esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 4 Fake Image Detection Methods--Using Handcrafted Feature Set
### Statistical Features  
It is observed that fake images have different statistical distribution cues compared to real news on social media. [PERSON] et al. [82] studied and found that people naturally share information with photos clicked with them from the incident site. Thus, ideally, the image's authenticity can be checked because various observers would also share other photos. At the same time, if it is fake, there are chances that multiple photos shared will have almost the same content. Thus, visual statistical features can determine the distributional difference between real and fake news and classify it as genuine or false. [PERSON] et al. [83] presented the spatial-temporal structural neural network architecture to model message diffusion from temporal and geographic perspectives for rumor identification. It was effective in spreading rumors, but it did not consider the spread of fraudulent photographs. [PERSON], [PERSON], and [PERSON] [84] used the GLRT-based statistical method. The detector's architecture is based on a JPEG image's reduced noise model, which considers pixel variance a quadratic function of pixel expectation. Two features of the proposed simplified noise model can be used as camera fingerprints to identify fake images. A training-free \"Generalized Likelihood Ratio Test\" (GLRT) is created using the framework of hypothesis testing theory, ensuring good detection performance for a predetermined false alarm rate. [PERSON] et al. [85] proposed various statistical features of an image by which this differentiation can be made. This paper suggested the following features:  
* Count: The presence of images in fake news. For example, how many images are present?
* Popularity: How popular is the event image over social media, such as comments and re-tweets?
* Dimension: What image size is gaining popularity compared to other images?
* The study suggested specific patterns in these statistical ratios, which are then used to classify the event as real or fake;* Issues with statistical methods:
* Statistical methods need to be researched further. Similar statistical observations can be observed with real news, too;
* Also, it does not accurately identify fake images. It only gives a diligent prediction pointing toward fake image probability.
## 3 Methodology
### Overview and Preliminary Information
#### 3.1.2 Long Short-Term Memory (LSTM) Basics  
In the basic LSTM network, the final output state can be obtained using the following equation based on a given sequence \(\{\mathbf{F}_{1},\mathbf{F}_{2},\cdots,\mathbf{F}_{t}\}\):  
\[h_{t}=\text{LSTM}(h_{t-1},\mathbf{F}_{t};\mathbf{W}) \tag{1}\]  
where \(\mathbf{W}\) is the weight matrix, \(\mathbf{b_{t}}\) is the bias vector, and \(h_{t}\) are the hidden states. The hidden states in the LSTM cell are mainly determined by the input gate \(i_{t}\), forget gate \(f_{t}\), output gate \(o_{t}\), and cell state \(c_{t}\), which can be expressed as follows:  
\[i_{t}=\sigma(\mathbf{W}_{i}[\mathbf{F}_{t},h_{t-1}]+\mathbf{b_{i}}) \tag{2}\]  
Figure 2: Schematic diagram of IPVO-LSTM structure, where “\(\oplus\)” denotes concatenation. The PO Dataset is the body pose dataset of pedestrians when they observe the blind area. The pedestrian three-dimensional (3D) pose selection method is adopted to select the pedestrian closer to the center of the road at time t and retain their 3D pose information. The pedestrian observation state calculation method is mainly used to calculate the observation state of the studied pedestrians in the blind area at time t.  
\[f_{t}=\sigma(\mathbf{W}_{f}[\mathbf{F}_{t},h_{t-1}]+\mathbf{b_{t}}) \tag{3}\] \[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot u_{t}\] (4) \[u_{t}=\tanh(\mathbf{W}_{c}[\mathbf{F}_{t},h_{t-1}]+\mathbf{b_{c}})\] (5) \[o_{t}=\sigma(\mathbf{W}_{o}[\mathbf{F}_{t},h_{t-1}]+\mathbf{b_{o}})\] (6) \[h_{t}=o_{t}\text{tanh}(c_{t}) \tag{7}\]  
where \(\odot\) denotes the element-wise product, \(u_{t}\) denotes the cell state update value, \(\mathbf{W}_{i}\), \(\mathbf{W}_{f}\), \(\mathbf{W}_{c}\), and \(\mathbf{W}_{o}\) denote the different weight matrices, \(\sigma(\cdots)\) denotes the sigmoid activation function, and \(\mathbf{b_{i}}\), \(\mathbf{b_{f}}\), \(\mathbf{b_{c}}\), and \(\mathbf{b_{o}}\) denote the different bias vectors [40].
## 3 Results
### Interaction between the Thermal and Acoustic Environment  
#### 3.2.1 The Effect of the Sound and Thermal Stimuli  
Table 5 shows the mean rating change of the acoustic comfort evaluation, relative to the condition, with and without thermal stimuli. With the introduction of thermal factors, the mean ratings changed, compared with the condition of no thermal factors. When the temperature was low, the changes of acoustic comfort ratings were mostly less than 0.03; when the temperature was medium and high, acoustic comfort was reduced; and with the increase of temperature, the reduced decline was more obvious. No matter the type of sound source, when the humidity was low, the effect on the acoustic comfort was the most obvious, but when the humidity was medium or high, it was non-significant. In the case of shouting, crying, and pager sounds, the effects of temperature and humidity were more obvious, and high temperature and low humidity had negative influences. However, with a medium temperature and humidity, the effect of improving acoustic comfort got better. In sum, the effect of thermal factors on the acoustic comfort of shouting, crying, and pager sounds was the most evident, and that of speech, TV sounds, and walking sounds was the least evident. Table 5 also shows the influence of measured time on the acoustic comfort evaluation. The occasional sound had no noticeable effect at different times, but the speech sounds and activity sounds show significant differences at different time periods. During the lunch break, for example, there was a significant reduction in these sounds due to human behavior, resulting in a higher score for the acoustic environment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Indicator**} & \multirow{2}{*}{**Sound**} & \multicolumn{2}{c}{**Without Thermal**} & \multirow{2}{*}{**Measured Time**} & \multicolumn{2}{c}{**Thermal Conditions**} \\  & & **Stimuli** & & & **Temperature** & **Humidity** \\ \hline \multirow{4}{*}{Speech sound} & Speech sound of companions & 0.18 & \multirow{4}{*}{0.11} & \multirow{4}{*}{**Temperature**} & \multirow{4}{*}{**Humidity**} \\  & Chatting sound of other users & & & & \\ \cline{1-1}  & Speech sound of staff & & & & \\ \cline{1-1}  & Shout & \(-\)0.23 & & & \\ \cline{1-1}  & Phone call & \(-\)0.11 & & & \\ \cline{1-1}  & Cry & \(-\)0.34 & & & \\ \cline{1-1}  & TV sound & 0.11 & & & \\ \cline{1-1} Activity sound & & & & & \\ \cline{1-1}  & Pager sound & \(-\)0.02 & & & \\ \cline{1-1}  & Walking sound & 0.02 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean rating change of acoustic comfort, relative to the thermal condition.
###### Abstract  
Gas detection based on photoacoustic spectroscopy (PAS) has attracted extensive attention due to its high sensitivity and large range of linearity. Herein, to achieve the simultaneous detection of the light carbon gases (CH\({}_{4}\), C\({}_{2}\)H\({}_{4}\), and C\({}_{2}\)H\({}_{6}\)), a gas detection system was constructed using a single mid-infrared tunable diode laser (central wavelength 3345 nm) source based on differential Helmholtz resonance spectroscopy (DHRS). The detection parameters (driving current, modulation depth, phase angle, etc.) were optimized under ambient pressure using the \(\mathrm{{\it l}}\)f demodulation method. With an integration time of 2 s, the detection limit reached 98.8 ppb, 252 ppb, and 33 ppb for methane, ethylene, and ethane, respectively. The validation test of the three-component mixture shows that the cross-interference can be effectively reduced by multiwavelength linear regression, and single wavelength linear regression causes large errors in the quantification of methane and ethylene.  
photoacoustic spectroscopy (PAS); multicomponent detection; interband cascade laser (ICL); Helmholtz resonance +
Footnote †: journal: Applied
## 2 The Lozi Chaotic Map
### Classic Chaotic Properties of the Dissipative Map ( \(|b|<1\))
#### 2.2.2 Fixed Points, Invariant Manifolds, and Basin of Attraction  
It is straightforward to explicitly compute the fixed points and periodic orbits of any order of \(\mathcal{L}_{a,b}\) (3) due to the piecewise linearity of the absolute value.  
When \(1-a<b<a+1\) (for \(a>0\), the regions \(\mathcal{M}^{-}\) and \(\mathcal{M}^{+}\) are both in the cone defined by theses inequalities), there exist two fixed points:  
\[\mathcal{P}^{+}=\left(\frac{1}{a+1-b},\frac{b}{a+1-b}\right),\quad\mathcal{P}^ {-}=\left(\frac{1}{1-a-b},\frac{b}{1-a-b}\right).\]  
\(\mathcal{P}^{+}\) belongs to the quadrant \(\{x\geq 0,y\geq 0\}\) and \(\mathcal{P}^{-}\) to the quadrant \(\{x<0,y<0\}\).  
For \(a>|b|+1\), that is, in the domain of the parameter space where both \(\mathcal{P}^{+}\) and \(\mathcal{P}^{-}\) coexist, it is easy to compute the local stability of these points by evaluating the corresponding eigenvalues of the Jacobian matrix of \(\mathcal{L}_{a,b}\). It can be shown that both points are saddles.  
Figure 3: The set \(\mathcal{M}\) of the plane of parameters in which the Lozi map has a strange attractor, following the first proof of Misiurewicz (4). Pink lines: \(b=0\) and \(b=1\), blue line: \(b=a\)-1, green line: \(b=4\)-2\(a\), brown line: \(b=a\sqrt{2}\)-2, red curve: \(b=\frac{a^{2}-1}{2a+1}\).  
Moreoever, Misiurewicz [10] showed that the chaotic attractor of this map belongs to the unstable invariant manifold of \(\mathcal{P}^{+}\). More exactly, the chaotic (and strange) attractor \(\tilde{\mathcal{S}}\) can be constructed from the successive forward iterations of a trapping triangular region \(T\),  
\[\tilde{\mathcal{S}}=\bigcap_{n=0}^{\infty}\mathcal{L}^{n}(T),\]  
with \(T\) being the triangle with vertices at the points \(I\), \(\mathcal{L}(I)\), and \(\mathcal{L}^{2}(I)\), where \(I\) is the point given by the intersection of the unstable manifold of the fixed point \(\mathcal{P}^{+}\) with the horizontal axis  
\[I=\left(\frac{2+a+\sqrt{a^{2}+4b}}{2(1+a-b)},0\right).\]  
The basin of attraction of the attractor is modeled by parts of the stable manifold of the fixed point \(\mathcal{P}^{-}\), as shown by Baptista et al. [14]. They considered the point \(X\) intersection of this stable manifold with the vertical axis. Straightforward computation provides  
\[X=\left(0,\frac{b(2-a+\sqrt{a^{2}+4b}}{(a-\sqrt{a^{2}+4b})(a+b-1)}\right).\]  
They considered certain point \(U\) belonging on the horizontal axis as well, first defined by Ishii [15], for which the expression is  
\[U=\left(\frac{b(2-a+\sqrt{a^{2}+4b}(a(1+b)+(1-b)\sqrt{a^{2}+4b})}{(1-a-b)(a- \sqrt{a^{2}+4b})(a-\sqrt{a^{2}+4b})(2b(b-1)+a^{2}(1+2b)+a(1-2b)\sqrt{a^{2}+4b} )},0\right).\]  
Baptista et al. showed that the basin of attraction is bounded by a polygonal line entirely characterized by the points \(U\) and \(X\) along with their successive preimages.
# Research on Frequency-Selective Output Constraint Algorithm for Active Vibration Control
## 4 Simulation Study  
### Simulation Setup  
A SDOF (single-degree of freedom) active vibration isolation platform model using an electromagnetic actuator has been chosen for the numerical simulation research [34; 35; 36; 37; 38]. The platform model is shown in Figure 3. \(F_{d}\) is the disturbance force acting on the base. It is detected by the sensor as a vibration signal \(x(n)\) and fed into the controller. \(F_{r}\) and are the response force of \(F_{d}\) and the control force generated by the AVC system. \(e(n)\) is the residual vibration detected at the test point by an accelerometer sensor. The meanings and values of the rest parameters are illustrated in Table 1. The secondary path \(S(\mathrm{s})\) from the drive signal \(u(\mathrm{n})\) to the acceleration response at the test point on mass \(m\) can be derived as Formula (22) [21]. Then, \(S(\mathrm{s})\) is transformed into the Z-domain as \(S(z)\) by using the zero-order hold method and a sample time of T\({}_{s}\) = 0.001 s (or a sample frequency \(f_{s}\) = 1000 Hz), as shown in Formula (23). An FIR model \(\hat{S}(z)\) with 128 taps has been estimated offline using the LMS system identification method with adaptation step size \(\mu_{si}=10^{-6}\) and a delay \(\Delta=1\) at sample frequency \(f_{si}=1000\)[20; 21; 22; 38]. The bode diagrams of \(S(z)\) and \(\hat{S}(z)\) are shown in Figure 4. To simplify, the primary path \(P(z)\) is set to 1 in the simulation. The adaption step size \(\mu\) and vibration source \(x(n)\) will be chosen according to different purposes.  
\[S(s)=\frac{Bls^{2}}{(ms^{2}+cs+k)(R_{e}+sL_{e})+(Bl)^{2}s} \tag{22}\]  
\[S(z)=\frac{3.546z^{2}-7.183z+3.637}{z^{3}-1.921z^{2}+0.9698z+7.125\times 10^{-20}} \tag{23}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Units** \\ \hline Proof mass & \(m\) & 0.1 & kg \\ Stiffness & \(k\) & \(5.0\times 10^{3}\) & N/m \\ Damping & \(c\) & 2.5 & \(-\) \\ Transduction coefficient & \(Bl\) & 1.5 & N/A \\ Coil resistance & \(R_{e}\) & 4 & \(\Omega\) \\ Coil inductance & \(L_{e}\) & \(1.0\times 10^{-5}\) & H \\ Amplifier gain & \(K_{g}\) & 1 & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the isolation platform for the simulation.  
Figure 3: A SDOF active vibration isolation platform model based on an electromagnetic actuator.
# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 4 Experiments and Results
### Evaluation Approach  
Evaluating ensemble classifiers is a crucial aspect of understanding their effectiveness and suitability in various areas. Prior studies have explored the assessment of ensemble techniques, shedding light on their effectiveness across different datasets and problem domains. Ensemble classifiers, which amalgamate the predictions of multiple foundational models to augment overall accuracy and resilience, have garnered considerable attention in the realm of machine learning literature. Several studies show limitations in genuine detection when employing ensemble phishing categorization, wherein research papers fail to provide insights into the methodology of detecting ensemble phishing emails [38; 39]. To circumvent these constraints, we adhered to the following guidelines that were taken into consideration to augment the resilience of the proposed approach and forestall the acquisition of deceptive outcomes.  
* Comprehensive Ensemble Composition Description: We ensured detailed and transparent presentation of the composition of our ensemble classifiers. We describe the underlying classifiers used and how they are configured. This transparency is critical for attaining replication and makes it easier to understand how the ensemble uses the unique capabilities of individual classifiers in the context of phishing email detection.
* Realistic Phishing Email Sample Deployment: We deployed realistic phishing email samples to ascertain the adaptability and effectiveness of our ensemble detection system in identifying contemporary phishing threats.
* Addressing Imbalanced Class Ratios Appropriately: We conducted evaluations of our ensemble classifiers using imbalanced class ratios to simulate real-world scenarios where benign emails substantially outnumber phishing ones. This guideline guarantees the robustness and efficacy of our ensemble approach in dealing with the practical challenges associated with class imbalance in phishing detection.
* Metric Selection Aligned with Class Imbalance: Metrics such as precision, recall, and F1 score prove more suitable for imbalanced datasets as compared to metrics influenced by class distribution, such as accuracy [40].
* Feature Importance Analysis: Many studies have often neglected the examination of feature importance in ensemble classifiers for phishing email detection. By comprehending the importance of different features, researchers can gain insights into the underlying patterns that contribute to effective phishing detection and enhance interpretability.
# A Multiarmed Bandit Approach for LTE-U/Wi-Fi Coexistence in a Multicell Scenario
## 3 Evaluation Scenario and Reference Results
For the proposed framework, two sets of simulations are proposed with different offered data rates. These two sets are detailed in Section 6. These offered data rates are modeled as UDP full buffer, as recommended by 3 GPP [3]. Besides, we want to analyze the coexistence when there is always data to transmit. In this case, the transmitter is always trying to access the wireless channel. Our goal is to maximize system throughput in a coexistence scenario between LTE-U and Wi-Fi. This is performed by automatically choosing the DC value to adapt to dynamic system conditions. First, our problem is formulated as a reinforcement learning model, which is better explained in Section 4. Then, we apply the MAB formulation as a simplification of the RL problem.
# A Novel Impact Rotary-Linear Motor Based on Decomposed Screw-Type Motion of Piezoelectric Actuator
## 4 Experimental Tests and Discussions
### Prototype Motor
#### 4.2.3 Load Capacity  
Load capacity is an important characteristic of actuators. The load capacity of rotary motion and linear motion are tested individually.  
Figure 10: The linear velocities of the prototype motor under different driving frequencies.  
Figure 9: The output motions of the prototype motor under different driving frequencies: (a) 2 kHz; (b) 4 kHz; (c) 6 kHz.  
The nylon wire is used to tow the mass and is connected to the rotary shaft. When the driving voltage is fixed at 720 V\({}_{\text{P-P}}\) with the driving frequency of 2 kHz under the duty ratio of 0%, the average rotary velocities of the motor will be tested under different loads. The load testing system and the results of average rotary velocities under different torques are shown in Figure 11.  
The nylon wire is used to tow the mass and is connected to the center of the shaft. When the driving voltage is fixed at 720 V\({}_{\text{P-P}}\) with the driving frequency of 11 kHz under the duty ratio of 100%, the average linear velocities of the motor will be tested under different loads. The load-testing system and results of average linear velocities are shown in Figure 12.  
The results in Figures 11 and 12 show that the maximum output torque and output force of the prototype are 4 \(\upmu\)Nm and 0.45 N, respectively.  
In Table 2, some proposed motors which can realize rotary and linear motions are compared with the prototype motor in terms of the working principle, the main size when removing the output shaft, the peak-to-peak driving voltage, the maximum rotary velocity, the output torque, the maximum linear velocity, and the output force. Table 2 shows that the prototype motor has higher velocity compared to the inchworm actuator which is proposed by [PERSON] [13] and the load capacity is stronger than the ultrasonic actuator which is proposed by [PERSON] [14]. The speed of motion of the prototype motor is similar to the impact motor which is proposed by [PERSON] [12]. The prototype motor has more compact structure compared to the proposed motors in references [12; 13], and the control of the prototype motor is simple compared to the proposed motor in reference [14].  
Figure 11: Load capacity of rotary motion: (**a**) testing system; (**b**) average rotary velocity under different external loads at 2 kHz.  
Figure 12: Load capacity of linear motion: (**a**) testing system; (**b**) average linear velocity under different external loads at 11 kHz.
# Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques
## 4 Results and Discussion
### MARS Modeling  
Likewise, the data were divided into two sets for training and testing with percentages of 80% and 20%, respectively. As mentioned before, MARS is sensitive to its initial parameters. Thus, there is a need for determining the most effective parameters on the MARS model with their suitable ranges. MARS effective parameters with their suitable range/value are shown in Table 2. It should be noted that the MARS model was constructed in a MATLAB environment to estimate the slope deformation of earth dams.  
Examining different conditions for designing models can give various results. Using these results, the main objective of this research can be achieved by presenting a new model for assessing slope deformation of earth dams due to earthquake shaking. Several MARS models based on ranges mentioned in Table 3 were constructed to predict slope deformation of earth dams. Then, the best MARS model was selected according to the obtained prediction performance. As mentioned earlier, MARS is able to propose a relationship between model inputs and model output. In the following, two different methods of MARS techniques are used to evaluate the slope deformation of earth dams.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Range/Value** \\ \hline Self-interactions & No \\ Prune & Yes \\ Max Functions & 5–20 \\ Generalized cross-validation penalty per knot & 0,1,2 \\ Max interactions & 2–4 \\ Threshold & \(1.00\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multivariate adaptive regression splines (MARS) effective parameters with their range/value used in the study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Basis Function** & **Values/Expression** \\ \hline BF1 = & max(0, x3 – 0.04) \\ BF2 = & max(0, x3 – 0.1) \\ BF3 = & BF2 * max(0, x6 – 0.6) \\ BF4 = & BF2 * max(0,0.6 – x6) \\ BF5 = & max(0,7.8 – x4) \\ BF6 = & max(0, x4 – 7.3) \\ BF7 =
## 2 Materials and Methods
### Physical-Layer Security Analysis: Colluding Eavesdroppers
#### 2.1.6 Intercept Probability  
The intercept probability \((P_{int})\) estimates the probability that the eavesdropper is able to intercept the information. This occurs when the wiretap channel conditions are more reliable than the main channel conditions. Mathematically, \(P_{int}\) is expressed as  
\[P_{int}=P_{r}(C_{s}<0)=P_{r}(\gamma_{B}<\gamma_{E})=1-P_{nsc}. \tag{23}\]  
Substituting (21) and (22) into (23) yields the exact and asymptotic intercept probability, respectively. It is worth mentioning that while the probability of non-zero secrecy capacity highlights the reliability level of the main channel, the intercept probability measures the intercept capabilities of the eavesdropper instead. This aids in comprehending the security implications of both channels.