esa-sceva/satcom-chunk-collection huggingface dataset:
# A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention
## 4 Experimental Results and Analysis
### Analysis  
All Evaluations of fusion effects in the two experiments are listed in Figures 7 and 8.  
The experiment results show that our method can locate appropriate stitching lines based on HVS and blend input images commendably. The output mosaic images have less discontinuous edges and visible stitching-line pixels, with most of them located in low-saliency or masking areas where human eyes will ignore flaws. In our method, the combination of \(\mu_{1}\), \(\mu_{2}\), \(\mu_{3}\), and \(\mu_{4}\), is crucial for tracing the stitching line. Computing with a visual property alone is likely to produce obvious mosaic trace, but an appropriate combination of parameters can stitch images seamlessly by our technique. The weight map with parameter set 2 takes the impact of several HVS properties into consideration comprehensively and has a stable performance for photos taken in similar environments. If the average luminance difference of image pairs is significant, we can increase the value of \(\mu_{2}\) so that we can make Equation (9) suit this situation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No. of Images**} & \multirow{2}{*}{**Weight Map**} & \multicolumn{2}{c}{**Canny (\(\sigma\) = 0.01)**} & \multicolumn{2}{c}{**Canny (\(\sigma\) = 1)**} \\ \cline{2-6}  & & \(\mathbf{N}\) & \(\mathbf{P}\) & \(\mathbf{N}\) & \(\mathbf{P}\) \\ \hline a & VN & 297 & 0.1250 & 96 & 0.0404 \\ b & LD & 222 & 0.1278 & 110 & 0.0633 \\ c & VM & 256 & 0.1590 & 64 & 0.0398 \\ d & VS & 233 & 0.1356 & 85 & 0.0495 \\ e & parameter set 1 & 285 & 0.1495 & 101 & 0.0530 \\ f & parameter set 2 & 274 & 0.1443 & 102 & 0.0537 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluations of fusion effects in Figure 6.  
Figure 7: Evaluations of fusion effects of white building pictures in two experiments (DS (directly seemed); MB (multi-band blending)). (**a**) \(N\) with \(\sigma\) = 0.01, (**b**) \(N\) with \(\sigma\) = 1, (**c**) \(P\) with \(\sigma\) = 0.01, and (**d**) \(P\) with \(\sigma\) = 1.
## 3 Influence Factors of the UAV-LDV System
### Similarity Relationship  
The number and distribution of the measuring points are related to the location of the excitation and structural sealant failure during the test. Theoretically, the number of measuring points should be as high as possible to accurately localize the occurrence of structural adhesive damage. However, a large number of measuring points reduce detection efficiency and increase the difficulty of analyzing the acquired data. This acts against the original purpose of developing a UAV-LDV system for rapid and mobile inspection. Due to the difference in the frequency response function at different locations of the glass curtain wall panel system [55], this section examines the effect of the measuring point location on the identification.  
As depicted in Figure 3, twelve measuring points were arranged diagonally across the panel. The method of excitation at the measuring points is employed to obtain a driving point Frequency Response Function (FRF). Point A is the hammering excitation point which is located at the geometric center of the glass panel to obtain the average first-order natural frequency. In the area where measurement points 2, 4, and 6 are located, a simulated excitation experiment was performed according to the literature [13]. The first- to fifth-order modal frequencies of the glass panel obtained by modal analysis and LDV tests are shown in Table 3.  
After testing and analysis, the normalized frequency response functions of the acceleration at points 2, 4, and 6 are shown in Figure 4. It shows that point 4 has five resonance peaks between 0 and 500 Hz, while point 2 has four resonance peaks, and point 6 only has three resonance peaks. Table 3 shows that in the first fifth-order frequency range, the resonance peak of the frequency response function does not include the second-order modal frequency at point 2, and only includes the first-, second- and fifth-order modal frequencies at point 6. As the acceleration time history should be recorded at a location where more modal frequencies can be obtained [55], point 4 is selected as a relatively preferred location.  
Similarly, the optimal locations for the measuring points' arrangement are selected at points 3, 4, 9, and 10. In order to complete the damage assessment mapping based on the SSD indicator, the number of measuring points was finalized to 9, arranged in a square (similar to the geometry of the glass curtain wall). Points 3, 4, 9, and 10 are the corners of the square as shown in Figure 5.  
Figure 4: Normalized FRF at different locations.  
Figure 5: Arrangement of measuring points on HFSGCW.
# CNN-Based Pill Image Recognition for Retrieval Systems
## 1 Introduction
Examples of such automation are the RxList Pill Identification Tool [11] and the Healthline Pill Identifier [12], which are web-based applications offering pill identification services. The concept of identifying pills from images has been studied, particularly using deep neural networks, with promising results. However, unlike these studies, our proposed approach does not only use neural networks, but also incorporates the non-parametric classifier known as k-Nearest Neighbors (k-NN) [13; 14]. The classifier k-NN is effective in developing arbitrary decision regions and can complete in polynomial time. Moreover, k-NN can obtain more convoluted decision boundaries than the usual mapping technique used in the prediction layer of a generic convolutional neural network. We summarize the contributions of our work as follows:  
1. We investigate the challenging problem of image retrieval, specifically targeting pill images. 2. We develop an efficient image retrieval system based on deep learning and the k-Nearest Neighbor (k-NN) classifier. 3. We employ a real-life dataset of pill images to evaluate the proposed system against accuracy and runtime, as well as compare the results with relevant image retrieval systems from the literature. 4. Our proposed model increased the accuracy of identifying pills form images by 10% while maintaining the same runtime as comparable methods. The paper is organized as follows. Section 2 surveys the literature for related work that has been conducted on information retrieval in general and information identification from images, specifically. The proposed method and architecture are discussed in Section 3. Experimental settings and results are detailed in Section 4. A discussion of the results and comparisons are presented in Section 5. Finally, the paper concludes in Section 6.
## 3 Optimization of Discharge Chamber Size and Magnetic Field
### Improvement of the Saddle Magnetic Field  
It can be inferred from the ionization conditions in the discharge chamber under the influence of the three conventional magnetic field distributions in the preceding section that the saddle-shaped magnetic field is more favorable for plasma production. On this basis, the influence of the magnetic field distribution on the discharge characteristics in the discharge chamber is estimated by adjusting the shape of the saddle-shaped magnetic field and the position of the central valley point.  
By varying the size of the magnet while keeping the position of the peak point constant, the center valley point of the saddle-shaped magnetic field can be smaller than 875 G, equal to 875 G, or larger than 875 G. The distances between the valley point magnetic field strength and 875 G are, successively, \(-119\) G, \(-88\) G, \(-43\) G, \(0\) G, \(41\) G, \(88\) G, and \(125\) G. The specific distributions of these seven axial magnetic fields are shown in the seven curves in Figure 11a.  
The influence of saddle-shaped magnetic fields with varied valley locations on the discharge chamber's discharge is displayed in Figure 11b. Because they are all saddle-shaped magnetic fields with little difference, the electron density distribution inside the space is similar. Nevertheless, Figure 11b demonstrates that the ionization properties under various magnetic fields remain distinct. In the same size of the discharge chamber, the variation in particle number, average electron density, and maximum electron density follow the same trend, and all three are oblique \"N\"-shaped. When the valley point is below 875 G, the particle number, average electron density, and maximum electron density increase with the height of the valley point. Moreover, they decrease and then increase when the valley point is above 875 G. This may be because the area of the resonance surface keeps rising as the valley point approaches 875 G, leading to an increase in the ionization efficiency. In a specific range above 875 G, the resonance surface decreases with the rise of the valley point, and the ionization efficiency decreases. However, a heating mechanism different from the ECR above a specific position improves the overall ionization efficiency.  
According to Figure 11b, setting the valley point within a small range lower than 875 G or at 875 G can improve the ionization efficiency inside the discharge chamber to a certain extent with a relatively simple design. Consequently, the performance of the ion source is enhanced.
## 2 Background
### Text Mining and Text Classification  
Text mining, also known as text analysis, is the automated process that uses natural language processing to transform unstructured text data into information that provides valuable insights. Text mining combines statistics, linguistics, and machine learning to create models that can predict results on new information based on their previous experience.  
Different methods and techniques for text mining can be distinguished. The basic elementary methods include word frequency, collocation, and concordance. Word frequency defines the list of the most frequently occurring words or concepts in a given text. Collocation is used to help identify words that commonly co-occur. Concordance is used to identify the context and instances of words or a set of words. These basic approaches are often used in two advanced text mining techniques, including text classification and extraction.  
Text classification is the process of assigning predefined tags or categories to unstructured text. Popular text classification tasks include topic analysis, sentiment analysis, language detection, and intent detection. Topic analysis aims to understand the main themes or subjects of a text. Sentiment analysis can be described as identifying the emotions that underlie any given text. The objective of language detection is to classify a text based on its language. Finally, intent detection aims to recognize the intentions or the purpose behind a text. Text classification is often performed using a rule-based system that uses linguistic rules to automatically detect the different linguistic structures and assign the corresponding tags. As a result, these rules typically consist of references to syntactic, morphological, and lexical patterns. In machine learning-based text classification, data is used to train a machine learning model. Hybrid systems combine rule-based systems with machine learning-based systems, typically to further increase the accuracy of the results.
## 5 Experimental Study and Discussion
### Similarity Calculation by ACI  
The local similarity between \(c_{i}\) and \(nc\) can be obtained as shown in Table 11. Recall that \(a_{1}\), \(a_{2}\), \(a_{3}\), and \(a_{4}\) are categorical attributes and \(a_{5}\) is a semantic attribute. Hence, the similarity measures \(a_{1}\), \(a_{2}\), \(a_{3}\), and \(a_{4}\) are calculated using Equation (3), and the similarity measures of \(a_{5}\) are calculated using Equation (6).  
After obtaining the nonadditive measures and local similarity, the ACI is used to aggregate them and calculate the global similarity.  
Using ACI, the similarity between \(c_{1}\) and \(nc\) is calculated from Equation (2), as follows:  
\[Sim(c_{1},nc)=\log_{\lambda}\left(\sum_{i=1}^{n}\left[\mu(A_{i})- \mu(A_{i+1})\right]\lambda^{F(x_{\mu(i)})}\right)\] \[=\log_{\lambda}([\mu(a_{3},a_{5},a_{1},a_{2},a_{4})-\mu(a_{5},a_{ 1},a_{2},a_{4})]\lambda^{sim_{3}}+[\mu(a_{5},a_{1},a_{2},a_{4})-\mu(a_{1},a_{2 },a_{4})]\lambda^{sim_{5}}+\] \[[\mu(a_{1},a_{2},a_{4})-\mu(a_{2},a_{4})]\lambda^{sim_{1}}+[\mu(a _{2},a_{4})-\mu(a_{4})]\lambda^{sim_{2}}+[\mu(a_{4})-\mu(\varnothing)]\lambda^ {sim_{4}})\] \[=\log_{10^{2}}((1-0.8029)\cdot 10^{2}\,+(0.8029-0.3360)\cdot 1 0^{2\cdot 17}\,+(0.3360-0.2765)\cdot 10^{2}+\] \[(0.2765-0.2121)\cdot 10^{2^{1}}\,+(0.2121-0)\cdot 10^{2^{1}})\] \[=0.7709\]  
The values of \(\mu(A_{i})\) are taken from Table 10. In the experiment, the similarity between \(c_{1}\), \(c_{2}\), \(c_{3}\), and \(c_{4}\) and \(nc\) are 0.7709, 0.9455, 0.9373, and 0.2752, and the corresponding \(\lambda\) values are set to \(10^{2}\), \(10^{6}\), \(10^{4}\) and \(10\) respectively. The value of \(\lambda\) is determined by the decision maker, which varies with the decision maker's different attitudes towards \(c_{i}\) and \(nc\). Notably, the decision maker is most optimistic about \(c_{2}\) and most pessimistic about \(c_{4}\); therefore, we set a larger \(\lambda\) for \(c_{2}\) and a smaller \(\lambda\) for \(c_{4}\).
# Smartphone Sensors in Motion: Advancing Traffic Safety with Mobile Technology
###### Abstract  
This research investigates the feasibility of using smartphones as reliable instruments to measure vehicle deceleration under different conditions and compares their accuracy and reliability with traditional accelerometers. The research was conducted using a passenger vehicle (Audi A6 Avant) on different road surfaces--dry, wet, and gravel--at several speed intervals (30, 50, 70, and 90 km/h). The vehicle was equipped with an XL Meter decelerometer and three different smartphones in different price ranges. Each device recorded deceleration data, which was then analyzed to evaluate accuracy and reliability. The findings show that while the smartphones show promising results on dry and gravel surfaces, their accuracy decreases at lower speeds and on wet surfaces due to the limitations of the sensors in detecting subtle deceleration values. The research also highlights that mid-range smartphones can perform comparably to higher-end models, suggesting that excessive investment in more expensive technology may not be necessary for scientific purposes. However, some differences in measurements are attributed to variations in device mounting and orientation sensitivity. In conclusion, this research supports the potential of integrating smartphone technology in vehicle testing for road safety, although it highlights critical limitations that need to be addressed for standardized use.  
vehicle deceleration; smartphone sensors; mobile measurement technology; cost-effective alternatives; sensor accuracy +
Footnote †: journal: Applied sciences
# Multi-Meta Information Embedding Enhanced BERT for Chinese Mechanics Entity Recognition
## 3 The Proposed Model
### The Encoding Layer  
An encoding layer is applied to learn the sequence feature of the output of MMIEE-BERT, usually using a CNN, long short-term memory (LSTM [38]), or a transformer encoder. This study used LSTM as the encoding model. LSTM was proposed to solve the gradient disappearance problem of traditional RNNs (recurrent neural networks). LSTM consists of three gates: a forget gate, input gate, and output gate. The basic structure of LSTM is provided in Figure 8.  
The input of the forget gate is the hidden state of previous time \(\mathbf{h_{t-1}}\) and the input vector of current time \(\mathbf{x_{t}}\). The corresponding output is \(\mathbf{f_{t}}\). The propagation formula can be written as  
\[\mathbf{f_{t}}=\delta(\mathbf{W_{f}h_{t-1}}+\mathbf{U_{f}x_{t}}+\mathbf{b_{f}}) \tag{9}\]  
where \(\mathbf{W_{f}}\), \(\mathbf{U_{f}}\), and \(\mathbf{b_{f}}\) are trainable parameters. The input of the input gate is the hidden state of previous time \(\mathbf{h_{t-1}}\) and the input vector of current time \(\mathbf{x_{t}}\). The corresponding outputs are \(\mathbf{i_{t}}\) and \(\mathbf{C_{t}^{\prime}}\). The propagation formula is \[\mathbf{i_{t}}=\delta(\mathbf{W_{i}}\mathbf{h_{t-1}}+\mathbf{U_{i}}\mathbf{x_{t}}+ \mathbf{b_{i}}) \tag{10}\]  
\[\mathbf{C^{{}^{\prime}}_{t}}=tanh(\mathbf{W_{C}}\mathbf{h_{t-1}}+\mathbf{U_{C}} \mathbf{x_{t}}+\mathbf{b_{C}}) \tag{11}\]  
where \(\mathbf{W_{i}}\), \(\mathbf{U_{i}}\), \(\mathbf{b_{i}}\), \(\mathbf{W_{C}}\), \(\mathbf{U_{C}}\), and \(\mathbf{b_{C}}\) are trainable parameters. The memory unit of LSTM is updated through the output of the forget gate and input gate. The update formula is  
\[\mathbf{C_{t}}=\mathbf{C_{t+1}}\otimes\mathbf{f_{t}}+\mathbf{i_{t}}\otimes \mathbf{C^{{}^{\prime}}_{t}} \tag{12}\]  
The input of the output gate is the hidden state of previous time \(\mathbf{h_{t-1}}\), the input vector of current time \(\mathbf{x_{t}}\), and the memory unit state of current time \(\mathbf{C_{t}}\). The outputs are \(\mathbf{o_{t}}\) and the hidden state \(\mathbf{h_{t}}\). The calculation formula is  
\[\mathbf{o_{t}}=\delta(\mathbf{W_{o}}\mathbf{h_{t-1}}+\mathbf{U_{o}}\mathbf{x_ {t}}+\mathbf{b_{o}}) \tag{13}\]  
\[\mathbf{h_{t}}=\mathbf{o_{t}}\otimes tanh(\mathbf{C_{t}}) \tag{14}\]  
However, LSTM can only model the input vector in a single direction. Therefore, this study used BiLSTM to learn the sequence information. A BiLSTM layer consists of a forward LSTM and a reverse LSTM. The forward LSTM is used to learn the future features, and the reverse LSTM is used to learn the historical features. As two LSTM units are used, the output of BiLSTM at time \(t\) is obtained by concatenating the output of the forward LSTM with the output of the reverse LSTM, which can be written as  
\[\mathbf{h_{t}}=[\mathbf{h_{t}^{F}},\mathbf{h_{t}^{B}}] \tag{15}\]  
where \(\mathbf{h_{t}}\) denotes the output of BiLSTM, \(\mathbf{h_{t}^{F}}\) denotes the output of the forward LSTM, and \(\mathbf{h_{t}^{B}}\) denotes the output of the reverse LSTM.
# Optimal Energy Management within a Microgrid: A Comparative Study
## 2 Model for Microgrid Components
### Electric Vehicle  
Nowadays, tendencies exist to make energy consumption more efficient to satisfy transportation needs. In this case, it is necessary to consider that an Electric Vehicle (EV) will be connected into a microgrid to charge its battery efficiently. Also, the EV will work as a storage system responsible for energy management and optimization within the microgrid.  
The structure of an EV is constituted mainly by an AC engine, a three-phase power rectifier (AC/DC), a storage system (battery), and a three-phase power inverter. This last component injects power from the storage system to the grid (Figure 5). The three-phase power inverter is considered as a Controlled Voltage Source (CVS), which has the ability to control the active and reactive power flow between the EV and the microgrid [27].  
The real power and the reactive power injected are represented through the Equations (6) and (7), respectively. Supposing that power loss does not exist on the CVS, the power storage in the battery and the active power injected by the CVS are the same with an equality constraint, Equation (20).  
\[P_{\mathrm{vsc}}=P_{ev-s} \tag{20}\]  
The State of Charge (SOC) of an EV in a determined time is described by the Equation (21) [28]. The energy consumption for an instant of time \(t\), \(E_{\mathrm{consumption}}(t)\), is the result of the sum of the energy storage at time \(t-1\), \(E_{\mathrm{storage}}(t-1)\), the energy consumed at time \(t\), \(E_{\mathrm{consumed}}(t)\), and the sum of the energy charged \(\sum\limits_{t=1}^{t_{2}}\left(P(t)_{\mathrm{charged}}\right)\) and the discharged energy \(\sum\limits_{t=1}^{t_{2}}\left(P(t)_{\mathrm{discharged}}\right)\) at time \(t\):  
\[\begin{split}\mathrm{SOC}(t)=E_{\mathrm{consumption}}(t)\\ =E_{\mathrm{storage}}(t-1)+\Delta t\cdot\eta c\cdot\sum\limits_{ t=1}^{t_{2}}\left(P(t)_{\mathrm{charged}}\right)\\ -E_{\mathrm{consumed}}(t)-\frac{1}{\eta d}\cdot\Delta t\cdot \sum\limits_{t=1}^{t_{2}}\left(P(t)_{\mathrm{discharged}}\right)\end{split} \tag{21}\]  
where \(\eta c\) is the charge efficiency coefficient of EV, \(\eta d\) the discharge efficiency coefficient of EV, and \(\Delta t\) the differential of operational time. It is necessary to consider that the charged power is negative \(\left(P(t)_{\mathrm{charged}}<0\right)\) and the discharged power is positive \(\left(P(t)_{\mathrm{discharged}}>0\right)\), where the sum of these powers represents the total power injected or consumed by the battery of the EV on the grid point.  
In addition, the voltage phasor in the connection node is represented by a magnitude \(V_{j}^{t_{z}}\) and an angle \(\theta_{j}^{t_{z}}\). Thus, the decision variables of the \(j\)th battery of the EV are \(\left[P_{\mathrm{charged}}^{t_{z}},P_{\mathrm{discharged}}^{t_{z}}\right]\in y_{B}\) (\(\forall t_{z}\in T\)) whereas \(\left[V_{j}^{t_{z}},\theta_{j}^{t_{z}}\right]\in y_{VH}\) (\(\forall t_{z}\in T\)).
## 3 Proposed Watermarking Framework
### Structure of Watermarking Network to Be Trained
#### 3.2.1 Pre-Processing Network for Host Image  
First, the host image pre-processing network maintains the original image's resolution and is composed of one convolutional layer (CL) with 64 filters whose strides are the same as 1. Since the embedding network's output should be similar to the host image, the input image, this network should not damage the host significantly. Thus, it consists of only one CL with 3 \(\times\) 3 filters. Nevertheless, it uses 64 filters (it means 64 channels are produced) to extract as many characteristics of the host image as possible.