esa-sceva/satcom-chunk-collection huggingface dataset:
# Date Fruit Classification Based on Surface Quality Using Convolutional Neural Network Models
## 3 Materials and Methods
### Evaluation Metrics  
Evaluation metrics are used to measure the performance of models and to show how accurately a model works on a given dataset. In our paper, we measured the accuracy of the system via accuracy, precision, and recall metrics. Then, we compared the results obtained from several models to find out which model works best with our dataset. In the following, we explain each metric and its formula.  
True positives and true negatives: This refers to the images that the models predict correctly.  
False positives and false negatives: This refers to the images that the models predict incorrectly.  
The accuracy metric calculates the percentage of images that are correctly classified; this metric measures how often our models can predict correctly in terms of the overall prediction. It measures this by calculating the true positives and true negatives over the total number of predictions. The formula is shown in Equation (1):  
\[\frac{\textit{True prediction}}{\textit{total prediction}} \tag{1}\]  
Precision is a measurement that measures the percentage of correct positives that are predicted correctly out of the total positives that are predicted either correctly or incorrectly. The formula is shown in Equation (2):  
\[\frac{\textit{True positive}}{(\textit{True positive}+\textit{False positive})} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Operation** & **Resolution** & **Channels** & **Layers** \\ \hline
1 & Conv3 \(\times\) 3 & 224 \(\times\) 224 & 32 & 1 \\
2 & MBConv1, k3 \(\times\) 3 & 112 \(\times\) 112 & 16 & 1 \\
3 & MBConv6, k3 \(\times\) 3 & 112 \(\times\) 112 & 24 & 2 \\
4 & MBConv6, k5 \(\times\) 5 & 56 \(\times\) 56 & 40 & 2 \\
5 & MBConv6, k3 \(\times\) 3 & 28 \(\times\) 28 & 80 & 3 \\
6 & MBConv6, k5 \(\times\) 5 & 14 \(\times\) 14 & 112 & 3 \\
7 & MBConv6, k5 \(\times\) 5 & 14 \(\times\) 14 & 192 & 4 \\
8 & MBConv6, k3 \(\times\) 3 & 7 \(\times\) 7 & 320 & 1 \\
9 & Conv1 \(\times\) 1 \& Pooling \& FC & 7 \(\times\) 7 & 1280 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EfficientNet-B0 baseline network.  
Figure 8: Classification module.  
Recall measures the percentage of positive predictions that were correctly predicted over the true positives and the false negatives. The formula is shown in Equation (3):  
\[\frac{\textit{True positive}}{(\textit{True positive}+\textit{False negative})} \tag{3}\]  
The mean average precision is the most common metric used in deep learning to evaluate object detection models. The mAP is calculated by taking the mean precision at the overall intersection over the union. The mean average precision measures the accuracy of the algorithm. It works by measuring the number of times your model predicts correctly. To measure the mAP, we need to first compute other metrics, that is, the precision, recall, precision recall curve, and average precision.  
\[mAP\frac{1}{n}\sum_{k=1}^{k=n}=AP_{k} \tag{4}\]
# Small Infrared Target Detection via a Mexican-Hat Distribution
## 4 Experimental Results and Analysis  
The proposed method for detecting a small IR target has been implemented in MATLAB using a PC with 2.6 GHz Intel Core i7 processor and 8 GB RAM. To evaluate the proposed method, different quantitative and qualitative evaluations have been carried out on IR images with complex backgrounds. The test data consists of 100 synthetic IR images with resolution of 584 \(\times\) 468 pixels (termed as Seq.1) and 32 real-world IR images with resolution of 240 \(\times\) 320 pixels (termed as Seq.2). Each image of Seq.1 has only one target. So does Seq.2. Here, the 100 synthetic images are made by adding targets with a Gaussian distribution to real-world cloud backgrounds.
## 3 Proposed Methodology
### Proposed Bert and Recurrent Neural Network Based (RNN_Bert_b)  
The combination of Bert and RNN for text classification tasks, such as the SST-2 sentiment analysis dataset, effectively leverages the strengths of both models. Bert provides deep contextual embeddings that capture the nuances of words in their specific contexts, while RNN excels at capturing the dependencies and order within the sequence of embeddings. This combination typically yields enhanced performance on classification tasks, as the model benefits from Bert's robust contextual understanding and RNN's sequential learning capabilities. Furthermore, RNNs, especially when enhanced with attention mechanisms, can handle longer sequences more effectively than transformers alone, making this hybrid approach particularly effective for complex text classification challenges. Attention mechanisms have been introduced to enhance the performance of RNNs by enabling the model to focus on specific elements of the input sequence, rather than processing all the information equally. The attention mechanism functions by assigning attention scores to various elements of the input sequence. The attention scores are typically calculated as a weighted sum based on the relevance of each input token (word, for example) to the current output. RNN has difficulty processing long-term dependencies due to issues such as vanishing or exploding gradients. This makes it challenging for them to capture information from distant time steps. The attention mechanism provides a solution to this problem by enabling the model to focus on specific relevant time steps, regardless of their distance in the past. Figure 6 shows the flowchart of integration of [PERSON] and RNN model.  
The hyperparameters that control the behavior and performance of the RNN model combined with Bert are as follows: The maximum length of the input sequences (max_seq_len) has been set to 50. The number of training iterations over the entire dataset (epochs) is set to five. The number of samples processed in a single batch is set to 16 and 32. The learning rate (\(lr\)), which represents the step size at each iteration while moving toward a minimum of the loss function, is set to \(2\times 10^{-5}\), \(3\times 10^{-5}\), and \(5\times 10^{-5}\). The number of epochs without improvement in validation accuracy before stopping the training early (patience) is set to 1, and the maximum norm of the gradients for clipping (max_grad_norm) is set to 10. Next, the number of features in the hidden state of the RNN model, which determines the capacity of the RNN to learn temporal dependencies in the data is set to 256, and the number of classes in the classification task is set to two.  
Figure 5: Integration of the Bert model with KNN.
###### Abstract
This type of research allows us to observe that dashboards are very diverse in the educational context, both in their functionalities and in their design, since these characteristics are what define the purpose of the instrument (and its efficiency). Due to these factors, methods have been sought, and proposals made to design educational and learning analytics dashboards so that they can be adapted according to their purposes and audience, because there is no one-size-fits-all approach [7]. In the educational context, dashboards not only seek to inform tutors about student performance but can also become tools to motivate students. They can even serve as tools for students to self-regulate and compare their own results. However, not all students may respond in the same way to the information shown on a dashboard about their performance [7]. Thus, it is not only the variety of user roles in the educational context but the variety of objectives and profiles among users with the same role, which makes the development of dashboards that present learning analysis an elaborate activity. In addition, the amount of data generated and its complex structure can make the process of knowledge discovery even more difficult for less technical profiles. As can be seen, dashboards in the educational context have increased in popularity due to the benefits that their use can bring. However, to take advantage of them, it is necessary to take into account the users and the context in which they will be used. However, not only dashboards that show learning variables can be found in the educational context. As these kinds of tools provide an important means to understand data and extract information and knowledge from them, they are also employed as didactic tools for motivation and learning [1], adding more complexity to the domain. For all these reasons, the present work describes a proposal to create a technological ecosystem for dynamically tailoring dashboards no matter the data context or the data domain. Specifically, this paper is focused on illustrating how dashboards can be generated through a web service, providing different use cases within the context of developing dashboards for educational purposes [1]. To sum up, we pose the following research question:  
**RQ1. ** Is a technological ecosystem approach applicable to provide dashboards in different contexts and data domains? The rest of this paper is organized as follows. Section 2 contains background regarding the automatic generation of dashboards and visualizations. Section 3 describes the methodology followed throughout this work. Section 4 presents the dashboard generator service architecture, and Section 5 illustrates the application and integration of these services to generate dashboards with different purposes. Finally, Sections 6 and 7 discuss the results and outline the conclusions obtained through this work.
## 3 Compression Algorithm Design
### Performance Analysis of the Proposed Algorithm  
In this section, a brief comparative analysis is presented of the proposed compression algorithm (Real Time Huffman) against LEC and ALEC approaches highlighting critical advantages offered. Considering the former comparison, all three algorithms are implemented in Matlab environment and are being evaluated with respect to the compression rate achieved and compression delay assuming execution on the same personal computer. The evaluation is based upon real EEG and ECG signals, extracted either from respective open data bases or from real measurements undertaken in the context of the specific evaluation effort, which comprise the most challenging modalities in epilepsy monitoring. A brief description of the sources of the datasets as well as the execution environment is as follows.
# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
## 4 Experimental Setting and Results Analysis
### Computational Efficiency  
In order to compare the computational efficiency of the proposed method with other methods, the average running time for a total of 60 images on two image datasets is calculated and presented in Table 6. As shown in Table 6, our method runs faster than CSF, AUIF, and VEHMD. The reason for this is that our method uses five typical operators directly applied to the pixels, while AUIF and VEHMD rely on the time-consuming operation of scale decomposition to obtain various sublayers, resulting in a substantial increase in running time. A few methods require less running time than the proposed method, primarily because the models are pre-trained, including VggML and Resnet50 methods. Despite the effectiveness of these methods, the proposed method yields better results in terms of subjective and objective metrics.
# A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump
## 1 Introduction
A unique feature extraction approach was proposed to handle field vibration data in which features were varying with the development of damage in the impellers. The efficiency of the devised strategy was proved with a case study, involving two field datasets. [PERSON] [13] developed a methodology for estimating the _RUL_ for slurry pumps, which was based on the fusion of data obtained from several vibration sensors. The fusion methodology allowed for the building of more reliable degradation indexes. After that, Kalman Filter method was used to estimate the parameters of a deterioration trend line for predicting the trend of the developed degradation index. Finally, by analytically extrapolating the state-space model to a pre-defined threshold, an interval estimation of the _RUL_ was achieved. [PERSON] [14] studied the degradation performance and the _RUL_ estimation of the impellers with the combination of a nonlinear state-space model and an efficient unscented transform method. [PERSON] [15] also proposed a moving-average wear degradation index and combined it with a general sequential Monte Carlo method for predicting the _RUL_ of a slurry pump. All the above-mentioned techniques were developed by the curve-fitting methods for processing data-driven _RUL_ prognosis and health management. Furthermore, the presented Health Degradation Trends (HDTs) in these studies were progressing in a smooth way towards the threshold lines. The problem involved with the curve-fitting method is its issue of under- and over-fitting, particularly when it is applied to an HDT having many deep crest- and trough-like structures. This situation leads towards the doubt that whether the predicted curve is indicating the true degradation of the pump impellers or not. To encounter this shortcoming of the curve-fitting method, this study opted for an unsupervised learning technique of neural networks for predicting the online _RUL_. As an outline, the conducted research aimed to develop a novel framework that can (i) filter the invalid datasets from the raw data automatically, (ii) construct and recognize the most appropriate HDT from the valid datasets with the help of PCA and a moving average method, (iii) automatically select the HDTs data points for initiating the iteration process of prediction, and (iv) develop a hybrid deep Long Short-Term Memory (LSTM) model embedded with a smart learning rate mechanism for _RUL_ prognosis, using the selected data points of HDTs.
# Research on Panorama Generation from a Multi-Camera System by Object-Distance Estimation
## 4 Experimental Results
### Visualized Analysis  
We conducted a performance comparison of our proposed method (Method C) with AutoStitch [19] (Method A) and the traditional cylindrical panoramic stitching method [32] (Method B) using both indoor and outdoor scenes, as depicted in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cam** & **\(\alpha\)/rad** & **\(\beta\)/rad** & **\(\gamma\)/rad** & **X/cm** & **Y/cm** & **Z/cm** \\ \hline
1–2 & 0.0174 & 0.7814 & \(-\)0.0029 & 4.8081 & 0.0295 & \(-\)1.9565 \\
1–3 & 0.0091 & 1.5850 & 0.0057 & 6.5616 & \(-\)0.0294 & 6.6810 \\
1–4 & 0.0046 & 2.3532 & 0.0038 & 4.8170 & 0.0683 & \(-\)11.5291 \\
1–5 & \(-\)0.0205 & \(-\)3.1253 & \(-\)0.0108 & \(-\)0.2132 & 0.1015 & \(-\)13.3953 \\
1–6 & \(-\)0.0140 & \(-\)2.3357 & \(-\)0.0212 & \(-\)4.7553 & 0.0871 & \(-\)11.2954 \\
1–7 & \(-\)0.0011 & \(-\)1.5729 & \(-\)0.0238 & \(-\)6.8198 & 0.0953 & \(-\)6.7039 \\
1–8 & 0.0105 & \(-\)0.7723 & \(-\)0.0197 & \(-\)4.6815 & 0.0800 & \(-\)1.8884 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relative orientation parameters of the combined cameras.  
Figure 9: Images for computing relative orientation parameters (2 groups are displayed).  
Figure 11 displayed the panoramas for an outdoor scene. In Figure 11a, stitching truncation (resulting in structural discontinuity) can be observed in the roadway, highlighted with an orange box. In Figure 11b, the repetition of road lamps is evident, highlighted with the blue box. In contrast, the panoramic image generated by our algorithm (Method C) demonstrates visual consistency within the highlighted overlapping regions, as depicted in Figure 11c.  
Figure 11: Panorama of an outdoor scene. (**a**) Method A; (**b**) Method B; (**c**) Method C.  
Figure 10: The image sequences of an outdoor and indoor scene (the numbers in the picture indicate which camera numbered from 1 to 8 obtained the sub-images).  
Figure 12 displays the panoramas for an indoor scene. In Figure 12a, more truncation and ghosting of the ceiling can be noticed, as highlighted with the orange box. Furthermore, the panoramic image shows significant gaps, particularly in areas with a low-texture environment lacking distinct feature points, which are highlighted with red boxes in the overlapping regions. In Figure 12b, you can observe that the ceilings are repeatedly generated, highlighted with an orange box. In contrast, the panoramic image generated by our algorithm (Method C) is complete and seamless in the area highlighted within the same orange box, as depicted in Figure 12c.  
Figure 12: Panorama of an indoor scene. (**a**) Method A; (**b**)Method B; (**c**)Method C.  
Therefore, it becomes evident that when compared to Method A and Method B, our approach (Method C) maintains visual consistency in both indoor and outdoor scenes and effectively eliminates stitching gaps, especially in scenes with low-texture regions.
## 3 System Design
### Energy Model  
The battery-powered sensor nodes deplete their energy on continuous data transmission. Due to the deployment of SNs in the farthest location, it is difficult to recharge the batteries. Consider \(\mathrm{E}(f,D)_{t}\) as energy consumed by the SN to broadcast \(f\)-bit data to neighboring nodes and \(D\) implies the distance of two neighboring nodes. Each node senses data and broadcast to the neighboring nodes repeatedly, thereby depleting the energy of SNs. The energy consumption of SNs depends on the single-hop or multi-hop transmission. The mathematical expression for the computation of \(\mathrm{E}(f,D)_{t}\) is given by,  
\[\mathrm{E}(f,D)_{t}=\left\{\begin{array}{l}f.\mathrm{E}_{e}+f.\ e_{Pl}.D^{2},D<D_{0}\\ f.\mathrm{E}_{e}+f.\ e_{Ml}.D^{4},D\geq D_{0}\end{array}\right. \tag{1}\]  
Figure 1: WMSN model.  
The term E\({}_{e}\) represents electronics energy, \(e_{FI}\) and \(e_{Mi}\) indicates free space energy loss and multipath energy loss, respectively; \(D^{2}\) and \(D^{4}\) depicts power loss in single-hop and multi-hop transmission, respectively. Moreover, the threshold distance \(D_{0}=\sqrt{\frac{e_{FI}}{e_{Mi}}}\) helps to find whether the data transmission is single-hop or multi-hop. The energy consumption according to transmission distance is computed as follows,  
\[\mathrm{E}_{r}(f)=f\cdot\mathrm{E}_{e} \tag{2}\]  
The above expression describes the energy consumption of SN to receive \(f\) data bits at distance \(D\).
## 2 The Concept of an EMC Topological Model
### The Matrix Description of the EMC Topological Model  
Considering the topological model of a ship's structure, we can determine the effective value of \(E_{l}^{(t)}\)--the resultant EM disturbance that influences the operation of the selected \(l\)-th EuT--as presented in Figure 1 and described by Equation (1). The value of the first component \(E_{l}^{(i)}\) in Equation (1), i.e.,the components \(E_{lm}^{(i)}\) in Equation (2), where \(m\in 1,2,\ldots,\ M\), can be calculated by solving the following matrix equation:  
\[ \begin{bmatrix}E_{l}^{(i)}\\ \end{bmatrix}  = \begin{bmatrix}E_{l1}^{(i)}\\ \end{bmatrix} \] (6) \[= \begin{bmatrix}d^{(i)}\\ \end{bmatrix} _{\text{MxM}}\cdot \begin{bmatrix}E^{(i)}(0)\\ \end{bmatrix} _{\text{Mx1}}-\sum_{i=1}^{M}\left(\left(\left(\begin{  
\(\mu\). In practice, the attenuation value is calculated or measured at the given frequencies [13]. The results of this can be recorded in matrix form, i.e., \(\left[a_{w}^{(i)}\right]\). The values of the other components included in Equation (1), i.e., \(E_{l}^{(o)}\) and \(E_{l}^{(e)}\), should be calculated in a similar way to (6)-(12), i.e.,  
\[ \begin{array}{c}\left[E_{l}^{(o)}\right]\\ \left[E_{l}^{(o)}\right]\\ \cdot\\ \cdot\\ E_{lj}^{(o)}\\ \cdot\\ \cdot\\ E_{lj}^{(o)}\end{array}  \tag{13}\] \[= \left[d^{(o)}\right]_{J\times J}\cdot\left[E^{(o)}(0)\right]_{J \times 1}-\sum\limits_{i=1}^{J}\left(\left(\left[sc^{(o)}\right]_{J\times P} \cdot\left[a_{w}^{(o)}\right]_{P\times J}\right)\left[E_{il}\right]_{J\times J} \right)\left[e_{i}\right]_{J\times 1}\right)-\left[a_{s}^{(o)}\right]_{J\times 1},\]  
in which  
* \(\left[d^{(o)}\right]_{J\times J}\) is a diagonal matrix of the activation coefficients of the ship's own external disturbing sources, which has the same role as and a similar form to Equation (7);
* \(\left[E^{(o)}(0)\right]_{J\times 1}\) is a vector of the source values of the ship's own external disturbing signals, which has a similar form to Equation (8);
* \(\left[sc^{o}\right]_{J\times P}\) is a matrix of the presence coefficients of the shielding planes on the propagation path of the ship's own internal disturbing signals, which has a similar form to Equation (9);
* \(\left[a_{w}^{(o)}\right]_{P\times J}\) is a matrix of the loss (attenuation) values of the ship's own external disturbing signals when passing through the shielding planes, which has a similar form to Equation (11);
* \(\left[a_{s}^{(o)}\right]_{J\times 1}\) is a vector of the space attenuation values of the disturbing signals along their propagation path from sources to the EuT, which has a similar form to Equation (12) and  
\[ \begin{array}{c}\left[E_{l}^{(e)}\right]\\ \left[E_{l}^{(e)}\right]\\ \cdot\\ \cdot\\ E_{lk}^{(e)}\\ \cdot\\ E_{lk}^{(e)}\end{array}
## 3 Bi-Level Optimal Model of a Distribution Network Comprising a Micro Energy Grid  
The power-supply structure of the micro energy grid and the distribution network were established in the previous section. Based on that structure, a bi-level optimal dispatching model of a distribution network with a micro energy grid was established. The upper level was the distribution network, and the lower level was the micro energy grid. The upper model used the minima of three objectives--the integrated operating cost of the distribution network, the network's active power loss, and the standard deviation of the voltage deviation in the distribution network. The lower model used the minimum integrated operating cost for the micro energy grid as the objective function. The exchanged power between the micro energy grid and the distribution network was set to the optimized output of the upper level, which was regarded as a known quantity in the lower optimization model. The integrated operating cost for the micro energy grid was set to the optimized output of the lower level, which was regarded as a known quantity in the upper optimization model. The upper model was optimized according to the optimal result of the lower level, and repeated until the optimal solution was achieved. The dispatching period was 24 h. The bi-level optimization model is shown in Figure 3.  
Figure 2: Operating structure of a distribution network comprising a micro energy grid.