esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Collected X Datasets**} \\ \cline{2-5}  & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **Accuracy (\%)** \\ \hline LR & 79.02 & 95.41 & 62.23 & 75.45 \\ \hline SVM & 79.24 & 95.47 & 62.26 & 75.5 \\ \hline LSTM & 76.53 & 95.01 & 56.92 & 71.04 \\ \hline CNN & 79.02 & 95.9 & 61.53 & 75.11 \\ \hline Doc2 Vec+SVM & 59.74 & 77.90 & 67.62 & 62.94 \\ \hline RoBERTa & 84.32 & 95.21 & 75.34 & 80.88 \\ \hline BILSTM + ELMo + Attention mechanism & 78.12 & 94.30 & 84.11 & 79.23 \\ \hline Our model (DLCTC) & 79.7890 & 99.02 & 87.64 & 85.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the results of four commonly used base models and three hybrid models on the collected datasets. data are both clean and semantically rich. This meticulous approach enables the model to analyze and interpret the tweets more effectively, leading to higher performance metrics. When comparing RoBERTa with DLCTC and BiLSTM + ELMo + Attention, the distinctions become evident. RoBERTa leverages transformer architecture, using self-attention across tokens to capture bidirectional context and long-range dependencies [45]. This allows it to perform very well on many NLP tasks; however, unlike DLCTC, it does not explicitly use character or concept-level embeddings, which are vital for understanding informal social media language. While RoBERTa may reach high levels of precision and recall, its approach is more generalized compared to the multi-level embedding strategy of DLCTC, which is tailored for the nuances of X data. The BiLSTM + ELMo + Attention model, while capable of capturing word-level context and emphasizing critical sequence parts through attention, still falls short in comparison to DLCTC due to its lack of character and concept embeddings. ELMo's deep contextual word representations are powerful for text understanding, but the absence of character embeddings limits the model's ability to manage variations in spelling and abbreviation common in tweets. Furthermore, without concept embeddings like those in DLCTC, the model has a limited capacity for semantic disambiguation. Thus, although both RoBERTa and BiLSTM + ELMo + Attention models show strong performance on various NLP tasks, DLCTC's design, which integrates multi-level embeddings and advanced preprocessing, offers a distinct advantage for processing complex and informal social media text.
## 5 Drug Delivery Systems
### Chitosan  
Chitosan is a naturally occurring polysaccharide composed of randomly distributed \(\beta\)-(1\(\rightarrow\)4)-linked D-glucosamine and \(N\)-acetyl-D-glucosamine. In neutral pH, the polysaccharide is water-insoluble. However, even a slight decrease in pH causes protonation of the amine group, making it water-soluble. Chitosan is not toxic and is known to be highly mucoadhesive. From an economic standpoint, it is easy to produce and is readily biodegradable.  
Additionally, chitosan has been shown to have antimicrobial properties that are possibly linked to the disruption of bacterial cell wall permeability [80]. Moreover, no significant drawbacks to its utilization have been found so far. It has a wide variety of industrial uses (aside from purely chemical to recently approved bio packaging and wound dressing), but in this paper, we will focus only on its potential as a nanocarrier [91]. Chitosan has been utilized as a nanocarrier for many EOs. A couple of studies assessed the antimicrobial effects of this drug delivery method. [PERSON] et al. [80] summarized the research on this topic, referencing studies that show the antimicrobial potential of chitosan-loaded EOs against _Staphylococcus epidermidis_, _S. aureus_, _B. cereus_, _Listeria monocytogenes_, _E. coli_, _Salmonella_ Typhi, _Shigella dysenteriae_, and _Xanthomonas campestris_. Since then, a couple of additional studies have been conducted. [PERSON] et al. [92] demonstrated curry leaf EO encapsulation and its release mechanism. [PERSON] et al. [93] showed that _Eurommia ulmooides_ seed EO encapsulated in chitosan had better antimicrobial and antioxidant properties in vitro. [PERSON] et al. [94] demonstrated that St. John's Wort EO encapsulated in a hydrogel had worse antimicrobial properties than the hydrogel loaded with only chitosan. However, when applied in a wound healing study, the combination of St. John's Wort EO and chitosan in the hydrogel effectively reduced wound size. Apart from bactericidal properties, some studies demonstrated that this delivery method can also be a viable strategy against fungi in vitro and in vivo using an animal model [95; 96; 97].  
Despite the apparent lack of significant disadvantages of this drug delivery system, in vivo human studies are still lacking. Much research has been conducted to establish chitosan-encapsulated EOs in the food and agriculture industry; however, most studies were performed in vitro only. Current in vivo studies have only been conducted on farm animals, which does not give us a clear picture of the effect on humans.
## 3 Experimental Results  
### Effect of Vibration on 1C Discharge Capacity  
#### 3.1.1 Probability Density Function of Capacity Samples  
In this paper, 32 lithium-ion cells are investigated, of which the 1C capacity before vibration are within the range of 2930 mAh to 3065 mAh and the 1C capacity after vibration are within the range of 2854 mAh to 3016 mAh. In order to obtain the distribution of 1C capacity of samples before and after vibration, samples are grouped in the first place and the number of groups is determined by the equation as follows:  
\[m\geq 1+3.3 lgn, \tag{4}\]  
where \(n\) represents the number of tested battery samples (32). Therefore, the capacity of samples should be divided into six groups.  
The median value of each group is in accordance with the abscissa, and the observed probability density of each group as the ordinate is calculated as follows:  
\[f_{n}(c)=\frac{n_{i}}{n\Delta c_{i}}, \tag{5}\]  
where \(n_{i}\) represents the number of samples in each group, \(n\) represents the number of the total samples and \(\Delta c_{i}\) represents group distance.  
Based on Equation (5), the frequency histogram of the capacity before and after vibration is obtained; then, according to the sample distribution, the normal distribution probability density curve is also obtained, as shown in Figures 5 and 6 respectively.  
Figure 4: Axis convention of cells.  
Figure 5: The frequency histogram and theoretical normal distribution density function of the capacity samples before vibration.  
From the above figures, it can be seen that the frequency histogram is well in accordance with the corresponding standard normal distribution. In more detail, the frequency histogram before vibration is in accordance with the normal distribution \(N\) (2988.9, 24.1794\({}^{2}\)), and the frequency histogram after vibration is in accordance with the normal distribution \(N\) (2920.1, 29.3257\({}^{2}\)).
## 5 Results and Evaluation
### Performance Metrics Comparison
Shallow trees, also known as weak learners, are expected to perform poorly because they do not capture enough information about the problem. On the other hand, deeper trees capture too many aspects, which leads to overfitting the training dataset, making it difficult to make an accurate prediction on unseen data. As a result, the decision tree model was created under the depth parameter of 4. This model obtained 90% overall accuracy and classification error was 0.103. While recall reached a promising value of 98%, the precision achieved only 71%, which also affected the lower F1 score of 85%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **Precision** & **F1 Score** \\ \hline Naive Bayes (NB) & 0.77 & 0.72 & 0.93 & 0.82 \\ Random Forest (RF) & 0.93 & 0.96 & 0.86 & 0.91 \\ Neural Network (NN) & 0.88 & 0.89 & 0.86 & 0.88 \\ Logistic Regression (LR) & 0.93 & 0.98 & 0.79 & 0.90 \\ Support Vector Machines (SVM) & 0.92 & 0.96 & 0.79 & 0.88 \\ Decision Tree (DT) & 0.90 & 0.98 & 0.71 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the performance measures of applied classification algorithms.
# Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
## 1 Introduction
Different statistical and ML techniques, viz., Gaussian Process Regression (GPR), Linear Regression Model (LRM), Support Vector Machine (SVM), Tree Regression (TR), and NN are examined for the uncertainty of PV and wind power prediction. Moreover, a deep learning model, the LSTM network, is also used to predict the PV and wind power output. The proposed KRLS forecasting method can handle regression issues with a smaller, non-static lexicon and obtain better outcomes than other ML models. The model-fitting approach makes it perfect for smart grid applications where numerous generators share a highly interconnected power system and spatial dependency is desired. The remainder of this paper is organized as follows. In Section 2, a review of state-of-the-art technologies is presented. In Section 3, the methodology of the proposed link scheduling and KRLS technique is described. Section 4 includes an examination of different statistical and ML approaches. Finally, Section 5 provides the concluding remarks of this article.
## 4 Experiment Overview
### Evaluation Criteria  
As explained, for each received message, the CC2420 module allowed logging the RSSI and LQI values. The RSSI is an estimation of the signal power at the receiver. In the TelosB data-sheet [28], the received signal power in dBm, \(P_{r}\), is defined by:  
\[P_{r}=RSSI\_VAL-RSSI\_OFFSET \tag{6}\]  
where \(RSSI\_OFFSET\), empirically found in the data-sheet [28], is equal to \(-45\) dBm.  
In addition to the RSSI, the LQI is also a widely used metric to characterize the quality of the radio link in low power wireless sensor networks. However, in this study, we preferred to use the PRR instead of LQI as a link quality metric, as the PRR is more meaningful at the network layer, and it is calculated over a longer time interval (i.e., several message receptions). Practically, we used the sequence number of the received messages to calculate the PRR value over batches of 20 messages.  
Figure 4 shows how the link is defined as a function of the measured PRR, following guidelines similar to those in [14]: if no packet is received between two nodes, there is _No Link_ between this pair of nodes. A link on which less than 10% of packets are received is considered as _Poor_. If 10-90% of packets are received, the link is considered as _Intermediate_. A link on which more than 90% of packets are received is considered as _Good_, and when 100% of packets are received the link is considered _Perfect_.  
Another important property is the link symmetry: between two nodes A and B, we have the oriented link A \(\rightarrow\) B that materializes the messages transmitted by A, and B \(\rightarrow\) A for messages where B is the sender. Depending on the quality of links A \(\rightarrow\) B and B \(\rightarrow\) A, we have a symmetrical (the two links exist) or asymmetrical (only one of the two links exists, either A \(\rightarrow\) B or B \(\rightarrow\) A) link between nodes A and B. To measure the asymmetry of a link, we used both the PRR and RSSI, by designing two metrics, \(asm_{AB}^{PRR}\) and \(asm_{AB}^{RSSI}\), as follows:  
\[asm_{AB}^{PRR}=(|PRR_{AB}-PRR_{BA}|) \tag{7}\]  
where \(PRR_{AB}\) is the PRR measured from A to B, and  
\[asm_{AB}^{RSSI}=(|\overline{RSSI}_{AB}-\overline{RSSI}_{BA}|) \tag{8}\]  
where \(\overline{RSSI}_{AB}\) is the mean RSSI measured for messages transmitted from A to B for each PRR measured by B. Please note that the latter metric is only meaningful if at least one message is received in both directions. Otherwise, the link is considered unidirectional. Finally, the percentage of unidirectional links, \(U_{I}\), is also an indicator we used to measure the symmetry of the links in the network.
## 2 Description of the IMM-UKF Filtering Problem
### Non-Gaussian Distribution and Sensor Fault Problems for IMM-UKF
#### 2.3.2 Sensor Faults  
When sensor faults exist, the third-order accuracy condition is no longer met for the standard UKF [42]. Therefore, for the internal IMM-UKF link, the filtering problem to be solved is how to design an effective adaptive UKF algorithm for systems given by Equation (2), when the sensor fault \(\delta\mathbf{h}_{out,k}\) exists.
## 3 Stochastic Scheduling Model of DC-Linked MGs
### Objective Function  
The objective function of scheduling cost of DC-linked multi-MGs is formulated as:  
\[\begin{array}{ll}min&\sum\limits_{t=1}^{T}C_{d}(\sum\limits_{\
u=1}^{V}P_{PV }(t)+\sum\limits_{w=1}^{W}P_{WT}(t))+\sum\limits_{t=1}^{T}\sum\limits_{g=1}^{ G}C_{g}(t)\\ &+\sum\limits_{t=1}^{T}\sum\limits_{m=1}^{M}(\eta_{1}\cdot max(P_{m}(t),0)+ \eta_{2}\cdot min(P_{m}(t),0))\end{array} \tag{5}\]  
where \(C_{d}\) is the unit generation cost of RES; \(\eta_{1}\) and \(\eta_{2}\) are the prices of purchasing electricity from DNO and selling to DNO, respectively; \(P_{m}(t)\) is the exchanging power between MG and DNO at time \(t\) (positive for purchasing and negative for selling); \(T\) is the scheduling time horizon; \(V\), \(W\), \(G\) and \(M\) are the numbers of PVs, WTs, synchronous generators and MGs, respectively.  
In order to deal with the uncertainties, the output powers of PV, WT and load demand are modeled as random variables \(\mathrm{P}_{PV\omega}(t)\), \(\mathrm{P}_{WT\omega}(t)\) and \(\mathrm{P}_{l\omega}(t)\). In stochastic programming, \(\mathrm{P}_{PV\omega}(t)\), \(\mathrm{P}_{WT\omega}(t)\) and \(\mathrm{P}_{l\omega}(t)\) are denoted using a finite set of scenarios \(\omega\in\Omega\). Each scenario \(\omega\) consists of a vector of PV, WT, and load:  
\[\begin{array}{cc}\textit{Scenario}&\omega=\left\{\mathrm{P}_{PV\omega}(t), \mathrm{P}_{WT\omega}(t),\mathrm{P}_{l\omega}(t)\right\}_{t\in T}\end{array} \tag{6}\]  
where \(\mathrm{P}_{PV\omega}(t)\), \(\mathrm{P}_{WT\omega}(t)\) and \(\mathrm{P}_{l\omega}(t)\) are the power values of PV, WT and load in scenario \(\omega\) at time \(t\), respectively.  
Each scenario \(\omega\) has a probability of occurrence \(p(\omega)\), in such a way that the sum of the probabilities over all scenarios is equal to 1, i.e., \(\sum\limits_{\omega=1}^{\Omega}p(\omega)=1\).  
Using the stochastic programming, the objective function can be reformulated by adding random variables and conditional value-at-risk (\(CVaR\)):  
\[\begin{array}{cc}\textit{min}&C_{\textit{ert}}+\beta CVaR\end{array} \tag{7}\]  
where \(C_{\textit{ert}}\) is the expected cost of scheduling, and the \(\beta CVaR\) is \(CVaR\) multiply a risk coefficient \(\beta\). Due to the cost of the scheduling is related to the value of \(\beta\), the system operator can make reasonable operating scheduling decision according to the risk preference degree.  
\[\begin{array}{cc}C_{\textit{ert}}=\sum\limits_{\omega=1}^{\Omega}p(\omega) \big{[}\sum\limits_{t=1}^{T}C_{d}(\sum\limits_{v=1}^{V}\mathrm{P}_{PV\omega}(t )+\sum\limits_{w=1}^{W}P_{WT\omega}(t))+\sum\limits_{t=1}^{T}\sum\limits_{g=1 }^{G}C_{g}(t)\\ \hskip 14.226378 pt+\sum\limits_{t=1}^{T}\sum\limits_{m=1}^{M}\left(\eta_{1} \cdot max(P_{m}(t),0)+\eta_{2}\cdot min(P_{m}(t)),0)\right)\big{]}\end{array} \tag{8}\]  
\[\begin{array}{cc}CVaR=\textit{Min}\left\{\zeta+\frac{1}{1-\alpha}\sum \limits_{\omega=1}^{\Omega}\left(C_{\textit{ert}}-\zeta\right)p(\omega)\right\} \end{array} \tag{9}\]  
where \(\zeta\) is an auxiliary variable; \(\alpha\) is the confidence level.
# Experimental Study and Numerical Simulation of W/O Emulsion in Developing Heavy Oil Reservoirs
## 3 Results and Discussion
### Effect of temperature  
Figure 5 displays the effect of temperature on the stability of the emulsion. Figure 6 shows the effect of temperature on the viscosity of the emulsion.  
As shown in Figure 5, the water bleeding rate of the emulsion basically showed an increasing trend with the increase in temperature. The water bleeding rate was relatively low as a function of residence time extension when the temperature was below 50 \({}^{\circ}\)C, indicating that the emulsion was relatively stable. A lower temperature led to a better stability of the emulsion. The increase in temperature increased the solubility of natural emulsifiers such as asphaltene and colloid in crude oil, which, on the one hand, increased the difficulty of emulsification and made the droplets formed by stirring larger; on the other hand, the adsorption amount of emulsifiers on the oil-water interface decreased, and tended to be arranged irregularly, such that the strength of the oil-water interface film decreased, the discharge speed of the interface film increased, and the stability became worse. At the same time, an increase in temperature led to the intense thermal movement of molecules in the emulsion system, and the probability of collision and coalescence of the inner phase water droplets increased, thus accelerating the demulsification of the emulsion [29].  
As shown in Figure 6, the viscosity of the emulsion and crude oil decreased with the increase in temperature, and the viscosity ratio of emulsion to crude oil increased with the increase in temperature.
# Forest Fire Detection Using New Routing Protocol
## 7 Evaluation Measurements
### E2E Delay  
This indicates the time of packets that are sent successfully from the \(S\) node to the \(D\) node within the network [41]. In addition, the calculation of E2E delay can be performed as shown in Equation (3), where \(n\) refers to the total number of packets that are sent successfully and \(p\) refers to the packet number.  
\[\text{E2E Delay }(\text{s})=\frac{\sum_{p=1}^{n}(arrive\;time_{p}-send\;time_{p})}{number \;of\;packets} \tag{3}\]
###### Abstract  
In recent years, computational imaging, which encodes scene information into a set of measurements, has become a research focus in the field of microwave imaging. As with other typical inverse problems, the key challenge is to reduce the mutual coherences in the measurement matrix which is composed of measurement modes. Since the modes are synthesized by antennas, there is a great deal of interest in the antenna optimization for the reduction. The mechanism underlying the generation of the coherences is critical for the optimization; however, relevant research is still inadequate. In this paper, we try to address the research gap by relating the coherences to the antennas' equivalent radiation sources via spectral Green's dyad. We demonstrate that the coherences in the measurement matrix are dependent on the spatial spectral coherences of the sources, while in this relationship the imaging scenario acts as a spectral low-pass filter. Increasing the imaging range narrows the spectral constraint, which eventually increases the coherences in the measurement matrix. Full-wave electromagnetic simulations are performed for validation. We hope that our work provides a possible direction for the antenna optimization in microwave computational imaging (MCI) applications and motivates further research in this field.  
Keywords:coherence; spatial spectrum; equivalent source; microwave computational imaging +
Footnote †: journal: Applied Physics Letters
# Research on the Prediction of Operator Users' Number Portability Based on Community Detection
## 2 Modeling and Experiment Comparison  
### Modeling Ideas  
The main thought behind using machine learning algorithms to establish a predictive model in this paper is to correlate the basic information of existing users and users who have ported their phone numbers. In this study, we adopt machine learning methods for modeling and finding out the relationship between the two parties, so as to predict customers with the potential for attrition among the existing customers. Thus, in this research, it was shown that this method can be used to effectively locate and retain customers while lowering costs for the company.  
The modeling concept presented in this paper is as follows (shown in Figure 1):  
Figure 1: Modeling concept.  
Firstly, to complete data preprocessing, this research divided the data into three categories, namely numerical data, discrete data, and regional data.  
Secondly, the user numerical data generated for 6 consecutive months were modeled based on the Word2 Vec method; the new attribute collection 1 is output, which reflects the behavior change characteristics of users in a period of time before; and feature engineering processing was performed on the discrete data and the new attribute collection 1.  
A single original feature (or variable) was usually a continuous feature, categorical feature, or ordinal feature. The processing method used for a single feature in this paper is shown in Table 1.  
After constructing a multilayer community network for regional data, in this research, the new attribute collection 2 is output based on an improved method.  
Thirdly, in this study, new attribute collection features are selected to determine the appropriate feature variables.  
Fourthly, a prediction model is built to predict the tendency of existing users to leave the network, and the prediction precision of different models is compared to, finally, obtain the most suitable prediction model to predict whether existing users would leave the network.
## 3 Model Development
### Entropy-Unscertained Measure Theory
#### 3.2.2 Single-Index Unascertained Measure Matrix  
For every assessment object \(X_{i}(i=1,2,\cdots,n)\), the matrix of \(\left(\mu_{ijk}\right)_{m\times p}\) is called as the single-index measure assessment matrix of \(X_{i}\), as shown in the expression of Equation (4).  
\[\left(\mu_{ijk}\right)_{m\times p}=\left[\begin{array}{cccc}\mu_{i11}&\mu_{ i12}&\cdots&\mu_{i1p}\\ \mu_{21}&\mu_{i22}&\cdots&\mu_{i2p}\\ \vdots&\vdots&\ddots&\vdots\\ \mu_{im1}&\mu_{im2}&\cdots&\mu_{imp}\end{array}\right] \tag{4}\]  
Before establishing the single-index measure matrix, it is necessary to establish a single index measure function. At present, the construction methods of single index measure function mainly include linear type, exponential type, parabolic type and sinusoidal type, etc. No matter what type of simulation function is used, it must satisfy the limiting conditions of Equations (1)-(3). These four types of function graph are shown in Figures 1-4[37; 42].  
The corresponding expression of unascertained measure functions are as follows:  
(1)  
Function expression of linear type  
\[\left\{\begin{array}{ll}\mu_{i}(x)=\begin{cases}\frac{-x}{a_{i+1}-a_{i}}+\frac{a _{i+1}}{a_{i+1}-a_{i}}&a_{i}<x\leq a_{i+1}\\ 0&x>a_{i+1}\\ \mu_{i+1}(x)=\begin{cases}0&x\leq a_{i}\\ \frac{x}{a_{i+1}-a_{i}}-\frac{a_{i}}{a_{i+1}-a_{i}}&a_{i}<x\leq a_{i+1}\\ \end{cases}\end{array}\right. \tag{5}\]  
(2)  
Function expression of parabolic type  
Figure 1: Linear-type function.  
Figure 3: Exponential-type function.  
Figure 2: Parabolic-type function.  
\[\left\{\begin{array}{l}\mu_{i}(x)=\begin{cases}1-\left(\frac{x-a_{i}}{a_{i+1}-a_{i }}\right)^{2}&a_{i}<x\leq a_{i+1}\\ 0&x>a_{i+1}\\ \mu_{i+1}(x)=\begin{cases}0&x\leq a_{i}\\ \left(\frac{x-a_{i}}{a_{i+1}-a_{i}}\right)^{2}&a_{i}<x\leq a_{i+1}\\ \end{cases}\end{array}\right. \tag{6}\]  
(3) Function expression of exponential type  
\[\left\{\begin{array}{l}\mu_{i}(x)=\begin{cases}1-\frac{1-e^{x-a_{i}}}{1-e^{x _{i+1}-a_{i}}}&a_{i}<x\leq a_{i+1}\\ 0&x>a_{i+1}\\ \mu_{i+1}(x)=\begin{cases}0&x\leq a_{i}\\ \frac{1-e^{x-a_{i}}}{1-e^{x_{i+1}-a_{i}}}&a_{i}<x\leq a_{i+1}\\ \end{cases}\end{array}\right.\end{array}\right. \tag{7}\]  
(4) Function expression of sinusoidal type  
\[\left\{\begin{array}{l}\mu_{i}(x)=\begin{cases}\frac{1}{2}-\frac{1}{2}\sin \frac{\pi}{a_{i+1}-a_{i}}\Big{(}x-\frac{a_{i+1}-a_{i}}{2}\Big{)}&a_{i}<x\leq a _{i+1}\\ 0&x>a_{i+1}\\ \mu_{i+1}(x)=\begin{cases}0&x\leq a_{i}\\ \frac{1}{2}+\frac{1}{2}\sin\frac{\pi}{a_{i+1}-a_{i}}\Big{(}x-\frac{a_{i+1}-a_{ i}}{2}\Big{)}&a_{i}<x\leq a_{i+1}\\ \end{cases}\end{array}\right. \tag{8}\]