esa-sceva/satcom-chunk-collection huggingface dataset:
# An Evolutionary Computation Approach for Twitter Bot Detection
## 3 Data Preparation
### Data Description  
_TwiBot-20_ stores information in JSON format and consists of four files: _train.json_, _dev.json_, _test.json_, and _support.json_. Each file contains an array of JSON objects where each JSON object is a user account with a set of properties including user ID, name, screen name, domain, followings, followers, tweets, and a label indicating whether the user is human (0) or bot (1). _TwiBot-20_ was collected from July 2020 to September 2020. Properties and semantic information were retrieved using Twitter API and a sample of 200 tweets, on average, was retrieved for each user along with properties available from Twitter API calls, while approximately 20 neighbors' IDs were collected by following user relationships (10 followers and 10 followings). Regarding the data annotation strategy adopted during the generation of the _TwiBot-20_ dataset, the characteristics of labeling bot profiles that were taken into account during this step can be summarized as follows: API usage, lack of originality and variety in published tweets, identical tweets repeated multiple times, and massive usage of URLs in the posts. The authors decided to partition the labeled users dataset, consisting of 11,826 user profiles, with a 7:2:1 random partition that generated, respectively, a training set (_train.json_), validation set (_dev.json_), and test set (_test.json_). The authors also provided unsupervised users with a support set (_support.json_) to preserve the dense graph structure and follow relationship forms. We adopt the training set to drive the evolutionary process of the proposed techniques, the validation set to choose the best model according to the generalization error, and the test set to evaluate the model. As we can see from Figure 1, each set is balanced with respect to the target class, with bot accounts that are a little more represented.
# Study on Disturbance Mechanism of Squeezed and Non-Squeezed Soil Piles on Soft Soil Foundation
###### Abstract  
The construction process of pile foundations can significantly disrupt the soil. Therefore, it is necessary to limit the degree of soil disturbance caused by pile foundation construction to an acceptable level. This paper examines the disturbance effects of pile driving on soft soil foundations, specifically analyzing the squeezing effect of squeezed soil piles and the unloading effect of non-squeezed soil piles. To investigate these effects, two typical squeezed soil piles, a hydrostatic pile, and a bag grouting pile, as well as a typical non-squeezed soil pile (a bored pile) are selected. Specifically, a novel construction method for numerical models, which simulates the mechanical processes of different pile types under standard grids, is proposed. Three crucial indicators--soil displacement field, stress field, and disturbance influence range--are chosen to compare the disturbance effects of three types of piles on the soil. Results indicate that the two types of squeezed soil piles cause significant disturbance to the soil displacement field, especially in the horizontal direction, while causing a relatively slight disturbance to the soil stress field. Among the two of them, the disturbance magnitude and range of the hydrostatic pile are greater than those of the bag grouting pile. For the non-squeezed soil pile, the soil displacement field changes minimally and the stress field remains basically unchanged during the pile driving process of the bored pile. To compare and quantify the disturbance effects of three types of piles on soil, the soil disturbance range in the horizontal direction of each pile is normalized by its radius. Results indicate that the horizontal disturbance values of maximum horizontal stress for all three types of piles are approximately 1/5 of the pile length above the pile tip, with normalized values of 7.6, 5.5, and 3.5, respectively. The maximum horizontal deformation disturbance range in the horizontal direction occurs near the ground surface and has normalized values of 15.2, 7.5, and 1.1 for the three types of piles, respectively. Therefore, the hydrostatic pile has the greatest disturbance effect, followed by the bag grouting pile and the bored pile. However, within the allowable range of disturbance in practical engineering, the optimal piling method can be selected by comprehensively considering factors such as the construction difficulty and economic costs.  
hydrostatic pile; bag grouting pile; bored pile; squeezing effect; unloading effect +
Footnote †: journal: Applied
## 2 Theoretical Background
### The Flow of the CMSR System  
The flow of the advancing coupled multi-stable stochastic resonance method with two first-order multi-stable stochastic resonance systems (CMSR) is shown in Figure 3.  
Figure 3: The flow of the CMSR system.  
The flow of the CMSR system in Figure 3 shows that \(a\), \(b\) and \(r\) can affect the performance of the CMSR system. Based on the adiabatic approximation theory of SR, the variable-scale method can be used to introduce the frequency compression ratio (R) to convert large frequency signals into small frequency signals. For the selection of parameters \(a\), \(b\), \(r\), \(R\), this paper uses the SOA, which takes output \(SNR\) as the fitness function to optimize four parameters synchronously. The SOA process includes the determination of search step-size and search direction, the update of the individual position of the seeker, and the implementation of the algorithm. The specific procedures are described as follows:  
* Signal preprocessing. The envelope signal obtained after filtering and demodulating the collected bearing vibration signal is recorded as \(s_{1}\), \(s_{2}=s_{1}-mean(s_{1})\), \(s=s_{2}/2\text{max}(abs(s_{2}))\), \(S\) is the input signal;
* Parameter initialization. Number of iterations, population size, and range of parameters are determined;
* Calculate the target value of each position using the output \(SNR\) formula;
* Parameter optimization. The \(SNR\) is considered as the fitness function of the seeker optimization algorithm (SOA) to adaptively optimize and determine the system parameters of the SR by using the subsampling technique;
* If the set value is equal to the current number of iterations, output the best system parameters \(a\), \(b\), \(r\), \(R\) and enter the next step; otherwise, return to the previous step;
* Signal detection. Import the pre-processed signal into the CMSR system with the determined parameters to get the output signal \(x_{1}\) Scale recovery of signal frequency and amplitude \(x=x_{1}\times 2\text{max}(abs(s_{2}))\);
* Fault detection. Fourier transform of \(x\) to complete fault detection.
###### Abstract  
This paper studies the problem of joint power allocation and user association in wireless heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based approach. This is a challenging problem since the action space is hybrid, consisting of continuous actions (power allocation) and discrete actions (device association). Instead of quantizing the continuous space (i.e., possible values of powers) into a set of discrete alternatives and applying traditional deep reinforcement approaches such as deep Q learning, we propose working on the hybrid space directly by using the novel parameterized deep Q-network (P-DQN) to update the learning policy and maximize the average cumulative reward. Furthermore, we incorporate the constraints of limited wireless backhaul capacity and the quality-of-service (QoS) of each user equipment (UE) into the learning process. Simulation results show that the proposed P-DQN outperforms the traditional approaches, such as the DQN and distance-based association, in terms of energy efficiency while satisfying the QoS and backhaul capacity constraints. The improvement in the energy efficiency of the proposed P-DQN on average may reach 77.6% and 140.6% over the traditional DQN and distance-based association approaches, respectively, in a HetNet with three SBS and five UEs.  
energy efficiency; power allocation; user clustering; reinforcement learning +
Footnote †: journal: Applied
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 4 OFDM in PLC Transmission
### Wavelet-OFDM  
Wavelets are zero mean waveforms of fixed duration. The difference between wavelets and sinusoids employed in Fourier analysis is that wavelets are usually irregular and asymmetric. The wavelet transform decomposes the signals into shifted and scaled versions of appropriate wavelets.  
The advantage of the wavelet transform over other transforms is that it is discrete both in time and in scale. The wavelet decomposition is implemented employing appropriate low-pass (LPF) and high-pass (HPF) filters, followed by a down-sampler to enhance the spectral efficiency of the wavelet transform. According to the above procedure, the wavelet transform decomposes a signal into its approximation coefficients (output of the LPF) and its detail coefficients (output of the HPF). The inverse procedure is performed employing the inverse wavelet transform. In W-OFDM, the discrete wavelet transform (DWT) and the inverse DWT (IDWT) replace the FFT and IFFT transforms used at the transmitter and the receiver in conventional OFDM as depicted by the block diagram of W-OFDM transmission given in Figure 1[17; 18].  
In Figure 1a, the transmitter uses a digital modulator which maps the input bits onto OFDM symbols \(\mathbf{X_{m}}\), forming \(N\) parallel data streams \(X_{m}(i)\), \(0\leq i\leq N-1\). Each stream \(X_{m}(i)\) is converted to a serial stream forming a vector \(\mathbf{X}\) which will be transposed into the approximation coefficients \(\mathbf{A}\). To implement IDWT at the transmitter, the signal is up-sampled by two and then filtered by an LPF. This operation retains the primarily important lower part of the signal spectrum. Moreover, a zero padding signal is fed to an HPF. To produce the orthogonal wavelets required for the implementation of the wanted IDWT-DWT pair, the LPF and HPF filters must be quadrature mirror filters. The lengths of \(\mathbf{A}\) and \(\mathbf{D}\) depend on the number of OFDM subcarriers, \(N\), and on the filters producing the wavelets selected [19]. The DWT procedure is implemented at the receiver as depicted in Figure 1b. The received data, \(\mathbf{U_{k}}\), are fed to the LPF and HPF employed at the receiver and are down-sampled by two. The \(\mathbf{a}\) signal output from the LPF is then converted to a parallel stream, \(U_{m}(i)\), which is fed to the demodulator. The \(\mathbf{d}\) signal output from the HPF is discarded [19].
## 4 Understanding Centralised Identity Systems
### LDAP: The Directory-Based Approach
#### 4.5.1 Technical Architecture  
Some main components of LDAP's client-server model are the Directory System Agent (DSA), a server component that manages the Directory Information Tree (DIT) and runs the LDAP service, and the Directory User Agent (DUA), a client-side component that interacts with the DSA to retrieve and update directory information. Each entry in the LDAP directory is uniquely identified by a Distinguished Name (DN) and Relative Distinguished Names (RDNs), which indicate levels within the DIT hierarchy [21].
## 3 Results and Discussion
### K-Fold Crossover Experiment  
To better represent the network performance, cross-validation experiments (10-fold, 15-fold, and 20-fold) were performed in this paper to find the best classification for the dataset and to avoid the selection of unexpected hyperparameters and models that cannot generalize due to particular classifications. The results in Table 2 show that the evaluation metrics, such as classification accuracy (PR), classification F1 score (F1), and overall accuracy (Acc) of the experiments, conducted with 20-fold cross-validation are better than the other methods, so the dataset partitioning method of randomly dividing the subjects into training, validation, and test sets in the ratio of 15:4:1 maximizes the model performance.
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 6 Numerical Results
This well validates the effectiveness of our proposed sub-optimal number of sensor nodes. Figure 4: Simulation and analysis results of the average MMSE \(\overline{D}(N)\) w.r.t. the number of sensor nodes \(N\) under different SNR \(\gamma_{0}\). Figure 5: The weighted-sum of freshness and fidelity tradeoff \(f(N)\) w.r.t. the number of sensor nodes \(N\) under different SNR \(\gamma_{0}\) and weighting factor \(\alpha\). Figure 6 depicts the relationship between the data freshness-fidelity weighted-sum tradeoff \(f(N)\) and the number of sensor nodes \(N\) under different weighting factor \(\alpha\) and arrival rate \(v\). In the simulation, the signal power \(\sigma_{X}^{2}\) is fixed to 10 dBm (10 mW) and the noise power remains the same, which means the MMSE distortion remains the same. Observing the circular solid curve and the square solid curve, we can see that under the same weighting factor \(\alpha=0.6\), a larger arrival rate \(v\) causes a smaller tradeoff. However, the optimal number of sensor nodes \(N^{\star}\) can be comparatively larger: When \(v=10\), \(N^{\star}=8\); When \(v=5\), \(N^{\star}=5\). This is explainable since a larger arrival rate \(v\) leads to smaller AoI, which means the curve will start climbing later. The improvement on system overall freshness and fidelity brought by the proposed sub-optimal solution \(N^{\star}\) is also validated.
# An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
###### Abstract  
Today, the internet is fundamental to social inclusion. There are many people that live in remote areas, and the only way to supply internet services is through the use of microwave terrestrial and satellite systems. Thus, it is important to have efficient tools to design and optimize these systems. In this paper, a tool with the objective to shorten the time spent in the design process of microwave terrestrial and satellite point-to-point links is presented. This tool can be applied in academia by engineering students, providing an extended analysis of many sections of a link project design, as well as in professional practice by telecommunication engineering departments, presenting a concise step-by-step interactive design process. This tool uses three-dimensional world visualization, with the Cesium Application Programming Interface (API), to display and analyze site-specific characteristics that can disrupt the link's quality of service (QoS). Using this visualization, two ray-tracing algorithms were developed to analyze signal diffraction and reflection mainly throughout terrestrial links. Using this new algorithm, an innovative process for signal diffraction and reflection calculations was created. Using updated standards provided by the International Telecommunication Union Radiocommunication Sector (ITU-R), the characteristics of the defined simulated links could be predicted, thus providing the user with the metrics of signal quality and system link budget.  
radio propagation; software planning tool; terrestrial microwave link; satellite microwave link +
Footnote †: journal: Future Internet
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## 4 Likelihood Derivation  
In this localization problem, we aim to estimate the _state_ vector \(\mathbf{x}=[\text{lat},\;\text{lon}]^{T}\) which represents the unknown position (latitude and longitude) of a ground IoT device in the geodetic coordinates system. To do so, we utilize a series of three measurements, namely Doppler and angle of arrival (AoA) in 2D (azimuth and off-nadir), \(\mathbf{Z}^{(\mathbf{k})}=[f^{(k)}_{\text{d}},\Phi^{(k)},\Theta^{(k)}]^{T}\), where \(k\) is the discrete-measurement index.
# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 4 Capacity Bounds Analysis  
### Single-Service Capacity Bounds  
#### 4.1.1 Capacity Bounds Due to the Interference  
It is well known that any user actively using a cell can cause interference with other users. User \(i\) should boost power as required to meet the node requirements when more users become active. We consider a single-service case. Since all users achieve the required \(SNRi\) (\(Si=SFi/SNRi=S\)), it is presumed that all users transmit a signal with the same spreading factor, \(SFi\). Additionally, for all code combinations, the non-orthogonality factor, \(\varepsilon_{ij}\), will be equal. Every active user in the cell causes some interference for the other users. A user's interference level increases with the number of users entering the cell; hence, this user needs to supply more transmission power. If the number of users in the cell reaches a certain limit, the system can no longer increase the received power to improve the signal-to-noise ratio (\(SNR\)) because of the linear relationship between the received power \(P_{R}\) of user \(i\) and the resulting interference level of all the other users. This limit is indicated by the following condition:  
\[S>(n-1).\frac{V\cdot\varepsilon}{F}\]Given the derivation of \(P_{R}\) in [32] and assuming that all users in the cell are using the same radio service and that \(n\) users are interfering with each other in the cell, thermal noise, \(N_{0}\), is generated in the cell and is derived as follows:  
\[N_{0}=P_{R}\bigg{(}S-(n-1)\cdot\frac{V\cdot\varepsilon}{F}\bigg{)} \tag{7}\]  
Because of the linear relationship between the received power of user \(i\), \(PP_{i}\), and the interference caused by other users in the cell, the system cannot improve the signal-to-noise ratio (\(SNR\)) when the number of active users, \(n_{max}\), reaches a specific limit. Therefore, from (7), we obtain the following:  
\[n_{\max}<\frac{S\cdot F}{V\cdot\varepsilon}+1 \tag{8}\]  
**Notations.**\(n_{\max}\) is the maximum number of active users in the cell, \(\varepsilon\) is the non-orthogonality factor of the user's signals, \(S\) is the service factor (\(S=SF/SNR\)), and \(SF\) is the spreading factor representing the number of chips per data symbol. Here, \(V\) is the activity factor for any type of user service, and \(F\) is the inter-cell interference factor.
# Dynamic Response of Dual-Disk Rotor System with Uncertainties Based on Chebyshev Convex Method
## 4 Numerical Analysis of the Rotor Dynamic Response Based on CCM
### Numerical Simulation Model and Deterministic Response  
As shown in Figure 5, the rotor system is modeled by beam element, mass element, and spring element with convex variable parameters. Detailed deterministic geometric and physical parameters of the rotor system studied are given in Table 1.  
Figure 4: Computational flowchart of CCM for rotor dynamic response analysis.  
The deterministic response is illustrated to characterize the dual-disk rotor system without uncertainty and to give an initial impression of its dynamic behaviors. Based on the deterministic model and numerical simulations, the first three critical speeds of the rotor are shown in the Table 2 and the transient-state vibration amplitude time when the two disks rotate at the same time, history of the disk geometric center is shown in Figure 6.  
By observing the value of the first three critical speeds and deterministic response of the dual-disk rotor, it can be found that the rotor system has two critical speeds in the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline \(E\) (GPa) & Young’s modulus & 210 \\ \(R\) (m) & Radius of the shaft & 0.005 \\ \(r\) (m) & Radius of disk 1, 2 & 0.0375 \\ \(L\) (m) & Length of shaft & 0.55 \\ \(L_{1}\), \(L_{2}\), \(L_{3}\), \(L_{4}\), \(L_{5}\) (m) & Length of shaft 1, 2, 3, 4, 5 & 0.10, 0.10, 0.15, 0.10, 0.10 \\ \(K_{1}\), \(K_{2}\) (N/m) & Stiffnesses of supports 1, 2 & \(1.20\times 10^{5},1.0\times 10^{5}\) \\ \(C\) (N\(\cdot\)s/m) & Damping coefficient & 32 \\ \(m_{1}\), \(m_{2}\) (kg) & Mass of disk 1, 2 & 0.483, 0.481 \\ \(\rho\) (kg/m\({}^{3}\)) & Density & 7800 \\ \(lp_{1}\), \(lp_{2}\) (kg\(\cdot\)m\({}^{2}\)) & Polar moment of inertia & \(3.242\times 10^{-4},3.228\times 10^{-4}\) \\ \(e_{1}\), \(e_{2}\) (m) & Eccentricity of disk 1, 2 & \(4\times 10^{-5},4\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of physical parameters of the dual-disk rotor.  
Figure 5: Schematic diagram of the dual-disk rotor model.  
Figure 6: The deterministic response of the dual-disk rotor.  
working speed range, and the first two peaks appear at 2838.67 rpm and 6406.81 rpm for both of the disks, respectively. Meanwhile, the second-order peak of disk 1 is significantly greater than that of disk 2. This phenomenon is analyzed by researchers and could be attributed to the different stiffness of bearings [33; 34; 35]. Moreover, the response law of the two disks is basically similar. The vibration state at the critical speed satisfies the actual operating law of the rotor system. In order to simplify the analysis process, the subsequent uncertainty analysis only analyzes disk 2.
## 4 Result  
### SIF, NDVI and kNDVI Responses to Water Stress  
Pearson correlation analysis was performed to explore the relationship among the three vegetation indices with SPEI3 (Figure 2). Our analysis reveals that these three vegetation indices predominantly exhibit positive correlation coefficients with SPEI3. Specifically, the percentages of pixels showing a positive correlation with NDVI-GPP, kNDVI-GPP, and SIF-GPP are 64.29%, 65.61%, and 66.72%, respectively. Notably, these indices demonstrate significant variation in their distribution across different regions and types of vegetation cover. In the Yunnan-Guizhou Plateau, for instance, SIF, NDVI, and kNDVI uniformly respond positively to SPEI3, exhibiting analogous spatial distribution traits. On contrast, in the Himalayan regions characterized by grassland cover, NDVI displays a markedly negative correlation with SPEI3, while kNDVI and SIF predominantly show positive correlations. Furthermore, in the lower altitude areas east of \(110^{\circ}\)E, particularly in Guangdong and Hunan provinces, the correlation between SIF and SPEI3 is comparatively low, which stands in contrast to the stronger correlation observed with NDVI. When considering the four types of vegetation cover--farmland, forest, shrub, and grassland--the median correlation coefficients between SIF and SPEI3 are positive in all categories, suggesting a robust general relationship between SIF and SPEI3 in these regions. In grassland areas, although NDVI and kNDVI demonstrate a higher degree of response variability, their median values are approximately zero, indicating a neutral overall response.