esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Design Based on Identified Model
is error between desired flow \(r_{flow}\) and measured flow \(y_{flow}\) and \(e_{\text{int},flow}\) is discrete time integral of \(e_{flow}\)  
\[e_{\text{int},flow}(k)=e_{\text{int},flow}(k-1)+T_{s}e_{flow}(k),e_{\text{int}, flow}(0)=0. \tag{6}\]  
The aim of controller design is to solve the optimization problem  
\[\min_{K_{utilizing}}\left\|W_{2r_{flow}}\right\|_{\infty}, \tag{7}\]  
where \(W_{2r_{flow}}\) is the transfer matrix between performance outputs \(z=\left[z_{p}\quad z_{u}\right]^{T}\) and flow reference signal \(r_{flow}\). It is well-known that, in practice, to achieve prescribed performance conditions is sufficient to find stabilizing controller \(K\)[30], which provides  
\[\left\|\left[W_{2r}\right]\right\|_{\infty}<1. \tag{8}\]  
This problem is solved by robust control MATLAB function _hinfsyn_[31]. The design is performed with several different performance weights. The requirements for system output performance and for the shape of the control signal are satisfied for the \(H\infty\) controller obtained with the weighed filters  
\[W_{p}= \begin{bmatrix}Z\left(\frac{5+50}{58+50}\right)&0&0\\ 0&Z\left(\frac{5+0.025}{58+0.025}\right)&0\\ 0&0&0.005\end{bmatrix} ,W_{u}=0.011, \tag{9}\]  
where \(Z[\bullet]\) denotes \(Z\)-transformation. The source code for the representation of the extended performance filter plant model and \(H\infty\) controller design is presented in Figure 7. The result obtained after minimization of (7) closed-loop system's \(H\infty\) norm is \(0.4717\), which is smaller than \(1\). Thus, the prescribed performance conditions are satisfied. The discrete-time state-space description of the \(H\infty\) controller is  
\[x_{c}((k+1)\;T_{S})=A_{\;c}x(k\;T_{S})+B_{c}\;y_{c}(k\;T_{S}), \tag{10}\]  
\[u(k\;T_{S})=C_{c}x_{c}(k\;T_{S})+D_{c}y_{c}(k\;T_{S}), \tag{11}\]  
where \(x_{c}=[x_{c,1},x_{c,2},\cdots,are the controller matrixes. The singular values of output sensitivity, complementary sensitivity, and control signal sensitivity are presented in Figures 8-10. As can be seen from Figure 8, the closed-loop system attenuates load disturbances sufficiently well (for example, the effect of disturbance with frequency 0.001 rad/s is reduced \(10^{5}\) times). Moreover, the system tracks flow reference signals with frequencies up to 3 rad/s without steady-state error and the closed-loop bandwidth is approximately 12 rad/s, which means that the system dynamic is sufficiently fast (Figure 9). Figure 10 shows that the control signal sensitivity to high frequency measurement noise is relatively small, which  
Figure 10: Control signal sensitivity. Figure 9: Complementary sensitivity.
# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 3 Problem Formulation
### Reinforcement Learning Formulation
#### 3.2.5 Transition Probability (P)  
The transition probability quantifies the generally stochastic likelihood of transitioning from one state to another given an action; its variability is driven by the sampled congestion and bandwidth fields described above. It is defined mathematically as  
\[P(S_{t+1}=s^{\prime}\mid S_{t}=s,A_{t}=a)\]  
where  
* \(S_{t}\) denotes the current state at time \(t\).
* \(A_{t}\) represents the action taken at time \(t\) from state \(S_{t}\).
* \(S_{t+1}\) is the state at time \(t+1\), resulting from taking action \(A_{t}\) in state \(S_{t}\).
# Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
## 3 Analyses and Discussion
### LST, NDVI, and SAR Correlation  
Several studies have investigated the correlation between the optical data and the SAR data for different land covers [37; 38; 39]. In this study, the monthly correlation between the optical, thermal, and radar data within a wetland area was investigated. Thus, the average value of the eleven dates from Landsat-8, and the twelve dates from Sentinel-1 representing one year investigated in this study were taken into consideration for every class previously determined with the UAV data. The correlation results are presented in Table 5. The full VH and VV results are presented in Figures A3 and A4 respectively.  
For the statistical comparison between the multi-sensor data were completed using correlation, which ranges between \(-1\) (indirect relationship) and \(1\) (perfect relationship), and the correlations were supported using statistical significance variable which if it is less than \(0.05\) it means the results are significant--or that they did not just occur by chance.  
It should be noted that the Landsat-8 and Sentinel-1 images were not taken at the same dates, but the used images present a one-year period presented with eleven images.  
Figure 9: February–June VH-VV classes comparison.
# Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks
## 3 Role of Blockchain in 6G Services  
This section provides insights on the 6G-based smart services, such as network slicing, spectrum sharing, resource management, and data sharing. Table 2 shows the comparative study on blockchain-based existing 6G services.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Author** & **Year** & **Objective** & **6G** & **Security** & **Approach** & **Pros** & **Cons** \\ \hline [PERSON] et al. [33] & 2019 & Smart public safety system. & ✗ & Blockchain. & BlendMAS. & The scheme offers secure and smart data sharing with access control. & Does not yet explore 6G networks. \\ \hline [PERSON] et al. [32] & 2020 & Safety and security in rideshare service. & ✗ & Using facial recognition API. & GPS tracking and 2-way rating system. & Developed mobile ridesharing scheme enhance the security & Did not consider latency and delay. \\ \hline [PERSON] et al. [34] & 2020 & Video surveillance systems & ✗ & Blockchain. & Lib-Pri scheme. & The scheme offers real-time video analytics by identifying fugitives using facial features. & Did not consider data confidentiality and scalability. \\ \hline [PERSON] et al. [35] & 2021 & To prevent cyberattack incidents. & ✗ & Blockchain. & Blockchain-enabled collaborative intrusion detection systems. & The scheme enabled node and application application authorization. & Does not discuss the scalability of the system. \\ \hline [PERSON] et al. [36] & 2022 & On-site safety for tower cranes. & ✗ & Blockchain. & Smart contract, consensus, and Hyperledger Fabric-based framework. & The scheme enhanced the safety performance. & Did not consider latency, throughput, and computation overhead analysis. \\ \hline [PERSON] et al. [37] & 2022 & Security and privacy of IoT data. & Blockchain and cryptography. & STCChain. & The scheme prevented data stealing and attacks. & Does not incorporate 6G-based solution. \\ \hline [PERSON] et al. [38] & 2022 & Crime and accident prevention for autonomous vehicles. & ✗ & Blockchain. & Multisignature-enabled access control using GPS data. & The scheme offers of the image and video data with lower latency. & The scheme limits verifying the reliability of GPS data. \\ \hline Proposed scheme & 2022 & Public safety to monitor criminal activity. & & & Layered architecture incorporating blockchain and 6G to detect crime. & The scheme uses different ML classifier to identify the attack and nonattack from the criminal activity dataset. & \\ \hline \end{tabular}
\end{table}
Table 1: A comparative analysis of proposed work with the existing studies.
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 2 Preliminaries  
In this section, we present the fundamental concept of fingerprint-based RSSI positioning. We then describe the conventional positioning algorithms, including the WKNN method and DNN-based regression (DNNR).
## 2 Methods
### Source Wavelet Inversion Based on L2-Norm Elastic Waveform Inversion (L2 EWI)  
Assuming that the near-surface velocity model is relatively accurate, the source wavelet function can be obtained by using the direct wave waveform inversion method. Using near-offset multi-component direct waves to construct the L2 norm objective function, we can obtain  
\[\sigma_{3}(\mathbf{s})=\sum_{i}^{ns}\sum_{j}^{nr}\bigg{[}\left\|\mathbf{U}_{i,j}^{x}(\mathbf{s})-\mathbf{D}_{i,j}^{x}\right\|^{2}+\left\|\mathbf{U}_{i,j}^{ z}(\mathbf{s})-\mathbf{D}_{i,j}^{z}\right\|^{2}\bigg{]}\, \tag{15}\]  
where \(\mathbf{U}\) and \(\mathbf{D}\) denote the synthetic direct waves and the observed direct waves, respectively, and \(\mathbf{s}\) denotes the source wavelet. According to the adjoint-state method, the derivative of the objective function \(\sigma_{3}\) with respect to the source wavelet can be written as  
\[\frac{\partial\sigma_{3}}{\partial\mathbf{s}_{i}}=\sum_{j}^{nr}\bigg{[}\bigg{(} \frac{\partial\mathbf{\bar{r}}}{\partial\mathbf{s}_{i}}\bigg{)}^{T}\bigg{(}L^ {-1}\bigg{)}^{T}\bigg{(}\mathbf{U}_{i,j}^{x}-\mathbf{D}_{i,j}^{x}\bigg{)}+ \bigg{(}\frac{\partial\mathbf{\bar{r}}}{\partial\mathbf{s}_{i}}\bigg{)}^{T} \bigg{(}L^{-1}\bigg{)}^{T}\bigg{(}\mathbf{U}_{i,j}^{z}-\mathbf{D}_{i,j}^{z} \bigg{)}\bigg{]}, \tag{16}\]where \(\mathbf{s}_{i}\) denotes the source wavelet function of the \(i\)th shot, \(\mathbf{F}\) denotes the source matrix, \(L\) denotes the wavefield propagation operator, and the superscript T denotes transpose. It can be seen from Equation (16) that when the initial value of the inverted source wavelet is the zero vector, it is equivalent to backpropagating the observed data. This is the wavefield backpropagation source wavelet estimation method. Consequently, the wavefield backpropagation-derived source wavelet can serve as the initial wavelet in the L2 EWI method. Given the capacity of the wavefield backpropagation algorithm to extract accurate phase information from the source wavelet, the resultant wavelet can be considered a dependable initial source wavelet. Nevertheless, similar to the L2 norm full-waveform inversion, the presence of amplitude errors in the observed data impacts the accuracy of the source wavelet estimated via Equation (16).
## 5 Field Test Results and Discussion
In the BCCB Trench case, the vibration mitigation performances were found to be approximately the same according to the PGA values, while in terms of PGV and PGD values, they were found to be reduced by approximately 9% and 13%, respectively. In addition, if the average mitigation performances of all barrier cases are evaluated in terms of the zone of adequate isolation limits proposed by Woods, the vibration isolation performance for PGD and PGV data is within or very close to these limits, while the PGA values are outside this zone. Barriers reflect vibrations that hit them perpendicularly. Therefore, when examining the ARR values for PGA, PGV, and PGD obtained from the M130 accelerometer positioned in front of the barrier, it can be noted that the vibration waves in the EW direction are not as affected by reflected waves as those in the NS direction. In other words, the barrier in the EW direction does not reflect the waves effectively. Considering the effective direction of vibrations (in NS direction), the isolation performance of the barrier decreases as the distance of the accelerometers behind the barrier increases. However, this behavior in the EW direction is valid up to a certain distance, beyond which other factors come into play. As observed in this study, the ARR values calculated for all spectral magnitudes from the M132 accelerometer located 2.6 m from the barrier increase significantly compared to those calculated immediately behind the barrier. These increases are around 80-90%. However, when examining the ARR values of the spectral magnitudes calculated from the M133 accelerometer located 9.6 m from the barrier, they are lower than those from the M132 accelerometer. This is because the barrier's performance decreases with distance, and the vibration amplitudes in the EW direction are lower. In other words, vibrations with reduced amplitudes due to the barrier are further attenuated by the ground as the distance increases. In the UD direction, the average VMPs of all cases according to the PGA, PGV, and PGD values obtained from the sensor right behind the barrier are calculated as 50%, 56%, and 73%, respectively. The performance of the barrier conditions with respect to each other is similar to that in the EW direction. For all spectral amplitudes, the Empty Trench case provides the best isolation performance, while the Styrofoam-filled BCCB Trench case has the worst isolation performance compared to the others. However, according to the M131 accelerometer data behind the barrier, the calculated vibration reduction performance for all spectral magnitudes and each barrier condition is lower than in the EW and NS directions. This suggests that the effectiveness of the barrier in the UD direction is less compared to the other directions. Additionally, vibrations behind the barrier generally decrease with distance. Examining the ARR values calculated from the spectral magnitudesof the M130 accelerometer in front of the barrier indicates that, similar to the EW direction, waves do not effectively reflect off the barrier.
## 3 Methodology
### Network Architectures
#### 3.2.3 Sparse Attention Mechanism  
In the classical transformer work, the attention weights are computed as the scaled dot product of \(Q\) and \(K\), normalized by the dimensionality of the vector space \(d_{k}\):  
\[A=\varepsilon\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}\bigg{)}V \tag{1}\]  
where \(\varepsilon\) is a softmax function. To reduce computational complexity, a sparse attention mechanism is applied by introducing a randomly generated sparse mask. Let the Query tensor \(Q\in\Re^{N_{q}\times d_{k}}\) and Key tensor \(K\in\Re^{N_{q}\times d_{k}}\), where \(N_{q}\) and \(N_{k}\) represent the number of positions for the Query and Key, respectively. Sparse mask generation can be mathematically expressed as follows:A random matrix \(R\in\Re^{N_{q}\times N_{k}}\) is generated with elements sampled from a uniform distribution. A binary sparse mask \(M\in\Re^{N_{q}\times N_{k}}\) is then constructed based on a predefined sparsity threshold \(\tau=0.1\):  
\[M_{ij}=\left\{\begin{aligned} & 1,\ \ if\ R_{ij}<\tau\\ & 0,\ \ otherwise\end{aligned}\right. \tag{2}\]  
The sparse mask \(M\) defines which Query-Key pairs are valid for attention computation. Specifically, \(M_{ij}=1\) indicates that the Query at position \(i\) corresponds to the Key at position \(j\), while \(M_{ij}=0\) disables the connection, thereby enforcing sparsity.  
The sparse attention weights are computed as  
\[A_{s}=\varepsilon\Big{(}\frac{QK^{T}}{\sqrt{d_{k}}}\odot M\Big{)}V \tag{3}\]
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Parameters and Basic Structure of 5G PHY Layer
#### 2.2.2 PHY Layer Frame Structure
One of the drawbacks of this scheme is that the band definition requires a guard band between the UL and the DL, and the receiver must be equipped with a duplex filter to suppress interference from the transmitter. In the Time Division Duplex, as a type of half duplex scheme, a single carrier frequency is utilized for both the UL and the DL, and their separation is achieved by using different time slots. An essential aspect of any half-duplex system in general is the possibility of providing a sufficiently large Guard Period (GP) or Guard Time (GT), where neither DL nor UL transmissions occur [58]. The length of GT typically increases for stations that cover larger areas. TDD uses unpaired bands, where the UL and DL transmissions do  
Figure 3: Frame structure comparison between 4G LTE and 5G NR. not overlap in time, from both the device's and the cell's viewpoint. The benefit of TDD is the channel reciprocity assumption, allowing improved channel estimation and link adaptation, including precoding and directional antennas. This is particularly beneficial for methods like beamforming. Semi-Static TDD introduces a higher degree of flexibility compared to static TDD. In Time Division Long Term Evolution (TD-LTE), UL/DL configurations were defined within a single 10 ms frame. However, in 5G NR higher layer configuration parameters can be employed to achieve a UL/DL allocation parameterization that is specific to a cell or even a UE. Therefore, the slot configuration is adaptable and can be modified periodically while still prioritizing the management of inter-cell interference, see Figure 4.  
Dynamic TDD is the most adaptable approach for configuring UL/DL. Signaling mechanisms play a crucial role in informing the UE about resource allocations for UL and DL transmissions. Firstly, dynamic signaling for the scheduled device involves the device monitoring DL control signals to determine whether to transmit in the UL direction or listen for DL transmissions, with the scheduler controlling the UL/DL allocation. Secondly, semi-static signaling via RRC allows for UL/DL allocation information to be transmitted, aiding in power conservation by reducing the need for constant device monitoring. Finally, dynamic slot-format indication shared among a group of devices utilizes a special downlink control message, the Slot Format Indicator (SFI), to dynamically signal UL/DL allocation, facilitating efficient resource management and channel quality assessments. These mechanisms collectively ensure adaptive resource allocation and effective traffic management in dynamic TDD scenarios. This method could be applicable for small cells, or even for standalone or isolated indoor cells that have overlapping coverage with neighboring cells, thereby reducing the impact of inter-cell interference.
## 3 Results and Discussion
### Adsorption Performances of DE and DE-LDH
#### 3.2.3 Effect of Dye Concentration  
The concentration of CR molecules plays a critical role in adsorption and it is an important parameter to evaluate the ability of adsorbents and to find the maximum removal concentration at an optimized dosage and pH. The concentration range of CR from 50 to 1000 mg/L has been selected for this study (Figure 5c). The CR removal efficiency of adsorbent DE gradually decreased from 16.3% to 2.9% with the increase in CR concentrations from 50 to 1000 mg/L. DE-LDH showed a decrease in its dye removal efficiency from 97.3% to 52.2% with an increase in the concentration of dye from 50 to 1000 mg/L. From the results, DE-LDH exhibited a higher removal efficiency compared to the DE even at 1000 mg/L. The dye removal efficiency decreases with the increase in concentration due to the unavailability of active sites on the DE and DE-LDH surfaces. However, DE-LDH adsorbent has shown higher removal efficiency than DE due to its larger surface area.
## 3 Delay Spread, Doppler Shift, and Doppler Spread
### Doppler Spread  
The Doppler spread is the maximum difference in instantaneous frequency over all significant propagation links [42]. At time \(t\), the Doppler spread between the direct and the \(i\)-th RIS links is  
\[D_{\text{Ii}}(t)=f_{c}\max_{i_{m},i_{n}}\bigg{|}\frac{d}{dt}\bigg{(}\tau_{i_{m}, i_{n}}(t)+\frac{\phi_{i_{m},i_{n}}(t)+\gamma_{i_{m},i_{n}}}{2\pi f_{c}}\bigg{)}- \frac{d}{dt}\tau_{0}(t)\bigg{|}, \tag{25}\]  
and the Doppler spread across the \(i\)-th RIS itself, \(D_{\text{RIS}}(t)\), can be obtained as  
\[D_{\text{RISi}}(t)= f_{c}\max_{i_{m},i_{n},i_{m^{\prime}},i_{n^{\prime}}}\bigg{|} \frac{d}{dt}\bigg{(}\tau_{i_{m^{\prime},i_{n}}}(t)+\frac{\phi_{i_{m},i_{n}}(t)+ \gamma_{i_{m},i_{n}}}{2\pi f_{c}}\bigg{)} \tag{26}\] \[-\frac{d}{dt}\bigg{(}\tau_{i_{m^{\prime}},i_{n^{\prime}}}(t)+ \frac{\phi_{i_{m^{\prime}},i_{n^{\prime}}}(t)+\gamma_{i_{m^{\prime}},i_{n^{ \prime}}}}{2\pi f_{c}}\bigg{)}\bigg{|}.\]  
Note that \(i_{m^{\prime}},i_{n^{\prime}}\) refers to the different element from the element \(i_{m},i_{n}\), in the same RIS \(i\). Therefore, for the \(i\)-th RIS link, at time \(t\), the Doppler spread can be obtained as  
\[D_{i}(t)=\max\bigg{\{}D_{\text{Ii}}(t),D_{\text{RISi}}(t)\bigg{\}},\;\;i=1,2, \ldots,K. \tag{27}\]  
Consequently, the Doppler spread for the \(K\) RISs is  
\[D(t)=\max_{i}\bigg{\{}D_{i}(t)\bigg{\}},\;\;i=1,2,\ldots,K. \tag{28}\]  
**Remark 3**.: _It is noteworthy that, similar to Doppler shift, the RIS and transceiver HWI also have no influence on Doppler spread._
## 1 Introduction
In recent years, text mining has become an invaluable tool for analyzing public opinions and sentiments towards specific topics. As a key application of Natural Language Processing (NLP) technology, text mining enables computers to understand human language, thereby facilitating the extraction of critical insights from large volumes of textual data [6]. This paper utilizes text mining techniques to perform a quantitative analysis of Weibo posts related to common prosperity, with the objective of deciphering public attitudes and sentiments towards this goal. The study seeks to address the following research questions: (1) What are the primary concerns of the public regarding common prosperity? (2) How do public attitudes align or diverge on this topic, and is there a clear dichotomy between positive and negative sentiments? (3) Among those expressing negative sentiments, what specific issues are they focused on? This study contributes to the literature on common prosperity in three ways. Firstly, it adopts an innovative approach by examining the issue of common prosperity from the perspective of public attitudes, thereby enriching the body of research in this field. Secondly, it delves into the attitudes and emotions of the Chinese populace regarding common prosperity, specifically focusing on investigating the root causes of negative sentiments. This analysis not only highlights the prevailing public attitudes but also identifies the main obstacles impeding progress towards common prosperity in China. Thirdly, the research extends the application of NLP technology. While NLP has been traditionally employed to extract public opinions on specific topics, this study pioneers its use in exploring public attitudes and emotions towards broader policy goals, thereby enhancing the versatility of NLP in social science research. The structure of this paper is organized as follows: Section 2 provides a literature review. Section 3 describes the research methods employed, including data collection, the extraction of high-frequency words, topic analysis, and sentiment analysis. Section 4 presents the research findings and discussion. Section 5 concludes the paper with a summary of the study, outlines the theoretical and practical implications of our research, and details the contributions, limitations, and directions for future work.
## 5 Performance Analysis
### Consistency in Number of CH  
Cluster headcount has a significant impact on protocol efficiency, as there will be additions in node energy consumption and over-consumption of energy. If the number of CHs is low, the number of SNs per CH will be high and the SN data transmission duration will be belonged. The total energy consumption of each round of networks grows as the number of CH increases; it decreases the network's data fusion efficiency and longevity. In Figure 6, it can be seen that our recommended EEWBP protocols' headcounts fluctuation number is better than LEACH, CACP, CPCP, and CUCA. It is easy to observe that the cluster headcount in the LEACH protocol swings between \(3\leq\) k \(\leq\) 19, \(4\leq\) k \(\leq\) 15 in CUCA, \(5\leq\) k \(\leq\) 16 in CACP, \(4\leq\) k \(\leq\) 18 in CPCA and our proposed protocol swings between \(3\leq\) k \(\leq\) 13 which is the most ideal.  
Figure 5: Number of clusters and time.  
Figure 6: Cluster count vs. number of rounds.  
Optimal cluster headcount should be calculated using the weighted value, which contains several energy characteristics, to reduce the unpredictability of cluster headcounts in the suggested protocol. This means that the cluster's total capacity will be reduced to balance energy usage if many dead nodes are present.