esa-sceva/satcom-chunk-collection huggingface dataset:
# Encoding Text Information with Graph Convolutional Networks for Personality Recognition
## 1 Introduction
We show that personality graph convolutional networks (personality GCN) can effectively use text information to detect user personality and can significantly improve performance, even on small datasets. The main contributions of this paper are summarized below:  
* We propose a novel personality recognition method based on the graph convolutional neural network method. According to our understanding, this is the first personality recognition study to model the entire user text information corpus as a heterogeneous graph and learn user, word, and document embeddings together through graph neural networks. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Traits** & **Description** \\ \hline Extroversion (EXT) & Is this person outgoing, talkative, and energetic or reserved and solitary? \\ Neuroticism (NEU) & Is this person sensitive and nervous or secure and confident? \\ Agreeableness (AGR) & Is this person trustworthy, outspoken, generous, and humble or unreliable, complex, meager, and boastful? \\ Conscientiousness (CON) & Is this person efficient and organized or careless and sloppy? \\ Openness (OPN) & Is this person creative and curious or dogmatic and cautious? \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the Big Five personality traits. * This paper focuses on the correlation between the five personality traits. Unlike previous research, the personality GCN model shares a set of basic feature information during user personality recognition, rather than using five sets of features for classification. The feature information sharing fully considers the correlation between the five personality characteristics during training. We propose an innovation model that is completely different from previous research. * The results on two public and authoritative benchmark datasets show that our method's accuracy and F1-score outperform the latest personality recognition methods, without using pretrained word embeddings or external knowledge. The rest of this paper is organized as follows. Section 2 presents the work carried out in the field of personality recognition. Section 3 introduces the background of the GCN. Section 4 describes a graph convolution model for personality recognition. Section 5 presents experiments that analyze the effectiveness of our model. Section 6 concludes and presents future work.
# Research on Non-Random Vibration Analysis of Concrete Pump Truck Boom Based on Dynamic Excitation
## 1 Introduction
Because of this non-uniformity, the external excitation force acting on the system is largely unknown, which causes the system's dynamic response to exhibit strongly random features. In order to thoroughly examine the method by which these uncertainty factors affect the system's vibration characteristics, this work attempts to develop a dynamic model of the boom system that takes into account the uncertainty of concrete rheological properties. This study examines the vibration response issue of the concrete pump truck boom during construction, cites a non-random vibration analysis method based on the interval process model, and suggests a rigid-dispersed body coupling method to investigate the impact of fluid concrete on the boom. The use of unknown excitation modeling diminishes reliance on probability distribution assumptions, and the dynamic equations are resolved by integrating the Newmark-\(\beta\) technique to formulate the displacement envelope under uncertain system characteristics. The experimental findings indicate that the approach can precisely forecast the dynamic displacement range of the boom's end, effectively regulate the deviation of the pouring location within the specified limits, and markedly enhance the pouring accuracy and dependability. This method offers a novel technological approach for vibration control in engineering construction and holds significant reference value for further optimizing vibration control strategies under construction settings.
# Securely Computing the Manhattan Distance under the Malicious Model and Its Applications
## 2 Related Work  
Manhattan distance is a secure multi-party computational geometry problem. Manhattan distance can be seen everywhere in real life, which can be called the CityBlock distance or the taxi distance, that is, the actual distance as taxis pass from one crossroads to another. In a standard coordinate system, the sum of the absolute wheelbase of two points, which is the distance of two points in the north-south direction plus the distance in the east-west direction, which is the Manhattan distance between two points, can be noted as \(d=\lvert X_{2}-X_{1}\rvert+\lvert Y_{2}-Y_{1}\rvert\). As shown in Figure 1.  
In Fang [29], the authors designed a new coding method. With the help of homomorphism, the problem of calculating the Manhattan distance between two points is flexibly and skillfully transformed into calculating the Hamming distance of two vectors. This conversion idea not only improves the protocol efficiency but also prevents the disclosure of intermediate information. However, the protocol cannot resist malicious attacks.  
Reference [30] invoked the absolute value of the difference and designed the MPC of Manhattan distance under different restrictions. The protocol's performance is poor.  
Reference [31] designed a new graph encryption scheme for shortest distance queries based on a 2-hop cover labeling, which uses symmetric-key primitives. This scheme can  
Figure 1: Manhattan distance.  
obtain the shortest distance between any two points in the graph, but it cannot resist malicious attacks.  
In the above references, the MPC protocols of Manhattan distance is designed based on the semi-honest model, which cannot resist the attack of malicious participants. The protocol for secure computing Manhattan distance under the malicious model needs to be designed. The protocol proposed in reference [32] is a classic problem in the malicious model, but it is used to solve the Millionaire problem, which is different from the problem scenario we solve.
# Intelligent Environment Monitoring System for University Laboratories
## 2 System Design and Implementation
### System Model Design and Function Realization  
To achieve the aforementioned functions, the proposed system consists of a GPRS module, STC12C5A60S2 (STC, Shenzhen, China) and sensor detection modules. In our system, the software Protel99 SE (Altium, Sydney, Austrilia) is used to drive each part of the system's hardware circuit and generate the system function development board. The overview of our system architecture is illustrated in Figure 1.  
Figure 1: Our system architecture.  
Each sensor module has a responsibility to collect the laboratory data and return the data to the administrator's mobile terminal by message. Moreover, the system can analyze dangerous situations (e.g., fire protection and security) during the process of collecting data and realize the buzzer alarm and the SMS alarm automatically. Furthermore, the system development board takes SMS command control, automatic data acquisition and data control into account. The serial port is integrated to the WIFI module and the system is programmed by the JAVA program, achieving data detection and remote control using mobile terminal APP.  
With the development of large scale laboratories, the proposed system can be used independently. It can also be used as a subsystem which is able to connect the public network through the specific interface. Then, the platform analyzes and processes the collected information. This process can be controlled by different terminals.
## 1 Introduction
Sample antennas were then fabricated to mimic the evaluation scenarios above, and the feasibility of the proposed approach was demonstrated through measurement. The results confirm that the proposed FoM can well-describe pattern distortions caused by both mutual coupling and platform effects.
# Controller-Pilot Data Link Communication Security
## 4 Threat Model
### Threat Actors  
According to [3], threat actors may be classified as passive or active.  
An attacker is _passive_ if he does not interfere with the medium. A passive attacker may have a moderate level of technical capability, i.e., can set up and use equipment such as an SDR and/or a V antenna. Given the range of uses for avionic data link, different attackers are thus likely to have various intentions. Primarily, an attacker is listening and collecting data to achieve competitive advantage or for some kind of surveillance. He does not harm the system directly, but the distribution of the specific knowledge about the aircraft may potentially be used by the active actors.  
An _active_ attacker interferes directly with the medium. He may be equipped with an SDR capable of transmission, amplifiers, transmit/receive (TX/RX) VHF antennas, and sensors in multiple locations, and may collect data in a mobile fashion. The corresponding equipment provides an active attacker  
\begin{table}
\begin{tabular}{c c c c} \hline
**Threat Type** & **Actor Type** & **Affected Attributes** & **Example Attack** \\ \hline Eavesdropping & Passive & Confidentiality & Reading control messages \\ Jamming & Active & Availability & Channel blocking \\ Flooding & Active & Availability & Ground station/aircraft flooding \\ Injection & Active & Availability Confidentiality Integrity Non-repudiation & Ground station/aircraft ghost messaging \\ Alteration & Active & Integrity & Modification of message content \\ Masquerading & Active & Authentication Non-repudiation & Ghost aircraft/ground station identity \\ \hline \end{tabular}
\end{table}
Table 1: Overview of possible types of attacks on CPDLC technology.  
with the capability of causing leaks of personal information, limited access to the avionic systems, message injection, modification, and deletion. Attackers seeking criminal gain might focus on financial or operational data, allowing them to steal assets or blackmail victims.
## 3 Results and Discussion
### The EYB Eyes Image Results
#### 3.1.4 Summary  
This section emphasizes the findings so far based on the results in Section 3.1. First, we found that combining the multiscale analysis methods with the curvature-based methods improved the accuracy results. The improvement varied between 1.84% and 7.84%. Second, we found that the best combination was with the mean curvature and max principal curvature. The highest accuracy was 76.04% from \(SS+X\) and 74.05% from \(SS+H\). The mean curvature and the max principal curvature successfully characterized the multiscaled eyes images to be more distinctive. Third, we found that the scale-space method decreased the accuracy when using one-quarter and one-eighth of the resolution of the original image. This decrement also happened in the scale space with the curvature-based method and the DWT with the curvature-based method. On the contrary, the decrement did not happen for the DWT. Fourth, the linear SVM was the best classifier tested against all variables in the whole experiment.
# Statistical Characterization of the Magnetic Field in Space during Magnetic Storms
## 1 Magnetic Storms
Therefore, in this paper, probability density estimation is first performed for the observed data. Since a priori knowledge is available only from observed data and the mathematical form of probability density functions of the components of the magnetic field during a magnetic storm cannot be obtained directly, a nonparametric estimation method is needed. The Parzen-Rosenblatt probability density estimation method was proposed by [PERSON] et al. [25; 26] for this problem. For observations \(x_{1}\), \(x_{2}\),..., \(x_{N}\), the probability density of the random variable \(X\) can be estimated using the available data through Equation (1):  
\[\hat{f}_{X}(x)=\frac{1}{N\hbar}\sum_{i=1}^{N}k\bigg{(}\frac{x-x_{i}}{h}\bigg{)} \tag{1}\]  
where \(f\) with a \"\(\cdots\)\" above is the estimated probability density function, \(k(x)\) is the kernel function, and \(h\) is the smoothing parameter, also called the bandwidth, and is used to control the value of the kernel function. The probability density estimation approach in Equation (1) is asymptotically unbiased [27]; i.e., the probability density estimate converges to the true probability density when the sample size is sufficiently large. Namely  
\[\lim_{N\to\infty}E\big{(}\hat{f}_{X}(x)\big{)}=f_{X}(x) \tag{2}\]  
where \(f\) denotes the actual probability density function, and \(E\) denotes the mathematical expectation operator. There are a variety of kernel functions for probability density function estimation. In this paper, we chose the Gaussian kernel function, the mathematical formula of which is shown in Equation (3). \[k(x)=\frac{1}{\sqrt{2\pi}}\exp\left(-\frac{x^{2}}{2}\right) \tag{3}\]  
where \(k\) is the kernel, as depicted in Equation (1). The sampled data are then substituted into Equations (1) and (3). The probability density of the amplitude of each component of the magnetic field could be obtained for the six magnetic storm events, as shown in Figure 2.  
A comparison of the Dst results in Figure 1 reveals that the Dst levels on 8 September 2017 and 27 September 2020 are lower than those on the other four days, and the probability density of the magnetic field amplitude generally follows a Gaussian distribution; however, the probability density of the magnetic field amplitude deviates from the Gaussian distribution when the Dst level is higher, which might result from the energy injection during a magnetic storm. Figure 2: _Cont._  
In fact, according to the central limit theorem, when the sample size is large enough, the limit of many distributions is Gaussian. However, when a magnetic storm occurs, the energy injection might bring some disturbance, and some of the values of observed data might deviate from that in a calm space environment, which would probably violet the necessary condition for the central limit theorem and have some impacts on the information entropy of the magnetic field, and thus result in the deviation from Gaussian distribution.
# Dynamic Response Analysis of Asphalt Pavement under Pavement-Unevenness Excitation
## 4 Numerical Results
### Influence of Interlayer Contact Conditions  
This section mainly studies the effect of different interlayer interface coefficients \(k_{r}\) on the mechanical response of road surfaces under dynamic load. The random load generated when the vehicle speed is 20 m/s driving on the B-grade pavement is selected, the value range of \(k_{r}\) is \(10^{7}\) Pa/m\({}^{3}\) to \(10^{12}\) Pa/m\({}^{3}\), \(k_{0}=106\) Pa/m\({}^{3}\), and \(k_{r}\) * = \(k_{r}/k_{0}\) are defined. The relevant parameters of the pavement are shown in Table 3. The discontinuous interface is located between the surface layer and the base layer, and the calculation results are shown in Figure 13.  
Figure 13 shows the variation and distribution of \(\sigma_{zz}\), \(\sigma_{xx}\), \(\epsilon_{xx}\), and \(\epsilon_{zz}\) at the bottom of the layer below the discontinuous interface, respectively. The mean and extreme values of \(\sigma_{zz}\), \(\sigma_{xx}\), \(\epsilon_{xxr}\), and \(\epsilon_{zz}\) at the bottom all decrease with the increase of the interlayer interface coefficient. As can be seen from Figure 13a,b, when the interlayer interface coefficient increases from 107 Pa/m\({}^{3}\) to 1012 Pa/m\({}^{3}\), the peak value of \(\sigma_{zz}\) decreases from 0.0466 MPa to 0.0431 MPa, decreasing by 7.5%. However, the peak value of \(\sigma_{xx}\) decreases from 0.0666 MPa to 0.0132 MPa, decreasing by 80.2%, and gradually approaches a fixed value at the interface coefficient of 1011 Pa/m\({}^{3}\). The horizontal stress decreases much more than that in the vertical direction. At the same time, the dispersion of horizontal stress is much more affected by the interlayer interface coefficient than the vertical one, which indicates that the interlayer interface coefficient has a more significant effect on horizontal stress. As can  
Figure 12: Influence of vehicle load on mechanical response at bottom of surface layer. (**a**) Bottom of the surface layer \(\sigma_{zz}\); (**b**) bottom of the surface layer \(\sigma_{xx}\); (**c**) bottom of the surface layer \(\epsilon_{xz}\); (**d**) bottom of the surface layer \(\epsilon_{zz}\).  
be seen from Figure 13c,d, with the increase of interlayer interface coefficient, the peak value of \(\varepsilon_{xx}\) decreases from 0.0303 to 0.0111, decreasing by 63.4%, and the peak value of \(\varepsilon_{zz}\) decreases from 0.0433 to 0.0255, decreasing by 41.1%. The distribution of horizontal strain and vertical strain at the bottom of the surface layer in the range of 25-75% and 1-99% gradually decreases with the increase of interlayer interface coefficient, the degree of dispersion decreases, and the probability of high horizontal strain decreases, indicating that good interface connection conditions can slow down the fatigue damage of the pavement and delay the service life.
# Could We Realize the Fully Flexible System by Real-Time Computing with Thin-Film Transistors? +
## 2 Materials and Methods  
### Flexible Electronic Device  
Table 1 shows the comparison of characteristics among CMOS (silicon-based MOSFET), A-Si:H-based thin-film transistor (TFT) and A-oxide-based TFT [17]. With the excellent physical properties including transparency and soft, flexible devices are irreplaceable to be applied in the fully flexible system. In addition, due to the low manufacturing temperature and low cost, the manufacturing cycle and price of flexible devices are much smaller than CMOS. However, the process variation of the flexible devices is large than CMOS due to the immature process. As the previous works report [17; 18], the carrier mobility of A-oxide TFTs is 10-50 cm\({}^{2}\)/Vs, which is an order of magnitude smaller than that of CMOS but still large than that of A-Si:H TFTs. That is, it is difficult to realize high-speed, high accuracy signal processing with such flexible devices.  
A-oxide TFTs technology provides 3-terminal thin-film transistors, with a typical channel length of 5 \(\upmu\)m from the In-Ga-Zn-O system [18]. The substrate of this technology is plastic foil so that the circuits are flexible and even rollable. The electronics behavior of Oxide TFT transistors is quite similar to the crystalline silicon-based transistors. Thus the model adopted in our simulation is like MOSFET models but the model parameters are quite different. During the simulations, the mobility data adopted in the oxide TFT model (In-Ga-Zn-O) are around 10 cm\({}^{2}\)/Vs, which could be referred to the experimental data from some TFT device research literature, such as [18], without loss of generality.
# Experimental Study on the Near-Red Flow Characteristics of Alluvial Channel with Seepage
## 4 Conclusions  
Laboratory experiments were conducted to detect the turbulent characteristics of flows with moderate Reynolds number in the case of downward seepage. An Acoustic Doppler Velocimeter (ADV) was used to measure the instantaneous velocities of flow. Evidence on the turbulent characteristics in the presence of seepage led to various conclusions. The study of the vertical distributions for the mean velocities revealed that the velocities at the near-bed are higher by 1% to 5% in comparison to no-seepage conditions, which cause  
Figure 9: Kurtosis distributions (**a**) \(K(u)\) and (**b**) \(K(w)\) for all experiments in this study.  
a higher degree of turbulence. In general, the profiles of the Reynolds shear stress (RSS) are somewhat scattered and increase with seepage. The RSS is found increased close to the channel bed owing to the increasing trend of turbulent fluctuation and also indicating a higher transfer of momentum towards the bed surface. In the vicinity of bed surface, the skewness displays positive profiles of \(S(u)\) and negative profiles of \(S(w)\). Therefore, the distributions of skewness reveal that sweep events are dominant in the flow close to the bed surface, which is related to the influx of high-speed fluid parcels. Finally, the higher value of kurtosis in the near-bed flow signifies an increased degree of turbulence intermittency when downward seepage occurs. The results of this study (i.e., moderate Reynolds number/clear-water conditions) show good agreement with the typical results for higher Reynolds numbers, with lower magnitudes for the turbulent parameters subjected to no-seepage and seepage runs.  
Conceptualization, A.S.; methodology, A.S. and B.K.; formal analysis, A.S. and B.K.; investigation, A.S. and B.K.; writing--original draft preparation, A.S.; writing--review and editing, A.S., B.K. and G.O.; visualization, G.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Methodology
### Shadow Direction Acquisition
#### 3.2.1 Shadow Filtered by Area  
Large-area shadows appear in the cloud-free image for two reasons. First, a low solar attitude and high-rise buildings generate large shadows. Second, a dense arrangement of buildings may cause shadows to connect into large continuous areas. Our objective is to screen out the shadows generated by high-rise buildings, as the edges of which can indicate shadow angles. The shadow angle \(\theta\) refers to the angle between the shadow orientation and the north-south axis, where \(\theta\in[-90^{\circ},90^{\circ}]\). The two steps to obtain the shadow angle are as follows.  
A morphological opening operation is applied to separate all the shadows. An open operation effect depends on the structuring element (SE) and its size. As the large-size shadows are the targets, the structuring size must be a relatively large value to separate large, conjoined shadows, maintaining the shape of single shadows. The disk was chosen as the SE. The shadow result after open operation is called SH\({}_{\text{b}}\).  
Set a threshold \(A\) for the area to extract the large-area shadows from high buildings. Filtered shadow results were recorded as SH\({}_{\text{c}}\). To simplify the operation and ensure a correct result for the shadow angle, we chose three thresholds. There is a large range for threshold choices, so we chose those around the top 8% value in the descending order of the area as the middle threshold \(A_{2}\). And the other two thresholds \(A_{1}\) and \(A_{3}\) are defined as:  
\[A_{1}=A_{2}-a \tag{1}\]  
\[A_{3}=A_{2}+a \tag{2}\]  
In Equations (1) and (2), the value of \(a\) depends on the image resolution, usually set from 100 to 1500 pixels. Usually, all of \(A_{1}\), \(A_{2}\) and \(A_{3}\) are useful for large-area shadow extraction, as shown in Figure 3. The threshold could also be set manually and empirically.