esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection
## 3 Methodology
### Enhancing Naive Bayes Models with Transfer Learning and Incremental Learning
#### 3.6.1 Transfer Learning  
Transfer learning, a methodology widely used in machine learning, involves leveraging knowledge gained from one task or domain to solve another task or problem in a different domain [19]. It provides an efficient approach to model training, particularly when the target task lacks sufficient labeled data or computational resources, and time is limited.  
The significance of transfer learning can be understood from the following perspectives:  
* Efficiency in Learning:Transfer learning allows the utilization of pre-trained models, reducing the need to train models from scratch and enhancing the speed and efficiency of model training [21].
* Addressing Data Scarcity: Many real-world problems suffer from a scarcity of labeled data. Transfer learning leverages pre-trained models on large-scale datasets, enabling model fine-tuning with a smaller amount of labeled data from the target task [22].
* Enhancing Model Performance:Transfer learning can improve model performance, especially when the target task's dataset is small or noisy [23].  
To illustrate transfer learning, consider the analogy of developing a deep learning model to recognize a specific breed of cat, such as the Maine Coon. Starting from scratch would require collecting and labeling a large number of Maine Coon images and training a deep learning model, a time-consuming and challenging process. However, with transfer learning, a pre-trained model, already trained on a large-scale dataset like ImageNet, can be fine-tuned with a smaller dataset of Maine Coon images. This approach leverages the knowledge encapsulated in the pre-trained model, resulting in a high-performing Maine Coon recognition model with less labeled data and training time.  
Applying this concept to fake news detection, the complex problem can benefit from knowledge learned in related tasks. For instance, a pre-trained model on a large-scale text corpus would have learned language nuances, grammar, and context, which can be transferred to a fake news detection model. By fine-tuning the model with a smaller labeled dataset of fake and real news, its ability to detect subtle cues and patterns indicative of fake news can be improved, leading to enhanced performance.  
In this work, the naive Bayes models are employed for identifying fake news and embracing transfer learning capabilities via utilizing the partial_fit function. This functionality enables the models to adapt and refine their parameters in real time, accommodating the dynamic nature of news distribution.
## 4 Discussion
The proposed system could enhance the music-listening experience for Thai users and promote Thai artists. Ref. [24]'s work further explores the prediction of valence-arousal values in Thai music. Ref. [25]'s findings shed light on the applicability of Western models to Thai music. Ref. [26]'s cascaded technique offers a different approach to emotion classification. Table 4 shows the comparisons of recent works with our proposed method. Our experimental results demonstrate the effectiveness of the random forest model in classifying songs into distinct emotion categories. However, there is room for further refinement. To enhance the model's performance, we can consider several strategies:  
* Data augmentation: Data augmentation techniques can address the class imbalance, particularly for the \"Angry\" class. This involves creating synthetic samples to increase the representation of minority classes, potentially improving the model's ability to discern these emotions;
* Feature engineering: Exploring new features could enhance the model's discriminative power. This could involve incorporating a sentiment analysis of the lyrics or utilizing additional audio features like pitch and tone, which might offer further insights into the emotional qualities of the music;
* Hyperparameter tuning: Optimizing the model's hyperparameters is crucial for achieving better performance on unseen data. Fine-tuning these parameters could improve generalization and classification accuracy on the test set;
* Ensemble methods: Combining predictions from multiple models could yield more robust and accurate results. Employing ensemble methods, such as bagging or boosting, could leverage the strengths of different models to enhance overall classification performance. The research has several limitations that should be considered when evaluating its value. The system only classifies songs into four emotion categories, which may not be sufficient to capture the full range of human emotions. Additionally, the system is based on a dataset of 1500 Thai song lyrics, which may not represent all Thai music. Moreover, the system is based on a machine-learning model, which may not accurately capture the nuances of human emotion. Despite these limitations, the research is valuable because it provides a proof of concept for a system that can generate personalized Thai music playlists using emotion analysis. The system can potentially enhance the music-listening experience for Thai users and promote Thai artists. The system is still under development, and the authors plan to address these limitations in future work.
## 4 Results
### Simulated Results
#### 4.2.2 Power Over-Load Supervisor  
Figure 11 shows the behaviour of the power over-load supervisor strategy presented in Figure 6. In Figure 11, \(s^{*}\) refers to the power set-point received from any external manager, while \(s^{*}_{int}\), represents the inner reference managed by the mentioned over-load supervisor strategy. Note that the current tracking is supposed to operate properly, as it is illustrated in Section 4.  
Figure 10: \(R_{v}\) (0 \(\Omega\) initially) effect during grid-disconnected to grid-connected transference at \(t=50\) ms.  
In Figure 11, three different study cases framed in yellow can be observed. The first one refers to a short-time over-load demand. The second one accumulates two over-load queries. Finally, the third one evaluates a prolonged over-load target. The maximum over-load time, \(T_{maxol}\), is set to 1 s.  
Regarding the short-time scenario, when \(s^{*}\) is higher than 1, the \(o_{l_{t}}\) and \(o_{l_{o}}\) values increase. When \(s\) is lower than 1, the \(o_{l_{t}}\) time is held while \(o_{l_{o}}\) decreases progressively according to Equation (1). In this sense, the generated thermal stress on the cooling system is limited. This situation is exemplified in the accumulated over-load region. Although the set-point \(s\) is reduced transiently, the \(o_{l_{t}}\) time continues increasing due to not achieving \(o_{l_{o}}\) equal to zero. Lastly, for a prolonged-time, an over-load region can be observed as to how the over-load strategy limits the inner set-point \(s^{*}_{int}\) to 0.8 [pu]. This situation is produced when \(o_{l_{t}}\) reaches a pre-configured \(T_{maxol}\) and remains unaltered until the \(o_{l_{o}}\) reaches zero. Note that the maximum over-load time, \(T_{maxol}\), can be configured for each phase according to any specific design of the cooling system. Thus, the 1 s previously selected is just an example.
## 3 Results and Discussion
### Verification of the Local Density Approximation  
One of the implications of the R-DMFT calculation for a system in a trap is that it can examine the validity of the local density approximation (LDA). Within the LDA, a local quantity of the system in an external potential can be treated as the one computed in the translation invariant system at the chemical potential shifted by the value of the trap potential at the particular location where the quantity is estimated. Here, in particular, we compare the radius of the ferrimagnetic core between the estimate based on the LDA and the shell boundary observed in the R-DMFT calculation that explicitly includes the trap potential.  
In order to obtain the LDA estimate of the core radius, the DMFT calculations are given for the average density and total spin of the three sublattices as a function of chemical potential \(\mu=\mu_{\uparrow}=\mu_{\downarrow}\), which are shown in Figure 3. It indicates that the transition to the normal metallic phase occurs at the chemical potential \(\mu_{c}\approx 0.62\). Then, the radius of the core shell can be estimated within the LDA by solving the equation,  
\[\mu_{c}=\mu_{0}-V(R_{c})\,, \tag{8}\]  
where \(\mu_{0}=U/2=1\) indicates the chemical potential used in the R-DMFT calculations, and \(V(R_{c})=\frac{1}{2}V_{0}R_{c}^{2}\) is the trap potential at the radius \(R_{c}\). The LDA estimate of the radius \(R_{c}\) depends only on the trap strength \(V_{0}\), once \(\mu_{c}\) is determined. Table 1 lists the values of \(R_{c}\) for three different choices of the trap potential strength to contain the cloud in the supercells of \(L=10\), \(20\), and \(30\). The LDA estimate of \(R_{c}\) agrees well with the shell boundary observed in the R-DMFT calculations, which is visualized for \(L=30\) in Figure 1a.  
Figure 3: (**a**) Average density \(n\) and (**b**) total spin \(S_{z}\) per unit cell computed as a function of chemical potential \(\mu\) in the absence of the trap. The translation invariant system with the three sublattices of the Lieb lattices was considered by using DMFT, to be used for the local density approximation. The transition to the normal metal occurs at \(\mu_{c}\approx 0.62\) marked with a dotted line.
## 4 Validation and Analysis
### Study Results and Discussion
During tunneling, it is often expected that the data collected from the finished part can be used to guide the construction of the unfinished part. Over a continuous period of time, as collected data increase, the interaction between TBM disc cutters and rocks becomes clearer and a more accurate prediction model can be built. To validate this assumption, the trainings of the proposed model on different lengths of time spanning a month, half a month, and a week were conducted, and their corresponding predictions of disc cutter replacements for the following two days were compared. The comparison results are given in Table 7.  
As Table 7 shows, the best result is not as good as the result after training on the whole dataset. This is mainly because some rock-cutter interactive modes are not included in the training set of a short period of time. However, from the hypothesis in Section 2.1, for a short piece of time, the rock properties usually do not change much. Therefore, Table 7 shows that data of a short time, about 15 days, can generate an accuracy of 90.0%. When the training time length decreased from one month to half a month, the average prediction precision rate increased from 59.1% to 98.7%. Although recall rate decreased from 83.0% to 73.6%, the increase in precision was more helpful in tunneling because false positives are far more unacceptable than false negatives. The prediction performance turned down when the training time length decreased from half one month to a week, due to the small sample size of one-week training data sets. In some extreme cases, no disc cutter replacements are carried out within a whole week; therefore, the proposed method cannot learn \"replacement needed\" conditions in such situations. In Figure 8, the receiver operating characteristic (ROC) curve of these classification models is presented. It can be seen that the area under the curve (AUC) of the proposed KSVM model based on the data of 15 continuous days is the highest. In Figure 9, the predicted values of these classification models are presented. The proposed KSVM model based on one-month data performed poorly. Its predicted value oscillated throughout the whole process. For disc cutters from normal to \"replacement needed\", the prediction of one-week model was fast and accurate. However, as the orange line in Figure 9 shows, the one-week model was prone to predicting wrongly when disc cutters are used. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Test Performance**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & \(\mathbf{F_{1}}\) \\ \hline Operational data KSVM & 94.4\% & 97.0\% & 94.1\% & 95.6\% \\ Operational and geological data KSVM & 98.1\% & 98.7\% & 98.3\% & 98.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average performance results of the proposed model with and without geological inputs.
## 2 Materials and Methods
### Terahertz Spectra Analysis
Since we analyze the spectral region distant from the maximum of the [PERSON] relaxation band (0.6 cm\({}^{-1}\)) and record only its high-frequency edge, a decrease/increase in the mean relaxation time \(\tau\) is registered in our spectral range similarly to an increase/decrease in the amplitude \(\Delta\varepsilon\). In other words, there is no need to vary both these parameters; therefore, the dielectric relaxation time \(\tau\) was fixed at a value corresponding to the dielectric relaxation time of water (\(\tau\) = 7.315 ps at 30 \({}^{\circ}\)C [51]). The values of the other six model parameters: \(\Delta\varepsilon\), \(\omega_{1}\), A\({}_{1}\), \(\gamma_{1}\), A\({}_{2}\), \(\gamma_{2}\)--were selected computationally using the fitting procedure. The algorithm consisted in an extensive search of values of the model parameters and a search for a minimum value of deviation (s) of the experimental dielectric function (2) from the model function (4):  
\[\mathrm{s=}\frac{1}{\mathrm{N}}\sqrt{\sum_{i=1}^{\mathrm{N}}\left(\frac{ \varepsilon^{\prime\prime\mathrm{\leavevmode\
obreak\ ex}(\
u_{i})- \varepsilon^{\prime\prime\mathrm{mod}}(\
u_{i})}}{\varepsilon^{\prime\prime \mathrm{mod}}(\
u_{i})}\right)^{2}} \tag{5}\]  
where \(\varepsilon^{\prime\prime\mathrm{\leavevmode\
obreak\ ex}}(\
u_{i})\) and \(\varepsilon^{\prime\prime\mathrm{mod}}(\
u_{i})\)--values of the experimental and model functions \(\varepsilon^{\prime\prime\mathrm{\leavevmode\
obreak\ (\
u)}}\) for wavenumber \(\
u_{i}\); N--number of experimental points in a spectrum (in this study N = 260). A set of values of model parameters was considered optimal when the minimum value of s was attained. The fitting procedure can be considered sufficiently accurate if the value of s does not exceed 0.001 which was met in the present work. Although the spectra were recorded in the range of 1.2-120 cm\({}^{-1}\), for fitting the function \(\varepsilon^{\prime\prime\mathrm{\leavevmode\
obreak\ ex}}(\
u)\) was used in the wavenumber range from 6 to 110 cm\({}^{-1}\). This is due to the presence of spectral distortions at the edges of the spectral range of the spectrometer, or more specifically, the occurrence of a systematic error. This is a fairly stable distortion, i.e., the spectra of different samples can be reliably compared with each other, whereas calculating the physical parameters with the use of these parts of the spectrum is incorrect. The fitting procedure was sequentially implemented for each \(\varepsilon^{\prime\prime\mathrm{\leavevmode\
obreak\ ex}}(\
u)\) function obtained from the experimental data. Separately, it is important to indicate that we use only the imaginary part (4) to select the parameters of the model dielectric function (3). This is due, as mentioned above, to our desire to achieve the greatest reproducibility and the smallest spread in the values of the calculated parameters. The fitting of two functions (the real and imaginary parts of function (3)) inevitably leads to an increase in scatter which reduces the accuracy of the approach used.
# Influence of Structural Parameters on Deformation Behavior of Metal Rhombic Chain Mesh
## 3 Results
### Effects of Initial Tension on Metal Netting Deformation  
Figure 12 demonstrates the vertical displacement variations in metal netting with different initial tensions under changing concentrated and distributed loads at a solidity of 0.11. Figure 13 illustrates the deformation patterns of netting with different solidities under a concentrated load of 100 N and a distributed load of 10 N at an initial pretension of 200 N. Figure 14 shows the deformation patterns of netting with varying pretension under a concentrated load of 100 N and a distributed load of 10 N at a netting solidity of 0.11.  
Figure 11: Variation in maximum force of mesh with increasing centralized load and uniform load for different densities of mesh coating.  
Figure 12: Variation in maximum displacement of mesh coat with increasing centralized load and uniform load under different initial tensile force.  
Under a solidity of 0.11, the vertical displacement of the chain net structure increases linearly with increasing concentrated and uniformly distributed loads. At the same load level, greater initial pretension results in smaller structural deformation. For example, under concentrated loading, when the initial pretension increases from 200 N to 300 N, the maximum vertical displacement decreases by approximately 3.52%, indicating that increased pretension enhances structural stiffness and provides a certain degree of deformation suppression.  
When the initial pretension is held constant at 200 N, increasing the solidity from 0.11 to 0.29 leads to a significant reduction in overall chain net deformation, with maximum displacement reductions of 53.07% and 68.68%, respectively. These results demonstrate that solidity is a key parameter influencing the flexural stiffness of the metallic chain net, and its increase can markedly improve deformation control performance.  
A further comparison of deformation performance under different initial pretension levels reveals that, although increasing pretension reduces displacement at measurement points, the degree of improvement is significantly lower than that achieved through increased solidity. This indicates that initial pretension acts as a secondary adjustment factor in structural optimization. Overall, both solidity and initial pretension positively influence the performance of the chain net structure; however, solidity plays a more dominant role in enhancing structural stiffness and controlling deformation, making it the primary parameter governing the mechanical behavior of the metallic chain net.net deformation.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 6 Conclusions  
A satellite access point powered by solar panels was developed to provide communication services to the sensors or IoT in remote areas. The advantage of this design is that it can be easily set up, maintained, and deployed anywhere. Because it is solar-powered, it can be fully self-sustaining without user intervention. Moreover, it is low-cost. For about USD $500 in equipment costs, an ad hoc infrastructure can be established in remote or marginalized areas.  
Conceptualization, A.W. and Y.T.C.; methodology, A.W. and Y.T.C.; software, Y.T.C.; resources, A.W.; writing--original draft preparation, Y.T.C.; writing--review and editing, A.W.; project administration, A.W.; funding acquisition, A.W. All authors have read and agreed to the published version of the manuscript.  
The work described in this paper was fully supported by the Open University of Hong Kong research grant (No. R5078).  
The authors declare no conflict of interest.
# An Energy-Efficient Convolutional Neural Network Processor Architecture Based on a Systolic Array
## 3 System Modules
### Data Buffers System  
The data buffers system of the processor is shown in Figure 11. All buffers interact with DDR3 by DMA through AXI4-Stream. Since the parameters of CNNs are highly reusable, the data buffers allow the data stream of the operation process to be kept mainly on-chip, avoiding frequent data exchanges with DDR3 and reducing the power of memory access. After the DMA carries data from DDR3, the scatter decides which buffer the data is sent to. This paper optimizes the storage system of the processor on the basis of making full use of bandwidth and hardware resources and maximizing the operation throughput. Table 2 shows the parameters of all data buffers in the processor.  
Figure 11: Diagram of the data buffers system.  
Figure 10: Diagram of the PU performing fully connected computation.  
1. Image buffer: The image buffer is used to transfer image data to the PE array. It consists of two levels of memory. The first level is SRAM, which can store up to 1 Mbit of image values. The second level are the register files (RF), implemented by distributed storage, which stores one image row. Compared with SRAM, RF has lower power per bit access and can, therefore, reuse image values with less energy. The data read from RF is broadcast to all PEs, and the address generator produces the read address of RF. For example, if the length of an image row is 5, the read address sequence is 1-2-3-2-3-4-3-4-5 when the convolution step is 1. Filter buffer: Each PU has its corresponding filter buffer, which contains three sub-buffers that provide weight values to the three PEs in the PU, respectively. The sub-filter buffer also includes SRAM and RF, which can store 32 kbit weight values. Each sub-filter buffer stores one weight row in each channel of the filters that is assigned to this buffer. For example, for one conventional layer with 256 filters with a size of 3 \(\times\) 3 and channels with a size of 4, each sub-filter buffer will store (256/128) \(\times\) 4 = 8 weight rows, i.e., 8 \(\times\) 3 = 24 values. Similar to the image data, the RF stores one row of weight values used in the current computation to achieve low power data reuse. The read addresses of all filter buffers are consistent, so only one address generator is required.
3. Bias buffer: The bias buffer is used to transmit bias values to the PE array and temporarily store the partial sum and the results of operation. Each PU has its corresponding bias buffer, which is composed of FIFO. There are two main reasons for this design: (1) since the computation of each output feature map only needs to add the initial bias values once, the initial bias values will no longer be used in the subsequent operation after the convolution of the first channel; and (2) according to the proposed computing strategy, the i-th psum row produced by the last channel convolution will be used as the bias values in the computation of the i-th psum row of the next channel convolution, which is consistent with the first-in-first-out rule of FIFO.