esa-sceva/satcom-chunk-collection huggingface dataset:
# Mining Textual and Imagery Instagram Data during the COVID-19 Pandemic
## 1 Introduction
### Similar Studies  
A similar study [41] to the one presented here was conducted by [PERSON] and [PERSON] in the USA, where the use of Instagram is explored. They case-studied the Centers for Disease Control in South Korea, analyzing the content of their Instagram photos using Microsoft Azure Cognitive Services. Results showed that the largest classes of photos were those of 'text' and 'people'. The authors also found out that images having more human faces or human faces taking up most of the image area did not achieve a high user engagement level with respect to the other images. Additionally, there was a negative association with images with faces expressing positive or neutral emotions and user engagement.  
[PERSON] and [PERSON] in their study [42] about the disease in the United States, found that social media in general, and Instagram in particular, form a potential venue where the public can be educated on human papillomavirus vaccines (HPV) and the vaccination process. Posts' analysis revealed an association of HPV with cancer (35%) and prevention of HPV (32%). With posts scoring more 'likes', it was more probable to mention cancer (\(p=0.016\)), as well as HPV screening (\(p=0.041\)). With posts that did mention the HPV and were anti-vaccine, the probability of mentioning prevention or cancer was rather low (\(p<0.001\)), on both cases.  
In a work focusing on the pandemic evolution in Italy, [PERSON] et al. [43], employ a combination of Graph Convolutional Neural Networks (GCNs) and Long-Short-Term Memory Networks (LSTMs) to infer the parameters of the epidemiological models SIR and SIRD (Susceptible, Infected, Recovered, Deceased). A dynamic graph is exploited to model the coronavirus spread, with vertices representing places and edges corresponding to the respective spread due to movement of infected individuals. Their derived model correctly predicts the contagion curve and is also capable of projecting the total number of infected individuals under different scenarios of varying contact rate.  
Sentiment Analysis (SA) or Opinion Mining, as is occasionally called, is the process where text documents are analyzed to detect specific 'affects' or other emotion patterns towards a related product or service. It can be considered as a sub-topic on the intersection of natural language processing (NLP) and text/web mining disciplines [44]. Analyzing sentiment may find significant applications, as in [45], where the authors detect a person's mood and emotions, as well as its general personality traits and propose a recommender system for music. Personality characteristics are measured using questionnaires and text mining algorithms on social media streams, in the context of the Big Five (OCEAN) Model: openness, conscientiousness, extraversion, agreeableness, and neuroticism.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 3 Methodology
### Encryption of Data  
A method that solves one of cryptography's most difficult problems is RSA encryption [2]. The creation of keys for data encryption is the first step in the RSA algorithm. It calls for the use of two prime numbers (\(S\) and \(T\)), which are determined by primality testing. The prime numbers used in RSA must be extremely large and sufficiently spaced apart. Smaller or closer numbers are much more likely to break. Despite this, the process has been simplified by using smaller digits. \(S\) and \(T\) are the two prime numbers that are used as key factors, then multiplex factor is given by Equation (13).  
\[R=S\times T \tag{13}\]  
Here \(R\) is a multiplex key of prime factors that can be scaled using Equation (14).  
\[\lambda(R)=lcm(S-1,T-1) \tag{14}\]  
where \(\lambda(R)\) is the scaling factor that is used to make the key values stable. Now the encryption can be done using Equation (15).  
\[E=Plaintext_{m}^{e}\ mod\ R \tag{15}\]  
where \(E\) represents the final data after encryption, which is known as Cipher text. Here \(e\) is the estimated values of the plaintext normally given and \(m\) is the mobility of the message that is also initialized at the start.  
\[D=\frac{1}{e}\ mod\ d1 \tag{16}\]  
Finally, the division matrix \(D\) of message and data packages is calculated using Equation (16), where \(d1\) is the initial distance between the nodes. This whole mathematical process is shown in Figure 2 to find the appropriate cluster head sensor node, as per its security parameters. Algorithm 1 is used to calculate the overall trust value of an IoT node participating in an IoT network.  
```
Input: Node, Security, Energy Output: Trust value calculation and communication.  Trust value of the node: T_Value = (Successful_Trans + Unsuccessful_Trans)/(Trans + Successful_Trans); if(T_Value = adequate for communication); Allow communication with the node. else calculate the trust value for the security model of the node. T_Security = Access Control + Encrypted Packets + Secure Routed Packets; if(T_Security = adequate for communication); Allow communication with the node. else calculate the trust value for the energy consumption if(T_Energy = adequate for communication); Allow communication with the node. else calculate the trust value of mobility: T_Energy = Mobility + Energy_cons; if(T_Mobility = adequate for the communication); Allow the communication. else calculate the trust for the node in the reliability model. T = SentData + Energy; if(T = adequate for communication); Allow communication; else calculate the overall trust for the node. Overall_Trust=T_Value + T_Security + T_Energy+T_Mobility; if(Overall_Trust = adequate for communication); Allow the communication. else Deny communication with the node.  end.
```  
**Algorithm 1**The pseudocode for calculating Trust Value
# Simulation of Partial Discharge Induced EM Waves Using FDTD Method--A Parametric Study +
## 4 Results of Surface Discharge Simulations
### Effect of PD Pulse Width  
The top row of three graphs in Figures 9 and 10 show the effect of varying the PD pulse width on the sensor signal amplitude voltage while keeping the pulse amplitude, the length of the PD source and the length of the UHF sensor at their base values. As can be seen, the amplitude of the emitted signal is strongly affected by the pulse width. For a given PD pulse width of 2 ns or less, and with the uniform electrode configuration (Figure 10), the signals emitted by Acrylic samples are smaller compared to Teflon and XLPE samples, whereas for the non-uniform field configuration, the signals are approximately equal for all material samples. When the pulse width becomes larger than about 4 ns, their frequency spectrum narrows, and hence it becomes more difficult to detect their radiated signals within the UHF frequency spectrum. Further investigation is needed to clarify the apparently non-linear decrease in signals amplitudes with pulse width. This presents a challenge in unbounded systems such as surface discharge in outdoor insulation or corona discharge in air. As expected, the signal amplitudes for all samples decrease as the sensor distance increases from the PD source.
## 1 Introduction
### Limitation(s) and Significance of This Work  
The limitation of our Booth architecture is that it supports polynomial multiplications only up to operand lengths of 4096 bits (see Section 3.5). We utilized the Vivado IDE tool to implement a parameterized Booth multiplier in Verilog HDL. Our implementation targeted various operand sizes, and we reported results after the post-place and route on the Xilinx Virtex-7 FPGA. For the largest considered operand size of \(1024\times 1024\), our Booth accelerator utilizes 1434 slices, operates at a frequency of 523.56 MHz, completes one polynomial multiplication in 0.977 us, and the total power consumption is 927 mW. The applicability of our proposed Booth multiplier accelerator across a broad spectrum of applications, including but not restricted to elliptic curves, error-correcting codes, RS codes, and signal-processing algorithms such as filtering, convolution, and correlation, highlights its significance. More precisely, let us consider the RS codes as an example to see how our proposed Booth multiplier accelerator can be utilized. Generally, the RS codes are a type of error-correcting code commonly used in digital communications and storage systems, which requires polynomial multiplications in associated encoding and decoding operations [19]. During the RS encoding, a message polynomial must be multiplied by a generator polynomial to obtain the codeword polynomial [20]. Our proposed Booth polynomial multiplier can be employed to efficiently compute this polynomial multiplication operation, thereby accelerating the encoding process. On the other hand, during the decoding process, error locations and magnitudes are estimated based on the received codeword polynomial, and this process often involves polynomial division and other associated arithmetic operations [20; 21]. Therefore, our proposed Booth polynomial multiplier can also be utilized during these arithmetic operations to accelerate the decoding process, particularly in cases where polynomial multiplication is required. In short, our proposed Booth polynomial multiplier can be effectively integrated into RS code implementations to accelerate polynomial multiplication operations involved in (both) encoding and decoding processes. In addition to these applications, our proposed Booth multiplier accelerator can be used for post-quantum cryptographic standards such as CRYSTALS-Dilithium [22], CRYSTALS-Kyber [23], etc., with some considerable modifications to multiply the polynomial coefficients by their sizes.  
The remainder of this article is formulated as follows: Section 2 describes the related mathematical knowledge for the Schoolbook and Booth multiplication approaches. The design trade-offs for these two multiplication methods are also provided in this section. Our proposed parameterized Booth multiplier architecture is described in Section 3. We present the implementation results and comparisons to existing non-digitized bit-serial multiplication accelerators in Section 4. Finally, the article is concluded in Section 5.
## 3 Results and Discussion
### Analysis of the Pellet Surface Area by Microwave Treatment  
A BET analysis was performed to determine the specific surface area of the pellet after microwave processing. Figure 7 shows the adsorption isotherms for each pellet sample, which confirm the presence of both micropores and mesopores. We confirmed that both the specific surface area and pore volume of the pellets treated with microwaves increased (Table 6) as the microwave energy caused etching on the surface, resulting in the formation of micropores. At 1 and 3 min, the specific surface area increased, with the highest value of 2236 m\({}^{2}\)/g at 3 min. The pellets processed for 5 min demonstrated lower specific surface areas of 2086 m\({}^{2}\)/g, respectively, than those processed for 3 min. This was because the surface of the pellet, which progressed at 5 min, collapsed owing to the excessive microwave energy.
## 4 Numerical Examples and Illustrations  
### Parameters and Coefficients  
The parameters adopted in the following numerical examples are shown in Table 1. Here, it is assumed that the density \(\rho\), cross-section area \(A\), and material properties (Young's modulus \(E\) and damping ratios \(\mu_{v,w}\)) are all independent of temperature variations. It is shown that the vibration characteristics are not sensitive to these temperature-dependent variables between \(-40\)\({}^{\circ}\)C and \(40\)\({}^{\circ}\)C. In this following, it is noted that the temperature changes under consideration are not continuous values, but rather several deterministic values.  
The first two in-plane symmetric and the out-of-plane mode frequencies are plotted versus the corresponding Irvine parameter \(\lambda^{2}\) in Figure 2. It is known that the one-to-one internal resonance occurs for a specific set of system parameters, e.g., the closeness of two natural frequencies. Here, the first symmetric mode frequencies are equal to the second out-of-plane ones around the first crossover point. The natural frequencies of the system may undergo unexpected changes due to temperature variations. Due to the shift of natural frequencies, the crossover point is also shifted, so the one-to-one internal resonant  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter (Unit)** & **Value** \\ \hline Density \(\rho\) (kg/m\({}^{3}\)) & 7800 \\ Area of cross-section \(A\) (m\({}^{2}\)) & \(7.069\times 10^{-2}\) \\ Young’s modulus \(E\) (Pa) & \(2.0\times 10^{11}\) \\ Thermal expansion coefficient \(\alpha\) (\(1/^{\circ}\)C) & \(1.2\times 10^{-5}\) \\ Cable span \(L\) (m) & 200 \\ Damping ratio \(\mu_{v}/\mu_{w}\) (1) & 0.005/0.006 \\ Temperature variations \(\Delta T\) (\({}^{\circ}\)C) & \(\pm 40\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the suspended cable.  
case will be exhibited in different Irvine parameters \(\lambda^{2}\) since the temperature increases or decreases.  
In this work, we investigate internal resonances between the in-plane and out-of-plane modes, assuming that their natural frequencies are in close proximity to each other. The tension variation factor, natural frequencies, and the quadratic and cubic non-linear coefficients are given in Table 2. There are significant differences in these quadratic and cubic coefficients in warming and cooling conditions. Internal resonances in this classical quadratic and cubic non-linear system lead to strongly coupled motions, so the effective interaction coefficients with thermal effects around the first crossover point are also given in Table 3. Specifically, the tension force is decreased (\(\chi_{\Delta T}<1.0\)) and the natural frequency is also decreased in the warming condition. The absolute values of all non-linear and effective interaction coefficients increase (decrease) when the temperature decreases (increases).
## 3 Spatial Correlation Network Analysis of Air Quality in China  
### Research Methods of Spatial Correlation Network  
In recent years, Chinese scholars have mostly used the gravity model to analyze the spatial correlation network [39; 40; 41]. The gravity model originated from universal gravitation in physics and is now widely used in economics, management, humanities andsocial sciences. The gravity model not only considers geographical factors but can also analyze the dynamic evolution trend of air quality. Its calculation formula is,  
\[\begin{array}{l}Grav_{ij}\ =\ k_{ij}\frac{A_{i}A_{j}}{\frac{A_{i}A_{j}}{D_{ij} \beta}}\\ k_{ij}\ =\ A_{i}/\left(A_{i}+A_{j}\right)\end{array} \tag{1}\]  
where, \(Grav_{ij}\), \(k_{ij}\) and \(D_{ij}\) respectively represent the gravity force of the air quality between province \(i\) and province \(j\), the contribution coefficient of air quality relationship, and the shortest distance between governments. \(A_{i}\) and \(A_{j}\) represent the air quality index of province \(i\) and province \(j\). \(b\) is the distance friction coefficient. When \(b\) equals 1, the formula is more realistic and can more reasonably reflect the intensity of the air quality correlation among provinces. When the gravity value from province \(i\) to province \(j\) is greater than the average gravity value, the air quality of province \(i\) will affect the air quality of province \(j\). When the gravity value from province \(i\) to province \(j\) is less than the average gravity value, the air quality of province \(i\) will not affect the air quality of province \(j\).
# Experimental Analysis of Ultrasonic Multiple Scattering Attenuation through the Air with Fine Dust
###### Abstract  
In this study, we experimentally evaluated the application of multiple scattering theory for measuring ultrasonic attenuation. Based on the independent approximation theory, the method adopted for calculating the attenuation of coherent waves through air with fine dust is discussed. To obtain a scattering wavefield, a unique ultrasonic scattering hardware was developed, and signal processing schemes were suggested. Four cases of standard particle doses (0, 0.004, 0.008, and 0.012 g) were investigated inside a chamber. The results obtained from the experiments demonstrate that the proposed signal processing approach successfully calculates the scattering attenuation, which correlates well with the applied doses of fine dust. In addition, we discuss the irregular shape and composition of fine dust relative to the scattering cross-section.  
ultrasound; multiple scattering; attenuation; independent scattering approximation 2021
## 2 Model Free Algorithms
### Model Free Adaptive Control (MFAC)  
The MFAC method starts from the possibility of describing an SISO system in the form:  
\[y_{k+1}=f\Big{(}y_{k},\cdots,y_{k-ny},u_{k},\cdots,u_{k-n_{u}}\Big{)}\,, \tag{1}\]  
where \(y_{k}\) and \(u_{k}\) are the output, and the input at the sample times \(k\), \(n_{y}\), \(n_{u}\) are two unknown integers which determine the horizon for which past values of the output signal or control signal are taken into account.  
The method considers two assumptions, so that the model described by Equation (1) can be linearized dynamically [7]:  
_Assumption 1_: The partial derivatives of \(f(\cdots)\) with respect to all variables are continuous for all \(k\) with finite exceptions.  
_Assumption 2_: The system (1) satisfies the general Lipschitz condition: \(|\Delta y_{k+1}|\leq b|\Delta u_{k}|\) for any \(k\) and \(\Delta u_{k}\
eq 0\), where \(b>0\) is a constant, \(\Delta y_{k+1}=y_{k+1}-y_{k}\) and \(\Delta u_{k}=u_{k}-u_{k-1}\).  
These assumptions imposed on the controlled system are considered in [7] to be acceptable and reasonable, with the first being a typical constraint for nonlinear systems,and the second imposing an upper bound on the change rate of the system output driven by the change of the control signal. Most physical systems, such as those for controlling temperature, level, pressure, and others, satisfy this assumption.
## 2 System Model and Problem Formulation
### Markov Decision Process  
As was mentioned above, when users are not influenced by co-channel interference, the anti-jamming channel selection problem can be formulated as a Markov decision process (MDP), and each user's strategy is independent. Motivated by [21; 22], we define MDP as:  
**Definition 1**.: _When users are free from the influence of co-channel interference, the Markov decision process of user \(n\) can be express as \(\left(S_{n},A_{n},R_{n},T_{n}\right)\), where:_  
* \(S_{n}\) _is the discrete set of user_ \(n\)_'s environment._ \(s_{n}\left(t\right)=\left(f_{n}\left(t\right),f_{j}\left(t\right)\right),s_{n} \left(t\right)\in S_{n}\) _is the environment state of user_ \(n\) _at time_ \(t\)_._ \(f_{n}\left(t\right)\)_, and_ \(f_{j}\left(t\right)\) _represent user_ \(n\)_'s transmission channel and jamming channel, respectively. In this case, user_ \(n\)_'s state is not influenced by other users._
* \(A_{n}\) _is the channel strategy set of user_ \(n\)_;_ \(a_{n}\left(t\right)\in A_{n}\) _denotes the channel selection strategy under the state of_ \(t\) _moment; similarly, user_ \(n\)_'s strategy is not influenced by others._
* _The reward function of user_ \(n\) _is_ \(R_{n}\)_, which satisfies_ \(S_{n}\times A_{n}\to R_{n}\)_. Specifically, for every state_ \(s_{n}\left(t\right)\)_, user_ \(n\) _can obtain a reward with action_ \(a_{n}\left(t\right)\)_._
* _The state transition function_ \(T_{n}\) _satisfies_ \(S_{n}\times A_{n}\to T_{n}\)_. Moreover, it also meets the Markov property, shown as:_ \[\begin{array}{l}P\left[s_{n}\left(t+1\right)\left|s_{n}\left(t\right),a_{n} \left(t\right),...,s_{n}\left(0\right),a_{n}\left(0\right)\right]\\ =P\left[s_{n}\left(t+1\right)\left|s_{n}\left(t\right),a_{n}\left(t\right) \right|,\ a_{n}\left(t\right)\in A_{n},s_{n}\left(t\right)\in S_{n}.\end{array}\] (8)  
_For each user in the UAV group, the corresponding Markov decision process can be solved using the single Q-learning method. Optimal anti-jamming selection strategies can be derived, as well._
## 2 Materials and Methods
### Apparatus and Methodology
#### 2.2.2 Volume Measurement and Porosity Calculated  
The length and diameter of the cylinder samples were measured by a precise vernier caliper. Then, the apparent volume (\(V_{t}\)) as calculated. The effective volume (\(V_{e}\)) of the cylinder samples, which includes the solid and closed pores [32] volumes of the sample, was measured by the AccuPyc II 1340 Gas Displacement Pycnometer (Micromeritic Instrument Corporation, Norcross, GA, USA). It is a non-destructive technique using the gas displacement method to measure the volume of material. The gas gas used in this study was nitrogen. The volume of the chamber used in this study was 100 cm\({}^{3}\) (46.2 mm in diameter and 61.8 mm in height). The apparatus demonstrated good reproducibility, with an accuracy of \(\pm 0.03\%\). The effective porosity \(\varphi_{e}\) of the cylinder samples can be calculated as follows: \(\varphi_{e}\ =\ \frac{V_{t}-V_{e}}{V_{t}}\ \times\ 100\%\).
# Improved ADRC-Based Autonomous Vehicle Path-Tracking Control Study Considering Lateral Stability
## 2 Vehicle Model  
### Vehicle Dynamics Model  
The two degrees of freedom (2-DOF) linear vehicle model is the basis for designing a control system. Based on the research content of this paper, ignoring the motion degrees of freedom such as vertical, roll, and pitch, and assuming that the suspension system is rigidly connected, the wheel characteristics of the left and right sides of the vehicle are the same. Therefore, we have a 2-DOF vehicle model that includes lateral motion degrees of freedom and yaw motion degrees of freedom. The simplified model is shown in Figure 1a, and the origin of the coordinate system is fixed at the center of mass of the vehicle, the \(x\)-axis indicates the longitudinal motion, the longitudinal velocity is considered as a constant value, and the \(y\)-axis indicates the lateral motion. The symbols L\({}_{1}\), L\({}_{2}\), R\({}_{1}\), and R\({}_{2}\) of the wheels correspond to the front-left, the rear-left, the front-right, and the rear-right, respectively.  
By small angle assumptions, the lateral and yaw equations of motion for the 2-DOF vehicle dynamics model are expressed as  
\[\left\{\begin{array}{l}m(\ddot{y}+v_{x}\dot{\varphi})=F_{yf}+F_{yr}\\ l_{z}\ddot{\varphi}=l_{f}F_{yf}-l_{r}F_{yr}+\Delta M_{z}\end{array}\right. \tag{1}\]  
where \(m\) is the vehicle's total mass, \(l_{z}\) is the yaw moment of inertia; \(v_{x}\) is the longitudinal velocity; \(y\) represents lateral displacement, \(\varphi\) is heading angle; \(l_{f}\), \(l_{r}\) are the distances from the front and rear axles to the center of gravity, respectively; \(\Delta M_{z}\) is the active external yaw  
Figure 1: Vehicle model.  
moment; \(F_{yf}\) is lateral force on the front axle; and \(F_{yr}\) is lateral force on the rear axle. \(F_{yf}\) and \(F_{yr}\) can be defined as  
\[\left\{\begin{array}{l}F_{yf}=2C_{f}\alpha_{f}\\ F_{yr}=2C_{r}\alpha_{r}\end{array}\right. \tag{2}\]  
where \(C_{f}\), \(C_{r}\) are expressed as the linear cornering stiffness; \(\alpha_{f}\), \(\alpha_{r}\) are the slip angles of the front and rear tires, respectively, which can be defined as  
\[\left\{\begin{array}{l}\alpha_{f}=\delta_{f}-\frac{v_{y}+l_{f}\dot{\varphi} }{v_{x}}\\ \alpha_{r}=\frac{l_{f}\dot{\varphi}-v_{y}}{v_{x}}\end{array}\right. \tag{3}\]  
where \(\delta_{f}\) is expressed as the front-wheel steering angle.  
By associating Equations (1)-(3), we can derive the dynamics model of a 2-DOF vehicle system as follows:  
\[\left\{\begin{array}{l}\ddot{y}=\frac{2C_{f}}{m}\bigg{(}\delta_{f}-\frac{v_ {y}+l_{f}\dot{\varphi}}{v_{x}}\bigg{)}+\frac{2C_{r}}{m}\frac{l_{r}\dot{\varphi }-v_{y}}{v_{x}}-v_{x}\dot{\varphi}\\ \ddot{\varphi}=\frac{1}{l_{z}}\bigg{[}2C_{f}l_{f}\bigg{(}\delta_{f}-\frac{v_{y }+l_{f}\dot{\varphi}}{v_{x}}\bigg{)}-2C_{r}l_{r}\frac{l_{r}\dot{\varphi}-v_{y }}{v_{x}}+\Delta M_{z}\bigg{]}\end{array}\right. \tag{4}\]