positioning CM optimization design usually involves a displacement amplification target. One can include other cost functions depending on the specific applications of the mechanism. To ensure the micromachining capability of the CFDM, it must make adequate output displacements in the cutting direction, and be available for high-speed machining. A tool exhibiting a high natural frequency can facilitate high-speed cutting. Therefore, in this optimization step, the two selected objective functions are: large displacement amplification ratio and high natural frequency. This paper implements the multi-objective design optimization problem using the NSGA-II algorithm in MATLAB.  
Instead of only solving a common multi-objective design optimization problem with deterministic constraints, this paper integrates the reliability analysis into the NSGA-II optimization process, which imposes the probability of violating the original conditions to form an RBDO problem. During the optimization process, the algorithm evaluates the reliability of each design in every evolution generation using the FORM. The uncertainty of the input parameters, such as cutting forces due to the inhomogeneity of the materials, the deviated dimensions of the fabricated CFDM prototype, and undesirable mismatching during the assembly, are considered and formulated as limit state functions.