esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point for multimodal information detection in an internet public opinion scenario. In this paper, we look at how to dynamically monitor the internet opinion information (mostly images and videos) that different websites post. Based on the most recent advancements in text recognition, this paper proposes a new method of visual multimodal text recognition and sentiment analysis (MTR-SAM) for internet public opinion analysis scenarios. In the detection module, a LK-PAN network with large sensory fields is proposed to enhance the CML distillation strategy, and an RSE-FPN with a residual attention mechanism is used to improve feature map representation. Second, it proposes that the original CTC decoder be replaced with a GTC method to solve earlier problems with text detection at arbitrary rotation angles. Additionally, the performance of scene text detection for arbitrary rotation angles is improved using a sinusoidal loss function for rotation recognition. Finally, the improved sentiment analysis model is used to predict the sentiment polarity of the text recognition results. The experimental results show that the new method proposed in this paper improves recognition speed by 31.77%, recognition accuracy by 10.78% on the video dataset, and the F1 score of the multimodal sentiment analysis model by 4.42% on the self-built internet public opinion dataset (lab dataset). The method proposed provides significant technical support for internet public opinion analysis in multimodal domains.  
public opinion analysis; sentiment analysis; multimodal; text recognition +
Footnote †: journal: Medical Image Analysis
## 3 Prefiltered Frequency-Domain Equalization
### Channel Equalization  
The matched filter output sequence \(\mathbf{r}\) in the FD can be obtained by taking the DFT of both sides of (5), as  
\[R_{k}=H_{k}S_{k}+Z_{k} \tag{8}\]  
where \(R_{k}\), \(H_{k}\), \(S_{k}\), and \(Z_{k}\) denote the FD representations of \(r_{n}\), \(s_{n}\), \(h_{n}\), and \(z_{n}\), respectively.  
According to the CPM SCFDE algorithm, the noise term \(z_{n}\) that is output by the matched filter correlates, so a noise whitening filter \(w_{n}\) is required for whitening equalization. Despite that the traditional noise whitening filter \(w_{n}\) can remove all relevant noise under optimal conditions, the filtered signal still possesses forward and backward information and performs symbol detection with the Viterbi algorithm or using only the current information.  
In this study, an improved filtering framework is presented in which a new transfer function, \(g_{n}\), is used to define a prefilter, which reduces the energy of backward information while maintaining some correlated noise. As a result, sequence \(\mathbf{r}\) can be converted into a signal sequence, \(\mathbf{a}\), with forward information, and then information at \(n\) and \(n-1\) can be used for symbol recognition simultaneously.  
Applying the prefilter to the received signal \(R_{k}\) yields  
\[R_{k}V_{k}=H_{k}S_{k}V_{k}+Z_{k}V_{k} \tag{9}\]  
where \(V_{k}=1/\sqrt{G_{k}}\), and \(G_{k}\) is given by DFE of \(g_{n}\).  
\[g_{n}\triangleq\left\{\begin{array}{l}\int_{0}^{+\infty}c(t+nT)c(t)dt\ \,\ n=0\\ \sqrt{2}\times\int_{0}^{+\infty}c(t+nT)c(t)dt,\ n>0\end{array}\right. \tag{10}\]  
The equation can be expressed as follows:  
\[\hat{B}_{k}=R_{k}V_{k}\widetilde{H}_{k} \tag{11}\]  
where an expression for the MMSE equalization coefficient \(\widetilde{H}_{k}\) is given by  
\[\widetilde{H}_{k}=\frac{\hat{H}_{k}^{*}V_{k}}{\left|H_{k}V_{k}\right|^{2}+N_{ 0}} \tag{12}\]
# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 3 Experiments and Results
### Geomagnetic Fingerprint Database Construction  
It is essential for geomagnetic positioning to build a high precision database. In our construction work, we set the reference point every 1.2 m, and then collected the geomagnetic data for 15 s on these points. Five geomagnetic features are extracted by using the method in Section 2.2.1. As for the uncollected area, the linear interpolation method is used to obtain the geomagnetic data. Five geomagnetic maps are generated as illustrated in Figure 11. From these figures, we can find that geomagnetic characteristics of different regions are very obvious, especially Figure 11a which shows the \(x\)-axis component is not close to zero in some experimental areas and this is contrary to theory. We analyzed that ferrous materials lead to the anomalous magnetic field. But they can better describe the magnetic field distribution and improve the specificity of the geomagnetic fingerprint. Therefore, \(x\)-axis components are extracted and combine the other four features to construct the fingerprint database.  
Figure 10: (**a**) Experimental area; (**b**) experimental path.
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 2 Related Work
Some extra technical advances are also included to provide history-based selection of potential fingerprints, multi-floor support, high-accuracy positioning using proximity to Bluetooth LE tags and WiFi APs, weighted positioning, and support of different mobile terminals with different radio sensibilities, among others.
## 3 Experiment Result and Analysis
At a load of 570 kN, the strengthening effect of the load-displacement relationship was 33%, and the strengthening effect of the load-strain relationship was 37.4%, showing a difference of 4%. Therefore, it can be concluded that it is difficult to confirm the exact strengthening effect through fragmentary information such as deflection and strain. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Load (kN)**} & \multicolumn{2}{c}{**Displacement (mm)**} & \multirow{2}{*}{**Strengthening Effect 3 (\%)**} & \multirow{2}{*}{**Remark**} \\ \cline{2-2} \cline{4-5}  & **Before 1** & & & \\ \hline
100 & 6.6 & 7.2 & - & \\
240 & 18.3 & 17.4 & - & Before 1 crack \\
400 & 39.7 & 30.3 & 23.7 & \\
470 & 51.7 & 37.2 & 28.0 & After 2 crack \\
570 & 77.1 & 51.3 & 33.0 & \\
1060 & - & 161.2 & - & \\ \hline \hline \end{tabular} \({}^{\dagger}\) Before strengthening; 2 After strengthening; 3 (Before strengthening–After strengthening)/Before strengthening.  
\end{table} 
Table 4: Comparison of displacement before and after strengthening. Figure 11: Load–strain relationship. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Load (kN)**} & \multicolumn{2}{c}{**Strain (\(\upmu\varepsilon\))**} & \multirow{2}{*}{**Strengthening Effect 3 (\%)**} & \multirow{2}{*}{**remark**} \\ \cline{2-2} \cline{4-5}  & **Before 1** & & & \\ \hline
100 & 90 & 94 & - & \\
240 & 227 & 220 & - & Before 1 crack \\
400 & 555 & 366 & 34.1 & \\
470 & 741 & 452 & 39.0 & After 2 crack \\
570 & 1115 & 698 & 37.4 & \\
1060 & - & 2357 & - & \\ \hline \hline \end{tabular} \({}^{\dagger}\) Before strengthening; 2 After strengthening; 3 (Before strengthening–After strengthening)/Before strengthening.  
\end{table} 
Table 5: Comparison of internal tendon strain before and after strengthening.
## 6 Simulation and Results
### Network Overhead  
To achieve a fair comparison between our scheme and the Cui et al. scheme [22], we have unified the sizes of common parameters. We have assumed that packets sent during the \"Setup and Key Management\" phase in RWFS and the ones sent during the \"Setup\" phase in the Cui et al. scheme are both of equal sizes (4000 bits). In addition, both sensor nodes' identities and timestamps were set to 8 and 64 bits, respectively.  
In RWFS, a packet contains a cipher-text \(c_{i}\) of size 16 bits, an embedded watermark of size 12 bits, the node's identifier, and a timestamp, which makes a total of 100 bits of payload per packet. In the Cui et al. scheme, the packet contains a cipher-text \(c_{i}\) of size 16 bits, a 48-bit homomorphic MAC, a MAC of size 160 bits, a node's identifier, and a timestamp. This makes the size of a packet to be 304 bits.  
The increase of the packet size is clearly reflected in the overall network overhead. From the comparison results in Figure 7, we can find that our scheme provides a great reduction in network overhead compared to the results of Cui et al.'s work for a duration of 10,000 rounds. The Cui et al. scheme transmits a total of 60% more bytes than our scheme. The reduction in transmitted packets can be directly linked to a longer network lifetime.
## 3 Probabilistic Neural Network Model Based on Symmetric Alpha Stable Distribution  
### Alpha Stable Distribution  
The \(\alpha\)-stable distribution, being the limiting distribution of the sum of infinite random variables with infinite possibility variance and independent distribution, is the only type of distribution pattern that complies with the generalized central limit theorem. It is a Gaussian distribution in a broad sense, however its probability density function has a thicker trailing, which can describe broader data, including those not satisfying the central limit theorem. Therefore, it has a more universal meaning.  
In this section, we peruse the \(\alpha\)-stable distribution and its CF, Suppose that \(-\infty<x<\infty\) and \(x\) is distributed according to a stable law, i.e., \(x\sim S\) (\(\alpha\), \(\beta\), \(\gamma\), \(\delta\)), is completely determined by four parameters; \(\alpha\) is the characteristic exponent and it determines the shape of the distribution, (\(0<\alpha\leq 2\)), \(\beta\) is the index of skewness, (\(-\)\(1\leq\beta\leq 1\)), \(\gamma\) is the dispersion or scale parameter of the distribution, (\(\gamma>0\)), and \(\delta\) is the location parameter, (\(\delta\in\mathbb{R}\)). The case \(\beta=0\) which is correspond to the symmetric \(\alpha\)-stable distribution. \(\alpha\)-stable distribution is usually provided by taking the inverse Fourier transform of its CF; however, a closed-form formula does not exist for its density function. The CF of the \(\alpha\)-stable distribution is defined as the following [37],  
\[\varphi(\omega;\alpha,\beta,\gamma,\delta)=E\Big{(}e^{j\omega X}\Big{)}=\left\{ \begin{array}{l}\exp\left\{-\gamma|\omega|^{\alpha}\big{(}1-j\beta sign( \omega)\tan\frac{\pi\alpha}{2}\big{)}+j\delta\omega\right\},\alpha\
eq 1\\ \exp\left\{-\gamma|\omega|\big{(}1+j\beta\frac{\pi}{\pi}sign(\omega)\ln|\omega| \big{)}+j\delta\omega\right\},\alpha=1\end{array}\right. \tag{3}\]  
where sign(\(\cdot\)) is the sign function. From the above equation, it is found that the expression for \(\alpha\)-stable CF has a discontinuity at \(\alpha=1\). The symmetric steady state distribution map in the state of \(\beta=0\) is shown in Figure 1 and the asymmetrical steady state distribution map in the state of \(\beta=1\) is shown in Figure 2.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 5 Performance Evaluation
### Evaluation Results  
In this section, in order to evaluate the performance of DDS, the achieved DSR, total energy consumption in the network, and the average E2E delay were collected. We compared the proposed algorithm with OAPF, OGP, SROD-MM, and SROD-WA under the effects of the delay bound, the required DSR, and the event rate.
## 5 Conclusions  
In this paper, for the first time, we proposed an ML-based TCN model for a RIS-assisted MIMO communication system. The goal of the proposed system is to analyze the implemented TCN model with a RIS channel for the observation of the BER and SER by  
Figure 8: (**a**) The BER results of the proposed TCN model effectiveness for different system configurations, (**b**) SER results of the proposed TCN model effectiveness for different system configurations.  
Figure 7: (**a**) The BER results for different optimizers with respect to SNR values for the BPSK modulation, (**b**) SER performance with respect to SNR values for the BPSK modulation scheme for different optimizers.  
changing the different optimizers during the training of the model. The effectiveness of the proposed model is analyzed with two different modulation schemes. The Adam, SGDm, and RMSprop optimizers are employed for loss function minimization. The simulation results of the proposed TCN model represented that the performance of the BER and SER with respect to different SNR values achieved satisfactory results. Thus, the TCN model can be a new candidate for wireless communication systems. In the future, the proposed TCN model can be tuned with different parameters and applied to more complex scenarios.  
Conceptualization, M.A.S.S. and M.H.R.; methodology, M.A.S.S., M.H.R., and M.A.A.; software, M.A.S.S. and M.H.R.; validation, M.A.S.S., M.H.R. and M.A.A.; formal analysis, M.A.S.S. and M.H.R.; investigation, M.A.S.S., M.H.R. and M.A.A.; resources, H.-K.S.; writing--original draft preparation, M.A.S.S. and M.H.R.; writing--review and editing, M.A.S.S., M.H.R. and H.-K.S.; visualization, M.A.S.S., M.H.R. and M.A.A.; supervision, Y.-H.Y. and H.-K.S.; project administration, Y.-H.Y. and H.-K.S.; funding acquisition, H.-K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute for Information & Communications Technology Promotion (IITP) grant, funded by the Korea government (MSIT) (No.2017-0-00217, Development of Immersive Signage Based on Variable Transparency and Multiple Layers). This research was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2020R1A6A1A03038540).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Synthesis of the Films  
The zinc oxide (ZnO) powder, with a high purity of 99% and a molecular weight of 81.37 g/mol (PA), was procured from Vetec. Magnesium nitrate (Mg(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O), which created charged particles in the solution for the applied potential difference (ddp), was acquired from Dinamica-Quimica Contemporanea Lda, Brazil. These were mixed in an ethanol (Synth) suspension at concentrations of 8 g/L and 0.1 g/L, respectively [19]. The fluorine-doped tin oxide (FTO), N179 dye, FTO with platinum, and the electrolyte Iodbyte AN-50 were acquired from Solaronix(r), Aubonne, Switzerland. N719 is a dye with the chemical formula C\({}_{58}\)H\({}_{66}\)N\({}_{8}\)O\({}_{8}\)RuS\({}_{2}\), also known as Rutheramer 535-bisTBA, belonging to the ruthenium dye family and used in the wavelength range up to 750 nm. AN-50 is a redox couple with the iodide /tri-iodide formula, regenerated by the electron that passes through the counter electrode in the cell, with a concentration of 50 mM.  
Electrophoretic deposition was carried out using a voltage source k33-300V, Kasvi.  
The electrophoresis deposition followed the following steps:  
1. The electrophoretic deposition took place in a simple two-electrode cell, with FTO serving as the anode and platinum as the cathode. The volume of the ZnO suspension in ethanol was 50 mL. Deposition occurred at applied voltages of 60 V, 70 V, and 80 V, with deposition times of 3 and 5 min, for each voltage. Figure 1 shows the scheme for cathodic deposition.
2. The FTO and the ZnO films were retrieved from the suspension and then calcined with a thermal treatment at 450 \({}^{\circ}\)C for 30 min.  
Figure 1: Scheme of the electrophoresis cell.  
The assembly of the cells followed the following steps:  
1. The FTO was immersed in N719 ruthenium-based dye for 24 h.
2. The FTO/dye was coupled with the platinum.
3. AN-50 electrolyte was injected between the sandwiched electrodes.
4. The cells were tested under light simulation following procedures outlined by [PERSON] et al. [34].  
In the EPD process, a uniform film was obtained through the electrophoresis of the charged ZnO particles in the suspension on the FTO substrate under the electric field ap-plied by the ddp source [35].
## 8 Chitosan-Based Nanoparticles for Posterior Segment of Eye
### Age-Related Macular Degeneration (AMD)  
After cataract, AMD is the leading cause of blindness in the elderly population [75]. Various flavanones like naringenin have been tested to improve the prognosis of the disease. However, the poor solubility of the drug has led researchers to develop a nontoxic carrier system using sulfobutyleter-\(\beta\)-cyclodextrintrin/chitosan nanoparticles, which demonstrated better in vivo corneal release and drug retention. Thus, the frequency of administration of a drug in AMD patients can be reduced to obtain better patient compliance [76]. The conventional therapy for age-related macular degeneration includes administration of various therapeutic proteins such as ranibizumab, directed to treat symptoms of wet AMD. Ranibizumab is a monoclonal antibody fragment that is anti-angiogenic, thus suppressing the action of vascular endothelial growth factor. The literature has suggested the administration of ranibizumab through biodegradable polymers like PLGA. The major issue associated with this is difficulty in protein loading, which can be circumvented by formulating system-within-system delivery devices. Chitosan-N-acetyl-L-cysteine nanoparticles were encapsulated in PLGA microparticle to enhance the entrapment efficiencies and slow-release profile with no initial burst. This may have promise to overcome the cost burden associated with intravitreal injections [77].  
The etiology of degenerative diseases such as AMD is typically associated with photo-oxidative damage, fluctuations in the PUFA levels, physical trauma or environmental and chemical factors. Antioxidant therapy is emerging as a choice of supplementary treatment that focuses on the delivery of antioxidants like lutein, zeaxanthin, rosmarinic acid, beta-carotene, and vitamin supplements which can be potent inhibitors of retinal neovascularization and macular degeneration. However, there are several challenges associated with absorption and targeting of the therapy. In the literature, the encapsulation of rosmarinic acid in chitosan-TPP nanoparticles demonstrated better permeability into the cells and alleviation of AMD [78].
# Fatigue Crack Calculation of Steel Structure Based on the Improved McEvily Model
## 3 The Verification of Engineering Feasibility  
In order to verify the feasibility of the improved McEvily model in practical engineering, the fatigue crack of the Q345 qD steel butt weld of a suspension bridge was analyzed. Based on the long-term strain monitoring data, the improved McEvily model, Paris model, and nominal stress method were used to calculate the fatigue damage value of the monitoring location.
## 3 Mathematical Model Construction Based on Theoretical Mechanics Methods
### Analysis of Mechanical Properties of Roof and Floor
\begin{array}{l}\sigma_{x}=-\frac{8\pi^{2}AE_{x}}{\delta^{2}(1-\mu^{2 })}\left[\sin^{2}\frac{\pi W}{a}\cos\frac{2\pi x}{b}+\mu\left(\frac{q}{b} \right)^{2}\sin^{2}\frac{\pi x}{a}\cos\frac{\pi W}{b}\right]\\ \sigma_{y}=-\frac{8\pi^{2}AE_{y}}{b^{2}(1-\mu^{2})}\left[\sin^{2}\frac{\pi x} {a}\cos\frac{2\pi W}{b}+\mu\left(\frac{q}{b}\right)^{2}\sin^{2}\frac{\pi y}{ b}\cos\frac{\pi x}{a}\right]\\ A=\frac{\frac{qq^{2}}{4\pi^{4}D\left[3+3\left(\frac{q}{b}\right)^{4}+2\left( \frac{q}{b}\right)^{2}\right]}}{4\pi^{4}D\left[3+3\left(\frac{q}{b}\right)^{4 }+2\left(\frac{q}{b}\right)^{2}\right]}\end{array} \right., \tag{18}\]  
Taking the tensile stress on the bottom surface of the roof, that is the \(z=-\delta/2\) plane, as the research object, the maximum tensile stress values in the \(x\) and \(y\) directions of the roof can be obtained as follows:  
\[\left\{ \begin{array}{l}\sigma_{\rm xmax}=\frac{4\pi^{2}AE_{x}}{\delta^{2}(1 -\mu^{2})}\\ \sigma_{\rm ymin}=\frac{4\pi^{2}AE_{x}}{\delta^{2}(1-\mu^{2})}\end{array} \right. \tag{19}\]  
Figure 7: Mechanical model of roof. For the roof supported by the TSR in actual work, its length \(a\) must be less than width \(b\), then the stress on the roof in the \(y\) direction must be greater than the stress on the roof in the \(x\) direction. From Equation (19), it can be obtained that  
\[\frac{12 qa^{2}}{\pi^{2}b^{2}\delta^{2}\left[3+3\left(\frac{a}{b}\right)^{4}+2 \left(\frac{a}{b}\right)^{2}\right]}<\sigma_{\mu}, \tag{20}\]  
where \(\sigma_{\mu}\) is the tensile strength limit of the roof material, Mpa. Considering the support strength \(p_{\min}\) of the TSR to the roof and the rock load \(q_{o}\) of the roof, Equation (20) can be rewritten as follows:  
\[p>q_{o}-\frac{\pi^{2}b^{2}\delta^{2}\sigma_{\mu}\left[3+3\left(\frac{a}{b} \right)^{4}+2\left(\frac{a}{b}\right)^{2}\right]}{12a^{2}}, \tag{21}\]  
Equation (21) provides the minimum allowable value of the TSR's support strength to the roof. To prevent the support force of the TSR from being too large and causing the floor to break, it is necessary to calculate the maximum specific pressure \(p_{\max}\) that the floor can withstand according to the maximum compressive strength of the floor [29]. Since the actual contact area between the base of the TSR and the floor is smaller than the area of the base, the cutting, pushing, and other behaviors during the excavation process will directly damage the integrity of the floor and generate defects such as cracks and micro-cracks, these defects will reduce the bearing capacity of the floor. Therefore, the coefficient \(C\) represents the influence on the maximum specific pressure the floor can withstand. \[p_{\max}=C\sigma_{b}. \tag{22}\]  
where \(\sigma_{b}\) is the uniaxial compressive strength limit of the floor material.