esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Stance Detection Method by Fusing Sentiment Features
## 3 Methodology
### Multi-Stance Indicator System  
User reviews differ not only in polarity but also in the degree to which they express their stance. The traditional approach usually divides stances into three categories, which fails to differentiate distinctions within the same polarity (support or against) and fails to explain why these discrepancies exist. As a result, this study separates stances into five categories based on polarity and degrees:  
Figure 1: Research framework.  
1. Strong support (SS): a totally positive attitude towards the event and a favorable opinion of the event.
2. Weak support (WS): a generally positive view of events, affirming most of them despite concessions in some parts.
3. Neutral: neutrality towards events and the discussion of them.
4. Weak opposition (WO): a generally negative view of events, opposing most of them despite concessions in some parts.
5. Strong opposition (SO): a totally negative attitude towards the event and unfavorable to the event.  
In this paper, a multi-stance indicator system based on the LDA model is developed to establish the association between segmented stances and topics. We use Gensim to implement LDA mining, which is a Python library for topic modeling. Parameter settings are shown in Table 1. The five stances categorized according to the topic results can provide a more detailed understanding of users' attitudes toward the event and the reasons for the differences, thus providing a guide to public opinion management.
## 4 Experimental Simulations
### Experimental Results and Analysis
#### 4.3.2 Comparison Experiments of Different Feature Selection Methods  
In order to verify the effectiveness and applicability of the feature selection method proposed in this paper, a comparison experiment of different feature selection methods is set up in this section: the feature selection method (RFP) proposed in this paper is compared with existing feature selection methods such as PCA [23] and AE [24] under the same experimental conditions. The data obtained after pre-processing is first sampled using the ADRDB algorithm to balance the data set, and then the features of the NSL-KDD dataset are reduced to 28 dimensions; the features of the UNSW_NB15 dataset are reduced to 28 dimensions; and the features of the CIC-IDS2017 dataset are reduced to 52 dimensions by the above three methods, respectively. Finally, the classification experiments are conducted by the proposed model in the paper, and the model parameters are set as shown in Table 1. The results obtained are shown in Table 8. The bold text indicates the evaluation indicators of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Feature Selection Method**} & \multicolumn{4}{c}{**Evaluation Indicators (\%)**} \\ \cline{3-6}  & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{NSL-KDD} & RFP & **99.69** & **99.65** & **99.69** & **99.70** \\  & PCA & 98.26 & 98.79 & 98.26 & 98.48 \\  & AE & 97.61 & 97.63 & 97.61 & 97.59 \\ \cline{1-1}  & RFP & **86.25** & **86.92** & **86.25** & **86.59** \\ \cline{1-1} \cline{2-6}  & PCA & 79.37 & 79.70 & 79.37 & 76.92 \\ \cline{1-1} \cline{2-6}  & AE & 81.01 & 80.43 & 81.01 & 80.05 \\ \cline{1-1} \cline{2-6}  & RFP & **99.65** & **99.63** & **99.65** & **99.64** \\ \cline{1-1} \cline{2-6}  & PCA & 89.64 & 93.02 & 89.64 & 90.60 \\ \cline{1-1} \cline{2-6}  & AE & 98.66 & 98.70 & 98.66 & 98.67 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of different feature selection methods.  
Figure 9: Heat map of features in the UNSW_NB15 dataset.  
From Table 8, it can be seen that the results obtained after using the data processed by the RFP algorithm proposed in this paper in the model are better. Analysis of the reasons for this shows that: the PCA method relies more on the variance when downscaling the data, while the non-principal components with small variance may also contain important information on the differences of the samples, and the downscaling process will have an impact on the subsequent data processing; the AE method is more dependent on the training data when reconstructing the feature space, thus, neither one of the above two methods has achieved better results. The RFP algorithm proposed in this paper starts from the data itself and selects features according to their importance and the correlation between them, which can improve the classification accuracy of the model.
# Drive-by Bridge Damage Detection Using Continuous Wavelet Transform
## 2 Numerical Models
\[r(x)=\sum_{i=1}^{N}\sqrt{2G(n_{i})\Delta n}~{}cos(2\pi n_{i}x+\varphi_{i}) \tag{2}\]  
where \(\varphi_{i}\) is the random phase angle that varies between 0 and \(2\pi\), \(x\) denotes a location on the surface where the irregularity \(r(x)\) is defined, and \(N\) represents the number of spatial frequencies, which is calculated as follows:  
\[N=\frac{n_{\text{max}}-n_{\text{min}}}{\Delta n}+1 \tag{3}\] \begin{table}
\begin{tabular}{c l l} \hline
**Bridge** & Mass per length & \(\rho=2~{}\text{t}/\text{m}\) \\  & Young’s modulus & \(E=30~{}\text{GPa}\) \\  & Moment of inertia & \(I=0.20~{}\text{m}^{4}\) \\  & First mode frequency & \(f_{b,1}=4.35~{}\text{Hz}\) \\  & Second mode frequency & \(f_{b,2}=17.23~{}\text{Hz}\) \\  & Third mode frequency & \(f_{b,3}=38.06~{}\text{Hz}\) \\ \hline
**Vehicle** & Mass moment of Inertia & \(m_{\varphi}=5~{}\text{t}\) \\  & Mass moment of Inertia & \(J_{0}=3.5~{}\text{t}\cdot\text{m}^{2}\) \\  & Stiffness coefficient (front) & \(k_{1}=5750~{}\text{kN}/\text{m}\) \\  & Stiffness coefficient (rear) & \(k_{2}=2875~{}\text{kN}/\text{m}\) \\  & Damping coefficient (front) & \(c_{1}=2.5~{}\text{kN}\cdot\text{s}/\text{m}\) \\  & Damping coefficient (rear) & \(c_{2}=2.5~{}\text{kN}\cdot\text{s}/\text{m}\) \\  & Axle distance to the mass & \(d_{1}=d_{2}=1~{}\text{m}\) \\  & Bounce frequency & \(f_{\varphi,h}=5.77~{}\text{Hz}\) \\  & Pitching frequency & \(f_{\varphi,r}=8.53~{}\text{Hz}\) \\ \hline \end{tabular}
\end{table} 
Table 1: The properties of bridge and instrumented vehicle [21]. The road roughness profile was generated at 10 mm intervals along the 25 m span of the bridge, as shown in Figure 2a. Figure 2b depicts the PSD of the generated road profile and the target PSD.
# Reduced Reference Quality Assessment for Image Retargeting by Earth Mover's Distance
## 2 EMD between Two Histograms
### Weighted EMD  
For each given reference and retargeted images, we obtain all the matching point pairs. We take an image patch around each matching point in the point pairs.  
If we use the above EMD as the distance measure in two histograms of image patch directly, we do not consider the spatial information of pixels in the patch. In fact, pixels near the center of matching point are more important than the others for computing similarity ([PERSON] and [PERSON], 2011). To solve this problem, we bring the weight to the pixels of different locations, and then we can calculate the EMD between the weighted histograms.  
Because the importance of the pixel is inversely proportional to the distance between the pixel and center point, we define the normalized weight value \(w(.)\) for every pixel in the image patch by  
\[w(i)=\left\{\begin{array}{cc}\frac{1}{d(i)}\bigg{/}\sum\limits_{j\in S} \frac{1}{d(j)}&\text{ \emph{otherwise}}\\ 1&\text{\emph{itshecenter}}\end{array}\right., \tag{2}\]  
where \(d(i)\) is the ground distance from the pixel \(i\) to center matching point in the patch \(S\). By using the weight value as the pixel's contribution to their histogram bins, we construct weighted histograms, and, by applying those weighted histograms to EMD, we can more accurately calculate the distance between the two image patches.  
In order to speed up the calculation, we choose the \(L^{1}\)-based distance as the ground distance \(d_{i,j}\). With this choice, [PERSON] has proven that the EMD between normalized weighted histograms equals to linear Wasserstein distance ([PERSON] and [PERSON], 2011). Under those conditions, the EMD can be written as:  
\[\text{EMD}(IH^{1},IH^{2})=\sum\limits_{i=1}^{n}\bigg{|}\sum\limits_{j=1}^{i} IH^{1}(j)-\sum\limits_{j=1}^{i}IH^{2}(j)\bigg{|}, \tag{3}\]  
where \(IH^{1}\) and \(IH^{2}\) are two normalized weighted histograms.
## 1 Introduction
### Motivation  
GNSS-based applications are increasingly expanding their reach to encompass more demanding scenarios, including urban and light-indoor environments. In areas with weak or compromised signals, they improve the availability of satellite signals [1,4]. This capability enhances the overall reliability and performance of GNSS receivers in diverse settings. This is significant for various applications, including location-based services (LBSs) and emergency response [17]. Operating effectively in conditions where traditional receivers may struggle, HS-GNSS receivers provide extended coverage in marginal signal conditions. The heightened need for navigation in challenging signal environments has led to a growing focus on handling faint signals [1]. This shift is motivating the development of receivers that operate in these challenging scenarios [6].  
The development of HS-GNSS receiver technology necessitates the implementation of non-standard features and a comprehensive description of the signal processing path from the antenna to the computation of the desired GNSS products. However, developing prototypes using off-the-shelf GNSS receivers poses difficulties. Researchers must contend with the increasing complexity and integration level of GNSS integrated circuits [18,19]. These systems do not offer an exact model of how the desired measurements are obtained, and they have limited reprogrammability.  
In light of these challenges, the primary motivation for the research presented in this paper is to develop a programmable, adaptable, and portable HS-GNSS receiver prototype. This prototype is aimed at researching experimental algorithms, particularly those focused on positioning in weak signal conditions.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 5 Discussion  
In this section, we demonstrate the security analysis of the proposed SRPL-RP and present the research findings and compare them with existing countermeasures to justify the effectiveness in terms of the network performance and the detection and mitigation accuracy.
###### Abstract
As a result, they achieve single-pulse temperature measurement of a single-mode fiber at a distance of more than 10 km with a spatial resolution of 6 m and a temperature recording error of 0.59 \({}^{\circ}\)C. The method is undoubtedly interesting from a practical point of view. However, in some cases, a significant modification of the experimental setup involving the use of special frequency chirps is not feasible. In [47], a spatially adaptive image denoising method is proposed to enhance the signal-to-noise ratio of BOTDR without compromising the spatial resolution. By analyzing the denoising principle, a spatially adaptive network denoising structure is created, effectively reducing noise and avoiding excessive smoothing. The method demonstrates promising results; however, the preliminary analysis of the original data requires additional time and resources. In [48], the authors present a novel approach to enhancing measurement speed by integrating quadratic least-squares filtering with moving average and median filters, utilizing a limited quantity of data for averaging. The incorporation of moving average and moving median algorithms in both the frequency and time domains results in a notable enhancement in the reliability and accuracy of Brillouin frequency shift extraction (BFS), accompanied by a reduction in calculation time. It should be noted, however, that averaging in any form is effective when the data corresponding to a single point is averaged rather than two neighboring points. Otherwise, the data must be edited properly to prevent the introduction of additional errors that offset the benefit of the averaging procedure. In this paper, we propose a processing method that allows averaging several BGS with minimal error, regardless of the points on the line at which they were obtained. Despite the high number of iterative procedures with large data sets in signal processing algorithms, all these operations can be implemented at the hardware level. This is due to the fact that most of them already work effectively at the hardware level in distributed sensors. The method allows for an increase in the accuracy of measurements in data obtained with a high-frequency scanning step and a low signal-to-noise ratio.
## 3 Differential GNSS
### Single Differencing  
Figure 11 illustrates the geometry of single differencing that is based on calculating the difference \(\Delta L_{AB}^{j}\) between range \(Aj\) (distance from receiver \(A\) to satellite \(j\)) and \(Bj\) (distance from receiver \(B\) to satellite \(j\)) according to Equation (2). That is [43]:  
\[\Delta L_{AB}^{j}=\Delta\rho_{AB}^{j}+c\Delta\tau_{AB}+\Delta Z_{AB}^{j}- \Delta I_{AB}^{j}+\Delta B_{AB}^{j}+\Delta\epsilon_{AB} \tag{3}\]  
First, it can be observed in Equation (3) that the satellite clock bias \(\tau^{j}\) is removed by calculating the range differences. Furthermore, in the case of short (\(<\)10 km) baselines (distance from receiver \(A\) to receiver \(B\)), the ionospheric and tropospheric effects are nearly the same, that is \(Z_{A}^{j}=Z_{B}^{j}\) and \(I_{A}^{j}=I_{B}^{j}\), so that Equation (3) can be further simplified to \(\Delta L_{AB}^{j}=\Delta\rho_{AB}^{j}+c\Delta\tau_{AB}+\Delta B_{AB}^{j}+ \Delta\epsilon_{AB}\)[44].  
Although many uncertainties can be removed by single differencing, the receiver clock biases \(\Delta\tau_{AB}\) are not removed. This can be achieved by using double differencing.
# Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm
## 2 Techniques and Methods  
### Basic Mathematical Model of the TSP  
The TSP can be described in words: a salesman wants to sell goods in several cities, and the geographical location of each city and the distance between any two cities are known. The salesman starts from any city, arrives in each city to make a sale, and finally returns to the starting point. A route needs to be found that offers the shortest total length [16]. This problem is a typical nondeterministic polynomial (NP) problem. More accurately,it is an NP-complete problem [17]. That is, it is impossible to determine whether there is a polynomial time solution to this kind of problem, but it can be verified whether the answer is accurate in polynomial time. The mathematical model of the TSP problem can be expressed as follows:  
\[\begin{array}{c}\underset{i=1}{min}\sum\limits_{i=1}^{N}\sum\limits_{j=1,i\
eq j }^{N}k_{i,j}d_{i,j}\\ s.t\left\{\begin{array}{l}\sum\limits_{i=1}^{N}k_{i,\ j}=1\\ \sum\limits_{j=1}^{N}k_{i,\ j}=1\\ k_{i,\ j}\in\{0,\ 1\}\end{array}\right.\end{array} \tag{1}\]  
where \(N\) represents the problem size, that is, the number of cities; \(k_{i,j}\) indicates whether there is a path between the two cities. If a road exists, it is 1, otherwise it is 0; \(d_{i,j}\) represents the distance between two cities. This paper discusses the symmetric TSP, that is, for any \(i\) and \(j\), \(d_{i,j}=d_{j,i}\). Furthermore, a new mathematical model was used to solve the path planning problem for conformal antenna surface detection, which is described in Section 4.2.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Parameters and Basic Structure of 5G PHY Layer  
Understanding key parameters and PHY layer access is crucial to assess network security. These parameters define aspects like spectrum allocation and initial access procedures, and they have influence on security challenges such as vulnerability to interference or eavesdropping.
# Coupling and Optical Analysis of a Round-Cornered Square-Shaped Microresonator
## 3 Optical Experimental Setup  
The designed optical rings are fabricated on a silicon-on-insulator platform with a 2 \(\upmu\)m thick cladding box and a 220 nm thick silicon layer, using the NanoSOI process available at Applied Nanotools Inc, NRC-NANO Building, 11,421 Saskatchewan Drive NW, Edmonton, Alberta T6G 2M9 Canada [35]. This process is based on direct-write 100 keV electron beam lithography technology (EBL), in which hydrogen silsesquioxane (HSQ) resists and an anisotropic ICP-RIE etch process with chlorine is used. Through the plasma-enhanced chemical vapor deposition (PECVD) process, and with tetraethyl orthosilicate (TEOS) at 300 \({}^{\circ}\)C as base, a 1 \(\upmu\)m oxide cladding was deposited on top. As a final step, 150 \(\times\) 150 \(\upmu\)m oxide windows were etched away to expose each of the devices to air. A scanning electron microscopy (SEM) image of the fabricated RCSS is shown in Figure 2.  
The experimental setup is built with a tunable Keysight 81600B laser source, emitting light in the O-band combined with an electronic polarizer, which is used to select and maintain TE and TM polarizations. Single-mode lensed fibers are used to butt-couple the light into the SiPh chip and to collect it at the throughput port of the device. In order to improve the coupling efficiency, edge strip couplers, tapered from 18 nm to the desired waveguide width, are designed at the input and output of the chip. At the end, light is detected using a Keysight N7744A optical detector sensor, which can be read through the Keysight N77x viewer software. The schematic figure of the setup is seen in Figure 3.  
Figure 2: Scanning electron microscopy (SEM) image of the fabricated RCSS (\(R_{c}\) = 15 \(\upmu\)m) microresonator for optical experiments.  
For the characterization of the optical transmission response, the light coupled with the device is optimized for the TE and TM polarizations, from an input power of 10 dBm set at the laser source. The data are collected using the Keysight insertion loss software, which sweeps from 1309 nm to 1311 nm wavelengths in order to obtain the O band responses. The step size used for the wavelength was 0.2 pm.
# Moving Ad Hoc Networks--A Comparative Study
## 7 Comparison of Short and Long-Distance Wireless Communication Technologies
Most of the working frequencies of digital radio stations use the 220-240 MHz or 400-470 MHz frequency bands, which are compatible with data phones, and offer good real-time data transmission, dedicated data transmission channels, and one-time investment. * A wireless network bridge is the product of the combination of wireless radio frequency technology and traditional wired network bridge technology. The wireless bridge is designed for point-to-point network interconnection using wireless technology for long-distance data transmission. It is a storage and forwarding device that realizes LAN interconnection at the link layer. It can be used for long-distance (up to 50 Km), high-speed (up to 100 Mbps) wireless networking between fixed digital devices and other fixed digital devices. * Satellite communication systems usually consist of two parts, namely the satellite end and the ground end. The satellite end is in the air and is mainly used to amplify the signal sent by the ground station and then forward it to other ground stations. The ground station is mainly used to control and track the satellites, and to realize the connection of the ground communication system to the satellite communication system. * Shortwave communication: According to the division of the International Radio Advisory Committee, \"shortwave\" refers to electromagnetic waves with a wavelength of 10 m-100 m and a frequency of 3 MHz-30 MHz. Shortwave communication refers to radio communication using short waves, also known as high frequency (HF) communication. Table 10 compares the commonly used range of wireless access technologies in terms of their transmission rate, spectrum characteristics and communication modes. Each radio system has its technical performance and application fields. It provides high-speed and high-reliability internet access but does not support applications with high real-time requirements. Telecom networks (such as GSM/CDMA) are mainly for voice services, so they can provide low latency. Ad hoc communication systems (such as DSRC) offer real-time support but poor reliability and are application-linked networks that can be used to create effective target systems only when close to the application.
###### Abstract
As a result, both supervised and unsupervised learning approaches have inherent limitations in landslide recognition tasks. Semi-supervised learning offers a promising alternative, albeit with relatively limited research to date. Therefore, this paper adopts a semi-supervised generative adversarial network to tackle the recognition task. This approach effectively addresses the issues of insufficient landslide samples and sample imbalances, ultimately enhancing the accuracy of landslide recognition and improving generalization. This paper selects the main urban area of Lanzhou City, covering about 1631 km\({}^{2}\), as the study area. The area is located in the transition zone from the Qinghai-Tibet Plateau to the Loess Plateau, with complex terrain and geological environment, which is a typical disaster-prone area [17]. At the same time, Lanzhou has a dense transportation network and large population flows. Frequent human activities such as engineering construction and mineral resource exploitation have also become important causes of landslide disasters [18; 19]. In the study of landslide disasters, the selection of influencing factors has a significant impact on the accuracy of landslide identification results. There are hundreds of factors that can affect landslides, categorized into the geomorphological and geological, human activity, and environmental conditions. Based on previous studies and the actual situation in the study area, this paper selects 10 landslide image factors as the sample set for the landslide identification task, and proposes to construct a semi-supervised generative adversarial network (SSGAN) to identify potential landslide hazards in the main urban area of Lanzhou City, develop a landslide identification program, and provide reference for landslide hazard identification in similar geological areas such as the main urban area of Lanzhou City.