esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
## 2 Related Work
### Bountakas' Helphed  
From the input of the original email to the categorization of the final output email, HELPHED, a phishing email detection model proposed by [PERSON] et al. [23], consists of six stages: the email parsing stage (S1), the content-based feature extraction stage (S2), the preprocessing stage (S3), the text-based feature extraction stage (S4), the feature selection stage (S5), and the ensemble classification stage (S6). The frame diagram of HELPHED is depicted in Figure 1.  
In Stage S1, the header and the body fields of an email are split and parsed to a vector structure, and they are associated with the respective email class according to the collection from which it originated (e.g., phishing or benign). Then, the content-based features of the message are extracted in the S2 phase, which includes several parts of the emails, such as body, syntax, header, and URL features. Subsequently, the texts are transformed into lowercase; the special characters, stopwords, and punctuation marks are deleted; the hyperlinks are converted to a consistent string; and the lemmatization process is employed in Stage S3. Later, Stage S4 utilizes the Word2 vec method [30] to automatically extract the textual features. The innovation in this phase is that both content-based and text-based features are extracted and merged into a robust hybrid feature set for the detection of the newer sophisticated phishing emails. Then, the redundant features that are identified by the mutual Information method are excluded from the hybrid feature set. In this way, only the most informative features will be used in the classification stage. Finally, Stage S6 deploys the selected hybrid features, which offer a comprehensive representation of emails to feed the ensemble learning algorithms for the detection of phishing emails by predicting the email class (namely phishing or benign). The classification algorithms involved in Stage 6 include stacking ensemble learning (SEL) and soft-voting ensemble learning (SVEL). SEL comprises two tiers, where Tier 1 employs the DT and KNN algorithms to perform the classification and classification tasks. The classification algorithms involved in Stage 6 include stacking ensemble learning (SEL) and soft-voting ensemble learning (SVEL).  
Figure 1: HELPHED architecture.  
initial classification, and Tier 2 employs the MLP algorithm to detect whether an email is phishing or benign based on the classification results of Tier 1. SVEL employs DT and KNN algorithms for the initial classification in Tier 1, and a voting function is used to judge the class in Tier 2. In our work, we have improved SVEL with an undersampling strategy and provide a detailed description in Section 3.
## 3 Results and Discussion
### Adsorption Kinetics and Adsorption Isotherms
Thus, it has been reported a chemisoprtion process (\(E_{a}\) of 32.2 kJ/mol) only for the adsorption of cadmium (II) on chitosan extracted from the _Penaus monodon_ shells [48]. \(K_{1}\) (mL/mg) and \(K_{2}\) (mg/g), affinity and capacity constants of the adsorption sites, respectively; \(K_{HB}\) (mg/g), hydrophobic binding constant which represents hydrophobic bonds between the non-polar adsorbate sites and the surface of the adsorbent; \(K_{f}\), maximum adsorption capacity constant (mg/g); and n affinity constant of the adsorbent. Other parameters are explained in the experimental section; \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **Parameter** & **SW** & **CM** & **AC1** & **AC2** & **CAC** \\ \hline \multirow{3}{*}{Langmuir} & \(K_{1}\) (L/mg) & 0.1899 & 0.65 & 0.07 & 0.003 & 0.0082 \\  & \(K_{2}\) (mg/g) & 61.74 & 10.00 & 264.8 & 5001 & 3285.5 \\  & r\({}^{2}\) & 0.9996 & 0.9729 & 0.9979 & 0.4995 & 0.3787 \\ \hline \multirow{3}{*}{Langmuir-like} & \(K_{1}\) (L/mg) & 60.16 & 9.96 & 1230.7 & 317 & 152.5 \\  & \(K_{2}\) (mg/g) & 0.20 & 0.51 & 0.014 & 0.06 & 0.25 \\  & \(K_{hb}\) (mg/g) & \(-\)0.03 & 0.50 & 0.34 & \(-\)2.6 & \(-\)2.3 \\  & \(R_{L}\) & 0.001 & 0.08 & 0.06 & 0.21 & 0.01 \\  & r\({}^{2}\) & 0.996 & 0.9879 & 0.9996 & 0.5572 & 0.4262 \\ \hline \multirow{3}{*}{Freundlich} & \(K_{f}\) (mg/g) & 9.57 & 3.90 & 17.13 & 207.8 & \(5.1\times 10^{7}\) \\  & n & 0.87 & 0.39 & 0.94 & 5.97 & 11.84 \\  & r\({}^{2}\) & 0.9987 & 0.9930 & 0.9984 & 0.9917 & 0.9382 \\ \hline \multirow{3}{*}{Sips} & \(N\) (L/g) & 53.56 & 15.23 & 5625.9 & 464.9 & 877.4 \\  & \(K\) (L/g) & 0.25 & 0.051 & 0.0022 & 0.011 & 0.025 \\  & n & 0.96 & 2.25 & 1.06 & 1.23 & 1.04 \\  & r\({}^{2}\) & 0.9996 & 0.9941 & 0.9984 & 0.439 & 0.3712 \\ \hline \multirow{3}{*}{Redlich-Peterson} & \(A\) (L/g) & 11.54 & 6758.8 & 112.8 & 59.82 & 26.88 \\  & \(K\) (L/mg) & 0.20 & 219,113 & \(2.1\times 10^{11}\) & 7922.0 & 0.009 \\  & \(\beta\) & 1.12 & 0.61 & 0.066 & 0.093 & 3.46 \\  & r\({}^{2}\) & 0.9996 & 0.9930 & 0.9984 & 0.3413 & 0.0000 \\ \hline \multirow{3}{*}{Evolich} & \(A\) (mg/g) & 51.4 & 19,932.5 & 270.7 & 22,600.3 & 37,707 \\  & \(K\) (L/mg) & 0.23 & \(6.7\times 10^{-5}\) & 0.068 & 0.0006 & 0.00071 \\  & r\({}^{2}\) & 0.9996 & 0.7137 & 0.9979 & 0.4995 & 0.3787 \\ \hline \multirow{3}{*}{Arrhenius} & \(E_{a}\) (kJ/mol) & 15.78 & 34.2 & 6.82 & 6.19 & 16.57 \\  & r\({}^{2}\) & 0.9754 & 0.9857 & 0.9998 & 0.9837 & 0.9087 \\ \hline \end{tabular}
\end{table} 
Table 5: Equilibrium adsorption and thermodynamic parameters for CV on several adsorbents. adsorbent load: 20 mg. temperature, 25 \({}^{\circ}\)C. The pH employed was 7.5, 7.2, 6.5, 7.0, and 7.2 for SW, CM, AC1, AC2, and CAC, respectively. Figure 3: \(Cont.\)
# Knowledge Sharing in Security-Sensitive Communities
## 2 Literature Review
### Homogeneous and Heterogeneous Communities  
In addition to trust, knowledge sharing, and network structure, this paper considers community homogeneity. Homogeneity refers to similarity among community members,while heterogeneity denotes diversity. Homophily, a relevant network feature, refers to the similarity between members, fostering interactions and connections [47]. In homogeneous contexts, homophilic relations form, creating strong connections [47]. Homogeneity is also linked to the echo chamber effect, where similar opinions strengthen connections [48].  
We argue that an organization's homogeneity, reflected in its shared culture regarding trust and security sensitivity, influences diversity. A community of security-sensitive organizations is homogeneous in terms of information security sensitivity. The group becomes heterogeneous if it includes both security-sensitive and non-sensitive organizations. The next section connects network properties to knowledge sharing in communities for comparison.
## 3 Proposed Methodology
### Data Acquisition  
This study conducted experiments using two publicly available datasets: CSI-HAR and StanWiFi. The details of both datasets are presented in Table 2.  
\begin{table}
\begin{tabular}{c l l l l} \hline
**Year** & **Classifier Model** & **Dataset** & \multicolumn{2}{c}{**Physical Activities**} & **Accuracy** \\ \hline
2019 & 1D-CNN [26] & Private & six casual activities & 88.13\% \\
2017 & LSTM [19] & StanWiFi & lie down; fall; walk; run; sit down; stand up & 90.50\% \\
2019 & ABLSTM [20] & StanWiFi & lie down; fall; walk; run; sit down; stand up & 97.30\% \\  & & Private & empty; jump; pick up; run; sit down; wave hand; walk & \\  & & & make phone call; check wristwatch; walk normal and & \\
2020 & Dense-LSTM [31] & Private & fast; run; jump; lie down; play guitar and piano; play & 90.00\% \\  & & & basketball & \\
2021 & LSTM-CNN [32] & Private & stand; sit, falling; standing up; stepping & 94.14\% \\
2021 & 2D-CNN [27] & CSI-HAR & lie down; fall; bend; run; sit down; stand up; walk & 95.00\% \\
2022 & CSITime [33] & StanWiFi & lie down; fall; walk; run; sit down; stand up & 98.00\% \\
2023 & MIMI-AE [28] & CSI-HAR & lie down; fall; bend; run; sit down; stand up; walk & 94.49\% \\ \hline \end{tabular}
\end{table}
Table 1: Previous works analysis using DL approaches for HAR based on CSI data.  
Figure 1: A CSI-based HAR framework using a hybrid DL network.
# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## 1 Introduction
Moreover, recent missions such as the Weather System Follow-on Microwave (WSF-M) and the upcoming Global Observation SATellite for Greenhouse gases and Water cycle (GOSAT-GW) are expected to further expand the capabilities of PMWI technology. These missions will operate at multiple frequencies, allowing for more detailed and accurate observations of atmospheric and surface parameters. Looking ahead, the launch of WSF-M1 in April 2024 and the planned launches of WSF-M2 in 2028, along with GOSAT-GW in \(\geq\)2025 carrying the Advanced Microwave Scanning Radiometer 3 (AMSR3), promise to
## 4 Experimental Results
### Restored Cover Image Analysis  
It is important to evaluate the quality of the restored image when compared with the original cover image. The reversibility cannot be guaranteed if the restored image is not the same as the original cover image. The quality of the restored image is determined using the \(PSNR\) and the \(SSIM\) values. The \(PSNR\) is the peak signal-to-noise ratio, which is a measure of the ratio of the maximum possible gray level of the image to the noise distortion that degrades the image quality. The units are decibels (\(dB\)). For a fully recovered image, if the mean squared error is zero, then the expected ideal value for the \(PSNR\) is \(\infty\)[58; 59].  
\(SSIM\) is the structural similarity index. It is a measure of the quality of the restored image based on visual perception. The \(SSIM\) value is \(1\) when the original and the recovered images are structurally similar.  
The \(PSNR\) and \(SSIM\) results for the four images, the baboon, the airplane, the boat, and the peppers, are given in Table 3. Some output results for the images that were generated during the experiment are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of** & **Block** & **Data Embedding** & **BER** \\
**the Image** & **Size** & **Rate (bpp)** & \\ \hline
**Airplane** & \(8\times 8\) & 0.0625 & 0 \\ \hline
**Baboon** & \(8\times 8\) & 0.0625 & 0 \\ \hline
**Boat** & \(8\times 8\) & 0.0625 & 0 \\ \hline
**Peppers** & \(8\times 8\) & 0.0625 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rate of information embedded and bit errors on airplane, baboon, boat and peppers images.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Name of the Image** & **Peak Signal-to-Noise Ratio (PSNR)** & **SSIM** \\ \hline
**Airplane** & \(\infty\) & 1 \\ \hline
**Baboon** & \(\infty\) & 1 \\ \hline
**Boat** & \(\infty\) & 1 \\ \hline
**Peppers** & \(\infty\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(PSNR\) and \(SSIM\) on airplane, baboon, boat and peppers images.
## 2 The Scenario, Architecture and Optimization Strategy of the UEMS for ADR
### The Optimal Operation Strategy of the UEMS for ADR  
In this paper, multiple rooms separately equipped with electric heaters are taken as a paradigm. The user comfort is defined as a temperature band. The optimal ADR strategy is described as a cooperative scheduling of the electric heaters in the multiple rooms that can obtain the minimal electricity bill while the user comfort is not violated. A concise but representative optimization problem can be formalized as Equation (1):  
\[\begin{array}{l}\min\sum\limits_{n^{\prime}=1}^{N^{\prime}}\sum\limits_{m=1}^ {M}\left(price(m)\cdot u_{n^{\prime}}^{m}\cdot p_{n^{\prime}}\cdot\Delta t\right) \\ \text{s.t. }T_{I}\leq T(n^{\prime},m)\leq T_{h}\ \forall n^{\prime}\in N,\forall m \in M^{*}\\ T(n^{\prime},m)=T_{o}^{m}-(T_{o}^{m}-T(n^{\prime},m-1))\varepsilon_{n^{\prime} }+Q_{n^{\prime}}R_{n^{\prime}}(1-\varepsilon)u_{n^{\prime}}^{m-1}\\ \varepsilon_{n^{\prime}}=e^{-\Delta t/R_{n^{\prime}}}\varepsilon_{n^{\prime}} \end{array} \tag{1}\]  
where \(N^{\prime}\) is the total number of rooms, \(M\) is the total number of time slots and \(M=T^{opt}/\Delta t\), \(T^{opt}\) is the duration of the optimization problem (min) and \(\Delta t\) is the time step (min), \(price(m)\) is the electric price of the \(m\)th time slot, \(u_{n^{\prime}}^{m}\) denotes the operating state of the electric heater in the \(n^{\prime}\)th room at the \(n\)th time slot, \(p_{n^{\prime}}\) denotes the power of the electric heater in the \(n^{\prime}\)th room, \(T_{I}\) and \(T_{h}\) are the lowest and the highest temperature of users' comfort respectively. The equivalent thermal parameter (ETP) model [14] is adopted to quantitatively describe the dynamic change process of the indoor temperature. \(T(n^{\prime},m)\) denotes the indoor temperature (\({}^{\circ}\)C) in the \(n^{\prime}\)th room at the \(m\)th time slot, \(M^{*}\) is the set of the time slots with comfort demand, \(T_{o}^{m}\) is the ambient temperature (\({}^{\circ}\)C) at the \(m\)th time slot, \(Q_{n^{\prime}}\) is the heating rate of the electric heater at the \(n^{\prime}\)th room, which is determined by the product of power and the energy efficiency ratio, i.e., \(\mu_{n^{\prime}}p_{n^{\prime}}\), \(R_{n^{\prime}}\) is the equivalent thermal resistance (\({}^{\circ}\)C/W) of the \(n^{\prime}\)th room and \(C_{n^{\prime}}\) is the equivalent heat capacity (J/\({}^{\circ}\)C) of the \(n^{\prime}\)th room.For brevity but effectiveness, those rooms are deemed to be approximately thermally decoupled and thermal interaction between those rooms is omitted.  
According to the above optimization programming, a day-ahead electric heater control 0-1 matrix \(U_{M\times N^{\prime}}\) can be eventually obtained where 1 denotes the electric heater is turned on and 0 denotes the electric heater is turned off.
# An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
## 4 Conclusions  
This study presents an interpretable machine learning melanoma recognition method that emphasizes the importance of features. The method starts with pre-processing, feature extraction (texture features using GLCM, color features using color moments, and shape features using morphological techniques) and feature ranking of dermoscopic images. Subsequently, SVM classifiers (poly, rbf, linear, sigmoid), logistic regression and Gaussian Bayesian machine learning techniques are used for training.  
An important aspect of the method is the visual ranking of feature contributions by RFE during the feature-extraction phase. The obtained feature importance ranking explains the importance of texture features. In addition, the feature selection is more convincing. Another aspect is that in the classification stage, the model is cross-validated ten times and 100 randomized experiments are used to enhance the credibility of the results and the generalization ability of the model.  
The results confirm that the feature set optimized by feature importance ranking performs better. The model demonstrated significant generalization ability on different datasets. This approach provides insights into melanoma identification and emphasizes the value of feature selection and interpretation for robust model design.  
Conceptualization, Methodology, Writing--review and editing, Z.L.; Software, Formal analysis, Writing--original draft, Q.J.; Methodology, Formal analysis, X.Y. and Y.Z.; Data curation, S.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the key specialized research and development breakthrough of Henan province (Grant No. 232102210030 to Y.Z.).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The ISIC dataset utilized in this work is openly available at: [[https://challenge.isic-archive.com/data](https://challenge.isic-archive.com/data)]([https://challenge.isic-archive.com/data](https://challenge.isic-archive.com/data)). The PH2 dataset utilized in this work is openly available at: [[https://www.fc.up.pt/addi/ph2%20](https://www.fc.up.pt/addi/ph2%20) database.html]([https://www.fc.up.pt/addi/ph2%20](https://www.fc.up.pt/addi/ph2%20) database.html).  
**Conflicts of Interest**: The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline GaussianNB [33] & 65.93 \\ LR [46] & 72.00 \\ RF [47] & 74.28 \\ KNN [48] & 75.00 \\ Proposed GaussianNB & 69.13 \\ Proposed LR & 74.80 \\ Proposed SVM & 77.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparisons of proposed model with other recent models.
## 3 Experiment and Model Validation
### Model Validation
#### 3.2.2 Comparison between Experimental Data and Simulation Results  
For validating the accuracy of the model in this experiment, comparisons were carried out between the experimental data and numerical data. Figure 6 shows the comparisons of the VOF of salt solution in the upper and lower tube walls in partial experiments and simulations. As can be seen from these results that there is good agreement between them, and the results of experimental and numerical simulations have similar reasonable trends. The trends of the VOF of the salt solution on the upper tube wall (UW) decrease from large to small while those of the lower tube wall (LW) are opposite, and the trends approximately coincide at a certain position and remain stable. The maximum deviations between simulative results and experimental results are valid with the acceptable accuracy level. In conclusion, the calculation results of these numerical simulations can be applied in this study.  
Figure 4: Scheme on the distribution of the sampling soft rubber pipes.  
Figure 5: Relationship among electrical conductivity (EC), volume fraction (VOF), and concentration (C) of salt solution.
## 3 Results
### The Composition of STSs Homologues and Phylogeny of STSs  
Bioinformatic analysis using antiSMASH database and a BLASTP search for putative sesquiterpene synthases (STSs) present in the genome of the strain \(D\). _tabescens_ CPCC 401429 revealed twelve candidate sesquiterpene biosynthetic genes (_DtSTS1-DtSTS12_, accession no. OQ442799-OQ442810) coding for the STSs (Figure 4). Investigation of the flanking regions of the STSs gene allowed the discovery of other genes coding cytochrome P450 monoxygenase or SDR reductase. As shown in Figure 5, In the phylogenetic analysis, the STSs are clustered in three categories: (i) \(\Delta\)6 -protoilludene type (Clade III), this group includes DtSTS3, DtSTS6, and DtSTS7, which cluster with Pro1 from _A. gallica_[52], PRO1_ARMOS from _A. ostoyae_, Arg6 and Arg7 from _Agrocybe aegerita_[10]. This specificity also counts for protoilludene synthases from _Omphalotus olearius_ and _S. hirsutum_. It has been demonstrated that \(\Delta\)6-protoilludene is the precursor for melleolides that have shown antitumor and antimicrobial activities [4]. (ii) DtSTS5, DtSTS11, and DtSTS12 are closely related to Agr1 and Agr3 from _A. aegerita_[10], Cop1-Cop3 from _C. cinerea_[8], and Omp3 from _Omphalotus olearius_[9]. These STSs have been characterized in previous genome-wide studies, in which _S. cerevisiae_ and/or _E. coli_ served as heterologous systems [11]. The STSs of this group could utilize a 1,10-cyclization of (2\(E\), 6_E_)-farnesyl pyrophosphate (FPP) to generate sesquiterpenes derived from an (_E_, _E_)-germacradienyl cation. Cop1 and Cop2 synthesize germacrene A as the major product, while Cop3 and Omp1 are \(\alpha\)-muurolene synthases [8; 9]. Omp3 accumulated several sesquiterpenes including \(\alpha\)-muurolene, \(\beta\)-elemene, \(\delta\)-cadinene, and selina-4, 7-diene [8; 9]. (iii) The residual six STSs were grouped into Clade IV, this group includes few members, i.e., Cop6 from _C. cinerea_, ShSTS1 from _S. hirsutum_, Omp9 and Omp10 from _O. olearius_. Cop6 is \(\alpha\)-cuprenene synthase [8], Omp9 could synthesize \(\alpha\)-barbateate (57%) and \(\beta\)-barbateate (21%), while Omp10 synthesizes daucene (21%), trans-dauca-4 (11),8-diene (71%) [9]. This implied that STSs in Clade IV might produce diverse sesquiterpene compounds.  
Figure 4: The genes (gene clusters) encoding sesquiterpene synthases (STSs) obtained from \(D\). _tabescens_ CPCC 401429. Note: sesquiterpene synthases DtSTS1-DtSTS1. DH, dehydrogenase; \(\alpha\)-KGD, \(\alpha\)-KG dependent dioxygenase; P450, P450 monooxygenase. The arrows represent the genes.
## 3 Control Methods
### Docking Method
#### 3.3.2 Docking Controller  
According to the relative positioning, a piecewise controller based on the PID algorithm is proposed to guide docking as in Equation (5). The control inputs \(v\) and \(\omega\) refer to linear velocity and angular velocity, respectively.  
\[v,\omega=k_{d}f_{d}(d-d^{*})+k_{x}f_{x}(x-x^{*})+k_{\theta}f_{\theta}(\theta- \theta^{*}) \tag{5}\]The piecewise controller aims to minimize the errors of three variables \((\Delta x=x-x^{*},\Delta d=d-d^{*},\Delta\theta=\theta-\theta^{*})\) referring to the target feature values. The control flowchart and switching scheme between different states are shown in Figure 10.  
* Image-guided approaching controller. In the far range, the docking controller is based on an image-guided approaching scheme. \(v\) and \(\omega\) are controlled and \(k_{x}\) and \(k_{\theta}\) in image coordinates with pixel units are scaled up to minimize \(\Delta x\) and \(\Delta\theta\). The PID controller is used, expressed as \(f\). When the size of the color marker is larger than the threshold or the Apriltag or an infrared signal is detected, it switches to the next corresponding subroutine.
* Position-based approaching controller. In the middle range, the coordinates including the position and yaw angle can be calculated in time. \(k_{\theta}\) is scaled up to control \(\omega\) to align the heading and \(v\) is inversely proportional to \(\Delta\theta\) and directly proportional to \(\Delta d\). \(f\) is a PI controller. If the distance between two robots \(\Delta d\) is less than a certain value, the later robot moves backward. When the later robot moves into the infrared range, the infrared-based subroutine is called.
* Infrared-based docking controller. In the near range, \(k_{d}\) is scaled up to control \(v\) to minimize the distance and \(k_{\theta}\) is adjusted to control \(\omega\) until the infrared signal reaches a threshold. \(f\) is a PI controller.  
Once the corresponding sensor detects nothing in three subroutines, it will move backward to search for the markers and infrared signal. After docking, the CAN bus is connected and a confirmation frame is communicated.